monthly_energy_balance_workflow/results.py

from pathlib import Path
import pandas as pd
import hub.helpers.constants as cte


class Results:
  def __init__(self, city, path):
    self._city = city
    self._path = path

  def print(self):
    print_results = None
    file = 'city name: ' + self._city.name + '\n'
    array = [None] * 12
    for building in self._city.buildings:
      if cte.MONTH in building.heating.keys():
        heating_results = building.heating[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
      else:
        heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
      if cte.MONTH in building.cooling.keys():
        cooling_results = building.cooling[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
      else:
        cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
      if cte.MONTH in building.lighting_electrical_demand.keys():
        lighting_results = building.lighting_electrical_demand[cte.MONTH]\
          .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
      else:
        lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
      if cte.MONTH in building.appliances_electrical_demand.keys():
          appliances_results = building.appliances_electrical_demand[cte.MONTH]\
          .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
      else:
        appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
      if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
        dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\
          .rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
      else:
        dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])

      if cte.MONTH in building.heating_consumption.keys():
        heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH], columns=[f'{building.name} heating consumption Wh'])
      else:
        heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
      if cte.MONTH in building.cooling_consumption.keys():
        cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH], columns=[f'{building.name} cooling consumption Wh'])
      else:
        cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
      if cte.MONTH in building.domestic_hot_water_consumption.keys():
        dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH], columns=[f'{building.name} domestic hot water consumption Wh'])
      else:
        dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])

      if cte.MONTH in building.heating_peak_load.keys():
        heating_peak_load_results = building.heating_peak_load[cte.MONTH]
      else:
        heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])

      if cte.MONTH in building.cooling_peak_load.keys():
        cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
      else:
        cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])

      if print_results is None:
        print_results = heating_results
      else:
        print_results = pd.concat([print_results, heating_results], axis='columns')
      print_results = pd.concat([print_results,
                                 cooling_results,
                                 lighting_results,
                                 appliances_results,
                                 dhw_results,
                                 heating_consumption_results,
                                 cooling_consumption_results,
                                 dhw_consumption_results,
                                 heating_peak_load_results,
                                 cooling_peak_load_results], axis='columns')
      file += '\n'
      file += f'name: {building.name}\n'
      file += f'year of construction: {building.year_of_construction}\n'
      file += f'function: {building.function}\n'
      file += f'floor area: {building.floor_area}\n'
      if building.average_storey_height is not None and building.eave_height is not None:
        file += f'storeys: {int(building.eave_height / building.average_storey_height)}\n'
      else:
        file += f'storeys: n/a\n'
      file += f'heated_volume: {0.85 * building.volume}\n'
      file += f'volume: {building.volume}\n'

    full_path_results = Path(self._path / 'demand.csv').resolve()
    print_results.to_csv(full_path_results, na_rep='null')
    full_path_metadata = Path(self._path / 'metadata.csv').resolve()
    with open(full_path_metadata, 'w') as metadata_file:
      metadata_file.write(file)