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add 3dLocalstat functionality to support ANATICOR
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‎nipype/interfaces/afni/__init__.py‎

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@@ -18,7 +18,7 @@
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from .utils import (
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ABoverlap, AFNItoNIFTI, Autobox, Axialize, BrickStat, Bucket, Calc, Cat,
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CatMatvec, CenterMass, ConvertDset, Copy, Dot, Edge3, Eval, FWHMx,
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LocalBistat, MaskTool, Merge, Notes, NwarpApply, NwarpAdjust, NwarpCat,
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OneDToolPy, Refit, Resample, TCat, TCatSubBrick, TStat, To3D, Unifize,
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Undump, ZCutUp, GCOR, Zcat, Zeropad)
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LocalBistat, Localstat, MaskTool, Merge, Notes, NwarpApply, NwarpAdjust,
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NwarpCat, OneDToolPy, Refit, Resample, TCat, TCatSubBrick, TStat, To3D,
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Unifize, Undump, ZCutUp, GCOR, Zcat, Zeropad)
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from .model import (Deconvolve, Remlfit, Synthesize)

‎nipype/interfaces/afni/utils.py‎

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@@ -1453,6 +1453,188 @@ def _format_arg(self, name, spec, value):
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return super(LocalBistat, self)._format_arg(name, spec, value)
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class LocalstatInputSpec(AFNICommandInputSpec):
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in_file = File(
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exists=True,
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mandatory=True,
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argstr='%s',
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position=-1,
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desc='input dataset')
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neighborhood = traits.Either(
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traits.Tuple(traits.Enum('SPHERE', 'RHDD', 'TOHD'), traits.Float()),
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traits.Tuple(traits.Enum('RECT'), traits.Tuple(traits.Float(),
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traits.Float(),
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traits.Float())),
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mandatory=True,
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desc='The region around each voxel that will be extracted for '
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'the statistics calculation. Possible regions are: '
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'\'SPHERE\', \'RHDD\' (rhombic dodecahedron), \'TOHD\' '
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'(truncated octahedron) with a given radius in mm or '
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'\'RECT\' (rectangular block) with dimensions to specify in mm.',
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argstr="-nbhd '%s(%s)'")
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_stat_names = ['mean', 'stdev', 'var', 'cvar', 'median', 'MAD', 'min',
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'max', 'absmax', 'num', 'sum', 'FWHM', 'FWHMbar', 'rank',
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'frank', 'P2skew', 'ALL', 'mMP2s', 'mmMP2s']
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stat = InputMultiObject(
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traits.Either(
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traits.Enum(_stat_names),
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traits.Tuple(traits.Enum('perc'),
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traits.Tuple(traits.Float, traits.Float, traits.Float))),
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mandatory=True,
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desc='statistics to compute. Possible names are :'
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' * mean = average of the values'
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' * stdev = standard deviation'
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' * var = variance (stdev*stdev)'
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' * cvar = coefficient of variation = stdev/fabs(mean)'
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' * median = median of the values'
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' * MAD = median absolute deviation'
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' * min = minimum'
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' * max = maximum'
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' * absmax = maximum of the absolute values'
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' * num = number of the values in the region:'
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' with the use of -mask or -automask,'
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' the size of the region around any given'
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' voxel will vary; this option lets you'
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' map that size. It may be useful if you'
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' plan to compute a t-statistic (say) from'
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' the mean and stdev outputs.'
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' * sum = sum of the values in the region:'
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' * FWHM = compute (like 3dFWHM) image smoothness'
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' inside each voxel\'s neighborhood. Results'
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' are in 3 sub-bricks: FWHMx, FHWMy, and FWHMz.'
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' Places where an output is -1 are locations'
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' where the FWHM value could not be computed'
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' (e.g., outside the mask).'
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' * FWHMbar= Compute just the average of the 3 FWHM values'
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' (normally would NOT do this with FWHM also).'
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' * perc:P0:P1:Pstep = '
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' Compute percentiles between P0 and P1 with a '
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' step of Pstep.'
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' Default P1 is equal to P0 and default P2 = 1'
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' * rank = rank of the voxel\'s intensity'
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' * frank = rank / number of voxels in neighborhood'
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' * P2skew = Pearson\'s second skewness coefficient'
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' 3 * (mean - median) / stdev '
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' * ALL = all of the above, in that order '
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' (except for FWHMbar and perc).'
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' * mMP2s = Exactly the same output as:'
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' median, MAD, P2skew'
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' but it a little faster'
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' * mmMP2s = Exactly the same output as:'
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' mean, median, MAD, P2skew'
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'More than one option can be used.',
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argstr='-stat %s...')
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mask_file = traits.File(
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exists=True,
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desc='Mask image file name. Voxels NOT in the mask will not be used '
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'in the neighborhood of any voxel. Also, a voxel NOT in the '
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'mask will have its statistic(s) computed as zero (0) unless '
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'the parameter \'nonmask\' is set to true.',
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argstr='-mask %s')
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automask = traits.Bool(
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desc='Compute the mask as in program 3dAutomask.',
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argstr='-automask')
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nonmask = traits.Bool(
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desc='Voxels not in the mask WILL have their local statistics '
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'computed from all voxels in their neighborhood that ARE in '
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'the mask.'
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' * For instance, this option can be used to compute the '
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' average local white matter time series, even at non-WM '
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' voxels.',
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argstr='-use_nonmask')
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reduce_grid = traits.Either(
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traits.Float,
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traits.Tuple(traits.Float, traits.Float, traits.Float),
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argstr='-reduce_grid %s',
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desc='Compute output on a grid that is reduced by the specified '
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'factors. If a single value is passed, output is resampled '
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'to the specified isotropic grid. Otherwise, the 3 inputs '
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'describe the reduction in the X, Y, and Z directions. This '
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'option speeds up computations at the expense of resolution. '
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'It should only be used when the nbhd is quite large with '
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'respect to the input\'s resolution, and the resultant stats '
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'are expected to be smooth.')
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reduce_restore_grid = traits.Either(
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traits.Float,
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traits.Tuple(traits.Float, traits.Float, traits.Float),
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argstr='-reduce_restore_grid %s',
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desc='Like reduce_grid, but also resample output back to input'
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'grid.')
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reduce_max_vox = traits.Float(
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argstr='-reduce_max_vox %s',
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desc='Like reduce_restore_grid, but automatically set Rx Ry Rz so'
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'that the computation grid is at a resolution of nbhd/MAX_VOX'
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'voxels.')
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grid_rmode = traits.Enum(
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'NN',
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'Li',
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'Cu',
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'Bk',
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argstr='grid_rmode %s',
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desc='Interpolant to use when resampling the output with the'
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'reduce_restore_grid option. The resampling method string '
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'RESAM should come from the set {\'NN\', \'Li\', \'Cu\', '
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'\'Bk\'}. These stand for \'Nearest Neighbor\', \'Linear\', '
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'\'Cubic\', and \'Blocky\' interpolation, respectively.')
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quiet = traits.Bool(
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argstr='-quiet',
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desc='Stop the highly informative progress reports.')
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overwrite = traits.Bool(
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desc='overwrite output file if it already exists',
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argstr='-overwrite')
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out_file = traits.File(
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desc='Output dataset.',
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argstr='-prefix %s',
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name_source='in_file',
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name_template='%s_localstat',
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keep_extension=True,
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position=0)
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class Localstat(AFNICommand):
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"""3dLocalstat - computes statistics at each voxel,
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based on a local neighborhood of that voxel.
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For complete details, see the `3dLocalstat Documentation.
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<https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dLocalstat.html>`_
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Examples
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========
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>>> from nipype.interfaces import afni
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>>> localstat = afni.Localstat()
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>>> localstat.inputs.in_file = 'bold.nii.gz'
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>>> localstat.inputs.mask_file = 'label-WM_desc-eroded_roi.nii.gz'
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>>> localstat.inputs.neighborhood = ('SPHERE', 45)
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>>> localstat.inputs.stat = 'mean'
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>>> localstat.inputs.nonmask=True
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>>> localstat.inputs.outputtype = 'NIFTI_GZ'
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>>> localstat.cmdline
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"3dLocalstat -prefix bold_localstat.nii.gz \
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-mask label-WM_desc-eroded_roi.nii.gz \
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-nbhd 'SPHERE(45.0)' \
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-use_nonmask \
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-stat mean \
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bold.nii.gz"
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>>> wmlocal = localstat.run() # doctest: +SKIP
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"""
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_cmd = '3dLocalstat'
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input_spec = LocalstatInputSpec
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output_spec = AFNICommandOutputSpec
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def _format_arg(self, name, spec, value):
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if name == 'neighborhood' and value[0] == 'RECT':
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value = ('RECT', '%s,%s,%s' % value[1])
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if name == 'stat':
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value = ['perc:%s:%s:%s' % v[1] if len(v) == 2 else v for v in value]
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if name == 'reduce_grid' or name == 'reduce_restore_grid':
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if len(value) == 3:
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value = '%s %s %s' % value
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return super(Localstat, self)._format_arg(name, spec, value)
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class MaskToolInputSpec(AFNICommandInputSpec):
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in_file = File(
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desc='input file or files to 3dmask_tool',

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