costs_workflow/unittests/test_costs_workflow.py

"""
TestCostsWorkflow test
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2022 Concordia CERC group
Copyright © 2022 Project Coder Atiya atiya.atiya@mail.concordia.ca
"""

from pathlib import Path
from unittest import TestCase
import pandas as pd
import helpers.constants as cte
from helpers.monthly_values import MonthlyValues
from imports.geometry_factory import GeometryFactory
from imports.construction_factory import ConstructionFactory
from imports.usage_factory import UsageFactory
from imports.weather_factory import WeatherFactory
from peak_loads import PeakLoads
from costs_workflow.capital_cost import CapitalCost
from costs_workflow.life_cycle_costs import LifeCycleCosts
from catalog_factories.costs_catalog_factory import CostCatalogFactory


class TestPeakLoadsWorkflow(TestCase):
  """
  TestPeakLoadsWorkflow class
  """
  def setUp(self) -> None:
    """
        Test setup
        :return: None
        """
    self._city = None
    self._complete_city = None
    self._example_path = (Path(__file__).parent / 'tests_data').resolve()
    self._output_path = (Path(__file__).parent / 'tests_outputs').resolve()

  def _get_citygml(self, file):
    file_path = (self._example_path / file).resolve()
    self._city = GeometryFactory('citygml', path=file_path).city
    self.assertIsNotNone(self._city, 'city is none')
    return self._city

  @property
  def _read_sra_file(self) -> []:
    path = (self._example_path / "one_building_in_kelowna_sra_SW.out").resolve()
    _results = pd.read_csv(path, sep='\s+', header=0)
    id_building = ''
    header_building = []
    _radiation = []
    for column in _results.columns.values:
      if id_building != column.split(':')[1]:
        id_building = column.split(':')[1]
        if len(header_building) > 0:
          _radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
        header_building = [column]
      else:
        header_building.append(column)
    _radiation.append(pd.concat([MonthlyValues().month_hour, _results[header_building]], axis=1))
    return _radiation

  def _set_irradiance_surfaces(self, city, irradiance_format):
    """
    saves in building surfaces the correspondent irradiance at different time-scales depending on the mode
    if building is None, it saves all buildings' surfaces in file, if building is specified, it saves only that
    specific building values
    :parameter city: city
    :return: none
    """
    for radiation in self._read_sra_file:
      city_object_name = radiation.columns.values.tolist()[1].split(':')[1]
      building = city.city_object(city_object_name)
      for column in radiation.columns.values:
        if column == cte.MONTH:
          continue
        header_id = column
        surface_id = header_id.split(':')[2]
        surface = building.surface_by_id(surface_id)
        new_value = pd.DataFrame(radiation[[header_id]].to_numpy(), columns=[irradiance_format])
        surface.global_irradiance[cte.HOUR] = new_value

  def _enrich_city(self, city, weather_file, weather_format, irradiance_format, construction_format, usage_format):
    WeatherFactory(weather_format, city, file_name=weather_file).enrich()
    self._set_irradiance_surfaces(city, irradiance_format)

    for building in city.buildings:
      building.year_of_construction = 2006
      if building.function is None:
        building.function = cte.LARGE_OFFICE

    ConstructionFactory(construction_format, city).enrich()
    UsageFactory(usage_format, city).enrich()

  def test_workflow(self):
    outputs_path = (Path(__file__).parent / 'tests_outputs').resolve()

    gml_file = 'one_building_in_kelowna.gml'
    city = self._get_citygml(gml_file)

    weather_file = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
    weather_format = 'epw'
    irradiance_format = 'sra'
    construction_format = 'nrel'
    usage_format = 'comnet'
    number_of_years = 40
    consumer_price_index = 0.1
    discount_rate = 2.5
    self._enrich_city(city, weather_file, weather_format, irradiance_format, construction_format, usage_format)
    # PeakLoads(city, outputs_path, weather_format, irradiance_format)
    municipality = "montreal"
    catalog = CostCatalogFactory('montreal_catalog').catalog
    content = catalog.entries()
    for building in city.buildings:
      building_volume = 0.0
      building_area = 0.0
      total_opaque_area = 0.0
      total_transparent_area = 0.0
      for internal_zone in building.internal_zones:
        for thermal_zone in internal_zone.thermal_zones:
          for thermal_boundary in thermal_zone.thermal_boundaries:
            if thermal_boundary.opaque_area is not None:
              total_opaque_area += thermal_boundary.opaque_area
            if thermal_boundary.windows_areas is not None:
              total_transparent_area += thermal_boundary.windows_areas

        building_area += internal_zone.area
        building_volume += internal_zone.volume

      bulding_name, heating_load, cooling_load = PeakLoads(city, outputs_path, weather_format, irradiance_format)._results[0]
      print(bulding_name, heating_load, cooling_load)
      capital_costs_at_year_0 = CapitalCost.calculate_capital_cost(building_area, municipality, building_volume, total_opaque_area, total_transparent_area, content, heating_load, cooling_load, building.floor_area)
      print("capital_costs_at_year_0  ", capital_costs_at_year_0)
      # end_of_life_cost = 0.0
      # items = []
      # fuels = city.fuels
      # concepts = []
      # LifeCycleCosts(city, number_of_years, consumer_price_index, discount_rate, end_of_life_cost,
      #            capital_costs_at_year_0, items, fuels, concepts).calculate_capital_costs


    # print(PeakLoads(city, outputs_path, weather_format, irradiance_format)._results[][1])