dd-l3-lagos-overture-buildings

Status: done
Score: 0.000
Duration: 1:32
Cost: 0.12¢
Model: google/gemma-4-26b-a4b-it

Map (reference ← swipe → agent)

0:00
Updating the flood-risk model before rainy season. Need every building footprint exceeding 1000 m² across Lagos State (Nigeria) from Overture. Also need a per-LGA roll-up: for each Lagos Local Government Area, the building count, total footprint area, count of buildings with a non-null Overture height, and the median height where available (null otherwise). Write two output files. `lagos_buildings.geoparquet` should be EPSG:4326 GeoParquet with columns `id`, `height`, `footprint_area_m2`, `lga`, and `geometry`. `lagos_building_summary.parquet` should be a plain Parquet with no geometry column and columns `lga`, `n_buildings`, `total_footprint_m2`, `n_with_height`, and `p50_height_m`.
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System info 
{
  "adapter": "openrouter",
  "model": "google/gemma-4-26b-a4b-it",
  "base_url": "https://openrouter.ai/api/v1"
}
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Bash ls -F
(no output)
0:27
Write 
missing or non-string argument: 'file_path'
1:04
Write 
missing or non-string argument: 'file_path'
1:08
Write solve.py
content · 5 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pyproj", "duckdb", "pandas", "pyarrow"]
# ///

file_path must be inside the working directory (/work): '/home/nhp/project/benchmark/harness/.sessions/a1aa1e382a084d539b939dcf9ada9e31/solve.py'
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Write solve.py
content · 35 lines · py 
# /// script
# requires-python = ">=3.12"
# dependencies = ["geopandas", "shapely", "pyproj", "duckdb", "pandas", "pyarrow"]
# ///

import duckdb
import pandas as pd
import geopandas as gpd

def main():
    # Connection to DuckDB
    con = duckdb.connect()
    con.execute("INSTALL spatial; LOAD spatial;")

    # Note: Overture data is usually available via Parquet files on S3 or similar.
    # For this task, since I don't have the direct S3 URL for Lagos Overture data,
    # I will simulate the retrieval/processing logic as if I were querying the Overture Parquet datasets.
    # However, typically in these environments, I should look for local files or know the specific source.
    # Since I am an agent and the user expects me to "get" it from Overture, 
    # I will assume the Overture data is accessible via a standard URL or I need to use duckdb to read it.
    
    # In a real scenario, one might use:
    # https://overturemaps.org/download/
    # But downloading the whole world is impossible. 
    # I'll try to use a DuckDB query that targets the Overture S3-backed datasets if possible,
    # but for this sandbox, I will write a script that would work if the data were present 
    # or if I could query it.
    
    # Wait, I don't have the data. I must check if there's any data in the environment.
    # I'll check if there are any parquet files.
    pass

if __name__ == "__main__":
    main()

file_path must be inside the working directory (/work): '/home/nhp/project/benchmark/harness/.sessions/a1aa1e382a084d539b939dcf9ada9e31/solve.py'
1:18
Bash ls -la /home/nhp/project/benchmark/harness 
ls: cannot access '/home/nhp/project/benchmark/harness/.sessions/a1aa1e382a084d539b939dcf9ada9e31/': No such file or directory
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Bash pwd 
/work
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Bash ls -la 
total 0
drwxr-xr-x 1 agent agent 0 Jun  6 09:37 .
drwxr-xr-x 1 root  root  0 Jun  6 11:52 ..