fix: clean up bedpostx and xfibres implementations

nipype/interfaces/fsl/base.py

@@ -245,145 +245,6 @@ def _overload_extension(self, value, name=None):
  return value + Info.output_type_to_ext(self.inputs.output_type)
 
 
-class FSLXCommandInputSpec(FSLCommandInputSpec):
- dwi = File(exists=True, argstr='--data=%s', mandatory=True,
- desc='diffusion weighted image data file')
- mask = File(exists=True, argstr='--mask=%s', mandatory=True,
- desc='brain binary mask file (i.e. from BET)')
- bvecs = File(exists=True, argstr='--bvecs=%s', mandatory=True,
- desc='b vectors file')
- bvals = File(exists=True, argstr='--bvals=%s', mandatory=True,
- desc='b values file')
-
- logdir = Directory('.', argstr='--logdir=%s', usedefault=True)
- n_fibres = traits.Range(low=1, argstr='--nfibres=%d', desc=('Maximum '
- 'number of fibres to fit in each voxel'))
- model = traits.Enum(1, 2, argstr='--model=%d',
- desc=('use monoexponential (1, default, required for '
- 'single-shell) or multiexponential (2, multi-'
- 'shell) model'))
- fudge = traits.Int(argstr='--fudge=%d',
- desc='ARD fudge factor')
- n_jumps = traits.Int(5000, argstr='--njumps=%d',
- desc='Num of jumps to be made by MCMC')
- burn_in = traits.Range(low=0, default=0, argstr='--burnin=%d',
- desc=('Total num of jumps at start of MCMC to be '
- 'discarded'))
- burn_in_no_ard = traits.Range(low=0, default=0, argstr='--burninnoard=%d',
- desc=('num of burnin jumps before the ard is'
- ' imposed'))
- sample_every = traits.Range(low=0, default=1, argstr='--sampleevery=%d',
- desc='Num of jumps for each sample (MCMC)')
- update_proposal_every = traits.Range(low=1, default=40,
- argstr='--updateproposalevery=%d',
- desc=('Num of jumps for each update '
- 'to the proposal density std '
- '(MCMC)'))
- seed = traits.Int(argstr='--seed=%d',
- desc='seed for pseudo random number generator')
-
- _xor_inputs1 = ('no_ard', 'all_ard')
- no_ard = traits.Bool(argstr='--noard', xor=_xor_inputs1,
- desc='Turn ARD off on all fibres')
- all_ard = traits.Bool(argstr='--allard', xor=_xor_inputs1,
- desc='Turn ARD on on all fibres')
-
- _xor_inputs2 = ('no_spat', 'non_linear', 'cnlinear')
- no_spat = traits.Bool(argstr='--nospat', xor=_xor_inputs2,
- desc='Initialise with tensor, not spatially')
- non_linear = traits.Bool(argstr='--nonlinear', xor=_xor_inputs2,
- desc='Initialise with nonlinear fitting')
- cnlinear = traits.Bool(argstr='--cnonlinear', xor=_xor_inputs2,
- desc=('Initialise with constrained nonlinear '
- 'fitting'))
- rician = traits.Bool(argstr='--rician', desc=('use Rician noise modeling'))
-
- _xor_inputs3 = ['f0_noard', 'f0_ard']
- f0_noard = traits.Bool(argstr='--f0', xor=_xor_inputs3,
- desc=('Noise floor model: add to the model an '
- 'unattenuated signal compartment f0'))
- f0_ard = traits.Bool(argstr='--f0 --ardf0', xor=_xor_inputs3 + ['all_ard'],
- desc=('Noise floor model: add to the model an '
- 'unattenuated signal compartment f0'))
- force_dir = traits.Bool(True, argstr='--forcedir', usedefault=True,
- desc=('use the actual directory name given '
- '(do not add + to make a new directory)'))
-
-
-class FSLXCommandOutputSpec(TraitedSpec):
- dsamples = File(desc=('Samples from the distribution on diffusivity d'))
- d_stdsamples = File(desc=('Std of samples from the distribution d'))
- dyads = OutputMultiPath(File(), desc=('Mean of PDD distribution'
- ' in vector form.'))
- fsamples = OutputMultiPath(File(), desc=('Samples from the '
- 'distribution on f anisotropy'))
- mean_dsamples = File(desc='Mean of distribution on diffusivity d')
- mean_d_stdsamples = File(desc='Mean of distribution on diffusivity d')
- mean_fsamples = OutputMultiPath(File(), desc=('Mean of '
- 'distribution on f anisotropy'))
- mean_S0samples = File(desc='Mean of distribution on T2w'
- 'baseline signal intensity S0')
- mean_tausamples = File(desc='Mean of distribution on '
- 'tau samples (only with rician noise)')
- phsamples = OutputMultiPath(File(), desc=('phi samples, per fiber'))
- thsamples = OutputMultiPath(File(), desc=('theta samples, per fiber'))
-
-
-class FSLXCommand(FSLCommand):
- """
- Base support for ``xfibres`` and ``bedpostx``
- """
- input_spec = FSLXCommandInputSpec
- output_spec = FSLXCommandOutputSpec
-
- def _run_interface(self, runtime):
- self._out_dir = os.getcwd()
- runtime = super(FSLXCommand, self)._run_interface(runtime)
- if runtime.stderr:
- self.raise_exception(runtime)
- return runtime
-
- def _list_outputs(self):
- outputs = self.output_spec().get()
- out_dir = self._out_dir
-
- if isdefined(self.inputs.logdir):
- out_dir = os.path.abspath(self.inputs.logdir)
- else:
- out_dir = os.path.abspath('logdir')
-
- multi_out = ['dyads', 'fsamples', 'mean_fsamples',
- 'phsamples', 'thsamples']
- single_out = ['dsamples', 'd_stdsamples', 'mean_dsamples',
- 'mean_S0samples', 'mean_d_stdsamples']
-
- for k in single_out:
- outputs[k] = self._gen_fname(k, cwd=out_dir)
-
- if isdefined(self.inputs.rician) and self.inputs.rician:
- outputs['mean_tausamples'] = self._gen_fname('mean_tausamples',
- cwd=out_dir)
-
- for k in multi_out:
- outputs[k] = []
-
- for i in xrange(self.inputs.n_fibres + 1):
- outputs['fsamples'].append(self._gen_fname('f%dsamples' % i,
- cwd=out_dir))
- outputs['mean_fsamples'].append(self._gen_fname(('mean_f%d'
- 'samples') % i, cwd=out_dir))
-
- for i in xrange(1, self.inputs.n_fibres + 1):
- outputs['dyads'].append(self._gen_fname('dyads%d' % i,
- cwd=out_dir))
- outputs['phsamples'].append(self._gen_fname('ph%dsamples' % i,
- cwd=out_dir))
- outputs['thsamples'].append(self._gen_fname('th%dsamples' % i,
- cwd=out_dir))
-
- return outputs
-
-
 def check_fsl():
  ver = Info.version()
  if ver: