Rechanged the Minimal.idf to give the necessary results, without additional stuff not used by the tool

# Conflicts:
#	main.py

input_files/eilat.geojson

@@ -1,249 +0,0 @@
-{
-  "type": "FeatureCollection",
-  "features": [
-    {
-      "type": "Feature",
-      "id": 1,
-      "properties": {
-        "heightmax": 9,
-        "ANNEE_CONS": 1978,
-        "CODE_UTILI": "residential"
-	  },
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              34.95217088371581,
-              29.56694805860026
-            ],
-            [
-              34.95262396587913,
-              29.566952667742285
-            ],
-            [
-              34.95261999147337,
-              29.567024109421467
-            ],
-            [
-              34.952169558914704,
-              29.567019500282157
-            ],
-            [
-              34.95217088371581,
-              29.56694805860026
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    },
-	{
-      "type": "Feature",
-      "id": 3,
-      "properties": {
-		"heightmax": 16,
-        "ANNEE_CONS": 2012,
-        "CODE_UTILI": "dormitory"
-	  },
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              34.95176644317411,
-              29.56827388702702
-            ],
-            [
-              34.95176550020565,
-              29.568180388329026
-            ],
-            [
-              34.95179850408434,
-              29.568180388329026
-            ],
-            [
-              34.95179850408434,
-              29.5681303582886
-            ],
-            [
-              34.95176644317411,
-              29.5681303582886
-            ],
-            [
-              34.95176644317411,
-              29.568038499789708
-            ],
-            [
-              34.951874884488376,
-              29.568038499789708
-            ],
-            [
-              34.951874884488376,
-              29.568058183760357
-            ],
-            [
-              34.95192391882168,
-              29.568058183760357
-            ],
-            [
-              34.951922032885705,
-              29.56804178045124
-            ],
-            [
-              34.95205216246262,
-              29.568042600617147
-            ],
-            [
-              34.952051219494166,
-              29.568129538124154
-            ],
-            [
-              34.95201821561636,
-              29.5681303582886
-            ],
-            [
-              34.95201821561636,
-              29.568176287507143
-            ],
-            [
-              34.95204839059062,
-              29.568176287507143
-            ],
-            [
-              34.95205027652662,
-              29.56827552735433
-            ],
-            [
-              34.95195503676348,
-              29.568274707190284
-            ],
-            [
-              34.95195597973188,
-              29.56825830391628
-            ],
-            [
-              34.951849424353696,
-              29.56825830391628
-            ],
-            [
-              34.951849424353696,
-              29.568274707190284
-            ],
-            [
-              34.95176644317411,
-              29.56827388702702
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    },
-    {
-      "type": "Feature",
-      "id": 2,
-      "properties": {
-		"heightmax": 24,
-        "ANNEE_CONS": 2002,
-        "CODE_UTILI": "Hotel employ"
-	  },
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              34.94972280674813,
-              29.566224752287738
-            ],
-            [
-              34.94974316291999,
-              29.56597561012454
-            ],
-            [
-              34.94989147217407,
-              29.565980668855033
-            ],
-            [
-              34.94987402402688,
-              29.566233605043536
-            ],
-            [
-              34.94972280674813,
-              29.566224752287738
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    },
-    {
-      "type": "Feature",
-      "id": 4,
-      "properties": {
-		"heightmax": 24,
-        "ANNEE_CONS": 2002,
-        "CODE_UTILI": "Hotel employ"
-	  },
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              34.9492572120038,
-              29.565899295950373
-            ],
-            [
-              34.949411568175805,
-              29.565906093785898
-            ],
-            [
-              34.94939398329561,
-              29.56615761339161
-            ],
-            [
-              34.94924158099852,
-              29.566149116117728
-            ],
-            [
-              34.9492572120038,
-              29.565899295950373
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    },
-    {
-      "type": "Feature",
-      "id": 5,
-      "properties": {
-		"heightmax": 9,
-        "ANNEE_CONS": 1978,
-        "CODE_UTILI": "residential"
-	  },
-      "geometry": {
-        "coordinates": [
-          [
-            [
-              34.95213398400861,
-              29.567197394967593
-            ],
-            [
-              34.95258172690612,
-              29.56720586098278
-            ],
-            [
-              34.95258172690612,
-              29.56727217807996
-            ],
-            [
-              34.952132361751325,
-              29.56726935607685
-            ],
-            [
-              34.95213398400861,
-              29.567197394967593
-            ]
-          ]
-        ],
-        "type": "Polygon"
-      }
-    }
-  ]
-}

main.py

@@ -12,10 +12,9 @@ from sra_engine import SraEngine
 
 
 try:
-  file_path = (Path(__file__).parent / 'input_files' / 'eilat.geojson')
-  climate_reference_city = 'Montreal'
-  construction_format = 'eilat'
-  usage_format = 'eilat'
+  file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
+  construction_format = 'nrcan'
+  usage_format = 'nrcan'
   energy_systems_format = 'montreal_custom'
 
   out_path = (Path(__file__).parent / 'output_files')
@@ -24,26 +23,30 @@ try:
   print('[simulation start]')
   city = GeometryFactory('geojson',
                          path=file_path,
-                         height_field='heightmax',
-                         year_of_construction_field='ANNEE_CONS',
-                         function_field='CODE_UTILI',
-                         function_to_hub=Dictionaries().eilat_function_to_hub_function).city
+                         height_field='height',
+                         year_of_construction_field='year_of_construction',
+                         function_field='function',
+                         function_to_hub=Dictionaries().montreal_function_to_hub_function).city
 
   print(f'city created from {file_path}')
   ConstructionFactory(construction_format, city).enrich()
   print('enrich constructions... done')
   UsageFactory(usage_format, city).enrich()
   print('enrich usage... done')
+  i = 1
+  x = len(city.buildings)
   for building in city.buildings:
-    building.energy_systems_archetype_name = 'system 1 gas pv'
+    if i < x:
+      building.energy_systems_archetype_name = 'system 1 gas'
+    else:
+      building.energy_systems_archetype_name = 'system 6 gas'
+    i = i + 1
   EnergySystemsFactory(energy_systems_format, city).enrich()
   print('enrich systems... done')
 
   print('exporting:')
-  sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
-  SraEngine(city, sra_file, tmp_folder)
+  SraEngine(city, tmp_folder)
   print('    sra processed...')
-
   MonthlyEnergyBalanceEngine(city, tmp_folder)
   print('    insel processed...')
 

peak_load_class.py

@@ -1,44 +0,0 @@
-from pathlib import Path
-import pandas as pd
-import hub.helpers.constants as cte
-
-def Peak_load (building):
-  array = [None] * 12
-  heating = 0
-  cooling = 0
-  for system in building.energy_systems:
-    for demand_type in system.demand_types:
-      if demand_type == cte.HEATING:
-        heating = 1
-      if demand_type == cte.COOLING:
-        cooling = 1
-  if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
-    peak_lighting = 0
-    peak_appliances = 0
-    for thermal_zone in building.internal_zones[0].thermal_zones:
-      lighting = thermal_zone.lighting
-      for schedule in lighting.schedules:
-        for value in schedule.values:
-          if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
-            peak_lighting = value * lighting.density * thermal_zone.total_floor_area
-      appliances = thermal_zone.appliances
-      for schedule in appliances.schedules:
-        for value in schedule.values:
-          if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
-            peak_appliances = value * appliances.density * thermal_zone.total_floor_area
-
-    monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
-    conditioning_peak = []
-    for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
-      if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
-        conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
-      else:
-        conditioning_peak.append(heating * value)
-      monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
-
-    electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
-                                                 , columns=[f'{building.name} electricity peak load W'])
-  else:
-    electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
-
-    return electricity_peak_load_results

results.py

@@ -9,68 +9,50 @@ class Results:
     self._path = path
 
   def print(self):
-    print_results = None
     file = 'city name: ' + self._city.name + '\n'
-    array = [None] * 12
     for building in self._city.buildings:
       if cte.MONTH in building.heating_demand.keys():
-        heating_results = building.heating_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
+        heating_results = building.heating_demand[cte.MONTH]
       else:
-        heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
+        heating_results = [None] * 12
       if cte.MONTH in building.cooling_demand.keys():
-        cooling_results = building.cooling_demand[cte.MONTH].rename(columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
+        cooling_results = building.cooling_demand[cte.MONTH]
       else:
-        cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
+        cooling_results = [None] * 12
       if cte.MONTH in building.lighting_electrical_demand.keys():
-        lighting_results = building.lighting_electrical_demand[cte.MONTH]\
-          .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
+        lighting_results = building.lighting_electrical_demand[cte.MONTH]
       else:
-        lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
+        lighting_results = [None] * 12
       if cte.MONTH in building.appliances_electrical_demand.keys():
-        appliances_results = building.appliances_electrical_demand[cte.MONTH]\
-          .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
+        appliances_results = building.appliances_electrical_demand[cte.MONTH]
       else:
-        appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
+        appliances_results = [None] * 12
       if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
-        dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]\
-          .rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
+        dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH]
       else:
-        dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
+        dhw_results = [None] * 12
 
       if cte.MONTH in building.heating_consumption.keys():
-        heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
-                                                   columns=[f'{building.name} heating consumption Wh'])
+        heating_consumption_results = building.heating_consumption[cte.MONTH]
       else:
-        heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
+        heating_consumption_results = [None] * 12
       if cte.MONTH in building.cooling_consumption.keys():
-        cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
-                                                   columns=[f'{building.name} cooling consumption Wh'])
+        cooling_consumption_results = building.cooling_consumption[cte.MONTH]
       else:
-        cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
+        cooling_consumption_results = [None] * 12
       if cte.MONTH in building.domestic_hot_water_consumption.keys():
-        dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
-                                               columns=[f'{building.name} domestic hot water consumption Wh'])
+        dhw_consumption_results = building.domestic_hot_water_consumption[cte.MONTH]
       else:
-        dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
+        dhw_consumption_results = [None] * 12
 
       if cte.MONTH in building.heating_peak_load.keys():
-        heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
-                                                 columns=[f'{building.name} heating peak load W'])
+        heating_peak_load_results = building.heating_peak_load[cte.MONTH]
       else:
-        heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
+        heating_peak_load_results = [None] * 12
       if cte.MONTH in building.cooling_peak_load.keys():
-        cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
-                                                 columns=[f'{building.name} cooling peak load W'])
+        cooling_peak_load_results = building.cooling_peak_load[cte.MONTH]
       else:
-        cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
-
-      if cte.MONTH in building.onsite_electrical_production.keys():
-        monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
-        onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
-                                                    columns=[f'{building.name} onsite electrical production Wh'])
-      else:
-        onsite_electrical_production = pd.DataFrame(array, columns=[f'{building.name} onsite electrical production Wh'])
-
+        cooling_peak_load_results = [None] * 12
       heating = 0
       cooling = 0
       for system in building.energy_systems:
@@ -82,7 +64,7 @@ class Results:
       if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
         peak_lighting = 0
         peak_appliances = 0
-        for thermal_zone in building.internal_zones[0].thermal_zones:
+        thermal_zone = building.thermal_zones_from_internal_zones[0]
         lighting = thermal_zone.lighting
         for schedule in lighting.schedules:
           for value in schedule.values:
@@ -103,25 +85,36 @@ class Results:
             conditioning_peak.append(heating * value)
           monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
 
-        electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
-                                                     , columns=[f'{building.name} electricity peak load W'])
+        electricity_peak_load_results = monthly_electricity_peak
       else:
-        electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
+        electricity_peak_load_results = [None] * 12
+
+      if cte.MONTH in building.onsite_electrical_production.keys():
+        monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
+        onsite_electrical_production = monthly_onsite_electrical_production
+      else:
+        onsite_electrical_production = [None] * 12
 
       if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
-        extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
-                                                    columns=[
-                                                      f'{building.name} electrical consumption for distribution Wh'])
+        extra_electrical_consumption = building.distribution_systems_electrical_consumption[cte.MONTH]
       else:
-        extra_electrical_consumption = pd.DataFrame(array,
-                                                    columns=[
-                                                      f'{building.name} electrical consumption for distribution Wh'])
+        extra_electrical_consumption = [None] * 12
 
-      if print_results is None:
-        print_results = heating_results
-      else:
-        print_results = pd.concat([print_results, heating_results], axis='columns')
-      print_results = pd.concat([print_results,
+      columns_names = [f'{building.name} heating demand J',
+                       f'{building.name} cooling demand J',
+                       f'{building.name} lighting demand J',
+                       f'{building.name} appliances demand J',
+                       f'{building.name} domestic hot water demand J',
+                       f'{building.name} heating consumption J',
+                       f'{building.name} cooling consumption J',
+                       f'{building.name} domestic hot water consumption J',
+                       f'{building.name} heating peak load W',
+                       f'{building.name} cooling peak load W',
+                       f'{building.name} electricity peak load W',
+                       f'{building.name} onsite electrical production J',
+                       f'{building.name} extra electrical consumption J'
+                       ]
+      print_results = pd.DataFrame([heating_results,
                                    cooling_results,
                                    lighting_results,
                                    appliances_results,
@@ -133,7 +126,8 @@ class Results:
                                    cooling_peak_load_results,
                                    electricity_peak_load_results,
                                    onsite_electrical_production,
-                                 extra_electrical_consumption], axis='columns')
+                                   extra_electrical_consumption]).T
+      print_results.columns = columns_names
       file += '\n'
       file += f'name: {building.name}\n'
       file += f'year of construction: {building.year_of_construction}\n'
@@ -145,137 +139,8 @@ class Results:
         file += f'storeys: n/a\n'
       file += f'volume: {building.volume}\n'
 
-    full_path_results = Path(self._path / 'demand.csv').resolve()
+      full_path_results = Path(self._path / f'demand_{building.name}.csv').resolve()
       print_results.to_csv(full_path_results, na_rep='null')
     full_path_metadata = Path(self._path / 'metadata.csv').resolve()
     with open(full_path_metadata, 'w') as metadata_file:
       metadata_file.write(file)
-
-  def outputsforgraph(self):
-      outputs_energy =pd.DataFrame()
-      array = [None] * 12
-      for building in self._city.buildings:
-        if cte.MONTH in building.heating_demand.keys():
-          heating_results = building.heating_demand[cte.MONTH].rename(
-            columns={cte.INSEL_MEB: f'{building.name} heating Wh'})
-        else:
-          heating_results = pd.DataFrame(array, columns=[f'{building.name} heating demand Wh'])
-        if cte.MONTH in building.cooling_demand.keys():
-          cooling_results = building.cooling_demand[cte.MONTH].rename(
-            columns={cte.INSEL_MEB: f'{building.name} cooling Wh'})
-        else:
-          cooling_results = pd.DataFrame(array, columns=[f'{building.name} cooling demand Wh'])
-        if cte.MONTH in building.lighting_electrical_demand.keys():
-          lighting_results = building.lighting_electrical_demand[cte.MONTH] \
-            .rename(columns={cte.INSEL_MEB: f'{building.name} lighting electrical demand Wh'})
-        else:
-          lighting_results = pd.DataFrame(array, columns=[f'{building.name} lighting electrical demand Wh'])
-        if cte.MONTH in building.appliances_electrical_demand.keys():
-          appliances_results = building.appliances_electrical_demand[cte.MONTH] \
-            .rename(columns={cte.INSEL_MEB: f'{building.name} appliances electrical demand Wh'})
-        else:
-          appliances_results = pd.DataFrame(array, columns=[f'{building.name} appliances electrical demand Wh'])
-        if cte.MONTH in building.domestic_hot_water_heat_demand.keys():
-          dhw_results = building.domestic_hot_water_heat_demand[cte.MONTH] \
-            .rename(columns={cte.INSEL_MEB: f'{building.name} domestic hot water demand Wh'})
-        else:
-          dhw_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water demand Wh'])
-
-        if cte.MONTH in building.heating_consumption.keys():
-          heating_consumption_results = pd.DataFrame(building.heating_consumption[cte.MONTH],
-                                                     columns=[f'{building.name} heating consumption Wh'])
-        else:
-          heating_consumption_results = pd.DataFrame(array, columns=[f'{building.name} heating consumption Wh'])
-        if cte.MONTH in building.cooling_consumption.keys():
-          cooling_consumption_results = pd.DataFrame(building.cooling_consumption[cte.MONTH],
-                                                     columns=[f'{building.name} cooling consumption Wh'])
-        else:
-          cooling_consumption_results = pd.DataFrame(array, columns=[f'{building.name} cooling consumption Wh'])
-        if cte.MONTH in building.domestic_hot_water_consumption.keys():
-          dhw_consumption_results = pd.DataFrame(building.domestic_hot_water_consumption[cte.MONTH],
-                                                 columns=[f'{building.name} domestic hot water consumption Wh'])
-        else:
-          dhw_consumption_results = pd.DataFrame(array, columns=[f'{building.name} domestic hot water consumption Wh'])
-
-        if cte.MONTH in building.heating_peak_load.keys():
-          heating_peak_load_results = pd.DataFrame(building.heating_peak_load[cte.MONTH],
-                                                   columns=[f'{building.name} heating peak load W'])
-        else:
-          heating_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} heating peak load W'])
-        if cte.MONTH in building.cooling_peak_load.keys():
-          cooling_peak_load_results = pd.DataFrame(building.cooling_peak_load[cte.MONTH],
-                                                   columns=[f'{building.name} cooling peak load W'])
-        else:
-          cooling_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} cooling peak load W'])
-
-        if cte.MONTH in building.onsite_electrical_production.keys():
-          monthly_onsite_electrical_production = building.onsite_electrical_production[cte.MONTH]
-          onsite_electrical_production = pd.DataFrame(monthly_onsite_electrical_production,
-                                                      columns=[f'{building.name} onsite electrical production Wh'])
-        else:
-          onsite_electrical_production = pd.DataFrame(array,
-                                                      columns=[f'{building.name} onsite electrical production Wh'])
-
-        heating = 0
-        cooling = 0
-        for system in building.energy_systems:
-          for demand_type in system.demand_types:
-            if demand_type == cte.HEATING:
-              heating = 1
-            if demand_type == cte.COOLING:
-              cooling = 1
-        if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
-          peak_lighting = 0
-          peak_appliances = 0
-          for thermal_zone in building.internal_zones[0].thermal_zones:
-            lighting = thermal_zone.lighting
-            for schedule in lighting.schedules:
-              for value in schedule.values:
-                if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
-                  peak_lighting = value * lighting.density * thermal_zone.total_floor_area
-            appliances = thermal_zone.appliances
-            for schedule in appliances.schedules:
-              for value in schedule.values:
-                if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
-                  peak_appliances = value * appliances.density * thermal_zone.total_floor_area
-
-          monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
-          conditioning_peak = []
-          for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
-            if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
-              conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
-            else:
-              conditioning_peak.append(heating * value)
-            monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
-
-          electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
-                                                       , columns=[f'{building.name} electricity peak load W'])
-        else:
-          electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
-
-        if cte.MONTH in building.distribution_systems_electrical_consumption.keys():
-          extra_electrical_consumption = pd.DataFrame(building.distribution_systems_electrical_consumption[cte.MONTH],
-                                                      columns=[
-                                                        f'{building.name} electrical consumption for distribution Wh'])
-        else:
-          extra_electrical_consumption = pd.DataFrame(array,
-                                                      columns=[
-                                                        f'{building.name} electrical consumption for distribution Wh'])
-
-        listgraph = [lighting_results.values.sum(),
-                     appliances_results.values.sum(),
-                     heating_consumption_results.values.sum(),
-                     cooling_consumption_results.values.sum(),
-                     dhw_consumption_results.values.sum(),
-                     extra_electrical_consumption.values.sum()
-                    ]
-        outputs_energy[f'building {building.name}'] = listgraph
-        outputs_energy.index = ['Lighting consumption', 'Appliances consumption', 'Heating consumption',
-                                'Cooling consumption', 'DHW consumption', 'Extra electrical consumption']
-        total_final_energy = (lighting_results.values.sum() + appliances_results.values.sum() + \
-                            heating_consumption_results.values.sum() + cooling_consumption_results.values.sum() + \
-                            dhw_consumption_results.values.sum() + extra_electrical_consumption.values.sum())/1000
-
-      return outputs_energy, total_final_energy
-
-

sra_engine.py

@@ -7,14 +7,13 @@ from hub.imports.results_factory import ResultFactory
 
 
 class SraEngine:
-  def __init__(self, city, file_path, output_file_path):
+  def __init__(self, city, output_file_path):
     """
     SRA class
-    :param file_path: _sra.xml file path
+    :param city: City
     :param output_file_path: path to output the sra calculation
     """
     self._city = city
-    self._file_path = file_path
     self._output_file_path = output_file_path
     if platform.system() == 'Linux':
       self._executable = 'sra'
@@ -29,6 +28,8 @@ class SraEngine:
     Calls the software
     """
     try:
-      subprocess.run([self._executable, str(self._file_path)], stdout=subprocess.DEVNULL)
+      subprocess.run([self._executable,
+                     (self._output_file_path / f'{self._city.name}_sra.xml')],
+                     stdout=subprocess.DEVNULL)
     except (SubprocessError, TimeoutExpired, CalledProcessError) as error:
       raise Exception(error)