39 lines
1.7 KiB
Python
39 lines
1.7 KiB
Python
from pathlib import Path
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import pandas as pd
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import hub.helpers.constants as cte
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class Results:
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def __init__(self, city, path):
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self._city = city
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self._path = path
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def print(self):
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print_results = None
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file = 'city name: ' + self._city.name + '\n'
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for building in self._city.buildings:
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heating_results = building.heating[cte.MONTH].rename(columns={'INSEL': f'{building.name} heating Wh'})
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cooling_results = building.cooling[cte.MONTH].rename(columns={'INSEL': f'{building.name} cooling Wh'})
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if print_results is None:
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print_results = heating_results
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else:
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print_results = pd.concat([print_results, heating_results], axis='columns')
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print_results = pd.concat([print_results, cooling_results,
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building.lighting_electrical_demand,
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building.appliances_electrical_demand,
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building.domestic_hot_water_heat_demand], axis='columns')
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file += '\n'
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file += 'name: ' + building.name + '\n'
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file += 'year of construction: ' + str(building.year_of_construction) + '\n'
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file += 'function: ' + building.function + '\n'
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file += 'floor area: ' + str(building.floor_area) + '\n'
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file += 'storeys: ' + str(int(building.eave_height / building.average_storey_height)) + '\n'
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file += 'heated_volume: ' + str(0.85 * building.volume) + '\n'
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file += 'volume: ' + str(building.volume) + '\n'
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full_path_results = Path(self._path / 'demand.csv').resolve()
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print_results.to_csv(full_path_results)
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full_path_metadata = Path(self._path / 'metadata.csv').resolve()
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with open(full_path_metadata, 'w') as metadata_file:
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metadata_file.write(file)
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