dd-l1-capetown-clinics-bbox

Status: done
Score: 1.000
Duration: 0:40
Cost: 0.17¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
I'm about to push the `capetown_clinics.csv` export through to our case-management system and I want a quick inventory check first. Can you produce a small JSON file with the total clinic count, the overall bounding box as a bbox array, and the count per subdistrict, so I can confirm every health subdistrict is represented? Please write it to `clinic_inventory.json` with keys `count`, `bbox`, and `count_per_subdistrict`.
0:00
System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:01
Bash ls -F 
capetown_clinics.csv
0:02
Read capetown_clinics.csv 
     1	clinic_id,name,subdistrict,wkt_geom
     2	1,Mokoena Western Clinic,Western,POINT(18.478309 -33.888756)
     3	2,Ndlovu Western Clinic,Western,POINT(18.421127 -33.924929)
     4	3,Pieterse Western Clinic,Western,POINT(18.389237 -33.976099)
     5	4,van der Merwe Western Clinic,Western,POINT(18.47354 -33.890933)
0:16
Write /work/solve.py
content · 43 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["pandas", "shapely"]
# ///
import pandas as pd
import json
from shapely import wkt

def main():
    df = pd.read_csv('capetown_clinics.csv')
    
    # Parse wkt_geom
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)
    
    # Total count
    total_count = len(df)
    
    # Bounding box
    # Combine all geometries into one multi-point for bbox calculation
    all_geoms = df['geometry'].tolist()
    if all_geoms:
        from shapely.ops import unary_union
        combined = unary_union(all_geoms)
        bbox = list(combined.bounds) # [minx, miny, maxx, maxy]
    else:
        bbox = []
        
    # Count per subdistrict
    subdistrict_counts = df['subdistrict'].value_counts().to_dict()
    
    # Output JSON
    output = {
        "count": total_count,
        "bbox": bbox,
        "count_per_subdistrict": subdistrict_counts
    }
    
    with open('clinic_inventory.json', 'w') as f:
        json.dump(output, f, indent=4)

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1046 chars).
0:24
Bash uv run solve.py 
Installed 5 packages in 203ms
0:29
Read clinic_inventory.json 
     1	{
     2	    "count": 80,
     3	    "bbox": [
     4	        18.380309,
     5	        -34.073855,
     6	        18.819549,
     7	        -33.701317
     8	    ],
     9	    "count_per_subdistrict": {
    10	        "Western": 12,
    11	        "Southern": 12,
    12	        "Tygerberg": 11,
    13	        "Northern": 10,
    14	        "Eastern": 10,
    15	        "Klipfontein": 9,
    16	        "Mitchells Plain": 8,
    17	        "Khayelitsha": 8
    18	    }
    19	}
0:39