def test_convertxfm():
filelist, outdir, cwd = create_files_in_directory()
cvt = fsl.ConvertXFM()
# make sure command gets called
yield assert_equal, cvt.cmd, "convert_xfm"
# test raising error with mandatory args absent
yield assert_raises, ValueError, cvt.run
# .inputs based parameters setting
cvt.inputs.in_file = filelist[0]
cvt.inputs.invert_xfm = True
cvt.inputs.out_file = "foo.mat"
yield assert_equal, cvt.cmdline, 'convert_xfm -omat foo.mat -inverse %s' % filelist[
0]
# constructor based parameter setting
cvt2 = fsl.ConvertXFM(in_file=filelist[0],
in_file2=filelist[1],
concat_xfm=True,
out_file="bar.mat")
yield assert_equal, cvt2.cmdline, \
"convert_xfm -omat bar.mat -concat %s %s" % (filelist[1], filelist[0])
clean_directory(outdir, cwd)
def test_convertxfm(create_files_in_directory_plus_output_type):
filelist, outdir, _ = create_files_in_directory_plus_output_type
cvt = fsl.ConvertXFM()
# make sure command gets called
assert cvt.cmd == "convert_xfm"
# test raising error with mandatory args absent
with pytest.raises(ValueError):
cvt.run()
# .inputs based parameters setting
cvt.inputs.in_file = filelist[0]
cvt.inputs.invert_xfm = True
cvt.inputs.out_file = "foo.mat"
assert cvt.cmdline == "convert_xfm -omat foo.mat -inverse %s" % filelist[0]
# constructor based parameter setting
cvt2 = fsl.ConvertXFM(
in_file=filelist[0], in_file2=filelist[1], concat_xfm=True, out_file="bar.mat"
)
assert cvt2.cmdline == "convert_xfm -omat bar.mat -concat %s %s" % (
filelist[1],
filelist[0],
)
def acpc_alignment(config=None,
acpc_target='whole-head',
mask=False,
wf_name='acpc_align'):
preproc = pe.Workflow(name=wf_name)
inputnode = pe.Node(util.IdentityInterface(fields=[
'anat_leaf', 'anat_brain', 'brain_mask',
'template_brain_only_for_anat', 'template_skull_for_anat',
'template_brain_for_acpc', 'template_head_for_acpc'
]),
name='inputspec')
output_node = pe.Node(util.IdentityInterface(
fields=['acpc_aligned_head', 'acpc_brain_mask']),
name='outputspec')
robust_fov = pe.Node(interface=fsl_utils.RobustFOV(),
name='anat_acpc_1_robustfov')
robust_fov.inputs.brainsize = config.anatomical_preproc['acpc_alignment'][
'brain_size']
robust_fov.inputs.out_transform = 'fov_xfm.mat'
# align head-to-head to get acpc.mat (for human)
if acpc_target == 'whole-head':
preproc.connect(inputnode, 'anat_leaf', robust_fov, 'in_file')
# align brain-to-brain to get acpc.mat (for monkey)
if acpc_target == 'brain':
preproc.connect(inputnode, 'anat_brain', robust_fov, 'in_file')
convert_fov_xfm = pe.Node(interface=fsl_utils.ConvertXFM(),
name='anat_acpc_2_fov_convertxfm')
convert_fov_xfm.inputs.invert_xfm = True
preproc.connect(robust_fov, 'out_transform', convert_fov_xfm, 'in_file')
align = pe.Node(interface=fsl.FLIRT(), name='anat_acpc_3_flirt')
align.inputs.interp = 'spline'
align.inputs.searchr_x = [30, 30]
align.inputs.searchr_y = [30, 30]
align.inputs.searchr_z = [30, 30]
preproc.connect(robust_fov, 'out_roi', align, 'in_file')
# align head-to-head to get acpc.mat (for human)
if acpc_target == 'whole-head':
preproc.connect(inputnode, 'template_head_for_acpc', align,
'reference')
# align brain-to-brain to get acpc.mat (for monkey)
if acpc_target == 'brain':
preproc.connect(inputnode, 'template_brain_for_acpc', align,
'reference')
concat_xfm = pe.Node(interface=fsl_utils.ConvertXFM(),
name='anat_acpc_4_concatxfm')
concat_xfm.inputs.concat_xfm = True
preproc.connect(convert_fov_xfm, 'out_file', concat_xfm, 'in_file')
preproc.connect(align, 'out_matrix_file', concat_xfm, 'in_file2')
aff_to_rig_imports = ['import os', 'from numpy import *']
aff_to_rig = pe.Node(util.Function(input_names=['in_xfm', 'out_name'],
output_names=['out_mat'],
function=fsl_aff_to_rigid,
imports=aff_to_rig_imports),
name='anat_acpc_5_aff2rigid')
aff_to_rig.inputs.out_name = 'acpc.mat'
preproc.connect(concat_xfm, 'out_file', aff_to_rig, 'in_xfm')
apply_xfm = pe.Node(interface=fsl.ApplyWarp(),
name='anat_acpc_6_applywarp')
apply_xfm.inputs.interp = 'spline'
apply_xfm.inputs.relwarp = True
preproc.connect(inputnode, 'anat_leaf', apply_xfm, 'in_file')
preproc.connect(inputnode, 'template_head_for_acpc', apply_xfm, 'ref_file')
preproc.connect(aff_to_rig, 'out_mat', apply_xfm, 'premat')
preproc.connect(apply_xfm, 'out_file', output_node, 'acpc_aligned_head')
if mask:
apply_xfm_mask = pe.Node(interface=fsl.ApplyWarp(),
name='anat_mask_acpc_7_applywarp')
apply_xfm_mask.inputs.interp = 'nn'
apply_xfm_mask.inputs.relwarp = True
preproc.connect(inputnode, 'brain_mask', apply_xfm_mask, 'in_file')
preproc.connect(inputnode, 'template_brain_for_acpc', apply_xfm_mask,
'ref_file')
preproc.connect(aff_to_rig, 'out_mat', apply_xfm_mask, 'premat')
preproc.connect(apply_xfm_mask, 'out_file', output_node,
'acpc_brain_mask')
return preproc
