Deconvolve extra options

Added a few extra options to Deconvolve

Author: rmarkello

nipype/interfaces/afni/model.py

@@ -33,47 +33,76 @@ class DeconvolveInputSpec(AFNICommandInputSpec):
     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] '
+        desc='   filename(s) 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.] '
+             '   [\'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.',
+             '   in the \'input1D\' option.',
         argstr='-input %s',
         mandatory=True,
         copyfile=False,
         sep=" ")
+    sat = traits.Bool(
+        desc='* 3dDeconvolve can check the dataset time series'
+             '  for initial saturation transients, which should'
+             '  normally have been excised before data analysis.'
+             '  If you want to have it do this somewhat time'
+             '  consuming check, use the option \'sat\'.',
+        argstr='-sat',
+        xor=['trans'])
+    trans = traits.Bool(
+        desc='* 3dDeconvolve can check the dataset time series'
+             '  for initial saturation transients, which should'
+             '  normally have been excised before data analysis.'
+             '  If you want to have it do this somewhat time'
+             '  consuming check, use the option \'trans\'.',
+        argstr='-trans',
+        xor=['sat'])
+    noblock = traits.Bool(
+        desc='Normally, if you input multiple datasets with'
+             '  \'input\', then the separate datasets are taken to'
+             '  be separate image runs that get separate baseline'
+             '  models.  If you want to have the program consider'
+             '  these to be all one big run, use \'noblock\'.'
+             '* If any of the input dataset has only 1 sub-brick,'
+             '  then this option is automatically invoked!'
+             '* If the auto-catenation feature isn\'t used, then'
+             '  this option has no effect, no how, no way.',
+        argstr='-noblock')
+    force_TR = traits.Int(
+        desc='Use this value of TR instead of the one in the -input dataset. '
+             '(It\'s better to fix the input using 3drefit.)',
+        argstr='-force_TR %d')
+    input1D = File(
+        desc='Filename of single (fMRI) .1D time series where time run downs '
+             'the column.',
+        argstr='-input1D %s',
+        exists=True)
     mask = File(
-        desc='filename of 3D mask dataset; '
+        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)')
+        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 '
+        desc='  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!',
+             '  to be excluded (0).',
         argstr='-censor %s',
         exists=True)
     polort = traits.Int(
-        desc='pnum = degree of polynomial corresponding to the '
-             ' null hypothesis  [default: pnum = 1]',
+        desc='Degree of polynomial corresponding to the null hypothesis '
+             '[default: 1]',
         argstr='-polort %d')
     ortvec = traits.Tuple(
         File(
@@ -90,75 +119,80 @@ class DeconvolveInputSpec(AFNICommandInputSpec):
              'include a lot of baseline regressors in one step. ',
         argstr='ortvec %s')
     x1D = File(
-        desc='save out X matrix',
+        desc='Save out X matrix',
         argstr='-x1D %s')
     x1D_stop = traits.Bool(
-        desc='stop running after writing .xmat.1D file',
+        desc='Stop running after writing .xmat.1D file',
         argstr='-x1D_stop')
     out_file = File(
-        desc='output statistics file',
+        desc='Output statistics file',
         argstr='-bucket %s')
     jobs = traits.Int(
-        desc='run the program with given number of sub-processes',
+        desc='Run the program with provided 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 '
+        desc='This option means to subtract specified 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',
+        desc='Number of stimulus timing files',
         argstr='-num_stimts %d')
     num_glt = traits.Int(
-        desc='number of general linear tests (i.e., contrasts)',
+        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',
+        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',
+        desc='Use local timing for stimulus timing files',
         argstr='-local_times',
         xor=['global_times'])
     fout = traits.Bool(
-        desc='output F-statistic for each stimulus',
+        desc='Output F-statistic for each stimulus',
         argstr='-fout')
     rout = traits.Bool(
-        desc='output the R^2 statistic for each stimulus',
+        desc='Output the R^2 statistic for each stimulus',
         argstr='-rout')
     tout = traits.Bool(
-        desc='output the T-statistic for each stimulus',
+        desc='Output the T-statistic for each stimulus',
         argstr='-tout')
     vout = traits.Bool(
-        desc='output the sample variance (MSE) for each stimulus',
+        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\'.',
+        desc='Generate a response model from a set of stimulus times'
+             ' given in file.',
         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',
+        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 '
+        desc='General linear tests (i.e., contrasts) using symbolic '
              'conventions',
-        argstr='-gltsym %s...')
+        argstr='-gltsym SYM: %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',
+        desc='General linear test (i.e., contrast) labels',
         argstr='-glt_label %d %s...',
         requires=['gltsym'])
 
 
+class DeconvolveOutputSpec(AFNICommandOutputSpec):
+    out_file = File(desc='output statistics file',
+                    exists=True)
+
+
 class Deconvolve(AFNICommand):
     """Performs OLS regression given a 4D neuroimage file and stimulus timings
 
@@ -194,7 +228,7 @@ def _parse_inputs(self, skip=None):
         return super(Deconvolve, self)._parse_inputs(skip)
 
     def _list_outputs(self):
-        outputs = self.output_spec().get()
+        outputs = super(Deconvolve, self)._list_outputs()
         if isdefined(self.inputs.x1D):
             if not self.inputs.x1D.endswith('.xmat.1D'):
                 outputs['x1D'] = self.inputs.x1D + '.xmat.1D'