def get_most_significant_input_dimensions(self, which_indices=None):
if which_indices is None:<EOL><INDENT>which_indices = np.argsort(self.input_sensitivity())[::-<NUM_LIT:1>][:<NUM_LIT:3>]<EOL><DEDENT>try:<EOL><INDENT>input_1, input_2, input_3 = which_indices<EOL><DEDENT>except ValueError:<EOL><INDENT>try:<EOL><INDENT>input_3 = None<EOL>input_1, input_2 = which_indices<EOL><DEDENT>except TypeError:<EOL><INDENT>input_1, input_2 = which_indices, None<EOL><DEDENT>except ValueError:<EOL><INDENT>input_1, input_2 = which_indices[<NUM_LIT:0>], None<EOL><DEDENT><DEDENT>return input_1, input_2, input_3<EOL>
Determine which dimensions should be plotted

Returns the top three most signification input dimensions

if less then three dimensions, the non existing dimensions are
labeled as None, so for a 1 dimensional input this returns
(0, None, None).

:param which_indices: force the indices to be the given indices.
:type which_indices: int or tuple(int,int) or tuple(int,int,int)