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FIX: Create out_reg_file correctly in RobustRegister #2205

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merged 5 commits into from
Oct 4, 2017
Merged

FIX: Create out_reg_file correctly in RobustRegister #2205

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eaf42e6
FIX: Create out_reg_file correctly in RobustRegister
effigies Oct 3, 2017
99c7ab6
STY: Flake8 fix RobustRegister
effigies Oct 3, 2017
758251a
make specs
effigies Oct 3, 2017
0eb6ba0
DOCTEST: Ellipsis
effigies Oct 3, 2017
efaaaa2
TEST: Fix RobustRegister cmdline checks
effigies Oct 4, 2017
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161 changes: 89 additions & 72 deletions nipype/interfaces/freesurfer/preprocess.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1457,76 +1457,102 @@ def _gen_filename(self, name):

class RobustRegisterInputSpec(FSTraitedSpec):

source_file = File(mandatory=True, argstr='--mov %s',
source_file = File(exists=True, mandatory=True, argstr='--mov %s',
desc='volume to be registered')
target_file = File(mandatory=True, argstr='--dst %s',
target_file = File(exists=True, mandatory=True, argstr='--dst %s',
desc='target volume for the registration')
out_reg_file = File(genfile=True, argstr='--lta %s',
desc='registration file to write')
registered_file = traits.Either(traits.Bool, File, argstr='--warp %s',
desc='registered image; either True or filename')
weights_file = traits.Either(traits.Bool, File, argstr='--weights %s',
desc='weights image to write; either True or filename')
est_int_scale = traits.Bool(argstr='--iscale',
desc='estimate intensity scale (recommended for unnormalized images)')
out_reg_file = traits.Either(
True, File, default=True, usedefault=True, argstr='--lta %s',
desc='registration file; either True or filename')
registered_file = traits.Either(
traits.Bool, File, argstr='--warp %s',
desc='registered image; either True or filename')
weights_file = traits.Either(
traits.Bool, File, argstr='--weights %s',
desc='weights image to write; either True or filename')
est_int_scale = traits.Bool(
argstr='--iscale',
desc='estimate intensity scale (recommended for unnormalized images)')
trans_only = traits.Bool(argstr='--transonly',
desc='find 3 parameter translation only')
in_xfm_file = File(exists=True, argstr='--transform',
desc='use initial transform on source')
half_source = traits.Either(traits.Bool, File, argstr='--halfmov %s',
desc="write source volume mapped to halfway space")
half_targ = traits.Either(traits.Bool, File, argstr="--halfdst %s",
desc="write target volume mapped to halfway space")
half_weights = traits.Either(traits.Bool, File, argstr="--halfweights %s",
desc="write weights volume mapped to halfway space")
half_source_xfm = traits.Either(traits.Bool, File, argstr="--halfmovlta %s",
desc="write transform from source to halfway space")
half_targ_xfm = traits.Either(traits.Bool, File, argstr="--halfdstlta %s",
desc="write transform from target to halfway space")
auto_sens = traits.Bool(argstr='--satit', xor=['outlier_sens'], mandatory=True,
desc='auto-detect good sensitivity')
outlier_sens = traits.Float(argstr='--sat %.4f', xor=['auto_sens'], mandatory=True,
desc='set outlier sensitivity explicitly')
least_squares = traits.Bool(argstr='--leastsquares',
desc='use least squares instead of robust estimator')
half_source = traits.Either(
traits.Bool, File, argstr='--halfmov %s',
desc="write source volume mapped to halfway space")
half_targ = traits.Either(
traits.Bool, File, argstr="--halfdst %s",
desc="write target volume mapped to halfway space")
half_weights = traits.Either(
traits.Bool, File, argstr="--halfweights %s",
desc="write weights volume mapped to halfway space")
half_source_xfm = traits.Either(
traits.Bool, File, argstr="--halfmovlta %s",
desc="write transform from source to halfway space")
half_targ_xfm = traits.Either(
traits.Bool, File, argstr="--halfdstlta %s",
desc="write transform from target to halfway space")
auto_sens = traits.Bool(
argstr='--satit', xor=['outlier_sens'], mandatory=True,
desc='auto-detect good sensitivity')
outlier_sens = traits.Float(
argstr='--sat %.4f', xor=['auto_sens'], mandatory=True,
desc='set outlier sensitivity explicitly')
least_squares = traits.Bool(
argstr='--leastsquares',
desc='use least squares instead of robust estimator')
no_init = traits.Bool(argstr='--noinit', desc='skip transform init')
init_orient = traits.Bool(argstr='--initorient',
desc='use moments for initial orient (recommended for stripped brains)')
init_orient = traits.Bool(
argstr='--initorient',
desc='use moments for initial orient (recommended for stripped brains)'
)
max_iterations = traits.Int(argstr='--maxit %d',
desc='maximum # of times on each resolution')
high_iterations = traits.Int(argstr='--highit %d',
desc='max # of times on highest resolution')
iteration_thresh = traits.Float(argstr='--epsit %.3f',
desc='stop iterations when below threshold')
subsample_thresh = traits.Int(argstr='--subsample %d',
desc='subsample if dimension is above threshold size')
iteration_thresh = traits.Float(
argstr='--epsit %.3f', desc='stop iterations when below threshold')
subsample_thresh = traits.Int(
argstr='--subsample %d',
desc='subsample if dimension is above threshold size')
outlier_limit = traits.Float(argstr='--wlimit %.3f',
desc='set maximal outlier limit in satit')
write_vo2vox = traits.Bool(argstr='--vox2vox',
desc='output vox2vox matrix (default is RAS2RAS)')
no_multi = traits.Bool(argstr='--nomulti', desc='work on highest resolution')
write_vo2vox = traits.Bool(
argstr='--vox2vox', desc='output vox2vox matrix (default is RAS2RAS)')
no_multi = traits.Bool(argstr='--nomulti',
desc='work on highest resolution')
mask_source = File(exists=True, argstr='--maskmov %s',
desc='image to mask source volume with')
mask_target = File(exists=True, argstr='--maskdst %s',
desc='image to mask target volume with')
force_double = traits.Bool(argstr='--doubleprec', desc='use double-precision intensities')
force_float = traits.Bool(argstr='--floattype', desc='use float intensities')
force_double = traits.Bool(argstr='--doubleprec',
desc='use double-precision intensities')
force_float = traits.Bool(argstr='--floattype',
desc='use float intensities')


class RobustRegisterOutputSpec(TraitedSpec):

out_reg_file = File(exists=True, desc="output registration file")
registered_file = File(desc="output image with registration applied")
weights_file = File(desc="image of weights used")
half_source = File(desc="source image mapped to halfway space")
half_targ = File(desc="target image mapped to halfway space")
half_weights = File(desc="weights image mapped to halfway space")
half_source_xfm = File(desc="transform file to map source image to halfway space")
half_targ_xfm = File(desc="transform file to map target image to halfway space")
registered_file = File(exists=True,
desc="output image with registration applied")
weights_file = File(exists=True, desc="image of weights used")
half_source = File(exists=True,
desc="source image mapped to halfway space")
half_targ = File(exists=True, desc="target image mapped to halfway space")
half_weights = File(exists=True,
desc="weights image mapped to halfway space")
half_source_xfm = File(
exists=True,
desc="transform file to map source image to halfway space")
half_targ_xfm = File(
exists=True,
desc="transform file to map target image to halfway space")


class RobustRegister(FSCommand):
"""Perform intramodal linear registration (translation and rotation) using robust statistics.
"""Perform intramodal linear registration (translation and rotation) using
robust statistics.

Examples
--------
Expand All @@ -1536,13 +1562,13 @@ class RobustRegister(FSCommand):
>>> reg.inputs.target_file = 'T1.nii'
>>> reg.inputs.auto_sens = True
>>> reg.inputs.init_orient = True
>>> reg.cmdline # doctest: +ALLOW_UNICODE
'mri_robust_register --satit --initorient --lta structural_robustreg.lta --mov structural.nii --dst T1.nii'
>>> reg.cmdline # doctest: +ALLOW_UNICODE +ELLIPSIS
'mri_robust_register --satit --initorient --lta .../structural_robustreg.lta --mov structural.nii --dst T1.nii'

References
----------
Reuter, M, Rosas, HD, and Fischl, B, (2010). Highly Accurate Inverse Consistent Registration:
A Robust Approach. Neuroimage 53(4) 1181-96.
Reuter, M, Rosas, HD, and Fischl, B, (2010). Highly Accurate Inverse
Consistent Registration: A Robust Approach. Neuroimage 53(4) 1181-96.

"""

Expand All @@ -1551,24 +1577,20 @@ class RobustRegister(FSCommand):
output_spec = RobustRegisterOutputSpec

def _format_arg(self, name, spec, value):
for option in ["registered_file", "weights_file", "half_source", "half_targ",
"half_weights", "half_source_xfm", "half_targ_xfm"]:
if name == option:
if isinstance(value, bool):
fname = self._list_outputs()[name]
else:
fname = value
return spec.argstr % fname
options = ("out_reg_file", "registered_file", "weights_file",
"half_source", "half_targ", "half_weights",
"half_source_xfm", "half_targ_xfm")
if name in options and isinstance(value, bool):
value = self._list_outputs()[name]
return super(RobustRegister, self)._format_arg(name, spec, value)

def _list_outputs(self):
outputs = self.output_spec().get()
outputs['out_reg_file'] = self.inputs.out_reg_file
if not isdefined(self.inputs.out_reg_file) and self.inputs.source_file:
outputs['out_reg_file'] = fname_presuffix(self.inputs.source_file,
suffix='_robustreg.lta', use_ext=False)
prefices = dict(src=self.inputs.source_file, trg=self.inputs.target_file)
suffices = dict(registered_file=("src", "_robustreg", True),
cwd = os.getcwd()
prefices = dict(src=self.inputs.source_file,
trg=self.inputs.target_file)
suffices = dict(out_reg_file=("src", "_robustreg.lta", False),
registered_file=("src", "_robustreg", True),
weights_file=("src", "_robustweights", True),
half_source=("src", "_halfway", True),
half_targ=("trg", "_halfway", True),
Expand All @@ -1577,21 +1599,16 @@ def _list_outputs(self):
half_targ_xfm=("trg", "_robustxfm.lta", False))
for name, sufftup in list(suffices.items()):
value = getattr(self.inputs, name)
if isdefined(value):
if isinstance(value, bool):
if value:
if value is True:
outputs[name] = fname_presuffix(prefices[sufftup[0]],
suffix=sufftup[1],
newpath=os.getcwd(),
newpath=cwd,
use_ext=sufftup[2])
else:
outputs[name] = value
outputs[name] = os.path.abspath(value)
return outputs

def _gen_filename(self, name):
if name == 'out_reg_file':
return self._list_outputs()[name]
return None


class FitMSParamsInputSpec(FSTraitedSpec):

Expand Down
2 changes: 1 addition & 1 deletion nipype/interfaces/freesurfer/tests/test_auto_RobustRegister.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,7 +53,7 @@ def test_RobustRegister_inputs():
no_multi=dict(argstr='--nomulti',
),
out_reg_file=dict(argstr='--lta %s',
genfile=True,
usedefault=True,
),
outlier_limit=dict(argstr='--wlimit %.3f',
),
Expand Down
6 changes: 4 additions & 2 deletions nipype/interfaces/freesurfer/tests/test_preprocess.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -17,6 +17,7 @@ def test_robustregister(create_files_in_directory):
filelist, outdir = create_files_in_directory

reg = freesurfer.RobustRegister()
cwd = os.getcwd()

# make sure command gets called
assert reg.cmd == 'mri_robust_register'
Expand All @@ -28,8 +29,9 @@ def test_robustregister(create_files_in_directory):
reg.inputs.source_file = filelist[0]
reg.inputs.target_file = filelist[1]
reg.inputs.auto_sens = True
assert reg.cmdline == ('mri_robust_register '
'--satit --lta %s_robustreg.lta --mov %s --dst %s' % (filelist[0][:-4], filelist[0], filelist[1]))
assert reg.cmdline == ('mri_robust_register --satit --lta '
'%s/%s_robustreg.lta --mov %s --dst %s' %
(cwd, filelist[0][:-4], filelist[0], filelist[1]))

# constructor based parameter setting
reg2 = freesurfer.RobustRegister(source_file=filelist[0], target_file=filelist[1], outlier_sens=3.0,
Expand Down