def rs_grouplevel(z, prefix, output_dir, work_dir):
from nipype.interfaces.fsl.model import Randomise
from nipype.interfaces import fsl as fsl
merger = fsl.utils.Merge()
merger.inputs.in_files = z
merger.inputs.dimension = 't'
merger.inputs.merged_file = op.join(work_dir, 'z_merge.nii.gz')
merger.run()
immaths = fsl.utils.ImageMaths()
immaths.inputs.in_file = op.join(work_dir, 'z_merge.nii.gz')
immaths.inputs.op_string = "-abs -bin -Tmin"
immaths.inputs.out_file = op.join(work_dir, 'mask.nii.gz')
immaths.run()
masker = fsl.maths.ApplyMask()
masker.inputs.in_file = op.join(work_dir, 'z_merge.nii.gz')
masker.inputs.mask_file = op.join(work_dir, 'mask.nii.gz')
masker.inputs.out_file = op.join(work_dir, 'z_merge.nii.gz')
masker.run()
meaner = fsl.utils.ImageMaths()
meaner.inputs.in_file = op.join(work_dir, 'z_merge.nii.gz')
meaner.inputs.op_string = "-Tmean"
meaner.inputs.out_file = op.join(work_dir, 'z.nii.gz')
meaner.run()
shutil.copyfile(op.join(work_dir, 'z.nii.gz'), op.join(output_dir, '{prefix}_z.nii.gz'.format(prefix=prefix)))
randomise = Randomise()
randomise.inputs.in_file = op.join(work_dir, 'z_merge.nii.gz')
randomise.inputs.mask = op.join(work_dir, 'mask.nii.gz')
randomise.inputs.one_sample_group_mean = True
randomise.inputs.raw_stats_imgs = True
randomise.inputs.vox_p_values = True
randomise.inputs.base_name = 'randomise'
os.chdir(work_dir)
randomise.run()
thresher = fsl.utils.ImageMaths()
thresher.inputs.in_file = op.join(work_dir, 'randomise_vox_corrp_tstat1.nii.gz')
thresher.inputs.op_string = "-thr 0.999"
thresher.inputs.out_file = op.join(work_dir, 'randomise_vox_corrp_tstat1_thr001.nii.gz')
thresher.run()
masker = fsl.maths.ApplyMask()
masker.inputs.in_file = op.join(work_dir, 'randomise_tstat1.nii.gz')
masker.inputs.mask_file = op.join(work_dir, 'randomise_vox_corrp_tstat1_thr001.nii.gz')
masker.inputs.out_file = op.join(work_dir, 'randomise_tstat1_thr001.nii.gz')
masker.run()
#copy data to directory vox_corrp_tstat1.nii.gz
shutil.copyfile(op.join(work_dir, 'randomise_tstat1.nii.gz'), op.join(output_dir, '{prefix}_tstat1.nii.gz'.format(prefix=prefix)))
shutil.copyfile(op.join(work_dir, 'randomise_vox_corrp_tstat1.nii.gz'), op.join(output_dir, '{prefix}_vox_corrp.nii.gz'.format(prefix=prefix)))
shutil.copyfile(op.join(work_dir, 'randomise_tstat1_thr001.nii.gz'), op.join(output_dir, '{prefix}_tstat1_thr001.nii.gz'.format(prefix=prefix)))
shutil.rmtree(work_dir)
def test_Randomise_inputs():
input_map = dict(
args=dict(argstr='%s', ),
base_name=dict(
argstr='-o "%s"',
position=1,
usedefault=True,
),
c_thresh=dict(argstr='-c %.2f', ),
cm_thresh=dict(argstr='-C %.2f', ),
demean=dict(argstr='-D', ),
design_mat=dict(
argstr='-d %s',
position=2,
),
environ=dict(
nohash=True,
usedefault=True,
),
f_c_thresh=dict(argstr='-F %.2f', ),
f_cm_thresh=dict(argstr='-S %.2f', ),
f_only=dict(argstr='--f_only', ),
fcon=dict(argstr='-f %s', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='-i %s',
mandatory=True,
position=0,
),
mask=dict(argstr='-m %s', ),
num_perm=dict(argstr='-n %d', ),
one_sample_group_mean=dict(argstr='-1', ),
output_type=dict(),
p_vec_n_dist_files=dict(argstr='-P', ),
raw_stats_imgs=dict(argstr='-R', ),
seed=dict(argstr='--seed=%d', ),
show_info_parallel_mode=dict(argstr='-Q', ),
show_total_perms=dict(argstr='-q', ),
tcon=dict(
argstr='-t %s',
position=3,
),
terminal_output=dict(nohash=True, ),
tfce=dict(argstr='-T', ),
tfce2D=dict(argstr='--T2', ),
tfce_C=dict(argstr='--tfce_C=%.2f', ),
tfce_E=dict(argstr='--tfce_E=%.2f', ),
tfce_H=dict(argstr='--tfce_H=%.2f', ),
var_smooth=dict(argstr='-v %d', ),
vox_p_values=dict(argstr='-x', ),
x_block_labels=dict(argstr='-e %s', ),
)
inputs = Randomise.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_Randomise_outputs():
output_map = dict(tstat_files=dict(),
t_p_files=dict(),
f_p_files=dict(),
f_corrected_p_files=dict(),
t_corrected_p_files=dict(),
fstat_files=dict(),
)
outputs = Randomise.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_Randomise_outputs():
output_map = dict(
f_corrected_p_files=dict(),
f_p_files=dict(),
fstat_files=dict(),
t_corrected_p_files=dict(),
t_p_files=dict(),
tstat_files=dict(),
)
outputs = Randomise.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_Randomise_inputs():
input_map = dict(
args=dict(argstr="%s"),
base_name=dict(argstr='-o "%s"', position=1, usedefault=True),
c_thresh=dict(argstr="-c %.2f"),
cm_thresh=dict(argstr="-C %.2f"),
demean=dict(argstr="-D"),
design_mat=dict(argstr="-d %s", position=2),
environ=dict(nohash=True, usedefault=True),
f_c_thresh=dict(argstr="-F %.2f"),
f_cm_thresh=dict(argstr="-S %.2f"),
f_only=dict(argstr="--f_only"),
fcon=dict(argstr="-f %s"),
ignore_exception=dict(nohash=True, usedefault=True),
in_file=dict(argstr="-i %s", mandatory=True, position=0),
mask=dict(argstr="-m %s"),
num_perm=dict(argstr="-n %d"),
one_sample_group_mean=dict(argstr="-1"),
output_type=dict(),
p_vec_n_dist_files=dict(argstr="-P"),
raw_stats_imgs=dict(argstr="-R"),
seed=dict(argstr="--seed=%d"),
show_info_parallel_mode=dict(argstr="-Q"),
show_total_perms=dict(argstr="-q"),
tcon=dict(argstr="-t %s", position=3),
terminal_output=dict(nohash=True),
tfce=dict(argstr="-T"),
tfce2D=dict(argstr="--T2"),
tfce_C=dict(argstr="--tfce_C=%.2f"),
tfce_E=dict(argstr="--tfce_E=%.2f"),
tfce_H=dict(argstr="--tfce_H=%.2f"),
var_smooth=dict(argstr="-v %d"),
vox_p_values=dict(argstr="-x"),
x_block_labels=dict(argstr="-e %s"),
)
inputs = Randomise.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
base_directory=output_dir,
template='glm_seed_copes/%s_*/cope.nii',
field_template=templates,
template_args=dict(roi=[['roi']])),
name='datagrabber')
# In[ ]:
## Level 2
# merge param estimates across all subjects per seed
merge = Node(Merge(dimension='t'), name='merge')
# FSL randomise for higher level analysis
highermodel = Node(Randomise(tfce=True,
raw_stats_imgs=True,
design_mat=group_mat,
tcon=group_con),
name='highermodel')
## Cluster results
# make binary masks of sig clusters
binarize = Node(Binarize(min=0.95, max=1.0),
name='binarize',
iterfield='in_file')
# mask T-map before clustering
mask_tmaps = Node(ApplyMask(), name='mask_tmaps')
# clusterize and extract cluster stats/peaks
clusterize = Node(Cluster(threshold=2.3,
def test_Randomise_inputs():
input_map = dict(one_sample_group_mean=dict(argstr='-1',
),
vox_p_values=dict(argstr='-x',
),
show_total_perms=dict(argstr='-q',
),
tfce2D=dict(argstr='--T2',
),
tfce_H=dict(argstr='--tfce_H=%.2f',
),
p_vec_n_dist_files=dict(argstr='-P',
),
cm_thresh=dict(argstr='-C %.2f',
),
f_only=dict(argstr='--f_only',
),
environ=dict(nohash=True,
usedefault=True,
),
in_file=dict(position=0,
mandatory=True,
argstr='-i %s',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
f_cm_thresh=dict(argstr='-S %.2f',
),
tfce=dict(argstr='-T',
),
tfce_E=dict(argstr='--tfce_E=%.2f',
),
args=dict(argstr='%s',
),
tfce_C=dict(argstr='--tfce_C=%.2f',
),
raw_stats_imgs=dict(argstr='-R',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
var_smooth=dict(argstr='-v %d',
),
design_mat=dict(position=2,
argstr='-d %s',
),
tcon=dict(position=3,
argstr='-t %s',
),
show_info_parallel_mode=dict(argstr='-Q',
),
base_name=dict(position=1,
usedefault=True,
argstr='-o "%s"',
),
f_c_thresh=dict(argstr='-F %.2f',
),
num_perm=dict(argstr='-n %d',
),
mask=dict(argstr='-m %s',
),
c_thresh=dict(argstr='-c %.2f',
),
x_block_labels=dict(argstr='-e %s',
),
seed=dict(argstr='--seed=%d',
),
demean=dict(argstr='-D',
),
output_type=dict(),
fcon=dict(argstr='-f %s',
),
)
inputs = Randomise.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
