Function create_default_index_implicit

xarray/core/indexes.py:1630–1678

Create a default index from a dimension variable. Create a PandasMultiIndex if the given variable wraps a pandas.MultiIndex, otherwise create a PandasIndex (note that this will become obsolete once we depreciate implicitly passing a pandas.MultiIndex as a coordinate).

(
    dim_variable: Variable,
    all_variables: Mapping | Iterable[Hashable] | None = None,
)

def create_default_index_implicit(
    dim_variable: Variable,
    all_variables: Mapping | Iterable[Hashable] | None = None,
) -> tuple[PandasIndex, IndexVars]:
    """Create a default index from a dimension variable.

    Create a PandasMultiIndex if the given variable wraps a pandas.MultiIndex,
    otherwise create a PandasIndex (note that this will become obsolete once we
    depreciate implicitly passing a pandas.MultiIndex as a coordinate).

    """
    if all_variables is None:
        all_variables = {}
    if not isinstance(all_variables, Mapping):
        all_variables = dict.fromkeys(all_variables)

    name = dim_variable.dims[0]
    array = getattr(dim_variable._data, "array", None)
    index: PandasIndex

    if isinstance(array, pd.MultiIndex):
        index = PandasMultiIndex(array, name)
        index_vars = index.create_variables()
        # check for conflict between level names and variable names
        duplicate_names = [k for k in index_vars if k in all_variables and k != name]
        if duplicate_names:
            # dirty workaround for an edge case where both the dimension
            # coordinate and the level coordinates are given for the same
            # multi-index object => do not raise an error
            # TODO: remove this check when removing the multi-index dimension coordinate
            if len(duplicate_names) < len(index.index.names):
                conflict = True
            else:
                duplicate_vars = [all_variables[k] for k in duplicate_names]
                conflict = any(
                    v is None or not dim_variable.equals(v) for v in duplicate_vars
                )

            if conflict:
                conflict_str = "\n".join(duplicate_names)
                raise ValueError(
                    f"conflicting MultiIndex level / variable name(s):\n{conflict_str}"
                )
    else:
        dim_var = {name: dim_variable}
        index = PandasIndex.from_variables(dim_var, options={})
        index_vars = index.create_variables(dim_var)

    return index, index_vars


# generic type that represents either a pandas or an xarray index