def test_TwoSampleTTestDesign_outputs(): output_map = dict(spm_mat_file=dict()) outputs = TwoSampleTTestDesign.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_TwoSampleTTestDesign_outputs(): output_map = dict(spm_mat_file=dict(), ) outputs = TwoSampleTTestDesign.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_TwoSampleTTestDesign_inputs(): input_map = dict( covariates=dict(field='cov', ), dependent=dict(field='des.t2.dept', ), 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', ), group1_files=dict( field='des.t2.scans1', mandatory=True, ), group2_files=dict( field='des.t2.scans2', mandatory=True, ), 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'], ), unequal_variance=dict(field='des.t2.variance', ), use_implicit_threshold=dict(field='masking.im', ), use_mcr=dict(), use_v8struct=dict( min_ver='8', usedefault=True, ), ) inputs = TwoSampleTTestDesign.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_TwoSampleTTestDesign_inputs(): input_map = dict(use_v8struct=dict(min_ver='8', usedefault=True, ), matlab_cmd=dict(), explicit_mask_file=dict(field='masking.em', ), paths=dict(), unequal_variance=dict(field='des.t2.variance', ), covariates=dict(field='cov', ), mfile=dict(usedefault=True, ), threshold_mask_absolute=dict(field='masking.tm.tma.athresh', xor=['threshold_mask_none', 'threshold_mask_relative'], ), global_calc_omit=dict(field='globalc.g_omit', xor=['global_calc_mean', 'global_calc_values'], ), ignore_exception=dict(nohash=True, usedefault=True, ), use_implicit_threshold=dict(field='masking.im', ), global_calc_mean=dict(field='globalc.g_mean', xor=['global_calc_omit', 'global_calc_values'], ), dependent=dict(field='des.t2.dept', ), group1_files=dict(field='des.t2.scans1', mandatory=True, ), group2_files=dict(field='des.t2.scans2', mandatory=True, ), threshold_mask_none=dict(field='masking.tm.tm_none', xor=['threshold_mask_absolute', 'threshold_mask_relative'], ), use_mcr=dict(), spm_mat_dir=dict(field='dir', ), global_normalization=dict(field='globalm.glonorm', ), no_grand_mean_scaling=dict(field='globalm.gmsca.gmsca_no', ), global_calc_values=dict(field='globalc.g_user.global_uval', xor=['global_calc_mean', 'global_calc_omit'], ), threshold_mask_relative=dict(field='masking.tm.tmr.rthresh', xor=['threshold_mask_absolute', 'threshold_mask_none'], ), ) inputs = TwoSampleTTestDesign.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_TwoSampleTTestDesign_inputs(): input_map = dict( covariates=dict(field="cov"), dependent=dict(field="des.t2.dept"), 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"), group1_files=dict(field="des.t2.scans1", mandatory=True), group2_files=dict(field="des.t2.scans2", mandatory=True), 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"] ), unequal_variance=dict(field="des.t2.variance"), use_implicit_threshold=dict(field="masking.im"), use_mcr=dict(), use_v8struct=dict(min_ver="8", usedefault=True), ) inputs = TwoSampleTTestDesign.input_spec() for key, metadata in input_map.items(): for metakey, value in metadata.items(): yield assert_equal, getattr(inputs.traits()[key], metakey), value