ENH: add interface for AFNI 3dTsmooth

nipype/interfaces/afni/__init__.py

@@ -13,7 +13,7 @@
     BlurInMask, BlurToFWHM, ClipLevel, DegreeCentrality, Despike, Detrend, ECM,
     Fim, Fourier, Hist, LFCD, Maskave, Means, OutlierCount, QualityIndex,
     ROIStats, Retroicor, Seg, SkullStrip, TCorr1D, TCorrMap, TCorrelate, TNorm,
-    TProject, TShift, Volreg, Warp, QwarpPlusMinus, Qwarp)
+    TProject, TShift, TSmooth, Volreg, Warp, QwarpPlusMinus, Qwarp)
 from .svm import (SVMTest, SVMTrain)
 from .utils import (
     ABoverlap, AFNItoNIFTI, Autobox, Axialize, BrickStat, Bucket, Calc, Cat,

nipype/interfaces/afni/preprocess.py

@@ -2853,6 +2853,79 @@ def _list_outputs(self):
         return outputs
 
 
+class TSmoothInputSpec(AFNICommandInputSpec):
+    in_file = File(
+        desc='input file to 3dTSmooth',
+        argstr='%s',
+        position=-1,
+        mandatory=True,
+        exists=True,
+        copyfile=False)
+    out_file = File(
+        name_template='%s_smooth',
+        desc='output file from 3dTSmooth',
+        argstr='-prefix %s',
+        name_source='in_file')
+    datum = traits.Str(
+        desc='Sets the data type of the output dataset',
+        argstr='-datum %s')
+    lin = traits.Bool(
+        desc='3 point linear filter: 0.15*a + 0.70*b + 0.15*c'
+        '[This is the default smoother]',
+        argstr='-lin')
+    med = traits.Bool(
+        desc='3 point median filter: median(a,b,c)',
+        argstr='-med')
+    osf = traits.Bool(
+        desc='3 point order statistics filter:'
+        '0.15*min(a,b,c) + 0.70*median(a,b,c) + 0.15*max(a,b,c)',
+        argstr='-osf')
+    lin3 = traits.Int(
+        desc='3 point linear filter: 0.5*(1-m)*a + m*b + 0.5*(1-m)*c'
+        "Here, 'm' is a number strictly between 0 and 1.",
+        argstr='-3lin %d')
+    hamming = traits.Int(
+        argstr='-hamming %d',
+        desc='Use N point Hamming windows.'
+        '(N must be odd and bigger than 1.)')
+    blackman = traits.Int(
+        argstr='-blackman %d',
+        desc='Use N point Blackman windows.'
+        '(N must be odd and bigger than 1.)')
+    custom = File(
+        argstr='-custom %s',
+        desc='odd # of coefficients must be in a single column in ASCII file')
+    adaptive = traits.Int(
+        argstr='-adaptive %d',
+        desc='use adaptive mean filtering of width N '
+        '(where N must be odd and bigger than 3).')
+
+
+class TSmooth(AFNICommand):
+    """Smooths each voxel time series in a 3D+time dataset and produces
+    as output a new 3D+time dataset (e.g., lowpass filter in time).
+
+    For complete details, see the `3dTsmooth Documentation.
+    <https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dTSmooth.html>`_
+
+    Examples
+    ========
+
+    >>> from nipype.interfaces import afni
+    >>> from nipype.testing import  example_data
+    >>> smooth = afni.TSmooth()
+    >>> smooth.inputs.in_file = 'functional.nii'
+    >>> smooth.inputs.adaptive = 5
+    >>> smooth.cmdline
+    '3dTsmooth -adaptive 5 -prefix functional_smooth functional.nii'
+    >>> res = smooth.run()  # doctest: +SKIP
+
+    """
+    _cmd = '3dTsmooth'
+    input_spec = TSmoothInputSpec
+    output_spec = AFNICommandOutputSpec
+
+
 class VolregInputSpec(AFNICommandInputSpec):
     in_file = File(
         desc='input file to 3dvolreg',

nipype/interfaces/afni/tests/test_auto_TSmooth.py

@@ -0,0 +1,55 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from __future__ import unicode_literals
+from ..preprocess import TSmooth
+
+
+def test_TSmooth_inputs():
+    input_map = dict(
+        adaptive=dict(argstr='-adaptive %d', ),
+        args=dict(argstr='%s', ),
+        blackman=dict(argstr='-blackman %d', ),
+        custom=dict(
+            argstr='-custom %s',
+            extensions=None,
+        ),
+        datum=dict(argstr='-datum %s', ),
+        environ=dict(
+            nohash=True,
+            usedefault=True,
+        ),
+        hamming=dict(argstr='-hamming %d', ),
+        in_file=dict(
+            argstr='%s',
+            copyfile=False,
+            extensions=None,
+            mandatory=True,
+            position=-1,
+        ),
+        lin=dict(argstr='-lin', ),
+        lin3=dict(argstr='-3lin %d', ),
+        med=dict(argstr='-med', ),
+        num_threads=dict(
+            nohash=True,
+            usedefault=True,
+        ),
+        osf=dict(argstr='-osf', ),
+        out_file=dict(
+            argstr='-prefix %s',
+            extensions=None,
+            name_source='in_file',
+            name_template='%s_smooth',
+        ),
+        outputtype=dict(),
+    )
+    inputs = TSmooth.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(inputs.traits()[key], metakey) == value
+def test_TSmooth_outputs():
+    output_map = dict(out_file=dict(extensions=None, ), )
+    outputs = TSmooth.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(outputs.traits()[key], metakey) == value