{
  "context": "This criteria evaluates how effectively the engineer uses h5netcdf's advanced dimension operations, specifically focusing on creating unlimited dimensions, dynamic dimension resizing, and proper management of multi-dimensional variables in NETCDF4 format.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "File creation",
      "description": "Uses h5netcdf.File() to create or open the netCDF4 file with appropriate mode ('w' for write, 'a' for append, or 'r+' for read-write)",
      "max_score": 10
    },
    {
      "name": "Unlimited dimensions",
      "description": "Creates both 'time' and 'station' dimensions as unlimited by setting their size to None (e.g., f.dimensions['time'] = None, f.dimensions['station'] = None)",
      "max_score": 20
    },
    {
      "name": "Variable creation",
      "description": "Uses create_variable() or direct assignment to create 'temperature' and 'humidity' variables with dimensions ('time', 'station') and appropriate dtype (float)",
      "max_score": 15
    },
    {
      "name": "Dimension resizing",
      "description": "Uses resize_dimension() method to manually resize unlimited dimensions when adding new time points or stations (e.g., f.resize_dimension('time', new_size))",
      "max_score": 25
    },
    {
      "name": "Data assignment",
      "description": "Uses NumPy-style indexing to write data to variables (e.g., temperature[time_idx, :] = data or temperature[:, station_idx] = data)",
      "max_score": 15
    },
    {
      "name": "Dimension size queries",
      "description": "Accesses dimension sizes using f.dimensions['dim_name'].size or len(f.dimensions['dim_name']) to query current dimension sizes",
      "max_score": 10
    },
    {
      "name": "File closing",
      "description": "Properly closes the file using f.close() or uses context manager (with h5netcdf.File(...) as f:) for automatic resource cleanup",
      "max_score": 5
    }
  ]
}