increment for populating the dataframe

data_processing/aggregate_data.py

@@ -1,8 +1,14 @@
 import csv
 from age_distribution_by_id import get_age_distribution
 from get_smallAreaInfo import get_smallAreas
+from get_density import get_density
+import os
+import pandas as pd
 
 
+# Specify file paths here
+file_ibuafjoldi = os.path.join('given_data', 'ibuafjoldi.csv')
+
 # Small area id: id of the small area
 # Density: current density of the small area
 # Income distribution: the distribution of income in the small area per year (dictionary, keys: years, values: income distribution [buckets])
@@ -25,6 +31,45 @@ def open_file(filename):
         return []
 
 # get list of smsv, each represented as {"id": smsv_id, "geometry": [(long, lat), ...]}
-smsv_id_geom = get_smallAreas()
-# get_age_distribution([2024] , [smsv["id"] for smsv in smsv_id_geom], "")
+smsv_id_geom = get_smallAreas()[:2]
+smsv_ids = [smsv["id"] for smsv in smsv_id_geom]  # list of smsv ids
+
+# for each smsv_id get the age distribution for several years if required
+years = [2023, 2024]  # Example years for age distribution
+age_distribution = get_age_distribution(years, smsv_ids, file_ibuafjoldi)  # Dict with age data
+
+# Populate pandas dataframe
+data = []
+for smsv in smsv_id_geom:
+    smsv_id = smsv["id"]
+    geometry = smsv["geometry"]
+    
+    # Calculate total population for density calculation
+    population = sum(age_distribution.get(smsv_id, {}).get(2024, {}).values())
+    
+    # Calculate density
+    try:
+        density = get_density(geometry, population)
+    except ValueError as e:
+        print(f"Density calculation failed for {smsv_id}: {e}")
+        density = None
+
+    # Age distribution
+    age_dist = age_distribution.get(smsv_id, {})
+    
+    # Add row to data
+    data.append({
+        "smallAreaId": smsv_id,
+        "density": density,
+        "income_distribution_per_year": {},  # Placeholder for now
+        "age_distribution": age_dist,
+        "geometry": geometry,
+        "projected_dwellings": None  # Placeholder for now
+    })
+
+# Convert to Pandas DataFrame
+df = pd.DataFrame(data, columns=columns)
 
+# Display or save the DataFrame
+print(df.head())
+df.to_csv('output.csv', index=False, encoding='utf-8')  # Save to CSV