Run df7df63d

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00146 — 35526ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd
import pyproj

# Read the input file
gdf = gpd.read_file("input.geojson")

# Diagnose the CRS
# The coordinates are large (millions), indicating a projected CRS in metres.
# Based on the task description, we determine this is EPSG:3857 (Web Mercator).
gdf.crs = "EPSG:3857"

# Reproject to WGS84 (EPSG:4326)
gdf_wgs84 = gdf.to_crs("EPSG:4326")

# Write the result to output.geojson
gdf_wgs84.to_file("output.geojson", driver="GeoJSON")

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00192 — 40707ms
# verifier: FAIL — verifier did not emit JSON; stdout tail: 
# no artifact produced

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00172 — 83968ms
# verifier: FAIL — output not in WGS84 Munich coords
# artifact: download (582 bytes)
import geopandas as gpd

# Load the input file
gdf = gpd.read_file("input.geojson")

# The coordinates are large (millions), indicating a projected CRS.
# As per the task instructions, we determine this is EPSG:3857.
if gdf.crs is None:
    gdf.crs = "EPSG:3857"

# Reproject to WGS84 (EPSG:4326)
gdf_wgs84 = gdf.to_crs("EPSG:4326")

# Write the output
gdf_wgs84.to_file("output.geojson", driver="GeoJSON")

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00190 — 71853ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 1 — rejected — cost 0.00172 — 32013ms
# verifier: FAIL — stats.json unreadable
# no artifact produced
import rasterio
import numpy as np
import json
from shapely import geometry

def main():
    # Load the raster
    with rasterio.open('grid.asc') as src:
        data = src.read(1)
        transform = src.transform
        shape = src.shape
        nodata = src.nodata

    # Load the polygon geometry
    with open('zone.geojson', 'r') as f:
        geojson_data = json.load(f)
    
    # Extract geometry from GeoJSON feature
    geom = geometry.shape(geojson_data['features'][0]['geometry'])

    # Create a boolean mask for pixels inside the polygon
    mask = rasterio.features.geometry_mask(
        [geom],
        transform=transform,
        out_shape=shape,
        invert=False
    )

    # Apply the mask to the raster data
    masked_values = data[mask]

    # Filter out nodata values
    if nodata is not None:
        masked_values = masked_values[masked_values != nodata]

    # Filter out NaN values
    masked_values = masked_values[~np.isnan(masked_values)]

    # Calculate the mean
    if len(masked_values) > 0:
        mean_val = np.mean(masked_values)
    else:
        mean_val = None

    # Write the result to stats.json
    result = {"mean": mean_val}
    with open('stats.json', 'w') as f:
        json.dump(result, f)

if __name__ == "__main__":
    main()

--- stderr ---
Traceback (most recent call last):
  File "/tmp/arena/solution.py", line 51, in <module>
    main()
  File "/tmp/arena/solution.py", line 22, in main
    mask = rasterio.features.geometry_mask(
           ^^^^^^^^^^^^^^^^^
AttributeError: module 'rasterio' has no attribute 'features'. Did you mean: '_features'?

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — rejected — cost 0.00218 — 75372ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00127 — 24848ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd
from pyproj import CRS

# Read input data
gdf = gpd.read_file("sites.geojson")

# Define coordinate reference systems
src_crs = CRS.from_epsg(4326)
tgt_crs = CRS.from_epsg(3857)

# Reproject to projected CRS for accurate buffering
gdf_proj = gdf.to_crs(tgt_crs)

# Create 1000m buffers
gdf_proj['geometry'] = gdf_proj.buffer(1000)

# Reproject back to WGS84
gdf_final = gdf_proj.to_crs(src_crs)

# Write output
gdf_final.to_file("buffers.geojson", driver="GeoJSON")

# attempt undefined — model: @cf/zai-org/glm-4.7-flash — exit 0 — accepted — cost 0.00150 — 30656ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5903 bytes)
import geopandas as gpd

# Read the input file
gdf = gpd.read_file('sites.geojson')

# Define coordinate reference systems
# Source: WGS84 (EPSG:4326)
# Target: Web Mercator (EPSG:3857) for accurate meter-based buffering
src_crs = 'EPSG:4326'
dst_crs = 'EPSG:3857'

# Reproject to projected CRS to ensure 1000m distance is accurate
gdf_projected = gdf.to_crs(dst_crs)

# Create 1000m buffers
gdf_projected['buffer'] = gdf_projected.geometry.buffer(1000)

# Extract buffer geometries and reproject back to WGS84
buffers_gdf = gpd.GeoDataFrame(geometry=gdf_projected['buffer'], crs=dst_crs)
buffers_gdf = buffers_gdf.to_crs(src_crs)

# Write the output file
buffers_gdf.to_file('buffers.geojson', driver='GeoJSON')

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00086 — 20975ms
# verifier: PASS — CRS reproject to WGS84 verified
# artifact: download (591 bytes)
import geopandas as gpd

gdf = gpd.read_file("input.geojson")
gdf = gdf.set_crs(epsg=3857, allow_override=True)
gdf = gdf.to_crs(epsg=4326)
gdf.to_file("output.geojson", driver="GeoJSON")

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00192 — 41038ms
# verifier: FAIL — verifier did not emit JSON; stdout tail: 
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00195 — 73802ms
# verifier: FAIL — verifier did not emit JSON; stdout tail: 
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00190 — 42037ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00190 — 45507ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — rejected — cost 0.00193 — 95205ms
# verifier: FAIL — stats.json unreadable
# no artifact produced

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00116 — 28078ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd

gdf = gpd.read_file("sites.geojson")
gdf_proj = gdf.to_crs("EPSG:3857")
gdf_proj["geometry"] = gdf_proj.buffer(1000)
gdf_out = gdf_proj.to_crs("EPSG:4326")
gdf_out.to_file("buffers.geojson", driver="GeoJSON")

# attempt undefined — model: @cf/moonshotai/kimi-k2.6 — exit 0 — accepted — cost 0.00145 — 31172ms
# verifier: PASS — 1000m buffers verified
# artifact: download (5935 bytes)
import geopandas as gpd

sites = gpd.read_file("sites.geojson")
if sites.crs is None:
    sites.set_crs(epsg=4326, inplace=True)

buffers = sites.to_crs(epsg=3857)
buffers.geometry = buffers.buffer(1000)
buffers = buffers.to_crs(epsg=4326)

buffers.to_file("buffers.geojson", driver="GeoJSON")