{
  "context": "This criteria evaluates how well the engineer uses h5netcdf's dimension management capabilities, including creating fixed and unlimited dimensions, associating dimensions with variables, and dynamically resizing unlimited dimensions.",
  "type": "weighted_checklist",
  "checklist": [
    {
      "name": "File creation",
      "description": "Uses h5netcdf.File() to create a new netCDF file in write mode ('w' or 'a')",
      "max_score": 10
    },
    {
      "name": "Fixed dimension creation",
      "description": "Creates fixed dimensions 'x' and 'y' using the dimensions dictionary assignment pattern (e.g., f.dimensions['x'] = 10) or equivalent method",
      "max_score": 15
    },
    {
      "name": "Unlimited dimension creation",
      "description": "Creates an unlimited dimension 'time' by assigning None or 0 as the size (e.g., f.dimensions['time'] = None)",
      "max_score": 20
    },
    {
      "name": "Dimension query",
      "description": "Uses the isunlimited() method or checks the dimension's size property to verify whether a dimension is unlimited",
      "max_score": 10
    },
    {
      "name": "Variable creation",
      "description": "Uses create_variable() or Group.create_variable() to create the temperature variable with correct dimensions tuple ('time', 'x', 'y') and dtype specification",
      "max_score": 20
    },
    {
      "name": "Data writing",
      "description": "Uses NumPy-style indexing on the variable object to write temperature data (e.g., var[time_index, :, :] = data or var[time_index] = data)",
      "max_score": 10
    },
    {
      "name": "Dimension resizing",
      "description": "Uses resize_dimension() method or equivalent to dynamically resize the unlimited time dimension to a new size",
      "max_score": 15
    }
  ]
}