New interface for Camino dtshape.

nipype/interfaces/camino/__init__.py

@@ -9,6 +9,6 @@
 from .dti import (DTIFit, ModelFit, DTLUTGen, PicoPDFs, Track, TrackPICo,
  TrackBayesDirac, TrackDT, TrackBallStick, TrackBootstrap,
  ComputeFractionalAnisotropy, ComputeMeanDiffusivity,
- ComputeTensorTrace, ComputeEigensystem)
+ ComputeTensorTrace, ComputeEigensystem, DTMetric)
 from .calib import (SFPICOCalibData, SFLUTGen)
 from .odf import (QBallMX, LinRecon, SFPeaks)

nipype/interfaces/camino/convert.py

@@ -563,7 +563,7 @@ class AnalyzeHeader(StdOutCommandLine):
 
  >>> import nipype.interfaces.camino as cmon
  >>> hdr = cmon.AnalyzeHeader()
- >>> hdr.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> hdr.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> hdr.inputs.scheme_file = 'A.scheme'
  >>> hdr.inputs.data_dims = [256,256,256]
  >>> hdr.inputs.voxel_dims = [1,1,1]

nipype/interfaces/camino/dti.py

@@ -8,7 +8,7 @@
 """
 from nipype.interfaces.base import (CommandLineInputSpec, CommandLine, traits,
  TraitedSpec, File, StdOutCommandLine,
- StdOutCommandLineInputSpec)
+ StdOutCommandLineInputSpec, isdefined)
 from nipype.utils.filemanip import split_filename
 import os
 
@@ -49,7 +49,7 @@ class DTIFit(StdOutCommandLine):
  >>> import nipype.interfaces.camino as cmon
  >>> fit = cmon.DTIFit()
  >>> fit.inputs.scheme_file = 'A.scheme'
- >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> fit.run() # doctest: +SKIP
  """
  _cmd = 'dtfit'
@@ -65,6 +65,107 @@ def _gen_outfilename(self):
  _, name , _ = split_filename(self.inputs.in_file)
  return name + '_DT.Bdouble'
 
+class DTMetricInputSpec(CommandLineInputSpec):
+ eigen_data = File(exists=True, argstr='-inputfile %s', mandatory=True,
+ desc='voxel-order data filename')
+
+ metric = traits.Enum('fa','md','rd','l1', 'l2', 'l3', 'tr', 'ra', '2dfa','cl','cp','cs',
+ argstr='-stat %s', mandatory=True,
+ desc=('Specifies the metric to compute. Possible choices are: '
+ '"fa", "md", "rd", "l1", "l2", "l3", "tr", "ra", "2dfa", "cl", "cp" or "cs".'))
+
+ inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+ argstr='-inputdatatype %s', usedefault=True,
+ desc=('Specifies the data type of the input data. '
+ 'The data type can be any of the following strings: '
+ '"char", "short", "int", "long", "float" or "double".'
+ 'Default is double data type'))
+
+ outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+ argstr='-outputdatatype %s', usedefault=True,
+ desc=('Specifies the data type of the output data. '
+ 'The data type can be any of the following strings: '
+ '"char", "short", "int", "long", "float" or "double".'
+ 'Default is double data type'))
+
+ data_header = File(argstr='-header %s', exists=True,
+ desc=('A Nifti .nii or .nii.gz file containing the header information. '
+ 'Usually this will be the header of the raw data file from which '
+ 'the diffusion tensors were reconstructed.'))
+
+ outputfile = File(argstr='-outputfile %s', genfile=True,
+ desc=('Output name. Output will be a .nii.gz file if data_header is provided and'
+ 'in voxel order with outputdatatype datatype (default: double) otherwise.'))
+
+class DTMetricOutputSpec(TraitedSpec):
+ metric_stats = File(exists=True, desc='Diffusion Tensor statistics of the chosen metric')
+
+class DTMetric(CommandLine):
+ """
+ Computes tensor metric statistics based on the eigenvalues l1 >= l2 >= l3
+ typically obtained from ComputeEigensystem.
+
+ The full list of statistics is:
+
+ <cl> = (l1 - l2) / l1 , a measure of linearity
+ <cp> = (l2 - l3) / l1 , a measure of planarity
+ <cs> = l3 / l1 , a measure of isotropy
+ with: cl + cp + cs = 1
+
+ <l1> = first eigenvalue
+ <l2> = second eigenvalue
+ <l3> = third eigenvalue
+
+ <tr> = l1 + l2 + l3
+ <md> = tr / 3
+ <rd> = (l2 + l3) / 2
+ <fa> = fractional anisotropy. (Basser et al, J Magn Reson B 1996)
+ <ra> = relative anisotropy (Basser et al, J Magn Reson B 1996)
+
+ <2dfa> = 2D FA of the two minor eigenvalues l2 and l3
+ i.e. sqrt( 2 * [(l2 - <l>)^2 + (l3 - <l>)^2] / (l2^2 + l3^2) )
+ with: <l> = (l2 + l3) / 2
+
+ Example
+ -------
+ Compute the CP planar metric as float data type.
+
+ >>> import nipype.interfaces.camino as cam
+ >>> dtmetric = cam.DTMetric()
+ >>> dtmetric.inputs.eigen_data = 'dteig.Bdouble'
+ >>> dtmetric.inputs.metric = 'cp'
+ >>> dtmetric.inputs.outputdatatype = 'float'
+ >>> dtmetric.run() # doctest: +SKIP
+ """
+ _cmd = 'dtshape'
+ input_spec=DTMetricInputSpec
+ output_spec=DTMetricOutputSpec
+
+ def _list_outputs(self):
+ outputs = self.output_spec().get()
+ outputs['metric_stats'] = os.path.abspath(self._gen_outfilename())
+ return outputs
+
+ def _gen_outfilename(self):
+ return self._gen_outputfile()
+
+ def _gen_outputfile(self):
+ outputfile = self.inputs.outputfile
+ if not isdefined(outputfile):
+ outputfile = self._gen_filename('outputfile')
+ return outputfile
+
+ def _gen_filename(self, name):
+ if name == 'outputfile':
+ _, name , _ = split_filename(self.inputs.eigen_data)
+ metric = self.inputs.metric
+ datatype= self.inputs.outputdatatype
+ if isdefined(self.inputs.data_header):
+ filename = name + '_' + metric + '.nii.gz'
+ else:
+ filename = name + '_' + metric + '.B' + datatype
+ return filename
+
 class ModelFitInputSpec(StdOutCommandLineInputSpec):
  def _gen_model_options(): #@NoSelf
  """
@@ -134,7 +235,7 @@ class ModelFit(StdOutCommandLine):
  >>> fit = cmon.ModelFit()
  >>> fit.model = 'dt'
  >>> fit.inputs.scheme_file = 'A.scheme'
- >>> fit.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> fit.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> fit.run() # doctest: +SKIP
  """
  _cmd = 'modelfit'
@@ -381,7 +482,7 @@ class TrackDT(Track):
 
  >>> import nipype.interfaces.camino as cmon
  >>> track = cmon.TrackDT()
- >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> track.inputs.seed_file = 'seed_mask.nii'
  >>> track.run() # doctest: +SKIP
  """
@@ -447,7 +548,7 @@ class TrackBayesDirac(Track):
 
  >>> import nipype.interfaces.camino as cmon
  >>> track = cmon.TrackBayesDirac()
- >>> track.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> track.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> track.inputs.seed_file = 'seed_mask.nii'
  >>> track.inputs.scheme_file = 'bvecs.scheme'
  >>> track.run() # doctest: +SKIP
@@ -547,7 +648,7 @@ class ComputeMeanDiffusivity(StdOutCommandLine):
 
  >>> import nipype.interfaces.camino as cmon
  >>> md = cmon.ComputeMeanDiffusivity()
- >>> md.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> md.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> md.inputs.scheme_file = 'A.scheme'
  >>> md.run() # doctest: +SKIP
  """
@@ -606,7 +707,7 @@ class ComputeFractionalAnisotropy(StdOutCommandLine):
 
  >>> import nipype.interfaces.camino as cmon
  >>> fa = cmon.ComputeFractionalAnisotropy()
- >>> fa.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> fa.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> fa.inputs.scheme_file = 'A.scheme'
  >>> fa.run() # doctest: +SKIP
  """
@@ -667,7 +768,7 @@ class ComputeTensorTrace(StdOutCommandLine):
 
  >>> import nipype.interfaces.camino as cmon
  >>> trace = cmon.ComputeTensorTrace()
- >>> trace.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> trace.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> trace.inputs.scheme_file = 'A.scheme'
  >>> trace.run() # doctest: +SKIP
  """
@@ -690,11 +791,21 @@ class ComputeEigensystemInputSpec(StdOutCommandLineInputSpec):
 
  inputmodel = traits.Enum('dt', 'multitensor', argstr='-inputmodel %s', desc='Specifies the model that the input data contains parameters for. Possible model types are: "dt" (diffusion-tensor data) and "multitensor"')
 
- maxcomponents = traits.Int(argstr='-maxcomponents %s', desc='The maximum number of tensor components in a voxel of the input data.')
+ maxcomponents = traits.Int(argstr='-maxcomponents %d', desc='The maximum number of tensor components in a voxel of the input data.')
 
- inputdatatype = traits.Enum("double", "char", "short", "int", "long", "float", argstr='-inputdatatype %s', desc='Specifies the data type of the input file. The data type can be any of the following strings: "char", "short", "int", "long", "float" or "double".')
+ inputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+ argstr='-inputdatatype %s', usedefault=True,
+ desc=('Specifies the data type of the input data. '
+ 'The data type can be any of the following strings: '
+ '"char", "short", "int", "long", "float" or "double".'
+ 'Default is double data type'))
 
- outputdatatype = traits.Enum("double", "char", "short", "int", "long", "float", argstr='-outputdatatype %s', desc='Specifies the data type of the output data. The data type can be any of the following strings: "char", "short", "int", "long", "float" or "double".')
+ outputdatatype = traits.Enum('double', 'float', 'long', 'int', 'short', 'char',
+ argstr='-outputdatatype %s', usedefault=True,
+ desc=('Specifies the data type of the output data. '
+ 'The data type can be any of the following strings: '
+ '"char", "short", "int", "long", "float" or "double".'
+ 'Default is double data type'))
 
 class ComputeEigensystemOutputSpec(TraitedSpec):
  eigen = File(exists=True, desc='Trace of the diffusion tensor')
@@ -716,7 +827,7 @@ class ComputeEigensystem(StdOutCommandLine):
 
  >>> import nipype.interfaces.camino as cmon
  >>> dteig = cmon.ComputeEigensystem()
- >>> dteig.inputs.in_file = 'tensor_fitted_data.Bfloat'
+ >>> dteig.inputs.in_file = 'tensor_fitted_data.Bdouble'
  >>> dteig.run() # doctest: +SKIP
  """
  _cmd = 'dteig'
@@ -730,4 +841,5 @@ def _list_outputs(self):
 
  def _gen_outfilename(self):
  _, name , _ = split_filename(self.inputs.in_file)
- return name + "_Eigen.img" #Need to change to self.inputs.outputdatatype
+ datatype= self.inputs.outputdatatype
+ return name + '_eig.B' + datatype

nipype/interfaces/camino/tests/test_auto_ComputeEigensystem.py

@@ -11,13 +11,15 @@ def test_ComputeEigensystem_inputs():
  ),
  inputmodel=dict(argstr='-inputmodel %s',
  ),
- outputdatatype=dict(argstr='-outputdatatype %s',
+ outputdatatype=dict(usedefault=True,
+ argstr='-outputdatatype %s',
  ),
  args=dict(argstr='%s',
  ),
- inputdatatype=dict(argstr='-inputdatatype %s',
+ inputdatatype=dict(usedefault=True,
+ argstr='-inputdatatype %s',
  ),
- maxcomponents=dict(argstr='-maxcomponents %s',
+ maxcomponents=dict(argstr='-maxcomponents %d',
  ),
  environ=dict(nohash=True,
  usedefault=True,

nipype/interfaces/camino/tests/test_auto_DTMetric.py

@@ -0,0 +1,46 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.camino.dti import DTMetric
+def test_DTMetric_inputs():
+ input_map = dict(ignore_exception=dict(nohash=True,
+ usedefault=True,
+ ),
+ outputfile=dict(argstr='-outputfile %s',
+ genfile=True,
+ ),
+ outputdatatype=dict(usedefault=True,
+ argstr='-outputdatatype %s',
+ ),
+ metric=dict(mandatory=True,
+ argstr='-stat %s',
+ ),
+ args=dict(argstr='%s',
+ ),
+ inputdatatype=dict(usedefault=True,
+ argstr='-inputdatatype %s',
+ ),
+ terminal_output=dict(mandatory=True,
+ nohash=True,
+ ),
+ environ=dict(nohash=True,
+ usedefault=True,
+ ),
+ eigen_data=dict(mandatory=True,
+ argstr='-inputfile %s',
+ ),
+ data_header=dict(argstr='-header %s',
+ ),
+ )
+ inputs = DTMetric.input_spec()
+
+ for key, metadata in input_map.items():
+ for metakey, value in metadata.items():
+ yield assert_equal, getattr(inputs.traits()[key], metakey), value
+def test_DTMetric_outputs():
+ output_map = dict(metric_stats=dict(),
+ )
+ outputs = DTMetric.output_spec()
+
+ for key, metadata in output_map.items():
+ for metakey, value in metadata.items():
+ yield assert_equal, getattr(outputs.traits()[key], metakey), value