fix: clean up bedpostx and xfibres implementations

Author: satra

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: