docs: def check_data(self, ds, name, dims):
          """
          A validation method for the data shape
  
          Parameters
          ----------
          name: list of lists of strings
              The variable names (see the
              :meth:`~psyplot.plotter.Plotter.check_data` method of the
              :attr:`plotter_cls` attribute for details)
          dims: list of dictionaries
              The dimensions of the arrays. It will be enhanced by the default
              dimensions of this plot method
          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):
              name = [name]
              dims = [dims]
          else:
              dims = list(dims)
          variables = [ds[safe_list(n)[0]] for n in name]
          decoders = [CFDecoder.get_decoder(ds, var) for var in variables]
          default_slice = slice(None) if self._default_slice is None else \
              self._default_slice
          for i, (dim_dict, var, decoder) in enumerate(zip(
                  dims, variables, decoders)):
              corrected = decoder.correct_dims(var, dict(chain(
                  six.iteritems(self._default_dims),
                  dim_dict.items())))
              # now use the default slice (we don't do this before because the
              # `correct_dims` method doesn't use 'x', 'y', 'z' and 't' (as used
              # for the _default_dims) if the real dimension name is already in
              # the dictionary)
              for dim in var.dims:
                  corrected.setdefault(dim, default_slice)
              dims[i] = [
                  dim for dim, val in map(lambda t: (t[0], safe_list(t[1])),
                                          six.iteritems(corrected))
                  if val and (len(val) > 1 or _is_slice(val[0]))]
          return self.plotter_cls.check_data(
              name, dims, [decoder.is_unstructured(var) for decoder, var in zip(
                  decoders, variables)])