Function _combine_single_variable_hypercube

xarray/structure/combine.py:739–795

Attempt to combine a list of Datasets into a hypercube using their coordinates. All provided Datasets must belong to a single variable, ie. must be assigned the same variable name. This precondition is not checked by this function, so the caller is assumed to know what it's doi

(
    datasets,
    fill_value,
    data_vars,
    coords,
    compat: CompatOptions | CombineKwargDefault,
    join: JoinOptions | CombineKwargDefault,
    combine_attrs: CombineAttrsOptions,
)

def _combine_single_variable_hypercube(
    datasets,
    fill_value,
    data_vars,
    coords,
    compat: CompatOptions | CombineKwargDefault,
    join: JoinOptions | CombineKwargDefault,
    combine_attrs: CombineAttrsOptions,
):
    """
    Attempt to combine a list of Datasets into a hypercube using their
    coordinates.

    All provided Datasets must belong to a single variable, ie. must be
    assigned the same variable name. This precondition is not checked by this
    function, so the caller is assumed to know what it's doing.

    This function is NOT part of the public API.
    """
    if len(datasets) == 0:
        raise ValueError(
            "At least one Dataset is required to resolve variable names "
            "for combined hypercube."
        )

    combined_ids, concat_dims = _infer_concat_order_from_coords(list(datasets))

    if fill_value is None:
        # check that datasets form complete hypercube
        _check_shape_tile_ids(combined_ids)
    else:
        # check only that all datasets have same dimension depth for these
        # vars
        _check_dimension_depth_tile_ids(combined_ids)

    # Concatenate along all of concat_dims one by one to create single ds
    concatenated = _combine_nd(
        combined_ids,
        concat_dims=concat_dims,
        data_vars=data_vars,
        coords=coords,
        compat=compat,
        fill_value=fill_value,
        join=join,
        combine_attrs=combine_attrs,
    )

    # Check the overall coordinates are monotonically increasing
    for dim in concat_dims:
        indexes = concatenated.indexes.get(dim)
        if not (indexes.is_monotonic_increasing or indexes.is_monotonic_decreasing):
            raise ValueError(
                "Resulting object does not have monotonic"
                f" global indexes along dimension {dim}"
            )

    return concatenated