class_docstr: Performs OLS regression given a 4D neuroimage file and stimulus timings
  
  For complete details, see the `3dDeconvolve Documentation.
  <https://afni.nimh.nih.gov/pub/dist/doc/program_help/3dDeconvolve.html>`_
  
  Examples
  ========
  
  >>> from nipype.interfaces import afni
  >>> deconvolve = afni.Deconvolve()
  >>> deconvolve.inputs.in_files = ['functional.nii', 'functional2.nii']
  >>> deconvolve.inputs.out_file = 'output.nii'
  >>> deconvolve.inputs.x1D = 'output.1D'
  >>> stim_times = [(1, 'timeseries.txt', 'SPMG1(4)')]
  >>> deconvolve.inputs.stim_times = stim_times
  >>> deconvolve.inputs.stim_label = [(1, 'Houses')]
  >>> deconvolve.inputs.gltsym = ['SYM: +Houses']
  >>> deconvolve.inputs.glt_label = [(1, 'Houses')]
  >>> deconvolve.cmdline
  "3dDeconvolve -input functional.nii functional2.nii -bucket output.nii -x1D output.1D -num_stimts 1 -stim_times 1 timeseries.txt 'SPMG1(4)' -stim_label 1 Houses -num_glt 1 -gltsym 'SYM: +Houses' -glt_label 1 Houses"
  >>> res = deconvolve.run()  # doctest: +SKIP
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code: def _format_arg(self, name, trait_spec, value):
      if name == "gltsym":
          for n, val in enumerate(value):
              if val.startswith("SYM: "):
                  value[n] = val.lstrip("SYM: ")
  
      return super()._format_arg(name, trait_spec, value)
  
imports: import os
  from ..base import CommandLineInputSpec, CommandLine, Directory, TraitedSpec, traits, isdefined, File, InputMultiPath, Undefined, Str
  from ...external.due import BibTeX
  from .base import AFNICommandBase, AFNICommand, AFNICommandInputSpec, AFNICommandOutputSpec, Info
label_desc: Use image node_id 1 for calling the Deconvolve obj's underlying member method code with example usage: obj._format_arg(name, trait_spec, value) and returns: super