Add TensorMetrics interface

Author: oesteban

nipype/interfaces/mrtrix3/__init__.py

@@ -2,7 +2,7 @@
 # vi: set ft=python sts=4 ts=4 sw=4 et:
 # -*- coding: utf-8 -*-
 
-from utils import Mesh2PVE, Generate5tt, BrainMask
+from utils import Mesh2PVE, Generate5tt, BrainMask, TensorMetrics
 from preprocess import ResponseSD, ACTPrepareFSL
 from tracking import Tractography
 from reconst import FitTensor, EstimateFOD

nipype/interfaces/mrtrix3/tests/test_auto_TensorMetrics.py

@@ -0,0 +1,53 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from nipype.testing import assert_equal
+from nipype.interfaces.mrtrix3.utils import TensorMetrics
+
+def test_TensorMetrics_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    component=dict(argstr='-num %s',
+    sep=',',
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_file=dict(argstr='%s',
+    mandatory=True,
+    position=-1,
+    ),
+    in_mask=dict(argstr='-mask %s',
+    ),
+    modulate=dict(argstr='-modulate %s',
+    ),
+    out_adc=dict(argstr='-adc %s',
+    ),
+    out_eval=dict(argstr='-value %s',
+    ),
+    out_evec=dict(argstr='-vector %s',
+    ),
+    out_fa=dict(argstr='-fa %s',
+    ),
+    terminal_output=dict(nohash=True,
+    ),
+    )
+    inputs = TensorMetrics.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_TensorMetrics_outputs():
+    output_map = dict(out_adc=dict(),
+    out_eval=dict(),
+    out_evec=dict(),
+    out_fa=dict(),
+    )
+    outputs = TensorMetrics.output_spec()
+
+    for key, metadata in output_map.items():
+        for metakey, value in metadata.items():
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value
+

nipype/interfaces/mrtrix3/utils.py

@@ -167,3 +167,62 @@ def _list_outputs(self):
         outputs = self.output_spec().get()
         outputs['out_file'] = op.abspath(self.inputs.out_file)
         return outputs
+
+
+class TensorMetricsInputSpec(CommandLineInputSpec):
+    in_file = File(exists=True, argstr='%s', mandatory=True, position=-1,
+                   desc='input DTI image')
+
+    out_fa = File(argstr='-fa %s', desc='output FA file')
+    out_adc = File(argstr='-adc %s', desc='output ADC file')
+    out_evec = File(argstr='-vector %s',
+                    desc='output selected eigenvector(s) file')
+    out_eval = File(argstr='-value %s',
+                    desc='output selected eigenvalue(s) file')
+    component = traits.List(
+        [1, 2, 3], argstr='-num %s', sep=',',
+        desc=('specify the desired eigenvalue/eigenvector(s). Note that '
+              'several eigenvalues can be specified as a number sequence'))
+    in_mask = File(exists=True, argstr='-mask %s',
+                   desc=('only perform computation within the specified binary '
+                         'brain mask image'))
+    modulate = traits.Enum('FA', 'none', 'eval', argstr='-modulate %s',
+                           desc='how to modulate the magnitude of the eigenvectors')
+
+class TensorMetricsOutputSpec(TraitedSpec):
+    out_fa = File(desc='output FA file')
+    out_adc = File(desc='output ADC file')
+    out_evec = File(desc='output selected eigenvector(s) file')
+    out_eval = File(desc='output selected eigenvalue(s) file')
+
+
+class TensorMetrics(CommandLine):
+
+    """
+    Convert a mesh surface to a partial volume estimation image
+
+
+    Example
+    -------
+
+    >>> import nipype.interfaces.mrtrix3 as mrt
+    >>> comp = mrt.TensorMetrics()
+    >>> comp.inputs.in_file = 'dti.mif'
+    >>> comp.inputs.out_fa = 'fa.mif'
+    >>> comp.cmdline                               # doctest: +ELLIPSIS
+    'tensor2metric -fa fa.mif dti.mif'
+    >>> comp.run()                                 # doctest: +SKIP
+    """
+
+    _cmd = 'tensor2metric'
+    input_spec = TensorMetricsInputSpec
+    output_spec = TensorMetricsOutputSpec
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+
+        for k in outputs.keys():
+            if isdefined(getattr(self.inputs, k)):
+                outputs[k] = op.abspath(getattr(self.inputs, k))
+
+        return outputs