run-20260619-015555Z / dd-l2-bangkok-multicrs-audit

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A contractor just shipped `bangkok_contractor_delivery.gpkg` and I need a defect cite-sheet before I sign off. Please walk every layer in the file and write one row per layer to `crs_audit.csv` with these columns: layer_name, declared_crs (formatted as `EPSG:NNNN`), geometry_type, feature_count, sample_x and sample_y for a sample coordinate taken from the layer as it sits on disk, and encoding_detected. For encoding_detected, look at the text attributes for anomalies and report either `latin1-mojibake` or `utf-8`.

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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 -l bangkok_contractor_delivery.gpkg

-rw-r--r-- 1 agent agent 2703360 Jun 19 02:42 bangkok_contractor_delivery.gpkg

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Write /work/solve.py

content · 86 lines · py

import geopandas as gpd
import fiona
import pandas as pd
import csv

def check_encoding(df):
    """
    Checks if the text columns in a dataframe contain mojibake.
    A simple way is to try to encode/decode or look for specific patterns.
    Here we will check if any string column contains characters that look like mojibake.
    Specifically, we'll check if strings can be converted from latin-1 to utf-8 without error 
    but look 'weird', or more simply, if we can detect common mojibake patterns.
    Actually, a more robust way is to check if strings contain common replacement characters 
    or if they were likely read as latin-1 when they were utf-8.
    """
    # For the sake of this task, let's see if we can identify latin1-mojibake.
    # A common sign of latin1-mojibake is seeing things like 'Ã©' instead of 'é'.
    
    # We'll check all object (string) columns
    for col in df.select_dtypes(include=['object']).columns:
        for val in df[col].dropna().unique():
            if isinstance(val, str):
                # Check for patterns like Ã followed by a character, which is common in utf-8 read as latin-1
                # or just try to see if it's "broken"
                try:
                    # If it's valid utf-8 and contains high-bit characters that look like mojibake
                    # This is a heuristic.
                    if any(ord(c) > 127 for c in val):
                        # Try to see if decoding it as latin-1 and re-encoding to utf-8 "fixes" it
                        # Or if it's already looking like mojibake.
                        # Let's look for the Ã pattern specifically.
                        if 'Ã' in val:
                            return 'latin1-mojibake'
                except:
                    pass
    return 'utf-8'

def audit_gpkg(file_path, output_csv):
    layers = fiona.listlayers(file_path)
    audit_results = []

    for layer in layers:
        gdf = gpd.read_file(file_path, layer=layer)
        
        # CRS
        if gdf.crs is not None:
            declared_crs = f"EPSG:{gdf.crs.to_epsg()}" if gdf.crs.to_epsg() else str(gdf.crs)
        else:
            declared_crs = "None"
            
        # Geometry Type
        if not gdf.empty:
            geom_type = gdf.geometry.type.iloc[0]
            # Sample coords
            sample_row = gdf.iloc[0]
            sample_geom = sample_row.geometry
            if sample_geom.is_empty:
                sample_x, sample_y = None, None
            else:
                sample_x, sample_y = sample_geom.centroid.x, sample_geom.centroid.y
        else:
            geom_type = "Unknown"
            sample_x, sample_y = None, None

        feature_count = len(gdf)
        
        # Encoding detection
        encoding = check_encoding(gdf)
        
        audit_results.append({
            'layer_name': layer,
            'declared_crs': declared_crs,
            'geometry_type': geom_type,
            'feature_count': feature_count,
            'sample_x': sample_x,
            'sample_y': sample_y,
            'encoding_detected': encoding
        })

    df_audit = pd.DataFrame(audit_results)
    df_audit.to_csv(output_csv, index=False)
    print(f"Audit complete. Results written to {output_csv}")

if __name__ == "__main__":
    audit_gpkg('bangkok_contractor_delivery.gpkg', 'crs_audit.csv')

Wrote /work/solve.py (3366 chars).

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Bash python solve.py

Audit complete. Results written to crs_audit.csv

/work/solve.py:20: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.
See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.
for col in df.select_dtypes(include=['object']).columns:
/work/solve.py:20: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.
See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.
for col in df.select_dtypes(include=['object']).columns:
/work/solve.py:20: Pandas4Warning: For backward compatibility, 'str' dtypes are included by select_dtypes when 'object' dtype is specified. This behavior is deprecated and will be removed in a future version. Explicitly pass 'str' to `include` to select them, or to `exclude` to remove them and silence this warning.
See https://pandas.pydata.org/docs/user_guide/migration-3-strings.html#string-migration-select-dtypes for details on how to write code that works with pandas 2 and 3.
for col in df.select_dtypes(include=['object']).columns:

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Bash cat crs_audit.csv

layer_name,declared_crs,geometry_type,feature_count,sample_x,sample_y,encoding_detected
parcels,EPSG:24047,Polygon,4000,657706.4571303247,1509274.702150785,utf-8
roads,EPSG:32647,LineString,5000,656996.3371533038,1509487.963830215,utf-8
markets,EPSG:4326,Point,1000,100.4533,13.655000000000001,utf-8