## 1. Creating the City Model from a GeoJSON File

Start by using the `GeometryFactory` class to create a city model from a GeoJSON file. This class reads building geometries and attributes from the file and constructs a city model.

```python
city = GeometryFactory(
  "geojson",
  input_file,
  height_field="height",
  year_of_construction_field="contr_year",
  function_field="function_c",
  adjacency_field="adjacency",
  function_to_hub=Dictionaries().montreal_function_to_hub_function
).city
```

- `"geojson"`: Specifies the format of the input file.
- `input_file`: The path to your GeoJSON file.
- `height_field="height"`: The attribute in the GeoJSON file that contains the building height.
- `year_of_construction_field="contr_year"`: The attribute for the building's year of construction.
- `function_field="function_c"`: The attribute for the building's function or usage.
- `adjacency_field="adjacency"`: The attribute indicating whether the building is attached or detached.
- `function_to_hub`: A dictionary that maps the building functions to standardized functions used by HUB.

The `city` object now contains all the buildings from the GeoJSON file with their associated attributes.

---

## 2. Enriching the City with Construction Details

Use the `ConstructionFactory` to enrich the city model with construction details and calculation of the thermal zones and total floor area.

```python
ConstructionFactory('nrcan', city).enrich()
```

- `'nrcan'`: Specifies the source of construction data, in this case, NRCan.
- `city`: The city model created in the previous step.
---
## 3. Assigning Energy Demands Using Archetypes

Finally, we assign energy demands to each building using the `ResultFactory` class and an archetype-based demand model. This basically maps buildings to predefined archetypes and assigning corresponding energy demand profiles.

```python
ResultFactory('archetypes', city, demand_file).enrich()
```
- `'archetypes'`: Specifies the handler we are using which in this case is archetype-based demand model.
- `city`: The city.
- `demand_file`: The path to the CSV file containing the archetype demand profiles.
---

## 4. Rules for Finding Archetypes and Assigning Demands

This mapping is based on the following building attributes:

- **Function**
- **Height**
- **Adjacency**: Whether the building is attached or is detached.
- **Year of Construction**

### **Mapping Rules**:

Based on the building's function, height, and adjacency:

- **Residential Buildings** ('residential', 'multifamily house', 'single family house'):
  - **Single Family House**:
    - Height less than 6 meters.
    - Adjacency: 'detached'.
    - `usage = 'Single Family'`.
  - **Row House**:
    - Height less than 6 meters.
    - Adjacency: 'attached'.
    - `usage = 'Row house'`.
  - **Duplex/Triplex**:
    - Height between 6 and 10 meters.
    - Adjacency: 'detached'.
    - `usage = 'Duplex/triplex'`.
  - **Small MURBs** (Multi-Unit Residential Buildings):
    - Height between 6 and 10 meters.
    - Adjacency: 'attached'.
    - `usage = 'Small MURBs'`.
  - **Medium MURBs**:
    - Height between 10 and 15 meters.
    - `usage = 'Medium MURBs'`.
  - **Large MURBs**:
    - Height greater than 15 meters.
    - `usage = 'Large MURBs'`.

- **Office Buildings** ('office', 'office and administration'):
  - `usage = 'Office'`.

- **Commercial Buildings** ('commercial', 'retail shop without refrigerated food', 'retail shop with refrigerated food', 'stand alone retail', 'strip mall'):
  - If adjacency is 'attached':
    - `usage = 'Commercial attached'`.
  - Else:
    - `usage = 'Commercial detached'`.

#### Determine Building Vintage (`vintage`):

Based on the building's year of construction:

- **Pre-1947**:
  - `year <= 1947`.
  - `vintage = 'Pre 1947'`.
- **1947-1983**:
  - `1947 < year <= 1983`.
  - `vintage = '1947-1983'`.
- **1984-2010**:
  - `1983 < year <= 2010`.
  - `vintage = '1984-2010'`.
- **Post-2010**:
  - `year > 2010`.
  - `vintage = 'Post 2010'`.

Combine usage and vintage to create the key:

```python
archetype_key = f"{usage} {vintage}"
```

This key is used to look up the corresponding demand profiles in the archetype demand file.

---

### Assigning Demands:

1. **Lookup Demand Profiles**:

   Using the `archetype_key`, retrieve the hourly demand profiles from the `demand_file`. Each archetype has associated hourly demands for different energy uses.

2. **Scale Demands by Floor Area**:

   Multiply the demand values by the building's total floor area.

3. **Assign Demands to Building**:

   Set the building's demand attributes:

   ```python
   building.heating_demand = [value * area for value in demand['Heating']]
   building.cooling_demand = [value * area for value in demand['Cooling']]
   building.domestic_hot_water_heat_demand = [value * area for value in demand['DHW']]
   building.appliances_electrical_demand = [value * area for value in demand['Equipment']]
   building.lighting_electrical_demand = [value * area for value in demand['Lighting']]
   ```

---

### Example:

For a residential building with the following attributes:
- **Function**: 'residential'
- **Height**: 9 meters
- **Adjacency**: 'attached'
- **Year of Construction**: 1986

#### **Mapping Steps**:

1. **Usage**:
   - Height is between 6 and 10 meters.
   - Adjacency is 'attached'.
   - Therefore, `usage = 'Small MURBs'`.

2. **Vintage**:
   - Year is 1986, which falls in 1984-2010.
   - Therefore, `vintage = '1984-2010'`.

3. **Archetype Key**:
   - `archetype_key = 'Small MURBs 1984-2010'`.