#spm.Normalize12().input_spec.gm_output_type = [True, False, False] # Should be modulated normalised
# Smooth
smooth = Node(interface=spm.Smooth(), name="smooth")
fwhmlist = [8]
smooth.iterables = (
'fwhm', fwhmlist
) # Iterables seem to follow the convention of variable.input[key] = value of the iterable.
# ## Set up the file management through ds to be able to wipe out unnecessary files?
# - There are all sorts of files labelled under the type of processing that produced them in each subject's folder, so I am unclear why this is a useful management step. It simply adds a "ds" folder next to the processing folders.
# Collect key output files in a useful place
ds = Node(interface=DataSink(), name="ds")
ds.inputs.base_directory = op.abspath(output_dir)
ds.substitutions = [('_subj_id', ''), ('_task', ''), ('_t1_', '')]
# ## Connect the nodes.
# #### Tip 4: Think of the connections like the "dependency" fields in SPM's batch editor.
# TODO Add first-level estimations
preproc_wf.connect([
(infosource, sf, [('subj_id', 'subj_id'), ('task', 'task'),
('t1', 't1'), ('timept', 'timept')]),
(infosource, ds, [('subj_id', 'container')]),
(sf, realign, [('func', 'in_files')]),
(realign, ds, [('realignment_parameters', 'motion.@param')]),
(sf, coreg, [('struct', 'target')]),
(
realign, coreg, [('mean_image', 'source'),
