[ENH] Added two AFNI interfaces

CHANGES

@@ -5,7 +5,7 @@ Next release
 * ENH: New interfaces in dipy: RESTORE, EstimateResponseSH, CSD and StreamlineTractography
        (https://github.com/nipy/nipype/pull/1090)
 * ENH: Added interfaces of AFNI (https://github.com/nipy/nipype/pull/1360,
-       https://github.com/nipy/nipype/pull/1361)
+       https://github.com/nipy/nipype/pull/1361, https://github.com/nipy/nipype/pull/1382)
 * ENH: Provides a Nipype wrapper for antsJointFusion (https://github.com/nipy/nipype/pull/1351)
 * ENH: Added support for PETPVC (https://github.com/nipy/nipype/pull/1335)
 * ENH: Merge S3DataSink into DataSink, added AWS documentation (https://github.com/nipy/nipype/pull/1316)

nipype/interfaces/afni/__init__.py

@@ -12,5 +12,6 @@
                          Fourier, Allineate, Maskave, SkullStrip, TCat, Fim,
                          BlurInMask, Autobox, TCorrMap, Bandpass, Retroicor,
                          TCorrelate, TCorr1D, BrickStat, ROIStats, AutoTcorrelate,
-                         AFNItoNIFTI, Eval, Means, Hist, FWHMx)
+                         AFNItoNIFTI, Eval, Means, Hist, FWHMx, OutlierCount,
+                         QualityIndex)
 from .svm import (SVMTest, SVMTrain)

nipype/interfaces/afni/preprocess.py

@@ -16,8 +16,7 @@
 
 from .base import (AFNICommandBase, AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec,
                    Info, no_afni)
-from ..base import CommandLineInputSpec
-from ..base import (Directory, TraitedSpec,
+from ..base import (CommandLineInputSpec, CommandLine, Directory, TraitedSpec,
                     traits, isdefined, File, InputMultiPath, Undefined)
 from ...external.six import string_types
 from ...utils.filemanip import (load_json, save_json, split_filename)
@@ -2372,3 +2371,133 @@ def _list_outputs(self):
 
         outputs['fwhm'] = tuple(sout)
         return outputs
+
+
+class OutlierCountInputSpec(CommandLineInputSpec):
+    in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
+    mask = File(exists=True, argstr='-mask %s', xor=['autoclip', 'automask'],
+                desc='only count voxels within the given mask')
+    qthr = traits.Range(value=1e-3, low=0.0, high=1.0, argstr='-qthr %.5f',
+                        desc='indicate a value for q to compute alpha')
+
+    autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip', xor=['in_file'],
+                           desc='clip off small voxels')
+    automask = traits.Bool(False, usedefault=True, argstr='-automask', xor=['in_file'],
+                           desc='clip off small voxels')
+
+    fraction = traits.Bool(False, usedefault=True, argstr='-fraction',
+                           desc='write out the fraction of masked voxels'
+                                ' which are outliers at each timepoint')
+    interval = traits.Bool(False, usedefault=True, argstr='-range',
+                           desc='write out the median + 3.5 MAD of outlier'
+                                ' count with each timepoint')
+    save_outliers = traits.Bool(False, usedefault=True, desc='enables out_file option')
+    outliers_file = File(
+        name_template="%s_outliers", argstr='-save %s', name_source=["in_file"],
+        output_name='out_outliers', keep_extension=True, desc='output image file name')
+
+    polort = traits.Int(argstr='-polort %d',
+                        desc='detrend each voxel timeseries with polynomials')
+    legendre = traits.Bool(False, usedefault=True, argstr='-legendre',
+                           desc='use Legendre polynomials')
+    out_file = File(
+        name_template='%s_outliers', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class OutlierCountOutputSpec(TraitedSpec):
+    out_outliers = File(exists=True, desc='output image file name')
+    out_file = File(
+        name_template='%s_tqual', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class OutlierCount(CommandLine):
+    """Create a 3D dataset from 2D image files using AFNI to3d command
+
+    For complete details, see the `to3d Documentation
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> toutcount = afni.OutlierCount()
+    >>> toutcount.inputs.in_file = 'functional.nii'
+    >>> toutcount.cmdline #doctest: +ELLIPSIS
+    '3dToutcount functional.nii > functional_outliers'
+    >>> res = toutcount.run() #doctest: +SKIP
+
+   """
+
+    _cmd = '3dToutcount'
+    input_spec = OutlierCountInputSpec
+    output_spec = OutlierCountOutputSpec
+
+    def _parse_inputs(self, skip=None):
+        if skip is None:
+            skip = []
+
+        if not self.inputs.save_outliers:
+            skip += ['outliers_file']
+        return super(OutlierCount, self)._parse_inputs(skip)
+
+    def _list_outputs(self):
+        outputs = self.output_spec().get()
+        if self.inputs.save_outliers:
+            outputs['out_outliers'] = op.abspath(self.inputs.outliers_file)
+        outputs['out_file'] = op.abspath(self.inputs.out_file)
+        return outputs
+
+
+class QualityIndexInputSpec(CommandLineInputSpec):
+    in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
+    mask = File(exists=True, argstr='-mask %s', xor=['autoclip', 'automask'],
+                desc='compute correlation only across masked voxels')
+    spearman = traits.Bool(False, usedefault=True, argstr='-spearman',
+                           desc='Quality index is 1 minus the Spearman (rank) '
+                                'correlation coefficient of each sub-brick '
+                                'with the median sub-brick. (default)')
+    quadrant = traits.Bool(False, usedefault=True, argstr='-quadrant',
+                           desc='Similar to -spearman, but using 1 minus the '
+                                'quadrant correlation coefficient as the '
+                                'quality index.')
+    autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip', xor=['mask'],
+                           desc='clip off small voxels')
+    automask = traits.Bool(False, usedefault=True, argstr='-automask', xor=['mask'],
+                           desc='clip off small voxels')
+    clip = traits.Float(argstr='-clip %f', desc='clip off values below')
+
+    interval = traits.Bool(False, usedefault=True, argstr='-range',
+                           desc='write out the median + 3.5 MAD of outlier'
+                                ' count with each timepoint')
+    out_file = File(
+        name_template='%s_tqual', name_source=['in_file'], argstr='> %s',
+        keep_extension=False, position=-1, desc='capture standard output')
+
+
+class QualityIndexOutputSpec(TraitedSpec):
+    out_file = File(desc='file containing the caputured standard output')
+
+
+class QualityIndex(CommandLine):
+    """Create a 3D dataset from 2D image files using AFNI to3d command
+
+    For complete details, see the `to3d Documentation
+    <http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> tqual = afni.QualityIndex()
+    >>> tqual.inputs.in_file = 'functional.nii'
+    >>> tqual.cmdline #doctest: +ELLIPSIS
+    '3dTqual functional.nii > functional_tqual'
+    >>> res = tqual.run() #doctest: +SKIP
+
+   """
+
+    _cmd = '3dTqual'
+    input_spec = QualityIndexInputSpec
+    output_spec = QualityIndexOutputSpec

nipype/interfaces/afni/tests/test_auto_OutlierCount.py

@@ -0,0 +1,80 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ....testing import assert_equal
+from ..preprocess import OutlierCount
+
+
+def test_OutlierCount_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    autoclip=dict(argstr='-autoclip',
+    usedefault=True,
+    xor=['in_file'],
+    ),
+    automask=dict(argstr='-automask',
+    usedefault=True,
+    xor=['in_file'],
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    fraction=dict(argstr='-fraction',
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_file=dict(argstr='%s',
+    mandatory=True,
+    position=-2,
+    ),
+    interval=dict(argstr='-range',
+    usedefault=True,
+    ),
+    legendre=dict(argstr='-legendre',
+    usedefault=True,
+    ),
+    mask=dict(argstr='-mask %s',
+    xor=['autoclip', 'automask'],
+    ),
+    out_file=dict(argstr='> %s',
+    keep_extension=False,
+    name_source=['in_file'],
+    name_template='%s_outliers',
+    position=-1,
+    ),
+    outliers_file=dict(argstr='-save %s',
+    keep_extension=True,
+    name_source=['in_file'],
+    name_template='%s_outliers',
+    output_name='out_outliers',
+    ),
+    polort=dict(argstr='-polort %d',
+    ),
+    qthr=dict(argstr='-qthr %.5f',
+    ),
+    save_outliers=dict(usedefault=True,
+    ),
+    terminal_output=dict(nohash=True,
+    ),
+    )
+    inputs = OutlierCount.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_OutlierCount_outputs():
+    output_map = dict(out_file=dict(argstr='> %s',
+    keep_extension=False,
+    name_source=['in_file'],
+    name_template='%s_tqual',
+    position=-1,
+    ),
+    out_outliers=dict(),
+    )
+    outputs = OutlierCount.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value

nipype/interfaces/afni/tests/test_auto_QualityIndex.py

@@ -0,0 +1,64 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ....testing import assert_equal
+from ..preprocess import QualityIndex
+
+
+def test_QualityIndex_inputs():
+    input_map = dict(args=dict(argstr='%s',
+    ),
+    autoclip=dict(argstr='-autoclip',
+    usedefault=True,
+    xor=['mask'],
+    ),
+    automask=dict(argstr='-automask',
+    usedefault=True,
+    xor=['mask'],
+    ),
+    clip=dict(argstr='-clip %f',
+    ),
+    environ=dict(nohash=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_file=dict(argstr='%s',
+    mandatory=True,
+    position=-2,
+    ),
+    interval=dict(argstr='-range',
+    usedefault=True,
+    ),
+    mask=dict(argstr='-mask %s',
+    xor=['autoclip', 'automask'],
+    ),
+    out_file=dict(argstr='> %s',
+    keep_extension=False,
+    name_source=['in_file'],
+    name_template='%s_tqual',
+    position=-1,
+    ),
+    quadrant=dict(argstr='-quadrant',
+    usedefault=True,
+    ),
+    spearman=dict(argstr='-spearman',
+    usedefault=True,
+    ),
+    terminal_output=dict(nohash=True,
+    ),
+    )
+    inputs = QualityIndex.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_QualityIndex_outputs():
+    output_map = dict(out_file=dict(),
+    )
+    outputs = QualityIndex.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value

nipype/interfaces/freesurfer/tests/test_auto_MRIsConvert.py

@@ -30,10 +30,12 @@ def test_MRIsConvert_inputs():
     ),
     origname=dict(argstr='-o %s',
     ),
-    out_datatype=dict(xor=['out_file'],
+    out_datatype=dict(mandatory=True,
+    xor=['out_file'],
     ),
     out_file=dict(argstr='%s',
     genfile=True,
+    mandatory=True,
     position=-1,
     xor=['out_datatype'],
     ),

nipype/interfaces/freesurfer/utils.py

@@ -852,7 +852,7 @@ def _format_arg(self, name, spec, value):
         if name == "out_file" and not os.path.isabs(value):
             value = os.path.abspath(value)
         return super(MRIsConvert, self)._format_arg(name, spec, value)
-    
+
     def _list_outputs(self):
         outputs = self.output_spec().get()
         outputs["converted"] = os.path.abspath(self._gen_outfilename())