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 clinic count
    total_count = len(df)
    
    # Bounding box
    # Assuming columns are 'latitude' and 'longitude' or similar.
    # Let's check columns first.
    print("Columns in CSV:", df.columns.tolist())
    
    # We need to identify lat/lon columns. Common names: latitude, longitude, lat, lon
    lat_col = None
    lon_col = None
    
    for col in df.columns:
        if col.lower() in ['latitude', 'lat']:
            lat_col = col
        if col.lower() in ['longitude', 'lon']:
            lon_col = col
            
    if not lat_col or not lon_col:
        print(f"Error: Could not find lat/lon columns. Found: {df.columns.tolist()}")
        return

    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 format: [min_lon, min_lat, max_lon, max_lat] or [min_lat, min_lon, max_lat, max_lon]
    # Usually, bbox is [min_x, min_y, max_x, max_y] which is [min_lon, min_lat, max_lon, max_lat]
    bbox = [min_lon, min_lat, max_lon, max_lat]
    
    # Count per subdistrict
    # We need to identify the subdistrict column. 
    # Let's guess common names: 'subdistrict', 'health_subdistrict', 'sub_district'
    subdistrict_col = None
    for col in df.columns:
        if 'subdistrict' in col.lower():
            subdistrict_col = col
            break
            
    if not subdistrict_col:
        print(f"Error: Could not find subdistrict column. Found: {df.columns.tolist()}")
        return
        
    counts_per_subdistrict = df[subdistrict_col].value_counts().to_dict()
    
    # Prepare JSON
    inventory = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {str(k): int(v) for k, v in counts_per_subdistrict.items()}
    }
    
    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 clinic count
    total_count = len(df)
    
    # Identify geometry column
    geom_col = None
    for col in df.columns:
        if 'wkt' in col.lower() or 'geom' in col.lower():
            geom_col = col
            break
            
    if not geom_col:
        print(f"Error: Could not find geometry column. Found: {df.columns.tolist()}")
        return

    # Parse geometries and calculate bbox
    # We can use geopandas for easier handling or just shapely
    geoms = df[geom_col].apply(wkt.loads)
    
    min_x, min_y, max_x, max_y = float('inf'), float('inf'), float('-inf'), float('-inf')
    
    for geom in geoms:
        bounds = geom.bounds # (minx, miny, maxx, maxy)
        if bounds[0] < min_x: min_x = bounds[0]
        if bounds[1] < min_y: min_y = bounds[1]
        if bounds[2] > max_x: max_x = bounds[2]
        if bounds[3] > max_y: max_y = bounds[3]
        
    bbox = [min_x, min_y, max_x, max_y]
    
    # Count per subdistrict
    subdistrict_col = None
    for col in df.columns:
        if 'subdistrict' in col.lower():
            subdistrict_col = col
            break
            
    if not subdistrict_col:
        print(f"Error: Could not find subdistrict column. Found: {df.columns.tolist()}")
        return
        
    counts_per_subdistrict = df[subdistrict_col].value_counts().to_dict()
    
    # Prepare JSON
    inventory = {
        "count": int(total_count),
        "bbox": bbox,
        "count_per_subdistrict": {str(k): int(v) for k, v in counts_per_subdistrict.items()}
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(inventory, f, indent=4)
    
    print("Successfully created clinic_inventory.json")

if __name__ == "__main__":
    main()