Added the full functionality to create meb

hub/city_model_structure/level_of_detail.py

@@ -14,6 +14,8 @@ class LevelOfDetail:
     self._geometry = None
     self._construction = None
     self._usage = None
+    self._weather = None
+    self._surface_radiation = None
 
   @property
   def geometry(self):
@@ -59,3 +61,33 @@ class LevelOfDetail:
     Set the city minimal usage level of detail, 1 or 2
     """
     self._usage = value
+
+  @property
+  def weather(self):
+    """
+    Get the city minimal weather level of detail, 0 (yearly), 1 (monthly), 2 (hourly)
+    :return: int
+    """
+    return self._weather
+
+  @weather.setter
+  def weather(self, value):
+    """
+    Set  the city minimal weather level of detail, 0 (yearly), 1 (monthly), 2 (hourly)
+    """
+    self._usage = value
+
+  @property
+  def surface_radiation(self):
+    """
+    Get the city minimal surface radiation level of detail, 0 (yearly), 1 (monthly), 2 (hourly)
+    :return: int
+    """
+    return self._surface_radiation
+
+  @surface_radiation.setter
+  def surface_radiation(self, value):
+    """
+    Set  the city minimal surface radiation level of detail, 0 (yearly), 1 (monthly), 2 (hourly)
+    """
+    self._surface_radiation = value

hub/helpers/constants.py

@@ -152,3 +152,7 @@ FULL_HVAC = 'Heating and cooling and ventilation'
 # Floats
 MAX_FLOAT = float('inf')
 MIN_FLOAT = float('-inf')
+
+# Tools
+SRA = 'sra'
+INSEL_MEB = 'insel meb'

hub/imports/results/insel_monthly_energry_balance.py

@@ -0,0 +1,53 @@
+"""
+Insel monthly energy balance
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2022 Concordia CERC group
+Project Coder Guillermo.GutierrezMorote@concordia.ca
+"""
+
+from pathlib import Path
+import pandas as pd
+import csv
+import hub.helpers.constants as cte
+
+class InselMonthlyEnergyBalance:
+  """
+  Import SRA results
+  """
+  def __init__(self, city, base_path):
+
+    self._city = city
+    self._base_path = base_path
+
+  @staticmethod
+  def _demand(insel_output_file_path):
+    heating = []
+    cooling = []
+    with open(Path(insel_output_file_path).resolve()) as csv_file:
+      csv_reader = csv.reader(csv_file)
+      for line in csv_reader:
+        demand = str(line).replace("['", '').replace("']", '').split()
+        for i in range(0, 2):
+          if demand[i] != 'NaN':
+            aux = float(demand[i]) * 1000  # kWh to Wh
+            demand[i] = str(aux)
+          else:
+            demand[i] = '0'
+        heating.append(demand[0])
+        cooling.append(demand[1])
+    monthly_heating = pd.DataFrame(heating, columns=[cte.INSEL_MEB])
+    monthly_cooling = pd.DataFrame(cooling, columns=[cte.INSEL_MEB])
+    return monthly_heating, monthly_cooling
+
+  def enrich(self):
+    for building in self._city.buildings:
+      file_name = building.name + '.out'
+      insel_output_file_path = Path(self._base_path / file_name).resolve()
+      if insel_output_file_path.is_file():
+        building.heating[cte.MONTH], building.cooling[cte.MONTH] = self._demand(insel_output_file_path)
+        building.heating[cte.YEAR] = pd.DataFrame(
+          [building.heating[cte.MONTH][cte.INSEL_MEB].sum()], columns=[cte.INSEL_MEB]
+        )
+        building.cooling[cte.YEAR] = pd.DataFrame(
+          [building.cooling[cte.MONTH][cte.INSEL_MEB].sum()], columns=[cte.INSEL_MEB]
+        )

hub/imports/results/simplified_radiosity_algorithm.py

@@ -32,7 +32,7 @@ class SimplifiedRadiosityAlgorithm:
       array = np.concatenate((array, np.full(total_hours, i + 1)))
     return pd.DataFrame(array, columns=[cte.MONTH])
 
-  def _get_mean_values(self, values):
+  def _get_monthly_mean_values(self, values):
     out = None
     if values is not None:
       if cte.MONTH not in values.columns:
@@ -41,6 +41,9 @@ class SimplifiedRadiosityAlgorithm:
       del out[cte.MONTH]
     return out
 
+  def _get_yearly_mean_values(self, values):
+    return values.mean()
+
   def _read_results(self):
     try:
       return pd.read_csv(self._input_file_path, sep='\s+', header=0)
@@ -79,9 +82,8 @@ class SimplifiedRadiosityAlgorithm:
         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=['sra'])
-
-        month_new_value = self._get_mean_values(new_value)
+        new_value = pd.DataFrame(radiation[[header_id]].to_numpy(), columns=[cte.SRA])
+        month_new_value = self._get_monthly_mean_values(new_value)
         if cte.MONTH not in surface.global_irradiance:
           surface.global_irradiance[cte.MONTH] = month_new_value
         else:
@@ -90,3 +92,6 @@ class SimplifiedRadiosityAlgorithm:
           surface.global_irradiance[cte.HOUR] = new_value
         else:
           pd.concat([surface.global_irradiance[cte.HOUR], new_value], axis=1)
+        if cte.YEAR not in surface.global_irradiance:
+          surface.global_irradiance[cte.YEAR] = self._get_yearly_mean_values(new_value)
+    self._city.level_of_detail.surface_radiation = 2

hub/imports/results_factory.py

@@ -10,6 +10,7 @@ from pathlib import Path
 from hub.helpers.utils import validate_import_export_type
 from hub.hub_logger import logger
 from hub.imports.results.simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
+from hub.imports.results.insel_monthly_energry_balance import InselMonthlyEnergyBalance
 
 
 class ResultFactory:
@@ -34,6 +35,12 @@ class ResultFactory:
     """
     SimplifiedRadiosityAlgorithm(self._city, self._base_path).enrich()
 
+  def _insel_meb(self):
+    """
+    Enrich the city with insel monthly energy balance results
+    """
+    InselMonthlyEnergyBalance(self._city, self._base_path).enrich()
+
   def enrich(self):
     """
     Enrich the city given to the class using the usage factory given handler

hub/imports/weather/epw_weather_parameters.py

@@ -9,6 +9,7 @@ import sys
 from pathlib import Path
 import pandas as pd
 import hub.helpers.constants as cte
+from hub.imports.weather.helpers.weather import Weather as wh
 
 
 class EpwWeatherParameters:
@@ -110,3 +111,10 @@ class EpwWeatherParameters:
         building.beam[cte.HOUR] = new_value
       else:
         pd.concat([building.beam[cte.HOUR], new_value], axis=1)
+    # create the monthly and yearly values out of the hourly
+    for building in self._city.buildings:
+      if cte.MONTH not in building.external_temperature:
+        building.external_temperature[cte.MONTH] = wh().get_monthly_mean_values(building.external_temperature[cte.HOUR][['epw']])
+      if cte.YEAR not in building.external_temperature:
+        building.external_temperature[cte.YEAR] = wh(). get_yearly_mean_values(building.external_temperature[cte.HOUR][['epw']])
+    self._city.level_of_detail.weather = 2

hub/imports/weather/helpers/weather.py

@@ -6,6 +6,9 @@ Project Coder Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
 """
 import math
 import hub.helpers.constants as cte
+import pandas as pd
+import calendar as cal
+import numpy as np
 
 
 class Weather:
@@ -28,3 +31,38 @@ class Weather:
               + 0.32 * (temperature + cte.KELVIN) - cte.KELVIN
       values.append(value)
     return values
+
+  def get_monthly_mean_values(self, values):
+    out = None
+    if values is not None:
+      if 'month' not in values.columns:
+        values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
+      out = values.groupby('month', as_index=False).mean()
+      del out['month']
+    return out
+
+  def get_yearly_mean_values(self, values):
+    return values.mean()
+
+  def get_total_month(self, values):
+    out = None
+    if values is not None:
+      if 'month' not in values.columns:
+        values = pd.concat([self.month_hour, pd.DataFrame(values)], axis=1)
+      out = pd.DataFrame(values).groupby('month', as_index=False).sum()
+      del out['month']
+    return out
+
+  @property
+  def month_hour(self):
+    """
+    returns a DataFrame that has x values of the month number (January = 1, February = 2...),
+    being x the number of hours of the corresponding month
+    :return: DataFrame(int)
+    """
+    array = []
+    for i in range(0, 12):
+      days_of_month = cal.monthrange(2015, i+1)[1]
+      total_hours = days_of_month * 24
+      array = np.concatenate((array, np.full(total_hours, i + 1)))
+    return pd.DataFrame(array, columns=['month'])

hub/persistence/models/city_object.py

@@ -16,7 +16,7 @@ class CityObject(Models):
   A model representation of an application
   """
   __tablename__ = 'city_object'
-  id = Column(Integer, Sequence('application_id_seq'), primary_key=True)
+  id = Column(Integer, Sequence('city_object_id_seq'), primary_key=True)
   city_id = Column(Integer, ForeignKey('city.id'), nullable=False)
   name = Column(String, nullable=False)
   alias = Column(String, nullable=True)

hub/persistence/models/simulation_results.py

@@ -17,7 +17,7 @@ class SimulationResults(Models):
   A model representation of an application
   """
   __tablename__ = 'simulation_results'
-  id = Column(Integer, Sequence('application_id_seq'), primary_key=True)
+  id = Column(Integer, Sequence('simulation_results_id_seq'), primary_key=True)
   city_id = Column(Integer, ForeignKey('city.id'), nullable=True)
   city_object_id = Column(Integer, ForeignKey('city_object.id'), nullable=True)
   name = Column(String, nullable=False)

hub/persistence/repositories/city.py

@@ -47,7 +47,7 @@ class City(Repository):
         pickle.dumps(city),
         city.name,
         city.level_of_detail.geometry,
-        'None' if city.climate_file is None else city.climate_file,
+        'None' if city.climate_file is None else str(city.climate_file),
         application_id,
         user_id,
         __version__)