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check_missing

Classes#

CheckMissing #

Bases: QualityChecker

Checks if any data are missing. A variable's data are considered missing if they are set to the variable's _FillValue (if it has a _FillValue) or NaN (NaT for datetime- like variables).

Functions#

run #
run(
    dataset: xr.Dataset, variable_name: str
) -> NDArray[np.bool_]
Source code in tsdat/qc/checkers/check_missing.py 
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def run(self, dataset: xr.Dataset, variable_name: str) -> NDArray[np.bool_]:
    results: NDArray[np.bool_] = dataset[variable_name].isnull().data

    if "_FillValue" in dataset[variable_name].attrs:
        fill_value = dataset[variable_name].attrs["_FillValue"]
        results |= dataset[variable_name].data == fill_value

    elif np.issubdtype(dataset[variable_name].data.dtype, str):  # type: ignore
        fill_value = ""
        results |= dataset[variable_name].data == fill_value

    return results