dd-l1-capetown-clinics-bbox

Rerun

import pandas as pd
import json

def main():
    # Load the CSV
    df = pd.read_csv('capetown_clinics.csv')
    
    # Total count
    total_count = len(df)
    
    # Bounding box calculation
    # Assuming columns are named 'latitude' and 'longitude' or similar. 
    # Let's check columns first.
    print("Columns found:", df.columns.tolist())
    
    # We'll use standard names for lat/lon or try to find them
    lat_col = None
    lon_col = None
    
    for col in df.columns:
        if 'lat' in col.lower():
            lat_col = col
        if 'lon' in col.lower() or 'lng' in col.lower():
            lon_col = col
            
    if not lat_col or not lon_col:
        # If not found, we might need to check if they are named differently
        # For now, let's assume we found them or error out.
        raise ValueError("Could not find latitude or longitude columns.")

    min_lat = df[lat_col].min()
    max_lat = df[lat_col].max()
    min_lon = df[lon_col].min()
    max_lon = df[lon_col].max()
    
    bbox = [min_lon, min_lat, max_lon, max_lat] # standard [minx, miny, maxx, maxy]
    
    # Count per subdistrict
    # Assuming a column named 'subdistrict' or 'health_subdistrict'
    subdistrict_col = None
    for col in df.columns:
        if 'subdistrict' in col.lower():
            subdistrict_col = col
            break
            
    if not subdistrict_col:
        raise ValueError("Could not find a subdistrict column.")
        
    count_per_subdistrict = df[subdistrict_col].value_counts().to_dict()
    
    # Prepare JSON
    inventory = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": count_per_subdistrict
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(inventory, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()

import pandas as pd
import json
from shapely import wkt

def main():
    # Load the CSV
    df = pd.read_csv('capetown_clinics.csv')
    
    # Total count
    total_count = len(df)
    
    # We have 'wkt_geom' column. Let's use it for bbox.
    # The WKT column probably contains POINT(...)
    
    # Convert WKT column to shapely geometries
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)
    
    # Calculate bounding box
    # We can use the combined bounds of all geometries
    # or manually iterate.
    all_bounds = []
    for geom in df['geometry']:
        all_bounds.append(geom.bounds) # (minx, miny, maxx, maxy)
        
    import numpy as np
    all_bounds_arr = np.array(all_bounds)
    min_lon = all_bounds_arr[:, 0].min()
    min_lat = all_bounds_arr[:, 1].min()
    max_lon = all_bounds_arr[:, 2].max()
    max_lat = all_bounds_arr[:, 3].max()
    
    bbox = [min_lon, min_lat, max_lon, max_lat]
    
    # Count per subdistrict
    # Column 'subdistrict' was found in previous run
    subdistrict_col = 'subdistrict'
    count_per_subdistrict = df[subdistrict_col].value_counts().to_dict()
    
    # Prepare JSON
    inventory = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": count_per_subdistrict
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(inventory, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()