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tsalotsalo
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Merge pull request #3 from rmarkello/afni_deconvolve
Afni deconvolve
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‎nipype/interfaces/afni/model.py

Lines changed: 127 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -30,7 +30,132 @@
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AFNICommandBase, AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec)
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class DeconvolveInputSpec(AFNICommandInputSpec):
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pass
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in_files = InputMultiPath(
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File(
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exists=True),
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desc='fname = filename of 3D+time input dataset '
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' [more than one filename can be given] '
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' here, and these datasets will be] '
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' [auto-catenated in time; if you do this,] '
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' [\'-concat\' is not needed and is ignored.] '
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'** You can input a 1D time series file here, '
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' but the time axis should run along the '
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' ROW direction, not the COLUMN direction as '
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' in the -input1D option. You can automatically '
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' transpose a 1D file on input using the \\\' '
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' operator at the end of the filename, as in '
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' -input fred.1D\\\' '
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' * This is the only way to use 3dDeconvolve '
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' with a multi-column 1D time series file.',
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argstr='-input %s',
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mandatory=True,
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copyfile=False)
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mask = File(
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desc='filename of 3D mask dataset; '
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'Only data time series from within the mask '
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'will be analyzed; results for voxels outside '
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'the mask will be set to zero.',
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argstr='-mask %s',
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exists=True)
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automask = traits.Bool(
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usedefault=True,
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argstr='-automask',
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desc='Build a mask automatically from input data '
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'(will be slow for long time series datasets)')
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censor = File(
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desc=' cname = filename of censor .1D time series '
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'* This is a file of 1s and 0s, indicating which '
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' time points are to be included (1) and which are '
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' to be excluded (0). '
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'* Option \'-censor\' can only be used once!',
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argstr='-censor %s',
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exists=True)
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polort = traits.Int(
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desc='pnum = degree of polynomial corresponding to the '
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' null hypothesis [default: pnum = 1]',
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argstr='-polort %d')
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ortvec = traits.Tuple(
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File(
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desc='filename',
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exists=True),
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Str(
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desc='label'),
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desc='This option lets you input a rectangular array '
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'of 1 or more baseline vectors from file \'fff\', '
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'which will get the label \'lll\'. Functionally, '
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'it is the same as using \'-stim_file\' on each '
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'column of \'fff\' separately (plus \'-stim_base\'). '
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'This method is just a faster and simpler way to '
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'include a lot of baseline regressors in one step. ',
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argstr='ortvec %s')
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x1d = File(
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desc='save out X matrix',
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argstr='-x1D %s')
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x1d_stop = traits.Bool(
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desc='stop running after writing .xmat.1D file',
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argstr='-x1D_stop')
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bucket = File(
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desc='output statistics file',
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argstr='-bucket %s')
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jobs = traits.Int(
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desc='run the program with given number of sub-processes',
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argstr='-jobs %d')
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stim_times_subtract = traits.Float(
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desc='This option means to subtract \'SS\' seconds from each time '
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'encountered in any \'-stim_times*\' option. The purpose of this '
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'option is to make it simple to adjust timing files for the '
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'removal of images from the start of each imaging run.',
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argstr='-stim_times_subtract %f')
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num_stimts = traits.Int(
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desc='number of stimulus timing files',
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argstr='-num_stimts %d')
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num_glt = traits.Int(
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desc='number of general linear tests (i.e., contrasts)',
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argstr='-num_glt %d')
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global_times = traits.Bool(
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desc='use global timing for stimulus timing files',
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argstr='-global_times',
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xor=['local_times'])
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local_times = traits.Bool(
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desc='use local timing for stimulus timing files',
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argstr='-local_times',
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xor=['global_times'])
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fout = traits.Bool(
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desc='output F-statistic for each stimulus',
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argstr='-fout')
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rout = traits.Bool(
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desc='output the R^2 statistic for each stimulus',
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argstr='-rout')
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tout = traits.Bool(
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desc='output the T-statistic for each stimulus',
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argstr='-tout')
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vout = traits.Bool(
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desc='output the sample variance (MSE) for each stimulus',
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argstr='-vout')
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stim_times = traits.List(
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traits.Tuple(traits.Int(desc='k-th response model'),
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File(desc='stimulus timing file',exists=True),
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Str(desc='model')),
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desc='Generate the k-th response model from a set of stimulus times'
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' given in file \'tname\'.',
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argstr='-stim_times %d %s %s')
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stim_label = traits.List(
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traits.Tuple(traits.Int(desc='k-th input stimulus'),
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Str(desc='stimulus label')),
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desc='label for kth input stimulus',
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argstr='-stim_label %d %s',
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requires=['stim_times'])
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gltsym = traits.List(
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Str(desc='symbolic general linear test'),
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desc='general linear tests (i.e., contrasts) using symbolic '
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'conventions',
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argstr='-gltsym %s')
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glt_labels = traits.List(
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traits.Tuple(traits.Int(desc='k-th general linear test'),
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Str(desc='GLT label')),
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desc='general linear test (i.e., contrast) labels',
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argstr='-glt_label %d %s',
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requires=['glt_sym'])
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class DeconvolveOutputSpec(TraitedSpec):
@@ -57,6 +182,7 @@ class Deconvolve(AFNICommand):
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'3dDeconvolve -input functional.nii -bucket output.nii -x1D output.1D -stim_times 1 stims1.txt SPMG1(4) 2 stims2.txt SPMG2(4)'
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>>> res = deconvolve.run() # doctest: +SKIP
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"""
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_cmd = '3dDeconvolve'
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input_spec = DeconvolveInputSpec
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output_spec = DeconvolveOutputSpec

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