def test_FactorialDesign_outputs(): output_map = dict(spm_mat_file=dict(), ) outputs = FactorialDesign.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_FactorialDesign_inputs(): input_map = dict( covariates=dict(field='cov', ), explicit_mask_file=dict(field='masking.em', ), global_calc_mean=dict( field='globalc.g_mean', xor=['global_calc_omit', 'global_calc_values'], ), global_calc_omit=dict( field='globalc.g_omit', xor=['global_calc_mean', 'global_calc_values'], ), global_calc_values=dict( field='globalc.g_user.global_uval', xor=['global_calc_mean', 'global_calc_omit'], ), global_normalization=dict(field='globalm.glonorm', ), ignore_exception=dict( nohash=True, usedefault=True, ), matlab_cmd=dict(), mfile=dict(usedefault=True, ), no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no', ), paths=dict(), spm_mat_dir=dict(field='dir', ), threshold_mask_absolute=dict( field='masking.tm.tma.athresh', xor=['threshold_mask_none', 'threshold_mask_relative'], ), threshold_mask_none=dict( field='masking.tm.tm_none', xor=['threshold_mask_absolute', 'threshold_mask_relative'], ), threshold_mask_relative=dict( field='masking.tm.tmr.rthresh', xor=['threshold_mask_absolute', 'threshold_mask_none'], ), use_implicit_threshold=dict(field='masking.im', ), use_mcr=dict(), use_v8struct=dict( min_ver='8', usedefault=True, ), ) inputs = FactorialDesign.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_FactorialDesign_inputs(): input_map = dict(ignore_exception=dict(nohash=True, usedefault=True, ), paths=dict(), use_implicit_threshold=dict(field='masking.im', ), global_calc_mean=dict(field='globalc.g_mean', xor=['global_calc_omit', 'global_calc_values'], ), use_v8struct=dict(min_ver='8', usedefault=True, ), threshold_mask_absolute=dict(field='masking.tm.tma.athresh', xor=['threshold_mask_none', 'threshold_mask_relative'], ), use_mcr=dict(), threshold_mask_none=dict(field='masking.tm.tm_none', xor=['threshold_mask_absolute', 'threshold_mask_relative'], ), covariates=dict(field='cov', ), global_calc_omit=dict(field='globalc.g_omit', xor=['global_calc_mean', 'global_calc_values'], ), matlab_cmd=dict(), global_calc_values=dict(field='globalc.g_user.global_uval', xor=['global_calc_mean', 'global_calc_omit'], ), mfile=dict(usedefault=True, ), no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no', ), explicit_mask_file=dict(field='masking.em', ), threshold_mask_relative=dict(field='masking.tm.tmr.rthresh', xor=['threshold_mask_absolute', 'threshold_mask_none'], ), spm_mat_dir=dict(field='dir', ), global_normalization=dict(field='globalm.glonorm', ), ) inputs = FactorialDesign.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value