code: def results_table(path_dict):
      """Return precalculated results images for subject info in `path_dict`
  
      Parameters
      ----------
      path_dict : dict
          containing key 'rootdir'
  
      Returns
      -------
      rtab : dict
          dict with keys given by run directories for this subject, values being a
          list with filenames of effect and sd images.
      """
      # Which runs correspond to this design type?
      rootdir = path_dict["rootdir"]
      runs = filter(
          lambda f: isdir(pjoin(rootdir, f)),
          ["results_run%03d" % i for i in range(1, 13)],
      )
  
      # Find out which contrasts have t-statistics,
      # storing the filenames for reading below
  
      results = {}
  
      for rundir in runs:
          rundir = pjoin(rootdir, rundir)
          for condir in listdir(rundir):
              for stat in ["sd", "effect"]:
                  fname_effect = abspath(
                      pjoin(rundir, condir, "effect.nii")
                  )
                  fname_sd = abspath(pjoin(rundir, condir, "sd.nii"))
              if exists(fname_effect) and exists(fname_sd):
                  results.setdefault(condir, []).append(
                      [fname_effect, fname_sd]
                  )
      return results
  
file_path: nipy/examples/ds105/ds105_util.py
imports: import os
  from os import listdir, makedirs
  from os.path import abspath, exists, isdir, splitext
  from os.path import join
  import numpy
  import pandas
  from nipy.io.api import load_image
label_desc: Use image node_id 6 for calling a global function with example usage: results_table(path_dict) and returns: results