Changes to extract the monthly calculation as a class for the game

main.py

@@ -9,16 +9,15 @@ from pathlib import Path
 from argparse import ArgumentParser
 import ast
 import pandas as pd
+import datetime
 
 from helpers import monthly_values as mv
 from populate import Populate
-from insel.insel import Insel
-from insel.templates.monthly_energy_balance import MonthlyEnergyBalance as templates
 from simplified_radiosity_algorithm import SimplifiedRadiosityAlgorithm
 from imports.geometry_factory import GeometryFactory
 from imports.weather_factory import WeatherFactory
 from city_model_structure.city import City
-import datetime
+from monthly_demand_calculation import MonthlyDemandCalculation
 
 parser = ArgumentParser(description='Monthly energy balance workflow v0.1.')
 required = parser.add_argument_group('required arguments')
@@ -52,6 +51,12 @@ else:
   file = Path(args.input_geometry_file).resolve()
   pickle_file = Path(str(file).replace('.gml', '.pickle'))
   city = GeometryFactory(args.geometry_type, file).city
+  print(len(city.buildings))
+  for building in city.buildings:
+    volume = building.volume
+    if str(volume) == 'inf':
+      sys.stderr.write(f'Building {building.name} has geometry errors. It has been removed from the city\n')
+      city.remove_city_object(building)
   city.save(pickle_file)
 
 print('begin_populating_time', datetime.datetime.now())
@@ -95,64 +100,51 @@ if not weather_step_in_pickle:
   else:
     total_number_of_buildings = len(city.buildings)
     if total_number_of_buildings > max_buildings_handled_by_sra:
-      radius = 100
+      radius = 80
       for building in city.buildings:
-        new_city = city.region(building.location, radius)
-        sra_new = SimplifiedRadiosityAlgorithm(city, Path(args.project_folder).resolve(), args.weather_file_name)
+        new_city = city.region(building.centroid, radius)
+        sra_new = SimplifiedRadiosityAlgorithm(new_city, Path(args.project_folder).resolve(), args.weather_file_name)
         sra_new.call_sra(keep_files=True)
-        sra_new.set_irradiance_surfaces(city, building_name=building.name)
+        sra_new.set_irradiance_surfaces(populated_city, building_name=building.name)
     else:
       sra.call_sra(keep_files=keep_files)
       sra.set_irradiance_surfaces(populated_city)
   pickle_file = Path(str(pickle_file).replace('.pickle', '_weather.pickle'))
   populated_city.save(pickle_file)
 
-print('begin_insel_time', datetime.datetime.now())
-# Step 4: Demand calculation (one model per building)
-print_results = None
-file = 'city name: ' + city.name + '\n'
+weather_format = 'epw'
+
+print('begin_user_assignment_time', datetime.datetime.now())
+# Step 4: Assign user defined parameters
 
 for building in populated_city.buildings:
-  if building.name != 'BLD122177':
-    # todo: default values to be defined at each specific workflow!
-    building.heated = True
-    building.cooled = False
-    building.attic_heated = 1
-    building.basement_heated = 1
-    building.usage_zones[0].internal_gains[0].average_internal_gain = 10.45
-    full_path_out = Path(args.project_folder + '/outputs/' + building.name + '_insel.out').resolve()
-    full_path_out.parent.mkdir(parents=True, exist_ok=True)
-    insel_file_name = building.name + '.insel'
-    try:
-      key = 'epw'
-      content = templates.generate_meb_template(building, full_path_out, key)
-      insel = Insel(Path(args.project_folder).resolve(), insel_file_name, content, mode=2, keep_files=keep_files).run()
-      building.heating['month'], building.cooling['month'] = templates.demand(full_path_out)
-      new_building = building.heating['month'].rename(columns={'INSEL': building.name})
-      demand_year = 0
-      for value in building.heating['month']['INSEL']:
-        if value == 'NaN':
-          value = '0'
-        demand_year += float(value)
-      yearly_heating = pd.DataFrame([demand_year], columns=['INSEL'])
-      building.heating['year'] = yearly_heating
-      if print_results is None:
-        print_results = new_building
-      else:
-        print_results = pd.concat([print_results, new_building], axis='columns')
-      file += '\n'
-      file += 'name: ' + building.name + '\n'
-      file += 'year of construction: ' + building.year_of_construction + '\n'
-      file += 'function: ' + building.function + '\n'
-      file += 'floor area: ' + str(building.thermal_zones[0].floor_area) + '\n'
-      file += 'storeys: ' + str(building.storeys_above_ground) + '\n'
-      file += 'heated_volume: ' + str(building.volume) + '\n'
-      file += 'volume: ' + str(building.volume) + '\n'
+  building.heated = True
+  building.cooled = False
+  building.attic_heated = 2
+  building.basement_heated = 0
+#    for value in building.heating['month']['INSEL']:
 
-    except ValueError:
-      print(sys.exc_info()[1])
-      print('Building ' + building.name + ' could not be processed')
-      continue
+print('begin_insel_time', datetime.datetime.now())
+MonthlyDemandCalculation(city, args.project_folder, weather_format).monthly_demand()
+
+print('begin_write_results_time', datetime.datetime.now())
+print_results = None
+file = 'city name: ' + city.name + '\n'
+for building in populated_city.buildings:
+  insel_file_name = building.name + '.insel'
+  building_results = building.heating['month'].rename(columns={'INSEL': building.name})
+  if print_results is None:
+    print_results = building_results
+  else:
+    print_results = pd.concat([print_results, building_results], axis='columns')
+  file += '\n'
+  file += 'name: ' + building.name + '\n'
+  file += 'year of construction: ' + building.year_of_construction + '\n'
+  file += 'function: ' + building.function + '\n'
+  file += 'floor area: ' + str(building.thermal_zones[0].floor_area) + '\n'
+  file += 'storeys: ' + str(building.storeys_above_ground) + '\n'
+  file += 'heated_volume: ' + str(building.volume) + '\n'
+  file += 'volume: ' + str(building.volume) + '\n'
 
 full_path_results = Path(args.project_folder + '/outputs/heating_demand.csv').resolve()
 print_results.to_csv(full_path_results)

monthly_demand_calculation.py

@@ -0,0 +1,49 @@
+"""
+Monthly demand calculation using the monthly energy balance methodology based on the norm...
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2020 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
+"""
+
+from pathlib import Path
+import pandas as pd
+import sys
+
+from insel.templates.monthly_energy_balance import MonthlyEnergyBalance as templates
+from insel.insel import Insel
+
+
+class MonthlyDemandCalculation:
+  def __init__(self, city, main_path, weather_format):
+    self._city = city
+    self._main_path = main_path
+    self._weather_format = weather_format
+
+  def monthly_demand(self):
+    for building in self._city.buildings:
+      full_path_out = Path(self._main_path + '/outputs/' + building.name + '_insel.out').resolve()
+      full_path_out.parent.mkdir(parents=True, exist_ok=True)
+      insel_file_name = building.name + '.insel'
+      try:
+        content = templates.generate_meb_template(building, full_path_out, self._weather_format)
+        insel = Insel(Path(self._main_path).resolve(), insel_file_name, content, mode=2, keep_files=True).run()
+        building.heating['month'], building.cooling['month'] = templates.demand(full_path_out)
+        heating_year = 0
+        for value in building.heating['month']['INSEL']:
+          if value == 'NaN':
+            value = '0'
+          heating_year += float(value)
+        yearly_heating = pd.DataFrame([heating_year], columns=['INSEL'])
+        building.heating['year'] = yearly_heating
+
+        cooling_year = 0
+        for value in building.cooling['month']['INSEL']:
+          if value == 'NaN':
+            value = '0'
+          cooling_year += float(value)
+        yearly_cooling = pd.DataFrame([cooling_year], columns=['INSEL'])
+        building.cooling['year'] = yearly_cooling
+
+      except ValueError:
+        print(sys.exc_info()[1])
+        print('Building ' + building.name + ' could not be processed')
+        continue