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oestebanoesteban
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add two new interfaces for AFNI
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‎nipype/interfaces/afni/__init__.py‎

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

‎nipype/interfaces/afni/preprocess.py‎

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@@ -2372,3 +2372,145 @@ def _list_outputs(self):
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outputs['fwhm'] = tuple(sout)
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return outputs
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class OutlierCountInputSpec(AFNICommandInputSpec):
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in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
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mask = File(exists=True, argstr='-mask %s', xor=['autoclip', 'automask'],
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desc='only count voxels within the given mask')
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qthr = traits.Range(value=1e-3, low=0.0, high=1.0, argstr='-qthr %.5f',
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desc='indicate a value for q to compute alpha')
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autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip', xor=['in_file'],
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desc='clip off small voxels')
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automask = traits.Bool(False, usedefault=True, argstr='-automask', xor=['in_file'],
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desc='clip off small voxels')
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fraction = traits.Bool(False, usedefault=True, argstr='-fraction',
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desc='write out the fraction of masked voxels'
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' which are outliers at each timepoint')
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interval = traits.Bool(False, usedefault=True, argstr='-range',
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desc='write out the median + 3.5 MAD of outlier'
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' count with each timepoint')
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save_outliers = traits.Bool(False, usedefault=True, desc='enables out_file option')
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outliers_file = File(name_template="%s_outliers", argstr='-save %s', name_source=["in_file"],
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keep_extension=True, desc='output image file name')
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polort = traits.Int(argstr='-polort %d',
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desc='detrend each voxel timeseries with polynomials')
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legendre = traits.Bool(False, usedefault=True, argstr='-legendre',
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desc='use Legendre polynomials')
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out_file = File(name_template='%s_outliers', name_source=['in_file'], argstr='> %s',
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keep_extension=False, position=-1, desc='capture standard output')
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class OutlierCountOutputSpec(AFNICommandOutputSpec):
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outliers_file = File(exists=True, desc='output image file name')
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outliers = traits.List(traits.Float,
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desc='parse standard output to get the count of outliers')
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class OutlierCount(AFNICommand):
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"""Create a 3D dataset from 2D image files using AFNI to3d command
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For complete details, see the `to3d Documentation
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<http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
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Examples
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========
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>>> from nipype.interfaces import afni
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>>> toutcount = afni.OutlierCount()
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>>> toutcount.inputs.in_file = 'functional.nii'
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>>> toutcount.cmdline #doctest: +ELLIPSIS
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'3dToutcount functional.nii > functional_outliers'
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>>> res = toutcount.run() #doctest: +SKIP
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"""
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_cmd = '3dToutcount'
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input_spec = OutlierCountInputSpec
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output_spec = OutlierCountOutputSpec
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def _parse_inputs(self, skip=None):
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if skip is None:
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skip = []
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if not self.inputs.save_outliers:
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skip += ['outliers_file']
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return super(OutlierCount, self)._parse_inputs(skip)
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def _list_outputs(self):
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outputs = self.output_spec().get()
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outputs['outliers_file'] = (Undefined if not self.inputs.save_outliers
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else self.inputs.outliers_file)
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with open(self.inputs.out_file, 'r') as fout:
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lines = fout.readlines()
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# remove general information and warnings
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outputs['outliers'] = [float(l)
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for l in lines if re.match("[0-9]+$", l.strip())]
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return outputs
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class QualityIndexInputSpec(AFNICommandInputSpec):
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in_file = File(argstr='%s', mandatory=True, exists=True, position=-2, desc='input dataset')
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mask = File(exists=True, argstr='-mask %s',
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desc='compute correlation only across masked voxels')
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spearman = traits.Bool(False, usedefault=True, argstr='-spearman',
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desc='Quality index is 1 minus the Spearman (rank) '
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'correlation coefficient of each sub-brick '
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'with the median sub-brick. (default)')
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quadrant = traits.Bool(False, usedefault=True, argstr='-quadrant',
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desc='Similar to -spearman, but using 1 minus the '
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'quadrant correlation coefficient as the '
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'quality index.')
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autoclip = traits.Bool(False, usedefault=True, argstr='-autoclip',
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desc='clip off small voxels')
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automask = traits.Bool(False, usedefault=True, argstr='-automask',
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desc='clip off small voxels')
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clip = traits.Float(argstr='-clip %f', desc='clip off values below')
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interval = traits.Bool(False, usedefault=True, argstr='-range',
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desc='write out the median + 3.5 MAD of outlier'
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' count with each timepoint')
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out_file = File(name_template='%s_tqual', name_source=['in_file'], argstr='> %s',
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keep_extension=False, position=-1, desc='capture standard output')
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class QualityIndexOutputSpec(AFNICommandOutputSpec):
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qi_value = traits.List(traits.Float, desc='output quality index')
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class QualityIndex(AFNICommand):
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"""Create a 3D dataset from 2D image files using AFNI to3d command
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For complete details, see the `to3d Documentation
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<http://afni.nimh.nih.gov/pub/dist/doc/program_help/to3d.html>`_
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Examples
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========
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>>> from nipype.interfaces import afni
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>>> tqual = afni.QualityIndex()
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>>> tqual.inputs.in_file = 'functional.nii'
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>>> tqual.cmdline #doctest: +ELLIPSIS
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'3dTqual functional.nii > functional_tqual'
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>>> res = tqual.run() #doctest: +SKIP
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"""
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_cmd = '3dTqual'
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input_spec = QualityIndexInputSpec
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output_spec = QualityIndexOutputSpec
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def _list_outputs(self):
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outputs = self.output_spec().get()
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with open(self.inputs.out_file, 'r') as fout:
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lines = fout.readlines()
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# remove general information
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lines = [l for l in lines if l[:2] != "++"]
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# remove general information and warnings
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outputs['qi_value'] = [float(l.strip()) for l in lines]
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return outputs

‎nipype/interfaces/afni/tests/test_auto_OutlierCount.py‎

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@@ -0,0 +1,76 @@
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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from ....testing import assert_equal
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from ..preprocess import OutlierCount
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5+
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def test_OutlierCount_inputs():
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input_map = dict(args=dict(argstr='%s',
8+
),
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autoclip=dict(argstr='-autoclip',
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usedefault=True,
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xor=['in_file'],
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),
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automask=dict(argstr='-automask',
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usedefault=True,
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xor=['in_file'],
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),
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environ=dict(nohash=True,
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usedefault=True,
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),
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fraction=dict(argstr='-fraction',
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usedefault=True,
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),
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ignore_exception=dict(nohash=True,
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usedefault=True,
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),
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in_file=dict(argstr='%s',
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mandatory=True,
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position=-2,
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),
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interval=dict(argstr='-range',
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usedefault=True,
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),
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legendre=dict(argstr='-legendre',
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usedefault=True,
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),
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mask=dict(argstr='-mask %s',
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xor=['autoclip', 'automask'],
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),
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out_file=dict(argstr='> %s',
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keep_extension=False,
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name_source=['in_file'],
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name_template='%s_outliers',
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position=-1,
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),
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outliers_file=dict(argstr='-save %s',
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keep_extension=True,
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name_source=['in_file'],
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name_template='%s_outliers',
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),
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outputtype=dict(),
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polort=dict(argstr='-polort %d',
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),
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qthr=dict(argstr='-qthr %.5f',
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),
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save_outliers=dict(usedefault=True,
56+
),
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terminal_output=dict(nohash=True,
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),
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)
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inputs = OutlierCount.input_spec()
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for key, metadata in list(input_map.items()):
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for metakey, value in list(metadata.items()):
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yield assert_equal, getattr(inputs.traits()[key], metakey), value
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def test_OutlierCount_outputs():
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output_map = dict(out_file=dict(),
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outliers=dict(),
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outliers_file=dict(),
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)
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outputs = OutlierCount.output_spec()
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for key, metadata in list(output_map.items()):
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for metakey, value in list(metadata.items()):
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yield assert_equal, getattr(outputs.traits()[key], metakey), value

‎nipype/interfaces/afni/tests/test_auto_QualityIndex.py‎

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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from ....testing import assert_equal
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from ..preprocess import QualityIndex
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def test_QualityIndex_inputs():
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input_map = dict(args=dict(argstr='%s',
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),
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autoclip=dict(argstr='-autoclip',
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usedefault=True,
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),
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automask=dict(argstr='-automask',
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usedefault=True,
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),
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clip=dict(argstr='-clip %f',
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),
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environ=dict(nohash=True,
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usedefault=True,
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),
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ignore_exception=dict(nohash=True,
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usedefault=True,
22+
),
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in_file=dict(argstr='%s',
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mandatory=True,
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position=-2,
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),
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interval=dict(argstr='-range',
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usedefault=True,
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),
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mask=dict(argstr='-mask %s',
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),
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out_file=dict(argstr='> %s',
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keep_extension=False,
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name_source=['in_file'],
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name_template='%s_tqual',
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position=-1,
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),
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outputtype=dict(),
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quadrant=dict(argstr='-quadrant',
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usedefault=True,
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),
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spearman=dict(argstr='-spearman',
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usedefault=True,
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),
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terminal_output=dict(nohash=True,
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),
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)
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inputs = QualityIndex.input_spec()
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for key, metadata in list(input_map.items()):
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for metakey, value in list(metadata.items()):
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yield assert_equal, getattr(inputs.traits()[key], metakey), value
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def test_QualityIndex_outputs():
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output_map = dict(out_file=dict(),
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qi_value=dict(),
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)
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outputs = QualityIndex.output_spec()
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for key, metadata in list(output_map.items()):
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for metakey, value in list(metadata.items()):
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yield assert_equal, getattr(outputs.traits()[key], metakey), value

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