Interface for 'Mode' of diffusion tensor maps

Author: Erik Ziegler

nipype/interfaces/dipy/__init__.py

@@ -1 +1,2 @@
 from .tracks import TrackDensityMap
+from .tensors import TensorMode

nipype/interfaces/dipy/tensors.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+from nipype.interfaces.base import (TraitedSpec, BaseInterface, BaseInterfaceInputSpec,
+                                    File, isdefined, traits)
+from nipype.utils.filemanip import split_filename
+import os.path as op
+import nibabel as nb
+import numpy as np
+from nipype.utils.misc import package_check
+import warnings
+
+from ... import logging
+iflogger = logging.getLogger('interface')
+
+try:
+    package_check('dipy')
+    import dipy.reconst.dti as dti
+except Exception, e:
+    warnings.warn('dipy not installed')
+
+
+class TensorModeInputSpec(TraitedSpec):
+    in_file = File(exists=True, mandatory=True,
+    desc='The input diffusion-weighted image file')
+    bvecs = File(exists=True, mandatory=True,
+    desc='The input b-vector file')
+    bvals = File(exists=True, mandatory=True,
+    desc='The input b-value track file')
+    out_filename = File('mode.nii', usedefault=True, desc='The output filename for the tracks in TrackVis (.trk) format')
+
+class TensorModeOutputSpec(TraitedSpec):
+    out_file = File(exists=True)
+
+class TensorMode(BaseInterface):
+	"""
+	Creates a tract density image from a TrackVis track file using functions from dipy
+
+	Example
+	-------
+
+	>>> import nipype.interfaces.dipy as dipy
+	>>> mode = dipy.TensorMode()
+	>>> mode.inputs.in_file = 'dwi.nii'
+	>>> mode.run()                                   # doctest: +SKIP
+	"""
+	input_spec = TensorModeInputSpec
+	output_spec = TensorModeOutputSpec
+
+	def _run_interface(self, runtime):
+		img=nb.load(self.inputs.in_file)
+		data=img.get_data()
+		affine=img.get_affine()
+
+		bvals=np.loadtxt(self.inputs.bvals)
+		gradients=np.loadtxt(self.inputs.bvecs).T
+
+		tensor = dti.Tensor(data,bvals,gradients,thresh=50)
+		
+		mode_data = tensor.mode
+		img = nb.Nifti1Image(mode_data,affine)
+		out_file = op.abspath(self.inputs.out_filename)
+		nb.save(img, out_file)
+		iflogger.info('Tensor mode image saved as {i}'.format(i=out_file))
+		return runtime
+
+	def _list_outputs(self):
+		outputs = self._outputs().get()
+		outputs['out_file'] = op.abspath(self.inputs.out_filename)
+		return outputs
+