setting to 0 values when the building cannot be calculated

insel/monthly_demand_calculation.py

@@ -9,6 +9,7 @@ import pandas as pd
 import sys
 import csv
 from insel.insel import Insel
+from hub.hub_logger import logger
 
 
 class MonthlyDemandCalculation(Insel):
@@ -22,28 +23,29 @@ class MonthlyDemandCalculation(Insel):
       file_name = building.name + '.out'
       full_path_out = Path(self._path / file_name).resolve()
       full_path_out.parent.mkdir(parents=True, exist_ok=True)
-      try:
+      if Path(full_path_out).is_file():
         building.heating['month'], building.cooling['month'] = self._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
+      else:
+        building.heating['month'] = pd.DataFrame([0] * 12, columns=['INSEL'])
+        building.cooling['month'] = pd.DataFrame([0] * 12, columns=['INSEL'])
+        logger.error(f'Building {building.name} could not be processed. Heating and cooling set to 0\n')
+        sys.stderr.write(f'Building {building.name} could not be processed. Heating and cooling set to 0\n')
 
-        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
+      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
 
-      except ValueError:
-        print(sys.exc_info()[1])
-        print('Building ' + building.name + ' could not be processed')
-        continue
+      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
 
   @staticmethod
   def _demand(insel_outputs_path):

monthly_energy_balance.py

@@ -26,7 +26,6 @@ _DAYS_A_MONTH = {cte.MONDAY: [5, 4, 4, 5, 4, 4, 5, 4, 4, 5, 4, 5],
 class MonthlyEnergyBalance:
   def __init__(self, city, path, attic_heated_case, basement_heated_case, weather_format):
     self._city = city
-    print(path)
     self._path = path
     self._weather_format = weather_format
     for building in self._city.buildings:
@@ -113,27 +112,31 @@ class MonthlyEnergyBalance:
     for building in self._city.buildings:
       lighting_demand = []
       appliances_demand = []
-      thermal_zone = building.internal_zones[0].thermal_zones[0]
-      area = thermal_zone.total_floor_area
+      if building.internal_zones[0].thermal_zones is None:
+        lighting_demand = [0] * 12
+        appliances_demand = [0] * 12
+      else:
+        thermal_zone = building.internal_zones[0].thermal_zones[0]
+        area = thermal_zone.total_floor_area
 
-      for month in range(0, 12):
-        total_lighting = 0
-        for schedule in thermal_zone.lighting.schedules:
-          total_day = 0
-          for value in schedule.values:
-            total_day += value
-          for day_type in schedule.day_types:
-            total_lighting += total_day * _DAYS_A_MONTH[day_type][month] * thermal_zone.lighting.density
-        lighting_demand.append(total_lighting * area)
+        for month in range(0, 12):
+          total_lighting = 0
+          for schedule in thermal_zone.lighting.schedules:
+            total_day = 0
+            for value in schedule.values:
+              total_day += value
+            for day_type in schedule.day_types:
+              total_lighting += total_day * _DAYS_A_MONTH[day_type][month] * thermal_zone.lighting.density
+          lighting_demand.append(total_lighting * area)
 
-        total_appliances = 0
-        for schedule in thermal_zone.appliances.schedules:
-          total_day = 0
-          for value in schedule.values:
-            total_day += value
-          for day_type in schedule.day_types:
-            total_appliances += total_day * _DAYS_A_MONTH[day_type][month] * thermal_zone.appliances.density
-        appliances_demand.append(total_appliances * area)
+          total_appliances = 0
+          for schedule in thermal_zone.appliances.schedules:
+            total_day = 0
+            for value in schedule.values:
+              total_day += value
+            for day_type in schedule.day_types:
+              total_appliances += total_day * _DAYS_A_MONTH[day_type][month] * thermal_zone.appliances.density
+          appliances_demand.append(total_appliances * area)
 
       building.lighting_electrical_demand = pd.DataFrame(lighting_demand,
                                                          columns=[f'{building.name} lighting electrical demand Wh'])

tmp/.gitignore

@@ -1,4 +0,0 @@
-# Ignore everything in this directory
-*
-# Except this file
-!.gitignore