Fixed ApplyTOPUP and TOPUP

Author: oesteban

nipype/interfaces/fsl/epi.py

@@ -17,7 +17,7 @@
 import numpy as np
 
 from nipype.interfaces.fsl.base import FSLCommand, FSLCommandInputSpec, Info
-from nipype.interfaces.base import (traits, TraitedSpec, OutputMultiPath, File,
+from nipype.interfaces.base import (traits, TraitedSpec, InputMultiPath, File,
  isdefined)
 
 
@@ -85,28 +85,28 @@ def _list_outputs(self):
 
 
 class TOPUPInputSpec( FSLCommandInputSpec ):
- in_file = File( exists=True, mandatory=True, desc='name of 4D file with images', argstr='--imain %s' )
- encoding_file = File( exists=True, mandatory=True, desc='name of text file with PE directions/times', argstr='--datain %s' )
- out_base = File( desc='base-name of output files (spline coefficients (Hz) and movement parameters)', argstr='--out %s' )
- out_field = File( argstr='--fout %s', desc='name of image file with field (Hz)' )
- out_corrected = File( argstr='--iout %s', desc='name of 4D image file with unwarped images' )
- out_logfile = File( argstr='--logout %s', desc='name of log-file' )
- warp_res = traits.Float( 10.0, argstr='--warpres %f', desc='(approximate) resolution (in mm) of warp basis for the different sub-sampling levels' )
- subsamp = traits.Int( 1, argstr='--subsamp %d', desc='sub-sampling scheme, default 1' )
- fwhm = traits.Float( 8.0, argstr='--fwhm %f', desc='FWHM (in mm) of gaussian smoothing kernel' )
- config = traits.String('b02b0.cnf', desc='Name of config file specifying command line arguments', argstr='--config %s', usedefault=True )
- max_iter = traits.Int( 5, argstr='--miter %d', desc='max # of non-linear iterations')
+ in_file = File( exists=True, mandatory=True, desc='name of 4D file with images', argstr='--imain=%s' )
+ encoding_file = File( exists=True, mandatory=True, desc='name of text file with PE directions/times', argstr='--datain=%s' )
+ out_base = File( desc='base-name of output files (spline coefficients (Hz) and movement parameters)', argstr='--out=%s' )
+ out_field = File( argstr='--fout=%s', desc='name of image file with field (Hz)' )
+ out_corrected = File( argstr='--iout=%s', desc='name of 4D image file with unwarped images' )
+ out_logfile = File( argstr='--logout=%s', desc='name of log-file' )
+ warp_res = traits.Float( 10.0, argstr='--warpres=%f', desc='(approximate) resolution (in mm) of warp basis for the different sub-sampling levels' )
+ subsamp = traits.Int( 1, argstr='--subsamp=%d', desc='sub-sampling scheme, default 1' )
+ fwhm = traits.Float( 8.0, argstr='--fwhm=%f', desc='FWHM (in mm) of gaussian smoothing kernel' )
+ config = traits.String('b02b0.cnf', desc='Name of config file specifying command line arguments', argstr='--config=%s', usedefault=True )
+ max_iter = traits.Int( 5, argstr='--miter=%d', desc='max # of non-linear iterations')
  # @oesteban: I don't know how to implement these 3 parameters, AFAIK there's no documentation.
  #lambda	Weight of regularisation, default depending on --ssqlambda and --regmod switches. See user documetation.
  #ssqlambda	If set (=1), lambda is weighted by current ssq, default 1
  #regmod	Model for regularisation of warp-field [membrane_energy bending_energy], default bending_energy
- estmov = traits.Enum( 1, 0, desc='estimate movements if set', argstr='--estmov %d' )
- minmet = traits.Enum( 0, 1, desc='Minimisation method 0=Levenberg-Marquardt, 1=Scaled Conjugate Gradient', argstr='--minmet %d' )
- splineorder = traits.Int( 3, argstr='--splineorder %d', desc='order of spline, 2->Qadratic spline, 3->Cubic spline' )
- numprec = traits.Enum( 'double', 'float', argstr='--numprec %s', desc='Precision for representing Hessian, double or float.' )
- interp = traits.Enum( 'spline', 'linear' , argstr='--interp %s', desc='Image interpolation model, linear or spline.' )
- scale = traits.Enum( 0, 1, argstr='--scale %d', desc='If set (=1), the images are individually scaled to a common mean' )
- regrid = traits.Enum( 1, 0, argstr='--regrid %d', desc='If set (=1), the calculations are done in a different grid' )
+ estmov = traits.Enum( 1, 0, desc='estimate movements if set', argstr='--estmov=%d' )
+ minmet = traits.Enum( 0, 1, desc='Minimisation method 0=Levenberg-Marquardt, 1=Scaled Conjugate Gradient', argstr='--minmet=%d' )
+ splineorder = traits.Int( 3, argstr='--splineorder=%d', desc='order of spline, 2->Qadratic spline, 3->Cubic spline' )
+ numprec = traits.Enum( 'double', 'float', argstr='--numprec=%s', desc='Precision for representing Hessian, double or float.' )
+ interp = traits.Enum( 'spline', 'linear' , argstr='--interp=%s', desc='Image interpolation model, linear or spline.' )
+ scale = traits.Enum( 0, 1, argstr='--scale=%d', desc='If set (=1), the images are individually scaled to a common mean' )
+ regrid = traits.Enum( 1, 0, argstr='--regrid=%d', desc='If set (=1), the calculations are done in a different grid' )
 
 class TOPUPOutputSpec( TraitedSpec ):
  out_fieldcoef = File( exists=True, desc='file containing the field coefficients' )
@@ -173,9 +173,9 @@ class ApplyTOPUPInputSpec( FSLCommandInputSpec ):
  in_files = InputMultiPath(File(exists=True), mandatory=True, desc='name of 4D file with images', argstr='%s' )
  encoding_file = File( exists=True, mandatory=True, desc='name of text file with PE directions/times', argstr='--datain=%s' )
  in_index = traits.List( argstr='%s', mandatory=True, desc='comma separated list of indicies into --datain of the input image (to be corrected)' )
- in_topup = File( mandatory=True, desc='basename of field/movements (from topup)', argstr='-t=%s' )
+ in_topup = File( mandatory=True, desc='basename of field/movements (from topup)', argstr='--topup=%s' )
 
- out_base = File( desc='basename for output (warped) image', argstr='-o=%s' )
+ out_base = File( desc='basename for output (warped) image', argstr='--out=%s' )
  method = traits.Enum( ('jac','lsr'), argstr='-m=%s', desc='use jacobian modulation (jac) or least-squares resampling (lsr)' )
  interp = traits.Enum( ('trilinear','spline'), argstr='-n=%s', desc='interpolation method' )
  datatype = traits.Enum( ('char', 'short', 'int', 'float', 'double' ), argstr='-d=%s', desc='force output data type' )
@@ -202,7 +202,7 @@ class ApplyTOPUP( FSLCommand ):
  >>> res = applytopup.run() # doctest: +SKIP
 
  """
- _cmd = 'topup'
+ _cmd = 'applytopup'
  input_spec = ApplyTOPUPInputSpec
  output_spec = ApplyTOPUPOutputSpec
 
@@ -217,7 +217,7 @@ def _format_arg(self, name, spec, value):
 
  formated = formated + "%s" % value[0]
  for fname in value[1:]:
- formated = formated + ",%s" % value
+ formated = formated + ",%s" % fname
  return formated
 
  def _parse_inputs( self, skip=None ):
@@ -226,7 +226,7 @@ def _parse_inputs( self, skip=None ):
 
  if not isdefined(self.inputs.out_base ):
  self.inputs.out_base = os.path.abspath( './nipype_applytopup' )
- return super(TOPUP, self)._parse_inputs(skip=skip)
+ return super(ApplyTOPUP, self)._parse_inputs(skip=skip)
 
 
  def _list_outputs(self):