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