(WIP) feat: add result factory for archetype mapping

hub/city_model_structure/simplified_building.py

@@ -14,6 +14,10 @@ class SimplifiedBuilding:
     self._postal_code = postal_code
     self._city = city
     self._type = 'building'
+    self.heating_demand = []
+    self.cooling_demand = []
+    self.electricity_demand = []
+    self.appliance_demand = []
 
   @property
   def name(self):

hub/imports/results/archetype_based_demand.py

@@ -0,0 +1,108 @@
+import pandas as pd
+
+class DemandEnricher:
+  """
+  DemandEnricher class to enrich buildings with demand data
+  """
+
+  def __init__(self, archetype_demand_file):
+    self.archetype_demand_file = archetype_demand_file
+    self.archetype_df = pd.read_csv(self.archetype_demand_file, encoding='latin1')
+    self.fix_type_of_building_encoding()
+    self.create_archetype_demands_dict()
+    self.create_function_mapping()
+
+  def fix_type_of_building_encoding(self):
+    def fix_encoding(text):
+      if isinstance(text, str):
+        try:
+          return text.encode('latin1').decode('utf-8')
+        except UnicodeEncodeError:
+          return text
+      return text
+
+    self.archetype_df['Type_of_building'] = self.archetype_df['Type_of_building'].apply(fix_encoding)
+
+  def create_archetype_demands_dict(self):
+    self.archetype_demands = {}
+    for archetype in self.archetype_df['Type_of_building'].unique():
+      df = self.archetype_df[self.archetype_df['Type_of_building'] == archetype]
+      self.archetype_demands[archetype] = df.reset_index(drop=True)
+
+  def create_function_mapping(self):
+    self.function_mapping = {
+      'residential': 'Maison Unifamiliale',
+      'single family house': 'Maison Unifamiliale',
+      'multifamily house': 'Apartements partie 3 du code',
+      'medium office': 'Bureaux',
+      'office and administration': 'Bureaux',
+      'commercial': 'Commercial attaché',
+      'warehouse': 'Commercial détaché',
+      'restaurant': 'Commercial attaché',
+      'hotel': 'Commercial attaché'
+    }
+
+  def get_vintage_range(self, year):
+    if year <= 1947:
+      return 'avant 1947'
+    elif 1947 < year <= 1983:
+      return '1947-1983'
+    elif 1983 < year <= 2010:
+      return '1984-2010'
+    elif year > 2010:
+      return 'après 2010'
+    else:
+      return None
+
+  def enrich_city(self, city):
+    for building in city.buildings:
+      if (building.year_of_construction is not None and
+          building.function is not None and
+          building.total_floor_area is not None):
+
+        building_function_lower = building.function.lower()
+        mapped_function = None
+        for key in self.function_mapping:
+          if key in building_function_lower:
+            mapped_function = self.function_mapping[key]
+            break
+
+        if mapped_function:
+          vintage_range = self.get_vintage_range(building.year_of_construction)
+
+          if vintage_range:
+            type_of_building = f"{mapped_function} {vintage_range}"
+
+            if type_of_building in self.archetype_demands:
+              demands_df = self.archetype_demands[type_of_building]
+
+              total_floor_area = building.total_floor_area
+              demands_df['Heating_total'] = demands_df['Heating'] * total_floor_area
+              demands_df['Cooling_total'] = demands_df['Cooling'] * total_floor_area
+              demands_df['Equipment_total'] = demands_df['Equipment'] * total_floor_area
+              demands_df['Lighting_total'] = demands_df['Lighting'] * total_floor_area
+
+              building.heating_demand = demands_df['Heating_total'].tolist()
+              building.cooling_demand = demands_df['Cooling_total'].tolist()
+              building.electricity_demand = demands_df['Lighting_total'].tolist()
+              building.appliance_demand = demands_df['Equipment_total'].tolist()
+            else:
+              building.heating_demand = []
+              building.cooling_demand = []
+              building.electricity_demand = []
+              building.appliance_demand = []
+          else:
+            building.heating_demand = []
+            building.cooling_demand = []
+            building.electricity_demand = []
+            building.appliance_demand = []
+        else:
+          building.heating_demand = []
+          building.cooling_demand = []
+          building.electricity_demand = []
+          building.appliance_demand = []
+      else:
+        building.heating_demand = []
+        building.cooling_demand = []
+        building.electricity_demand = []
+        building.appliance_demand = []

main.py

@@ -1,10 +1,12 @@
 from hub.imports.geometry_factory import GeometryFactory
 from hub.helpers.dictionaries import Dictionaries
+from hub.imports.results.archetype_based_demand import DemandEnricher
 import os
 import psycopg2
 import csv
 
 input_file = "data/cmm_points_function_vintage_surface.csv"
+output_file = "output_buildings.csv"
 
 # Initialize city object from GeometryFactory
 city = GeometryFactory(
@@ -17,3 +19,10 @@ city = GeometryFactory(
     function_to_hub=Dictionaries().montreal_function_to_hub_function,
     total_floor_area_field="supfi_etag"
 ).city
+
+demand_enricher = DemandEnricher('./data/energy_demand_data.csv')
+
+# Enrich the city object
+demand_enricher.enrich_city(city)
+
+print("done")