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Afni deconvolve #3

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Merged
merged 3 commits into from
Jun 22, 2017
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Afni deconvolve #3

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beb8f53
Merge pull request #1 from tsalo/afni-deconvolve
rmarkello Jun 22, 2017
a13f325
Deconvolve InputSpec first pass
rmarkello Jun 22, 2017
ac40d22
Merged from Taylor
rmarkello Jun 22, 2017
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128 changes: 127 additions & 1 deletion nipype/interfaces/afni/model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,132 @@
AFNICommandBase, AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec)

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


class DeconvolveOutputSpec(TraitedSpec):
Expand All @@ -57,6 +182,7 @@ class Deconvolve(AFNICommand):
'3dDeconvolve -input functional.nii -bucket output.nii -x1D output.1D -stim_times 1 stims1.txt SPMG1(4) 2 stims2.txt SPMG2(4)'
>>> res = deconvolve.run() # doctest: +SKIP
"""

_cmd = '3dDeconvolve'
input_spec = DeconvolveInputSpec
output_spec = DeconvolveOutputSpec
Expand Down