A validation method for the data shape

        Parameters
        ----------
        name: list of str with length 2
            The variable names (one for the first, two for the second array)
        dims: list with length 2 of lists with length 1
            The dimension of the arrays. Only 2D-Arrays are allowed (or 1-D if
            an array is unstructured)
        is_unstructured: bool or list of bool
            True if the corresponding array is unstructured.

        Returns
        -------
        %(Plotter.check_data.returns)s

def check_data(cls, name, dims, is_unstructured):
        """
        A validation method for the data shape

        Parameters
        ----------
        name: list of str with length 2
            The variable names (one for the first, two for the second array)
        dims: list with length 2 of lists with length 1
            The dimension of the arrays. Only 2D-Arrays are allowed (or 1-D if
            an array is unstructured)
        is_unstructured: bool or list of bool
            True if the corresponding array is unstructured.

        Returns
        -------
        %(Plotter.check_data.returns)s
        """
        if isinstance(name, six.string_types) or not is_iterable(name):
            name = [name]
            dims = [dims]
            is_unstructured = [is_unstructured]
        msg = ('Two arrays are required (one for the scalar and '
               'one for the vector field)')
        if len(name) < 2:
            return [None], [msg]
        elif len(name) > 2:
            return [False], [msg]
        valid1, msg1 = Simple2DBase.check_data(name[:1], dims[0:1],
                                               is_unstructured[:1])
        valid2, msg2 = BaseVectorPlotter.check_data(name[1:], dims[1:],
                                                    is_unstructured[1:])
        return valid1 + valid2, msg1 + msg2