def test_DTIFit_outputs(): output_map = dict(tensor_fitted=dict(), ) outputs = DTIFit.output_spec() for key, metadata in output_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_DTIFit_inputs(): input_map = dict(args=dict(argstr='%s', ), bgmask=dict(argstr='-bgmask %s', ), environ=dict(nohash=True, usedefault=True, ), ignore_exception=dict(nohash=True, usedefault=True, ), in_file=dict(argstr='%s', mandatory=True, position=1, ), non_linear=dict(argstr='-nonlinear', position=3, ), out_file=dict(argstr='> %s', genfile=True, position=-1, ), scheme_file=dict(argstr='%s', mandatory=True, position=2, ), terminal_output=dict(nohash=True, ), ) inputs = DTIFit.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_DTIFit_inputs(): input_map = dict(args=dict(argstr='%s', ), bgmask=dict(argstr='-bgmask %s', ), environ=dict(nohash=True, usedefault=True, ), ignore_exception=dict(nohash=True, usedefault=True, ), in_file=dict(argstr='%s', mandatory=True, position=1, ), non_linear=dict(argstr='-nonlinear', position=3, ), out_file=dict(argstr='> %s', genfile=True, position=-1, ), scheme_file=dict(argstr='%s', mandatory=True, position=2, ), terminal_output=dict(mandatory=True, nohash=True, ), ) inputs = DTIFit.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value