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autorecon1: convert_modalities output fix #1441

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merged 2 commits into from
Apr 19, 2016
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autorecon1: convert_modalities output fix #1441

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a041648
autorecon1: convert_modalities output fix
cni-md Apr 19, 2016
e3767b5
In contrast to 'create_featreg_preproc' 'create_parallelfeat_preproc'
cni-md Apr 19, 2016
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30 changes: 25 additions & 5 deletions nipype/workflows/fmri/fsl/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -113,6 +113,10 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
>>> preproc.base_dir = '/tmp'
>>> preproc.run() # doctest: +SKIP
"""
version = 0
if fsl.Info.version() and \
LooseVersion(fsl.Info.version()) > LooseVersion('5.0.6'):
version = 507

featpreproc = pe.Workflow(name=name)

Expand Down Expand Up @@ -358,7 +362,26 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
name='highpass')
featpreproc.connect(inputnode, ('highpass', highpass_operand), highpass, 'op_string')
featpreproc.connect(meanscale, 'out_file', highpass, 'in_file')
featpreproc.connect(highpass, 'out_file', outputnode, 'highpassed_files')

if version < 507:
featpreproc.connect(highpass, 'out_file', outputnode, 'highpassed_files')
else:
"""
Add back the mean removed by the highpass filter operation as of FSL 5.0.7
"""
meanfunc4 = pe.MapNode(interface=fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc4')

featpreproc.connect(meanscale, 'out_file', meanfunc4, 'in_file')
addmean = pe.MapNode(interface=fsl.BinaryMaths(operation='add'),
iterfield=['in_file', 'operand_file'],
name='addmean')
featpreproc.connect(highpass, 'out_file', addmean, 'in_file')
featpreproc.connect(meanfunc4, 'out_file', addmean, 'operand_file')
featpreproc.connect(addmean, 'out_file', outputnode, 'highpassed_files')


"""
Generate a mean functional image from the first run
Expand All @@ -368,11 +391,8 @@ def create_parallelfeat_preproc(name='featpreproc', highpass=True):
suffix='_mean'),
iterfield=['in_file'],
name='meanfunc3')
if highpass:
featpreproc.connect(highpass, 'out_file', meanfunc3, 'in_file')
else:
featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')

featpreproc.connect(meanscale, 'out_file', meanfunc3, 'in_file')
featpreproc.connect(meanfunc3, 'out_file', outputnode, 'mean')

return featpreproc
Expand Down
4 changes: 2 additions & 2 deletions nipype/workflows/smri/freesurfer/autorecon1.py
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Expand Up @@ -86,8 +86,8 @@ def convert_modalities(in_file=None, out_file=None):
convert.inputs.in_file = in_file
convert.inputs.out_file = out_file
convert.inputs.no_scale = True
convert.run()
out_file = os.path.abspath(convert.outputs.out_file)
out = convert.run()
out_file = os.path.abspath(out.outputs.out_file)
return out_file

T2_convert = pe.Node(Function(['in_file', 'out_file'],
Expand Down