def fisher_z_score_standardize(workflow, output_name, timeseries_oned_file, strat, num_strat, map_node=False): # call the fisher r-to-z sub-workflow builder fisher_z_score_std = get_fisher_zscore(output_name, map_node, 'fisher_z_score_std_%s_%d' \ % (output_name, num_strat)) node, out_file = strat[output_name] workflow.connect(node, out_file, fisher_z_score_std, 'inputspec.correlation_file') node, out_file = strat[timeseries_oned_file] workflow.connect(node, out_file, fisher_z_score_std, 'inputspec.timeseries_one_d') strat.append_name(fisher_z_score_std.name) strat.update_resource_pool({ '{0}_fisher_zstd'.format(output_name): (fisher_z_score_std, 'outputspec.fisher_z_score_img') }) return strat
def fisher_z_score_standardize(wf_name, label, input_image_type='func_derivative', opt=None): wf = pe.Workflow(name=wf_name) map_node = False if input_image_type == 'func_derivative_multi': map_node = True inputnode = pe.Node(util.IdentityInterface(fields=['correlation_file', 'timeseries_oned']), name='inputspec') fisher_z_score_std = get_fisher_zscore(label, map_node, 'fisher_z_score_std') wf.connect(inputnode, 'correlation_file', fisher_z_score_std, 'inputspec.correlation_file') wf.connect(inputnode, 'timeseries_oned', fisher_z_score_std, 'inputspec.timeseries_one_d') outputnode = pe.Node(util.IdentityInterface(fields=['out_file']), name='outputspec') wf.connect(fisher_z_score_std, 'outputspec.fisher_z_score_img', outputnode, 'out_file') return wf