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[FSL] Updated BEDPOSTX and XFibres
Old interfaces are still available as OldBEDPOSTX and OldXFibres
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‎CHANGES

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Next Release
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============
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* ENH: Updated FSL interfaces: BEDPOSTX and XFibres, former interfaces are still available with the prefix `Old`.
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* ENH: New FSL interface: EpiReg
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* ENH: New Freesurfer interface: Tkregister2 (for conversion of fsl style matrices to freesurfer format)
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* ENH: New FSL interfaces: WarpPoints, WarpPointsToStd.

‎nipype/interfaces/fsl/__init__.py

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from .epi import (PrepareFieldmap, TOPUP, ApplyTOPUP, Eddy, EPIDeWarp,
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SigLoss, EddyCorrect, EpiReg)
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from .dti import (BEDPOSTX, DTIFit, ProbTrackX, ProbTrackX2,
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from .dti import (BEDPOSTX, XFibres, DTIFit, ProbTrackX, ProbTrackX2,
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VecReg, ProjThresh, FindTheBiggest, DistanceMap,
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TractSkeleton, XFibres, MakeDyadicVectors)
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TractSkeleton, MakeDyadicVectors, OldBEDPOSTX, OldXFibres)
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from .maths import (ChangeDataType, Threshold, MeanImage, ApplyMask,
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IsotropicSmooth, TemporalFilter, DilateImage, ErodeImage,
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SpatialFilter, UnaryMaths, BinaryMaths, MultiImageMaths)

‎nipype/interfaces/fsl/base.py

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@@ -29,8 +29,10 @@
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import os
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import warnings
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from ...utils.filemanip import fname_presuffix
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from ..base import (CommandLine, traits, CommandLineInputSpec, isdefined)
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from ...utils.filemanip import fname_presuffix, split_filename, copyfile
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from ..base import (traits, isdefined,
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CommandLine, CommandLineInputSpec, TraitedSpec,
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File, Directory, InputMultiPath, OutputMultiPath)
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warn = warnings.warn
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warnings.filterwarnings('always', category=UserWarning)
@@ -243,6 +245,185 @@ def _overload_extension(self, value, name=None):
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return value + Info.output_type_to_ext(self.inputs.output_type)
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class FSLXCommandInputSpec(FSLCommandInputSpec):
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out_dir = Directory('.', mandatory=True, desc='output directory',
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usedefault=True, position=1, argstr='%s')
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dwi = File(exists=True, argstr='--data=%s', mandatory=True,
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desc='diffusion weighted image data file')
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mask = File(exists=True, argstr='--mask=%s', mandatory=True,
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desc='brain binary mask file (i.e. from BET)')
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bvecs = File(exists=True, argstr='--bvecs=%s', mandatory=True,
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desc='b vectors file')
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bvals = File(exists=True, argstr='--bvals=%s', mandatory=True,
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desc='b values file')
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logdir = Directory('logdir', argstr='--logdir=%s', usedefault=True)
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n_fibres = traits.Range(low=1, argstr='--nfibres=%d', desc=('Maximum '
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'number of fibres to fit in each voxel'))
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model = traits.Enum(1, 2, argstr='--model=%d',
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desc=('use monoexponential (1, default, required for '
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'single-shell) or multiexponential (2, multi-'
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'shell) model'))
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fudge = traits.Int(argstr='--fudge=%d',
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desc='ARD fudge factor')
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n_jumps = traits.Int(5000, argstr='--njumps=%d',
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desc='Num of jumps to be made by MCMC')
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burn_in = traits.Range(low=0, default=0, argstr='--burnin=%d',
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desc=('Total num of jumps at start of MCMC to be '
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'discarded'))
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burn_in_no_ard = traits.Range(low=0, default=0, argstr='--burninnoard=%d',
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desc=('num of burnin jumps before the ard is'
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' imposed'))
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sample_every = traits.Range(low=0, default=1, argstr='--sampleevery=%d',
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desc='Num of jumps for each sample (MCMC)')
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update_proposal_every = traits.Range(low=1, default=40,
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argstr='--updateproposalevery=%d',
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desc=('Num of jumps for each update '
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'to the proposal density std '
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'(MCMC)'))
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seed = traits.Int(argstr='--seed=%d',
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desc='seed for pseudo random number generator')
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_xor_inputs1 = ('no_ard', 'all_ard')
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no_ard = traits.Bool(argstr='--noard', xor=_xor_inputs1,
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desc='Turn ARD off on all fibres')
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all_ard = traits.Bool(argstr='--allard', xor=_xor_inputs1,
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desc='Turn ARD on on all fibres')
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_xor_inputs2 = ('no_spat', 'non_linear', 'cnlinear')
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no_spat = traits.Bool(argstr='--nospat', xor=_xor_inputs2,
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desc='Initialise with tensor, not spatially')
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non_linear = traits.Bool(argstr='--nonlinear', xor=_xor_inputs2,
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desc='Initialise with nonlinear fitting')
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cnlinear = traits.Bool(argstr='--cnonlinear', xor=_xor_inputs2,
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desc=('Initialise with constrained nonlinear '
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'fitting'))
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rician = traits.Bool(argstr='--rician', desc=('use Rician noise modeling'))
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_xor_inputs3 = ['f0_noard', 'f0_ard']
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f0_noard = traits.Bool(argstr='--f0', xor=_xor_inputs3,
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desc=('Noise floor model: add to the model an '
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'unattenuated signal compartment f0'))
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f0_ard = traits.Bool(argstr='--f0 --ardf0', xor=_xor_inputs3 + ['all_ard'],
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desc=('Noise floor model: add to the model an '
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'unattenuated signal compartment f0'))
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force_dir = traits.Bool(False, argstr='--forcedir', usedefault=True,
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desc=('use the actual directory name given '
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'(do not add + to make a new directory)'))
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class FSLXCommandOutputSpec(TraitedSpec):
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merged_thsamples = OutputMultiPath(File(exists=True), desc=('Samples from '
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'the distribution on theta'))
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merged_phsamples = OutputMultiPath(File(exists=True), desc=('Samples from '
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'the distribution on phi'))
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merged_fsamples = OutputMultiPath(File(exists=True),
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desc=('Samples from the distribution on '
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'anisotropic volume fraction.'))
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mean_thsamples = OutputMultiPath(File(exists=True), desc=('Mean of '
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'distribution on theta'))
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mean_phsamples = OutputMultiPath(File(exists=True), desc=('Mean of '
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'distribution on phi'))
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mean_fsamples = OutputMultiPath(File(exists=True), desc=('Mean of '
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'distribution on f anisotropy'))
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mean_dsamples = File(exists=True, desc='Mean of distribution on '
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'diffusivity d')
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mean_S0samples = File(exists=True, desc='Mean of distribution on T2w'
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'baseline signal intensity S0')
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mean_tausamples = File(exists=True, desc='Mean of distribution on '
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'tau samples (only with rician noise)')
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dyads = OutputMultiPath(File(exists=True), desc=('Mean of PDD distribution'
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' in vector form.'))
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dyads_disp = OutputMultiPath(File(exists=True), desc=('Uncertainty on the '
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' estimated fiber orientation'))
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fsamples = OutputMultiPath(File(exists=True), desc=('Samples from the '
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'distribution on anisotropic volume fraction'))
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class FSLXCommand(FSLCommand):
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"""
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Base support for ``xfibres`` and ``bedpostx``
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"""
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input_spec = FSLXCommandInputSpec
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output_spec = FSLXCommandOutputSpec
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def _run_interface(self, runtime):
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subjectdir = os.path.abspath(self.inputs.out_dir)
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out_dir = subjectdir + '.bedpostX'
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if isdefined(self.inputs.force_dir) and self.inputs.force_dir:
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out_dir = os.path.abspath(self.inputs.out_dir)
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self._out_dir = out_dir
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if not os.path.exists(out_dir):
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os.makedirs(out_dir)
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_, _, ext = split_filename(self.inputs.mask)
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copyfile(self.inputs.mask,
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os.path.join(subjectdir,
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'nodif_brain_mask' + ext))
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_, _, ext = split_filename(self.inputs.dwi)
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copyfile(self.inputs.dwi,
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os.path.join(subjectdir, 'data' + ext))
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copyfile(self.inputs.bvals,
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os.path.join(subjectdir, 'bvals'))
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copyfile(self.inputs.bvecs,
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os.path.join(subjectdir, 'bvecs'))
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runtime = super(FSLXCommand, self)._run_interface(runtime)
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if runtime.stderr:
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self.raise_exception(runtime)
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return runtime
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def _list_outputs(self):
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outputs = self.output_spec().get()
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out_dir = self._out_dir
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for k in outputs.keys():
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if k not in ('outputtype', 'environ', 'args', 'bpx_out_directory',
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'xfms_directory', 'mean_dsamples', 'mean_S0samples',
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'mean_tausamples'):
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outputs[k] = []
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outputs['mean_dsamples'] = self._gen_fname('mean_dsamples',
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cwd=out_dir)
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outputs['mean_S0samples'] = self._gen_fname('mean_S0samples',
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cwd=out_dir)
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if isdefined(self.inputs.rician) and self.inputs.rician:
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outputs['mean_tausamples'] = self._gen_fname('mean_tausamples',
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cwd=out_dir)
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for i in xrange(1, self.inputs.fibres + 1):
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outputs['merged_thsamples'].append(self._gen_fname(('merged_th%d'
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'samples') % i),
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cwd=out_dir)
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outputs['merged_phsamples'].append(self._gen_fname(('merged_ph%d'
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'samples') % i),
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cwd=out_dir)
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outputs['merged_fsamples'].append(self._gen_fname(('merged_f%d'
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'samples') % i),
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cwd=out_dir)
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outputs['mean_thsamples'].append(self._gen_fname(('mean_th%d'
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'samples') % i),
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cwd=out_dir)
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outputs['mean_phsamples'].append(self._gen_fname(('mean_ph%d'
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'samples') % i),
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cwd=out_dir)
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outputs['mean_fsamples'].append(self._gen_fname(('mean_f%d'
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'samples') % i),
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cwd=out_dir)
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outputs['dyads'].append(self._gen_fname('dyads%d' % i),
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cwd=out_dir)
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outputs['dyads_dispersion'].append(self._gen_fname(('dyads%d'
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'_dispersion') % i),
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cwd=out_dir)
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return outputs
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def check_fsl():
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ver = Info.version()

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