diff --git a/nipype/interfaces/fsl/preprocess.py b/nipype/interfaces/fsl/preprocess.py index 8f23699915..579307a4b5 100644 --- a/nipype/interfaces/fsl/preprocess.py +++ b/nipype/interfaces/fsl/preprocess.py @@ -118,15 +118,17 @@ class BET(FSLCommand): """Use FSL BET command for skull stripping. For complete details, see the `BET Documentation. - `_ + `_ Examples -------- >>> from nipype.interfaces import fsl - >>> from nipype.testing import example_data >>> btr = fsl.BET() - >>> btr.inputs.in_file = example_data('structural.nii') + >>> btr.inputs.in_file = 'structural.nii' >>> btr.inputs.frac = 0.7 + >>> btr.inputs.out_file = 'brain_anat.nii' + >>> btr.cmdline # doctest: +ALLOW_UNICODE + 'bet structural.nii brain_anat.nii -f 0.70' >>> res = btr.run() # doctest: +SKIP """ @@ -275,7 +277,7 @@ class FASTOutputSpec(TraitedSpec): mixeltype = File(desc="path/name of mixeltype volume file _mixeltype") - partial_volume_map = File(desc="path/name of partial volume file _pveseg") + partial_volume_map = File(desc='path/name of partial volume file _pveseg') partial_volume_files = OutputMultiPath(File( desc='path/name of partial volumes files one for each class, _pve_x')) @@ -288,18 +290,17 @@ class FAST(FSLCommand): """ Use FSL FAST for segmenting and bias correction. For complete details, see the `FAST Documentation. - `_ + `_ Examples -------- >>> from nipype.interfaces import fsl - >>> from nipype.testing import example_data - - Assign options through the ``inputs`` attribute: - >>> fastr = fsl.FAST() - >>> fastr.inputs.in_files = example_data('structural.nii') - >>> out = fastr.run() #doctest: +SKIP + >>> fastr.inputs.in_files = 'structural.nii' + >>> fastr.inputs.out_basename = 'fast_' + >>> fastr.cmdline # doctest: +ALLOW_UNICODE + 'fast -o fast_ -S 1 structural.nii' + >>> out = fastr.run() # doctest: +SKIP """ _cmd = 'fast' @@ -308,12 +309,12 @@ class FAST(FSLCommand): def _format_arg(self, name, spec, value): # first do what should be done in general - formated = super(FAST, self)._format_arg(name, spec, value) + formatted = super(FAST, self)._format_arg(name, spec, value) if name == 'in_files': # FAST needs the -S parameter value to correspond to the number # of input images, otherwise it will ignore all but the first - formated = "-S %d %s" % (len(value), formated) - return formated + formatted = "-S %d %s" % (len(value), formatted) + return formatted def _list_outputs(self): outputs = self.output_spec().get() @@ -526,7 +527,7 @@ class FLIRT(FSLCommand): """Use FSL FLIRT for coregistration. For complete details, see the `FLIRT Documentation. - `_ + `_ To print out the command line help, use: fsl.FLIRT().inputs_help() @@ -655,14 +656,18 @@ class MCFLIRT(FSLCommand): """Use FSL MCFLIRT to do within-modality motion correction. For complete details, see the `MCFLIRT Documentation. - `_ + `_ Examples -------- >>> from nipype.interfaces import fsl - >>> from nipype.testing import example_data - >>> mcflt = fsl.MCFLIRT(in_file=example_data('functional.nii'), cost='mutualinfo') - >>> res = mcflt.run() # doctest: +SKIP + >>> mcflt = fsl.MCFLIRT() + >>> mcflt.inputs.in_file = 'functional.nii' + >>> mcflt.inputs.cost = 'mutualinfo' + >>> mcflt.inputs.out_file = 'moco.nii' + >>> mcflt.cmdline # doctest: +ALLOW_UNICODE + 'mcflirt -in functional.nii -cost mutualinfo -out moco.nii' + >>> res = mcflt.run() # doctest: +SKIP """ _cmd = 'mcflirt' @@ -908,6 +913,9 @@ class FNIRTOutputSpec(TraitedSpec): class FNIRT(FSLCommand): """Use FSL FNIRT for non-linear registration. + For complete details, see the `FNIRT Documentation. + `_ + Examples -------- >>> from nipype.interfaces import fsl @@ -1208,6 +1216,9 @@ class SUSANOutputSpec(TraitedSpec): class SUSAN(FSLCommand): """ use FSL SUSAN to perform smoothing + For complete details, see the `SUSAN Documentation. + `_ + Examples --------