dd-l1-capetown-clinics-bbox

Status: done
Score: 100
Duration: 1:12
Cost: 0.13¢
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`.
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System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
0:13
Bash ls -F 
capetown_clinics.csv
0:23
Bash head -n 5 capetown_clinics.csv 
clinic_id,name,subdistrict,wkt_geom
1,Mokoena Western Clinic,Western,POINT(18.478309 -33.888756)
2,Ndlovu Western Clinic,Western,POINT(18.421127 -33.924929)
3,Pieterse Western Clinic,Western,POINT(18.389237 -33.976099)
4,van der Merwe Western Clinic,Western,POINT(18.47354 -33.890933)
0:41
Write /work/solve.py
content · 40 lines · py 
import pandas as pd
import json
from shapely import wkt

def main():
    # Load the data
    df = pd.read_csv('capetown_clinics.csv')

    # Convert wkt_geom to shapely geometries
    df['geometry'] = df['wkt_geom'].apply(wkt.loads)

    # Total clinic count
    total_count = len(df)

    # Bounding box
    # Concatenate all geometries to find the total bounds
    # A simpler way is to extract min/max x and y
    min_x = df['geometry'].map(lambda g: g.bounds[0]).min()
    min_y = df['geometry'].map(lambda g: g.bounds[1]).min()
    max_x = df['geometry'].map(lambda g: g.bounds[2]).max()
    max_y = df['geometry'].map(lambda g: g.bounds[3]).max()
    bbox = [min_x, min_y, max_x, max_y]

    # Count per subdistrict
    count_per_subdistrict = df['subdistrict'].value_counts().to_dict()

    # Prepare JSON data
    inventory = {
        "count": total_count,
        "bbox": bbox,
        "count_per_subdistrict": count_per_subdistrict
    }

    # Write to file
    with open('clinic_inventory.json', 'w') as f:
        json.dump(inventory, f, indent=4)

if __name__ == "__main__":
    main()

Wrote /work/solve.py (1101 chars).
0:43
Bash python solve.py
(no output)
0:48
Bash cat clinic_inventory.json 
{
    "count": 80,
    "bbox": [
        18.380309,
        -34.073855,
        18.819549,
        -33.701317
    ],
    "count_per_subdistrict": {
        "Western": 12,
        "Southern": 12,
        "Tygerberg": 11,
        "Northern": 10,
        "Eastern": 10,
        "Klipfontein": 9,
        "Mitchells Plain": 8,
        "Khayelitsha": 8
    }
}
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