def get_surface_workflow(name='surface_1sample'):
import nipype.pipeline.engine as pe
import nipype.interfaces.freesurfer as fs
import nipype.interfaces.io as nio
import nipype.interfaces.utility as niu
wf = pe.Workflow(name=name)
inputspec = pe.Node(niu.IdentityInterface(
fields=["copes", "regfiles", "surf_template", "subjects_dir"]),
name='inputspec')
formatter = pe.Node(niu.Function(input_names=['copes', 'regfiles'],
output_names=['out'],
function=do_format),
name='formatter')
preproc = pe.MapNode(fs.MRISPreproc(), name='preproc', iterfield=['hemi'])
preproc.inputs.hemi = ['lh', 'rh']
glmfit = pe.MapNode(fs.GLMFit(one_sample=True, surf=True),
name='glmfit',
iterfield=['hemi', 'in_file'])
glmfit.inputs.hemi = ['lh', 'rh']
outputspec = pe.Node(niu.IdentityInterface(fields=[
"beta_file", "dof_file", "error_file", "error_stddev_file",
"error_var_file", "estimate_file", "frame_eigenvectors", "ftest_file",
"fwhm_file", "gamma_file", "gamma_var_file", "mask_file", "sig_file"
]),
name='outputspec')
#connections
wf.connect(inputspec, 'copes', formatter, 'copes')
wf.connect(inputspec, 'regfiles', formatter, 'regfiles')
wf.connect(inputspec, 'surf_template', preproc, 'target')
wf.connect(inputspec, 'surf_template', glmfit, 'subject_id')
wf.connect(inputspec, 'subjects_dir', preproc, 'subjects_dir')
wf.connect(inputspec, 'subjects_dir', glmfit, 'subjects_dir')
wf.connect(formatter, 'out', preproc, 'vol_measure_file')
wf.connect(preproc, 'out_file', glmfit, 'in_file')
wf.connect([(glmfit, outputspec,
[('beta_file', 'beta_file'), ('dof_file', 'dof_file'),
('error_file', 'error_file'),
('error_stddev_file', 'error_stddev_file'),
('error_var_file', 'error_var_file'),
('estimate_file', 'estimate_file'),
('frame_eigenvectors', 'frame_eigenvectors'),
('ftest_file', 'ftest_file'), ('fwhm_file', 'fwhm_file'),
('gamma_file', 'gamma_file'),
('gamma_var_file', 'gamma_var_file'),
('mask_file', 'mask_file'), ('sig_file', 'sig_file')])])
return wf
if '/%s/' % s in f:
outlist.append(f)
continue
print(outlist)
return outlist
l2flow.connect(l2source, ('con', ordersubjects, subject_list), mergenode,
'in1')
l2flow.connect(l2source, ('reg', ordersubjects, subject_list), mergenode,
'in2')
"""
Concatenate contrast images projected to fsaverage
"""
l2concat = pe.Node(interface=fs.MRISPreproc(), name='concat')
l2concat.inputs.target = 'fsaverage'
l2concat.inputs.fwhm = 5
def list2tuple(listoflist):
return [tuple(x) for x in listoflist]
l2flow.connect(l2inputnode, 'hemi', l2concat, 'hemi')
l2flow.connect(mergenode, ('out', list2tuple), l2concat, 'vol_measure_file')
"""
Perform a one sample t-test
"""
l2ttest = pe.Node(interface=fs.OneSampleTTest(), name='onesample')
def test_mrispreproc():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(),
fsgd_file=dict(
xor=('subjects', 'fsgd_file', 'subject_file'),
argstr='--fsgd %s',
),
fwhm=dict(
xor=['num_iters'],
argstr='--fwhm %f',
),
fwhm_source=dict(
xor=['num_iters_source'],
argstr='--fwhm-src %f',
),
hemi=dict(
argstr='--hemi %s',
mandatory=True,
),
num_iters=dict(
xor=['fwhm'],
argstr='--niters %d',
),
num_iters_source=dict(
xor=['fwhm_source'],
argstr='--niterssrc %d',
),
out_file=dict(argstr='--out %s', ),
proj_frac=dict(argstr='--projfrac %s', ),
smooth_cortex_only=dict(argstr='--smooth-cortex-only', ),
source_format=dict(argstr='--srcfmt %s', ),
subject_file=dict(
xor=('subjects', 'fsgd_file', 'subject_file'),
argstr='--f %s',
),
subjects=dict(
xor=('subjects', 'fsgd_file', 'subject_file'),
argstr='--s %s...',
),
subjects_dir=dict(),
surf_area=dict(
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
argstr='--area %s',
),
surf_dir=dict(argstr='--surfdir %s', ),
surf_measure=dict(
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
argstr='--meas %s',
),
surf_measure_file=dict(
xor=('surf_measure', 'surf_measure_file', 'surf_area'),
argstr='--is %s...',
),
target=dict(
mandatory=True,
argstr='--target %s',
),
vol_measure_file=dict(argstr='--iv %s %s...', ),
)
instance = freesurfer.MRISPreproc()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(instance.inputs.traits()[key],
metakey), value