Update ep to include hot water, update how results are saved, update requirements.txt
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@ -5,6 +5,7 @@ from pathlib import Path
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import pandas as pd
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import platform
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import os
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import xlsxwriter
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from hub.imports.geometry_factory import GeometryFactory
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from hub.imports.weather_factory import WeatherFactory
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@ -28,14 +29,40 @@ class EnergyValidation:
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self.climate_file = Path(f'{self.storage_path}/{self.climate_file_name}.cli').resolve()
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self.meb_folder = Path('./results/meb').resolve()
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self.ep_folder = Path('./results/ep').resolve()
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self.result_file = Path('./results/energy_validation_results.xlsx').resolve()
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self.result_file = Path(f'./results/{datetime.datetime.now().strftime("%m-%d-%Y_%H-%M-%S")}'
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f'_energy_validation_results.xlsx').resolve()
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results_xlsx = xlsxwriter.Workbook(self.result_file)
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simulation_data_worksheet = results_xlsx.add_worksheet('Simulation data')
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metadata_worksheet = results_xlsx.add_worksheet('Metadata')
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bold = results_xlsx.add_format({'bold': True})
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simulation_data_worksheet.write('A1', 'Building', bold)
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simulation_data_worksheet.write('B1', 'Month', bold)
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simulation_data_worksheet.write('C1', 'Heating demand (kWh/m2)', bold)
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simulation_data_worksheet.write('D1', 'Cooling demand (kWh/m2)', bold)
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simulation_data_worksheet.write('E1', 'Appliances demand (kWh/m2)', bold)
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simulation_data_worksheet.write('F1', 'Lighting demand (kWh/m2)', bold)
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simulation_data_worksheet.write('G1', 'Domestic hot water demand (kWh/m2)', bold)
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simulation_data_worksheet.write('H1', 'Source of simulation (kWh/m2)', bold)
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metadata_worksheet.write('A1', 'Building id', bold)
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metadata_worksheet.write('B1', 'Storeys', bold)
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metadata_worksheet.write('C1', 'M2 per storey', bold)
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metadata_worksheet.write('D1', 'Total m2', bold)
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metadata_worksheet.write('E1', 'Total m3', bold)
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metadata_worksheet.write('F1', 'Year building', bold)
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metadata_worksheet.write('G1', 'Type of building', bold)
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results_xlsx.close()
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if platform.system() == 'Windows':
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self.encoding = 'windows-1252'
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else:
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self.encoding = 'utf-8'
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def _sort_buildings(self, buildings_to_simulate):
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@staticmethod
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def _sort_buildings(buildings_to_simulate):
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sorted_buildings = {}
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for building in buildings_to_simulate:
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code_utili = building['properties']['CODE_UTILI']
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@ -123,12 +150,13 @@ class EnergyValidation:
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index=False,
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header=False,
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)
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def _save_ep_results(self, demand, building_area, building_name):
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demand.drop('Date/Time', axis=1, inplace=True)
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# convert from J to kWh/m^2
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demand *= 2.77778e-7/building_area
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demand *= (2.77778e-7/building_area)
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# replace indexes with month/day/year format
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# insert date in month/day/year format
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months = [
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'1/1/2023',
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'2/1/2023',
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@ -146,15 +174,20 @@ class EnergyValidation:
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demand.insert(0, 'month', months)
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# insert building_name to first column
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# insert building_name
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demand.insert(0, 'building_name', building_name)
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# TODO: add water usage once working from ep
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demand['water_usage'] = 'NA'
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# insert hot water demand
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demand['water_usage'] = demand.iloc[:,5] - demand.iloc[:,2]
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# add simulation source as ep
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demand['source'] = 'ep'
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# drop unneeded columns
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demand.drop(4)
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demand.drop(5)
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demand.drop(6)
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# last, but not least, append our lovely reformatted data to the results spreadsheet
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with pd.ExcelWriter(self.result_file, engine='openpyxl', if_sheet_exists='overlay', mode='a') as writer:
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demand.to_excel(
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@ -215,53 +248,78 @@ class EnergyValidation:
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city.climate_file = self.climate_file
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city.name = f'{building_id}_energy_validation'
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# starting sra
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print(f'{building_id} starting sra')
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ExportsFactory('sra', city, self.tmp_folder,
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weather_file=self.weather_file, weather_format='epw').export()
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sra_file = (self.tmp_folder / f'{city.name}_sra.xml').resolve()
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sra_start = datetime.datetime.now()
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Sra(sra_file, self.tmp_folder).run()
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sra_end = datetime.datetime.now() - sra_start
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ResultFactory('sra', city, self.tmp_folder).enrich()
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try:
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# starting sra
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print(f'{building_id} starting sra')
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ExportsFactory('sra', city, self.tmp_folder,
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weather_file=self.weather_file, weather_format='epw').export()
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sra_file = (self.tmp_folder / f'{city.name}_sra.xml').resolve()
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sra_start = datetime.datetime.now()
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Sra(sra_file, self.tmp_folder).run()
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sra_end = datetime.datetime.now() - sra_start
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ResultFactory('sra', city, self.tmp_folder).enrich()
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# run meb
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print(f'{building_id} starting meb')
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for building in city.buildings:
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building.attic_heated = 0
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building.basement_heated = 1
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# run meb
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print(f'{building_id} starting meb')
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for building in city.buildings:
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building.attic_heated = 0
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building.basement_heated = 1
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EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, self.tmp_folder).export()
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meb_start = datetime.datetime.now()
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Meb(self.tmp_folder).run()
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meb_end = datetime.datetime.now() - meb_start
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ResultFactory('insel_meb', city, self.tmp_folder).enrich()
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results = MEBResults(city, Path('./results/meb/').resolve())
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results.print()
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EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, self.tmp_folder).export()
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meb_start = datetime.datetime.now()
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Meb(self.tmp_folder).run()
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meb_end = datetime.datetime.now() - meb_start
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ResultFactory('insel_meb', city, self.tmp_folder).enrich()
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results = MEBResults(city, Path('./results/meb/').resolve())
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results.print()
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# save meb results to energy_validation_results
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total_m2 = city.buildings[0].internal_zones[0].thermal_zones[0].total_floor_area
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meb_results = pd.read_csv(Path(f'{self.meb_folder}/demand.csv').resolve(),
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encoding=self.encoding)
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meb_metadata = pd.read_csv(Path(f'{self.meb_folder}/metadata.csv').resolve(),
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encoding=self.encoding)
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self._save_meb_results(meb_results, meb_metadata, total_m2)
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# run energyplus
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print(f'{building_id} starting energy plus')
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idf_file = EnergyBuildingsExportsFactory('idf', city, self.ep_folder).export()
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ep_start = datetime.datetime.now()
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idf_file.run()
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ep_end = datetime.datetime.now() - ep_start
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# run energyplus
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print(f'{building_id} starting energy plus')
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idf_file = EnergyBuildingsExportsFactory('idf', city, self.ep_folder).export_debug()
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ep_start = datetime.datetime.now()
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idf_file.run()
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ep_end = datetime.datetime.now() - ep_start
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# save meb results to energy_validation_results
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total_m2 = city.buildings[0].internal_zones[0].thermal_zones[0].total_floor_area
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meb_results = pd.read_csv(Path(f'{self.meb_folder}/demand.csv').resolve(),
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encoding=self.encoding)
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meb_metadata = pd.read_csv(Path(f'{self.meb_folder}/metadata.csv').resolve(),
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encoding=self.encoding)
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self._save_meb_results(meb_results, meb_metadata, total_m2)
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# save ep results to energy_validation_results
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ep_results = pd.read_csv(Path(f'{self.ep_folder}/{building_id}_energy_validation_mtr.csv').resolve(),
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encoding=self.encoding)
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self._save_ep_results(ep_results, total_m2, city.buildings[0].name)
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# save ep results to energy_validation_results
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ep_results = pd.read_csv(Path(f'{self.ep_folder}/{building_id}_energy_validation_out.csv').resolve(),
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encoding=self.encoding)
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self._save_ep_results(ep_results, total_m2, city.buildings[0].name)
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print(f"{building_id} sra time: {sra_end}")
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print(f"{building_id} meb time: {meb_end}")
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print(f"{building_id} ep time: {ep_end}")
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print(f"{building_id} sra time: {sra_end}")
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print(f"{building_id} meb time: {meb_end}")
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print(f"{building_id} ep time: {ep_end}")
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except:
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building_with_error = pd.DataFrame([
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building_id,
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'NA',
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'NA',
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'NA',
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'NA',
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'NA',
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'NA',
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'NA',
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'NA'
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]).transpose()
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print(building_with_error)
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# with pd.ExcelWriter(self.result_file, engine='openpyxl', if_sheet_exists='overlay',
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# mode='a') as writer:
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# building_with_error.to_excel(
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# writer,
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# startrow=writer.sheets['Simulation data'].max_row,
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# sheet_name='Simulation data',
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# index=False,
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# header=False,
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# )
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if cleanup is True:
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[os.remove(os.path.join(self.tmp_folder, file)) for file in os.listdir(self.tmp_folder)]
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@ -1 +1 @@
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cerc-hub
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xlsxwriter
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