summer_course_2024/city_model_structure/building.py

"""
Building module
SPDX - License - Identifier: LGPL - 3.0 - or -later
Copyright © 2020 Project Author Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
contributors: Pilar Monsalvete pilar_monsalvete@yahoo.es
"""
from typing import List

import matplotlib.patches as patches
import numpy as np
from matplotlib import pylab
from shapely import ops
from shapely.geometry import MultiPolygon
import pandas as pd
import helpers.constants as cte

from city_model_structure.attributes.surface import Surface
from city_model_structure.attributes.thermal_boundary import ThermalBoundary
from city_model_structure.attributes.thermal_zone import ThermalZone
from city_model_structure.attributes.usage_zone import UsageZone
from city_model_structure.city_object import CityObject
from city_model_structure.building_unit import BuildingUnit


class Building(CityObject):
  """
  Building(CityObject) class
  """
  def __init__(self, name, lod, surfaces, terrains, year_of_construction, function, lower_corner, attic_heated=0,
               basement_heated=0):
    super().__init__(lod, surfaces, name)
    self._basement_heated = basement_heated
    self._attic_heated = attic_heated
    self._terrains = terrains
    self._year_of_construction = year_of_construction
    self._function = function
    self._lower_corner = lower_corner
    self._average_storey_height = None
    self._storeys_above_ground = None
    self._foot_print = None
    self._usage_zones = []
    self._building_units = []
    self._type = 'building'
    self._m_heating = pd.DataFrame()
    self._h_heating = pd.DataFrame()
    self._heating = pd.DataFrame()
    self._m_cooling = pd.DataFrame()
    self._h_cooling = pd.DataFrame()
    self._cooling = pd.DataFrame()
    self._m_external_temperature = pd.DataFrame()
    self._h_external_temperature = pd.DataFrame()
    self._external_temperature = pd.DataFrame()

    # ToDo: Check this for LOD4
    self._thermal_zones = []
    if self.lod < 8:
      # for lod under 4 is just one thermal zone
      self._thermal_zones.append(ThermalZone(self.surfaces))

    for t_zones in self._thermal_zones:
      t_zones.bounded = [ThermalBoundary(s, [t_zones]) for s in t_zones.surfaces]
    surface_id = 0
    for surface in self._surfaces:
      surface.lower_corner = self._lower_corner
      surface.parent(self, surface_id)
      surface_id += 1

  @property
  def usage_zones(self) -> List[UsageZone]:
    """
    Get city object usage zones
    :return: [UsageZone]
    """
    return self._usage_zones

  @usage_zones.setter
  def usage_zones(self, values):
    """
    Set city objects usage zones
    :param values: [UsageZones]
    :return: None
    """
    # ToDo: this is only valid for one usage zone need to be revised for multiple usage zones.
    self._usage_zones = values
    for thermal_zone in self.thermal_zones:
      thermal_zone.usage_zones = [(100, usage_zone) for usage_zone in values]

  @property
  def terrains(self) -> List[Surface]:
    """
    Get city object terrain surfaces
    :return: [Surface]
    """
    return self._terrains

  @property
  def attic_heated(self):
    """
    Get if the city object attic is heated
    :return: Boolean
    """
    return self._attic_heated

  @attic_heated.setter
  def attic_heated(self, value):
    """
    Set if the city object attic is heated
    :param value: Boolean
    :return: None
    """
    self._attic_heated = value

  @property
  def basement_heated(self):
    """
    Get if the city object basement is heated
    :return: Boolean
    """
    return self._basement_heated

  @basement_heated.setter
  def basement_heated(self, value):
    """
    Set if the city object basement is heated
    :param value: Boolean
    :return: None
    """
    self._attic_heated = value

  @property
  def name(self):
    """
    City object name
    :return: str
    """
    return self._name

  @property
  def thermal_zones(self) -> List[ThermalZone]:
    """
    City object thermal zones
    :return: [ThermalZone]
    """
    return self._thermal_zones

  @property
  def heated_volume(self):
    """
    City object heated volume in cubic meters
    :return: float
    """
    # ToDo: this need to be the calculated based on the basement and attic heated values
    return self.volume

  @property
  def year_of_construction(self):
    """
    City object year of construction
    :return: int
    """
    return self._year_of_construction

  @property
  def function(self):
    """
    City object function
    :return: str
    """
    return self._function

  @property
  def average_storey_height(self):
    """
    Get city object average storey height in meters
    :return: float
    """
    return self._average_storey_height

  @average_storey_height.setter
  def average_storey_height(self, value):
    """
    Set city object average storey height in meters
    :param value: float
    :return: None
    """
    self._average_storey_height = value

  @property
  def storeys_above_ground(self):
    """
    Get city object storeys number above ground
    :return: int
    """
    return self._storeys_above_ground

  @storeys_above_ground.setter
  def storeys_above_ground(self, value):
    """
    Set city object storeys number above ground
    :param value: int
    :return:
    """
    self._storeys_above_ground = value

  @staticmethod
  def _tuple_to_point(xy_tuple):
    return [xy_tuple[0], xy_tuple[1], 0.0]

  def _plot(self, polygon):
    points = ()
    for point_tuple in polygon.exterior.coords:
      almost_equal = False
      for point in points:
        point_1 = Building._tuple_to_point(point)
        point_2 = Building._tuple_to_point(point_tuple)
        if self._geometry.almost_equal(point_1, point_2):
          almost_equal = True
          break
      if not almost_equal:
        points = points + (point_tuple,)
    points = points + (points[0],)
    pylab.scatter([point[0] for point in points], [point[1] for point in points])
    pylab.gca().add_patch(patches.Polygon(points, closed=True, fill=True))
    pylab.grid()
    pylab.show()

  @property
  def foot_print(self) -> Surface:
    """
    City object foot print surface
    :return: Surface
    """
    if self._foot_print is None:
      shapelys = []
      union = None
      for surface in self.surfaces:
        if surface.shapely.is_empty or not surface.shapely.is_valid:
          continue
        shapelys.append(surface.shapely)
        union = ops.unary_union(shapelys)
        shapelys = [union]
      if isinstance(union, MultiPolygon):
        Exception('foot print returns a multipolygon')
      points_list = []
      for point_tuple in union.exterior.coords:
        # ToDo: should be Z 0.0 or min Z?
        point = Building._tuple_to_point(point_tuple)
        almost_equal = False
        for existing_point in points_list:
          if self._geometry.almost_equal(point, existing_point):
            almost_equal = True
            break
        if not almost_equal:
          points_list.append(point)
      points_list = np.reshape(points_list, len(points_list) * 3)
      points = np.array_str(points_list).replace('[', '').replace(']', '')
      self._foot_print = Surface(points, remove_last=False, is_projected=True)
    return self._foot_print

  @property
  def type(self):
    """
    building type
    :return: str
    """
    return self._type

  @property
  def building_units(self) -> [BuildingUnit]:
    """
    Building units
    :return:
    """
    return self._building_units

  @building_units.setter
  def building_units(self, value):
    """
    Building units
    :param value: [BuildingUnit]
    """
    self._building_units = value

  @property
  def _monthly_heating(self) -> pd.DataFrame:
    """
    building monthly heating values in Watts-hour
    :return: DataFrame with 12 values and a header with the source of those
    """
    return self._m_heating

  @_monthly_heating.setter
  def _monthly_heating(self, value):
    """
    building monthly heating values in Watts-hour and a header with the source
    :param value: DataFrame(heating demand)
    """
    if self._m_heating.empty:
      self._m_heating = value
    else:
      self._m_heating = pd.concat([self._m_heating, value], axis=1)

  @property
  def _hourly_heating(self) -> pd.DataFrame:
    """
    building hourly heating values in Watts-hour
    :return: DataFrame with 8760 values and a header with the source of those
    """
    return self._h_heating

  @_hourly_heating.setter
  def _hourly_heating(self, value):
    """
    building hourly heating values in Watts-hour and a header with the source
    :param value: DataFrame(heating demand)
    """
    if self._h_heating.empty:
      self._h_heating = value
    else:
      self._h_heating = pd.concat([self._h_heating, value], axis=1)

  def heating(self, time_scale):
    """
    Get heating demand in Wh in a defined time_scale
    :param time_scale: string.
    :return: DataFrame(float)
    """
    if time_scale == cte.time_scale['hour']:
      self._heating = self._hourly_heating
    elif time_scale == cte.time_scale['month']:
      self._heating = self._monthly_heating
    else:
      raise NotImplementedError
    return self._heating

  @property
  def _monthly_cooling(self) -> pd.DataFrame:
    """
    building monthly cooling values in Watts-hour
    :return: DataFrame with 12 values and a header with the source of those
    """
    return self._m_cooling

  @_monthly_cooling.setter
  def _monthly_cooling(self, value):
    """
    building monthly cooling values in Watts-hour and a header with the source
    :param value: DataFrame(cooling demand)
    """
    if self._m_cooling.empty:
      self._m_cooling = value
    else:
      self._m_cooling = pd.concat([self._m_cooling, value], axis=1)

  @property
  def _hourly_cooling(self) -> pd.DataFrame:
    """
    building hourly cooling values in Watts-hour
    :return: DataFrame with 8760 values and a header with the source of those
    """
    return self._h_cooling

  @_hourly_cooling.setter
  def _hourly_cooling(self, value):
    """
    building hourly cooling values in Watts-hour and a header with the source
    :param value: DataFrame(cooling demand)
    """
    if self._h_cooling.empty:
      self._h_cooling = value
    else:
      self._h_cooling = pd.concat([self._h_cooling, value], axis=1)

  def cooling(self, time_scale):
    """
    Get cooling demand in Wh in a defined time_scale
    :param time_scale: string.
    :return: DataFrame(float)
    """
    if time_scale == cte.time_scale['hour']:
      self._cooling = self._hourly_cooling
    elif time_scale == cte.time_scale['month']:
      self._cooling = self._monthly_cooling
    else:
      raise NotImplementedError
    return self._cooling

  @property
  def _monthly_external_temperature(self) -> pd.DataFrame:
    """
    external temperature surrounding the building in grads Celsius monthly based
    :return: DataFrame with 12 values and a header with the source of those
    """
    return self._m_external_temperature

  @_monthly_external_temperature.setter
  def _monthly_external_temperature(self, value):
    """
    external temperature surrounding the building in grads Celsius monthly based
    :param value: DataFrame(external temperature)
    """
    if self._m_external_temperature.empty:
      self._m_external_temperature = value
    else:
      self._m_external_temperature = pd.concat([self._m_external_temperature, value], axis=1)

  @property
  def _hourly_external_temperature(self) -> pd.DataFrame:
    """
    external temperature surrounding the building in grads Celsius hourly based
    :return: DataFrame with 8760 values and a header with the source of those
    """
    return self._h_external_temperature

  @_hourly_external_temperature.setter
  def _hourly_external_temperature(self, value):
    """
    external temperature surrounding the building in grads Celsius hourly based
    :param value: DataFrame(external temperature)
    """
    if self._h_external_temperature.empty:
      self._h_external_temperature = value
    else:
      self._h_external_temperature = pd.concat([self._h_external_temperature, value], axis=1)

  def external_temperature(self, time_scale) -> pd.DataFrame:
    """
    Get cooling demand in Wh in a defined time_scale
    :param time_scale: string.
    :return: DataFrame(float)
    """
    if time_scale == cte.time_scale['hour']:
      self._external_temperature = self._hourly_external_temperature
    elif time_scale == cte.time_scale['month']:
      self._external_temperature = self._monthly_external_temperature
    else:
      raise NotImplementedError
    return self._external_temperature