def test_MELODIC_outputs():
output_map = dict(out_dir=dict(), report_dir=dict())
outputs = MELODIC.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 group_melodic_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='group_melodic',
inputs=[
DatasetSpec('smoothed_ts', nifti_gz_format),
FieldSpec('tr', float)
],
outputs=[DatasetSpec('group_melodic', directory_format)],
desc=("Group ICA"),
version=1,
citations=[fsl_cite],
**kwargs)
gica = pipeline.create_join_subjects_node(MELODIC(),
joinfield=['in_files'],
name='gica',
requirements=[fsl510_req],
wall_time=7200)
gica.inputs.no_bet = True
gica.inputs.bg_threshold = self.parameter('brain_thresh_percent')
gica.inputs.bg_image = self.parameter('MNI_template')
gica.inputs.dim = self.parameter('group_ica_components')
gica.inputs.report = True
gica.inputs.out_stats = True
gica.inputs.mm_thresh = 0.5
gica.inputs.sep_vn = True
gica.inputs.mask = self.parameter('MNI_template_mask')
gica.inputs.out_dir = 'group_melodic.ica'
pipeline.connect_input('smoothed_ts', gica, 'in_files')
pipeline.connect_input('tr', gica, 'tr_sec')
pipeline.connect_output('group_melodic', gica, 'out_dir')
return pipeline
def single_subject_melodic_pipeline(self, **kwargs):
pipeline = self.create_pipeline(
name='MelodicL1',
inputs=[
DatasetSpec('filtered_data', nifti_gz_format),
FieldSpec('tr', float),
DatasetSpec('brain_mask', nifti_gz_format)
],
outputs=[DatasetSpec('melodic_ica', directory_format)],
desc=("Single subject ICA analysis using FSL MELODIC."),
version=1,
citations=[fsl_cite],
**kwargs)
mel = pipeline.create_node(MELODIC(),
name='melodic_L1',
wall_time=15,
requirements=[fsl5_req])
mel.inputs.no_bet = True
pipeline.connect_input('brain_mask', mel, 'mask')
mel.inputs.bg_threshold = self.parameter('brain_thresh_percent')
mel.inputs.report = True
mel.inputs.out_stats = True
mel.inputs.mm_thresh = 0.5
mel.inputs.out_dir = 'melodic_ica'
pipeline.connect_input('tr', mel, 'tr_sec')
pipeline.connect_input('filtered_data', mel, 'in_files')
pipeline.connect_output('melodic_ica', mel, 'out_dir')
return pipeline
def group_melodic_pipeline(self, **name_maps):
pipeline = self.new_pipeline(name='group_melodic',
desc=("Group ICA"),
citations=[fsl_cite],
name_maps=name_maps)
pipeline.add(MELODIC(
no_bet=True,
bg_threshold=self.parameter('brain_thresh_percent'),
dim=self.parameter('group_ica_components'),
report=True,
out_stats=True,
mm_thresh=0.5,
sep_vn=True,
out_dir='group_melodic.ica',
output_type='NIFTI_GZ'),
inputs={
'bg_image': ('template_brain', nifti_gz_format),
'mask': ('template_mask', nifti_gz_format),
'in_files': ('smoothed_ts', nifti_gz_format),
'tr_sec': ('tr', float)
},
outputs={'group_melodic': ('out_dir', directory_format)},
joinsource=self.SUBJECT_ID,
joinfield=['in_files'],
name='gica',
requirements=[fsl_req.v('5.0.10')],
wall_time=7200)
return pipeline
def single_subject_melodic_pipeline(self, **name_maps):
pipeline = self.new_pipeline(
name='MelodicL1',
desc=("Single subject ICA analysis using FSL MELODIC."),
citations=[fsl_cite],
name_maps=name_maps)
pipeline.add('melodic_L1',
MELODIC(
no_bet=True,
bg_threshold=self.parameter('brain_thresh_percent'),
report=True,
out_stats=True,
mm_thresh=0.5,
out_dir='melodic_ica',
output_type='NIFTI_GZ'),
inputs={
'mask': (self.brain_mask_spec_name, nifti_gz_format),
'tr_sec': ('tr', float),
'in_files': ('filtered_data', nifti_gz_format)
},
outputs={'melodic_ica': ('out_dir', directory_format)},
wall_time=15,
requirements=[fsl_req.v('5.0.10')])
return pipeline
def test_MELODIC_inputs():
input_map = dict(
ICs=dict(argstr="--ICs=%s"),
approach=dict(argstr="-a %s"),
args=dict(argstr="%s"),
bg_image=dict(argstr="--bgimage=%s"),
bg_threshold=dict(argstr="--bgthreshold=%f"),
cov_weight=dict(argstr="--covarweight=%f"),
dim=dict(argstr="-d %d"),
dim_est=dict(argstr="--dimest=%s"),
environ=dict(nohash=True, usedefault=True),
epsilon=dict(argstr="--eps=%f"),
epsilonS=dict(argstr="--epsS=%f"),
ignore_exception=dict(nohash=True, usedefault=True),
in_files=dict(argstr="-i %s", mandatory=True, position=0, sep=","),
log_power=dict(argstr="--logPower"),
mask=dict(argstr="-m %s"),
max_restart=dict(argstr="--maxrestart=%d"),
maxit=dict(argstr="--maxit=%d"),
mix=dict(argstr="--mix=%s"),
mm_thresh=dict(argstr="--mmthresh=%f"),
no_bet=dict(argstr="--nobet"),
no_mask=dict(argstr="--nomask"),
no_mm=dict(argstr="--no_mm"),
non_linearity=dict(argstr="--nl=%s"),
num_ICs=dict(argstr="-n %d"),
out_all=dict(argstr="--Oall"),
out_dir=dict(argstr="-o %s", genfile=True),
out_mean=dict(argstr="--Omean"),
out_orig=dict(argstr="--Oorig"),
out_pca=dict(argstr="--Opca"),
out_stats=dict(argstr="--Ostats"),
out_unmix=dict(argstr="--Ounmix"),
out_white=dict(argstr="--Owhite"),
output_type=dict(),
pbsc=dict(argstr="--pbsc"),
rem_cmp=dict(argstr="-f %d"),
remove_deriv=dict(argstr="--remove_deriv"),
report=dict(argstr="--report"),
report_maps=dict(argstr="--report_maps=%s"),
s_con=dict(argstr="--Scon=%s"),
s_des=dict(argstr="--Sdes=%s"),
sep_vn=dict(argstr="--sep_vn"),
sep_whiten=dict(argstr="--sep_whiten"),
smode=dict(argstr="--smode=%s"),
t_con=dict(argstr="--Tcon=%s"),
t_des=dict(argstr="--Tdes=%s"),
terminal_output=dict(mandatory=True, nohash=True),
tr_sec=dict(argstr="--tr=%f"),
update_mask=dict(argstr="--update_mask"),
var_norm=dict(argstr="--vn"),
)
inputs = MELODIC.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_MELODIC_outputs():
output_map = dict(
out_dir=dict(),
report_dir=dict(),
)
outputs = MELODIC.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_MELODIC_inputs():
input_map = dict(
ICs=dict(argstr='--ICs=%s', ),
approach=dict(argstr='-a %s', ),
args=dict(argstr='%s', ),
bg_image=dict(argstr='--bgimage=%s', ),
bg_threshold=dict(argstr='--bgthreshold=%f', ),
cov_weight=dict(argstr='--covarweight=%f', ),
dim=dict(argstr='-d %d', ),
dim_est=dict(argstr='--dimest=%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
epsilon=dict(argstr='--eps=%f', ),
epsilonS=dict(argstr='--epsS=%f', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_files=dict(
argstr='-i %s',
mandatory=True,
position=0,
sep=',',
),
log_power=dict(argstr='--logPower', ),
mask=dict(argstr='-m %s', ),
max_restart=dict(argstr='--maxrestart=%d', ),
maxit=dict(argstr='--maxit=%d', ),
mix=dict(argstr='--mix=%s', ),
mm_thresh=dict(argstr='--mmthresh=%f', ),
no_bet=dict(argstr='--nobet', ),
no_mask=dict(argstr='--nomask', ),
no_mm=dict(argstr='--no_mm', ),
non_linearity=dict(argstr='--nl=%s', ),
num_ICs=dict(argstr='-n %d', ),
out_all=dict(argstr='--Oall', ),
out_dir=dict(
argstr='-o %s',
genfile=True,
),
out_mean=dict(argstr='--Omean', ),
out_orig=dict(argstr='--Oorig', ),
out_pca=dict(argstr='--Opca', ),
out_stats=dict(argstr='--Ostats', ),
out_unmix=dict(argstr='--Ounmix', ),
out_white=dict(argstr='--Owhite', ),
output_type=dict(),
pbsc=dict(argstr='--pbsc', ),
rem_cmp=dict(argstr='-f %d', ),
remove_deriv=dict(argstr='--remove_deriv', ),
report=dict(argstr='--report', ),
report_maps=dict(argstr='--report_maps=%s', ),
s_con=dict(argstr='--Scon=%s', ),
s_des=dict(argstr='--Sdes=%s', ),
sep_vn=dict(argstr='--sep_vn', ),
sep_whiten=dict(argstr='--sep_whiten', ),
smode=dict(argstr='--smode=%s', ),
t_con=dict(argstr='--Tcon=%s', ),
t_des=dict(argstr='--Tdes=%s', ),
terminal_output=dict(nohash=True, ),
tr_sec=dict(argstr='--tr=%f', ),
update_mask=dict(argstr='--update_mask', ),
var_norm=dict(argstr='--vn', ),
)
inputs = MELODIC.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
