def test_fslmerge(create_files_in_directory):
filelist, outdir, _ = create_files_in_directory
merger = fsl.Merge()
# make sure command gets called
assert merger.cmd == 'fslmerge'
# test raising error with mandatory args absent
with pytest.raises(ValueError):
merger.run()
# .inputs based parameters setting
merger.inputs.in_files = filelist
merger.inputs.merged_file = 'foo_merged.nii'
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI'
assert merger.cmdline == 'fslmerge -t foo_merged.nii %s' % ' '.join(
filelist)
# verify that providing a tr value updates the dimension to tr
merger.inputs.tr = 2.25
assert merger.cmdline == 'fslmerge -tr foo_merged.nii %s %.2f' % (
' '.join(filelist), 2.25)
# .run based parameter setting
merger2 = fsl.Merge(in_files=filelist,
merged_file='foo_merged.nii',
dimension='t',
output_type='NIFTI',
tr=2.25)
assert merger2.cmdline == \
'fslmerge -tr foo_merged.nii %s %.2f' % (' '.join(filelist), 2.25)
def test_fslmerge():
filelist, outdir, cwd, _ = create_files_in_directory()
merger = fsl.Merge()
# make sure command gets called
yield assert_equal, merger.cmd, 'fslmerge'
# test raising error with mandatory args absent
yield assert_raises, ValueError, merger.run
# .inputs based parameters setting
merger.inputs.in_files = filelist
merger.inputs.merged_file = 'foo_merged.nii'
merger.inputs.dimension = 't'
merger.inputs.output_type = 'NIFTI'
yield assert_equal, merger.cmdline, 'fslmerge -t foo_merged.nii %s' % ' '.join(filelist)
# verify that providing a tr value updates the dimension to tr
merger.inputs.tr = 2.25
yield assert_equal, merger.cmdline, 'fslmerge -tr foo_merged.nii %s %.2f' % (' '.join(filelist), 2.25)
# .run based parameter setting
merger2 = fsl.Merge(in_files=filelist,
merged_file='foo_merged.nii',
dimension='t',
output_type='NIFTI',
tr=2.25)
yield assert_equal, merger2.cmdline, \
'fslmerge -tr foo_merged.nii %s %.2f' % (' '.join(filelist), 2.25)
clean_directory(outdir, cwd)
def test_fslmerge(create_files_in_directory_plus_output_type):
filelist, outdir, _ = create_files_in_directory_plus_output_type
merger = fsl.Merge()
# make sure command gets called
assert merger.cmd == "fslmerge"
# test raising error with mandatory args absent
with pytest.raises(ValueError):
merger.run()
# .inputs based parameters setting
merger.inputs.in_files = filelist
merger.inputs.merged_file = "foo_merged.nii"
merger.inputs.dimension = "t"
merger.inputs.output_type = "NIFTI"
assert merger.cmdline == "fslmerge -t foo_merged.nii %s" % " ".join(filelist)
# verify that providing a tr value updates the dimension to tr
merger.inputs.tr = 2.25
assert merger.cmdline == "fslmerge -tr foo_merged.nii %s %.2f" % (
" ".join(filelist),
2.25,
)
# .run based parameter setting
merger2 = fsl.Merge(
in_files=filelist,
merged_file="foo_merged.nii",
dimension="t",
output_type="NIFTI",
tr=2.25,
)
assert merger2.cmdline == "fslmerge -tr foo_merged.nii %s %.2f" % (
" ".join(filelist),
2.25,
)
def test_merge():
input_map = dict(
args=dict(argstr='%s', ),
dimension=dict(
argstr='-%s',
mandatory=True,
),
environ=dict(),
in_files=dict(
mandatory=True,
argstr='%s',
),
merged_file=dict(argstr='%s', ),
output_type=dict(),
)
instance = fsl.Merge()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(instance.inputs.traits()[key],
metakey), value
def create_eddy_correct_pipeline(name="eddy_correct"):
"""Creates a pipeline that replaces eddy_correct script in FSL. It takes a
series of diffusion weighted images and linearly corregisters them to one
reference image.
Example
-------
>>> nipype_eddycorrect = create_eddy_correct_pipeline("nipype_eddycorrect")
>>> nipype_eddycorrect.inputs.inputnode.in_file = 'diffusion.nii'
>>> nipype_eddycorrect.inputs.inputnode.ref_num = 0
>>> nipype_eddycorrect.run() # doctest: +SKIP
Inputs::
inputnode.in_file
inputnode.ref_num
Outputs::
outputnode.eddy_corrected
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
import nipype.interfaces.fsl as fsl
inputnode = pe.Node(interface = util.IdentityInterface(fields=["in_file", "bvals"]),
name="inputnode")
pipeline = pe.Workflow(name=name)
split = pe.MapNode(fsl.Split(dimension='t'), name="split", iterfield=['in_file'])
pipeline.connect([(inputnode, split, [("in_file", "in_file")])])
getb0 = pe.Node(util.Function(input_names=['bvals'],
output_names=['b0'],
function=get_b0),
name='get_b0_indices')
pick_ref = pe.MapNode(util.Select(), name="pick_b0_scans", iterfield=["inlist"])
pick0 = pe.MapNode(util.Select(index=0), name="pick_first_scan", iterfield=["inlist"])
getmotion = pe.MapNode(util.Function(input_names=['flirt_out_mats'],
output_names=['motion_params'],
function=get_flirt_motion_parameters),
name='getmotion', iterfield=['flirt_out_mats'])
pipeline.connect(split, 'out_files', pick_ref, "inlist")
pipeline.connect(split, 'out_files', pick0, "inlist")
pipeline.connect(inputnode,'bvals', getb0, 'bvals')
pipeline.connect(getb0, 'b0', pick_ref, "index")
#pipeline.connect([(split, pick_ref, [("out_files", "inlist")]),
# (inputnode, pick_ref, [("ref_num", "index")])])
coregistration = pe.MapNode(util.Function(input_names=['in_file', 'reference'],
output_names=['out_file', 'out_matrix_file'],
function = mapnode_coregistration),
name = "coregister_b0",
iterfield=["in_file","reference"])
pipeline.connect([(pick_ref, coregistration, [("out", "in_file")]),
(pick0, coregistration, [("out", "reference")])])
art_mean = art_mean_workflow()
art_mean.inputs.inputspec.parameter_source='FSL'
pipeline.connect(coregistration,'out_matrix_file', getmotion, 'flirt_out_mats')
pipeline.connect(getmotion, 'motion_params', art_mean, 'inputspec.realignment_parameters')
merge = pe.MapNode(fsl.Merge(dimension="t"), name="merge", iterfield=["in_files"])
pipeline.connect([(coregistration, merge, [("out_file", "in_files")])
])
pipeline.connect(merge,'merged_file',art_mean,'inputspec.realigned_files')
outputnode = pe.Node(interface = util.IdentityInterface(fields=["eddy_corrected","mean_image","coreg_mat_files"]),
name="outputnode")
pipeline.connect(art_mean, 'outputspec.mean_image', outputnode, "mean_image")
coreg_all = coregistration.clone("coregister_all")
pipeline.connect(split, 'out_files', coreg_all, 'in_file')
pipeline.connect(art_mean,'outputspec.mean_image',coreg_all,"reference")
merge_all = merge.clone("merge_all")
pipeline.connect([(coreg_all, merge_all, [("out_file", "in_files")])
])
pipeline.connect(coreg_all, 'out_matrix_file', outputnode, "coreg_mat_files")
pipeline.connect(merge_all,'merged_file',outputnode,"eddy_corrected")
return pipeline
def remove_frames(scan, todel, matfile, tr, order=''):
""" given a 4d scan, a list of frames to remove, and an _extra.mat file,
will remove indicated frames from the time dimension of scan and update
_extra.mat file to reflect changes.
tr should reflect the actual TR of the acquisition data.
order indicates which scan is being edited for recording purposes only
and can be left blank
"""
# turn todel dict back into a list
to_remove = []
for k in sorted(todel.keys()):
for i in range(k, k + todel[k]):
to_remove.append(i)
to_remove = remove_dupes(to_remove) # remove duplicates in list of frames)
print 'removing the following frames from scan %s: %s' % (order, to_remove)
print 'splitting scan into volumes and removing frames'
# split 4D image into volumes
split = fsl.Split()
split.inputs.in_file = scan
split.inputs.dimension = 't'
split.inputs.output_type = 'NIFTI_GZ'
split.run() # outputs frames in same directory as volxxxx.nii.gz
# remove desired frames
for rem_ind in to_remove:
to_del = os.path.join(scan_dir, 'vol%04d.nii.gz' % (rem_ind))
os.remove(to_del)
# merge remaining frames back into single image
jnk, flnme = os.path.split(scan)
vols = sorted(glob(os.path.join(scan_dir, 'vol*')))
merge = fsl.Merge()
merge.inputs.in_files = vols
merge.inputs.dimension = 't'
merge.inputs.tr = tr
merge.inputs.merged_file = os.path.join(output_dir, flnme)
merge.inputs.output_type = 'NIFTI_GZ'
merge.inputs.ignore_exception
print 'creating new file %s' % (os.path.join(output_dir, flnme))
merge.run()
print 'updating _extra.mat file'
# update _extra.mat file
update_extra_mat(matfile, to_remove)
print 'cleaning up'
# clean up
vols = sorted(glob(os.path.join(scan_dir, 'vol*')))
for vol in vols:
os.remove(vol)