def test_FEAT_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
fsf_file=dict(
argstr='%s',
mandatory=True,
position=0,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
output_type=dict(),
terminal_output=dict(
mandatory=True,
nohash=True,
),
)
inputs = FEAT.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_FEAT_outputs():
output_map = dict(feat_dir=dict(), )
outputs = FEAT.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_FEAT_outputs():
output_map = dict(feat_dir=dict(),
)
outputs = FEAT.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_FEAT_inputs():
input_map = dict(args=dict(argstr='%s',
),
environ=dict(nohash=True,
usedefault=True,
),
fsf_file=dict(argstr='%s',
mandatory=True,
position=0,
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
output_type=dict(),
terminal_output=dict(nohash=True,
),
)
inputs = FEAT.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
feat_node.inputs.bet = True
feat_node.inputs.prewhitening = False
feat_node.inputs.motion_regression = False
feat_node.inputs.thresholding = 'uncorrected'
feat_node.inputs.hrf = 'doublegamma'
feat_node.inputs.open_feat_html = False
feat_node.inputs.highpass = 100
feat_node.inputs.contrasts = 'single-trial'
meta_wf.connect(select_files, 'func', feat_node, 'func_file')
meta_wf.connect(modelgen_wf, 'outputspec.session_info', feat_node,
'session_info')
meta_wf.connect(input_node, 'sub_id', feat_node, 'output_dirname')
meta_wf.connect(select_files, 'func_mask', feat_node, 'mask')
run_feat_node = Node(FEAT(), name='run_feat')
meta_wf.connect(feat_node, 'feat_dir', run_feat_node, 'fsf_file')
datasink = Node(interface=DataSink(), name='datasink')
datasink.inputs.parameterization = False
datasink.inputs.base_directory = out_dir
meta_wf.connect(feat_node, 'confound_file', datasink, 'confound_file')
meta_wf.connect(feat_node, 'ev_files', datasink, 'ev_files')
meta_wf.connect(run_feat_node, 'feat_dir', datasink, 'firstlevel_FEAT')
meta_wf.connect(concat_iterables_node, 'out', datasink, 'container')
meta_wf.base_dir = op.join(out_dir, 'workingdir')
meta_wf.run(plugin='MultiProc', plugin_args={'n_procs': 8})
def create_firstlevel_workflow_FEAT(name='level1feat'):
input_node = pe.Node(IdentityInterface(fields=[
'events_file', 'single_trial', 'sort_by_onset', 'exclude', 'func_file',
'TR', 'confound_file', 'which_confounds', 'extend_motion_pars',
'model_serial_correlations', 'hrf_base', 'hp_filter', 'contrasts'
]),
name='inputspec')
output_node = pe.Node(
IdentityInterface(fields=['fsf_file', 'ev_file', 'feat_dir']),
name='outputspec')
level1_design = pe.MapNode(interface=Level1Design(
bases={'dgamma': {
'derivs': True
}},
interscan_interval=2.0,
model_serial_correlations=True),
iterfield=['contrasts', 'session_info'],
name='level1_design')
feat = pe.MapNode(interface=FEAT(), iterfield=['fsf_file'], name='FEAT')
extract_task = pe.MapNode(interface=Extract_task,
iterfield=['in_file'],
name='extract_task')
rename_feat_dir = pe.MapNode(interface=Rename_feat_dir,
iterfield=['feat_dir', 'task'],
name='rename_feat_dir')
firstlevel_wf = pe.Workflow(name=name)
modelgen_wf = create_modelgen_workflow()
firstlevel_wf.connect(input_node, 'events_file', modelgen_wf,
'inputspec.events_file')
firstlevel_wf.connect(input_node, 'func_file', modelgen_wf,
'inputspec.func_file')
firstlevel_wf.connect(input_node, 'TR', modelgen_wf, 'inputspec.TR')
firstlevel_wf.connect(input_node, 'single_trial', modelgen_wf,
'inputspec.single_trial')
firstlevel_wf.connect(input_node, 'sort_by_onset', modelgen_wf,
'inputspec.sort_by_onset')
firstlevel_wf.connect(input_node, 'extend_motion_pars', modelgen_wf,
'inputspec.extend_motion_pars')
firstlevel_wf.connect(input_node, 'exclude', modelgen_wf,
'inputspec.exclude')
firstlevel_wf.connect(input_node, 'confound_file', modelgen_wf,
'inputspec.confound_file')
firstlevel_wf.connect(input_node, 'which_confounds', modelgen_wf,
'inputspec.which_confounds')
firstlevel_wf.connect(input_node, 'hp_filter', modelgen_wf,
'inputspec.hp_filter')
firstlevel_wf.connect(input_node, 'TR', level1_design,
'interscan_interval')
firstlevel_wf.connect(input_node, 'model_serial_correlations',
level1_design, 'model_serial_correlations')
firstlevel_wf.connect(input_node, 'hrf_base', level1_design, 'bases')
firstlevel_wf.connect(input_node, 'contrasts', level1_design, 'contrasts')
firstlevel_wf.connect(modelgen_wf, 'outputspec.session_info',
level1_design, 'session_info')
firstlevel_wf.connect(level1_design, 'fsf_files', feat, 'fsf_file')
firstlevel_wf.connect(level1_design, 'fsf_files', output_node, 'fsf_file')
firstlevel_wf.connect(level1_design, 'ev_files', output_node, 'ev_file')
firstlevel_wf.connect(input_node, 'func_file', extract_task, 'in_file')
firstlevel_wf.connect(extract_task, 'task_name', rename_feat_dir, 'task')
firstlevel_wf.connect(feat, 'feat_dir', rename_feat_dir, 'feat_dir')
firstlevel_wf.connect(rename_feat_dir, 'feat_dir', output_node, 'feat_dir')
return firstlevel_wf
