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Merge pull request #2590 from salma1601/afni_local_bistat
ENH: AFNI (3d)LocalBistat interface
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‎nipype/interfaces/afni/__init__.py

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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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MaskTool, Merge, Notes, NwarpApply, NwarpAdjust, NwarpCat, OneDToolPy,
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Refit, Resample, TCat, TCatSubBrick, TStat, To3D, Unifize, Undump, ZCutUp,
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GCOR, Zcat, Zeropad)
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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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from .model import (Deconvolve, Remlfit, Synthesize)

‎nipype/interfaces/afni/tests/test_auto_LocalBistat.py

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# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
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from __future__ import unicode_literals
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from ..utils import LocalBistat
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def test_LocalBistat_inputs():
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input_map = dict(
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args=dict(argstr='%s', ),
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automask=dict(
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argstr='-automask',
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xor=['weight_file'],
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),
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environ=dict(
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nohash=True,
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usedefault=True,
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),
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ignore_exception=dict(
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deprecated='1.0.0',
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nohash=True,
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usedefault=True,
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),
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in_file1=dict(
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argstr='%s',
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mandatory=True,
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position=-2,
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),
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in_file2=dict(
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argstr='%s',
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mandatory=True,
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position=-1,
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),
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mask_file=dict(argstr='-mask %s', ),
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neighborhood=dict(
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argstr="-nbhd '%s(%s)'",
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mandatory=True,
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),
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num_threads=dict(
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nohash=True,
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usedefault=True,
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),
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out_file=dict(
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argstr='-prefix %s',
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keep_extension=True,
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name_source='in_file1',
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name_template='%s_bistat',
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position=0,
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),
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outputtype=dict(),
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stat=dict(
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argstr='-stat %s...',
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mandatory=True,
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),
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terminal_output=dict(
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deprecated='1.0.0',
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nohash=True,
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),
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weight_file=dict(
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argstr='-weight %s',
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xor=['automask'],
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),
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)
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inputs = LocalBistat.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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assert getattr(inputs.traits()[key], metakey) == value
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def test_LocalBistat_outputs():
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output_map = dict(out_file=dict(), )
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outputs = LocalBistat.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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assert getattr(outputs.traits()[key], metakey) == value

‎nipype/interfaces/afni/utils.py

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@@ -1338,6 +1338,121 @@ def _list_outputs(self):
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return outputs
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class LocalBistatInputSpec(AFNICommandInputSpec):
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in_file1 = File(
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exists=True,
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mandatory=True,
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argstr='%s',
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position=-2,
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desc='Filename of the first image')
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in_file2 = 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='Filename of the second image')
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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 = ['pearson', 'spearman', 'quadrant', 'mutinfo', 'normuti',
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'jointent', 'hellinger', 'crU', 'crM', 'crA', 'L2slope',
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'L1slope', 'num', 'ALL']
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stat = InputMultiPath(
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traits.Enum(_stat_names),
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mandatory=True,
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desc='statistics to compute. Possible names are :'
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' * pearson = Pearson correlation coefficient'
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' * spearman = Spearman correlation coefficient'
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' * quadrant = Quadrant correlation coefficient'
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' * mutinfo = Mutual Information'
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' * normuti = Normalized Mutual Information'
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' * jointent = Joint entropy'
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' * hellinger= Hellinger metric'
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' * crU = Correlation ratio (Unsymmetric)'
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' * crM = Correlation ratio (symmetrized by Multiplication)'
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' * crA = Correlation ratio (symmetrized by Addition)'
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' * L2slope = slope of least-squares (L2) linear regression of '
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' the data from dataset1 vs. the dataset2 '
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' (i.e., d2 = a + b*d1 ==> this is \'b\')'
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' * L1slope = slope of least-absolute-sum (L1) linear '
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' regression of the data from dataset1 vs. '
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' the dataset2'
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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.'
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' * ALL = all of the above, in that order'
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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 mask '
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'will have its statistic(s) computed as zero (0).',
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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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xor=['weight_file'])
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weight_file = traits.File(
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exists=True,
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desc='File name of an image to use as a weight. Only applies to '
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'\'pearson\' statistics.',
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argstr='-weight %s',
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xor=['automask'])
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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_file1',
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name_template='%s_bistat',
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keep_extension=True,
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position=0)
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class LocalBistat(AFNICommand):
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"""3dLocalBistat - computes statistics between 2 datasets, at each voxel,
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based on a local neighborhood of that voxel.
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For complete details, see the `3dLocalBistat Documentation.
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<https://afni.nimh.nih.gov/pub../pub/dist/doc/program_help/3dLocalBistat.html>`_
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Examples
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========
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>>> from nipype.interfaces import afni
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>>> bistat = afni.LocalBistat()
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>>> bistat.inputs.in_file1 = 'functional.nii'
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>>> bistat.inputs.in_file2 = 'structural.nii'
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>>> bistat.inputs.neighborhood = ('SPHERE', 1.2)
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>>> bistat.inputs.stat = 'pearson'
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>>> bistat.inputs.outputtype = 'NIFTI'
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>>> bistat.cmdline
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"3dLocalBistat -prefix functional_bistat.nii -nbhd 'SPHERE(1.2)' -stat pearson functional.nii structural.nii"
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>>> res = automask.run() # doctest: +SKIP
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"""
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_cmd = '3dLocalBistat'
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input_spec = LocalBistatInputSpec
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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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return super(LocalBistat, 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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