------------------  Sample scripts for nonlinear warping  ------------------

The files in this directory give you examples of how to run afni_proc.py
with nonlinear spatial warping to MNI space.

anat_warped     = Directory with results from script s00.warper; it contains
                  nonlinearly warped anatomical dataset and the
                  warp transformation computed by 3dQwarp, via sswarper2
s00.warper      = Script to run sswarper2 to transform the subject's
                  dataset to MNI space and save the warp dataset
                  for later use in afni_proc.py; it also skullstrips the
                  anatomical dset

The output from s00.warper is stored in sub-directory anat_warped/
which the script creates. This data is supplied with the AFNI class
data so that you don't have to run this script. If you want to run
the script, you should delete the existing output first:
  \rm -rf anat_warped/
  tcsh s00.warper |& tee s00.warper.out

The afni_proc.py (AP) processing command that would use these
nonlinear warping results is stored one directory up, in the script:
s05.ap.uber.NL. You can run this, and see the similarities/differences
between the AP processing that includes only affine alignment to
template (via @auto_tlrc), in the script: s05.ap.uber.

+ Hint: The results are not dramatically different - but the purpose
  of using nonlinear warping is to give better alignment between
  subjects in a reference space. The differences across subject will
  be smaller (but never nonzero, esp. due to physiological differences
  like local variations in number of sulci/gyri across a population)
  after full nonlinear alignment. The purpose of this example
  directory is to show you how to setup analyses that us nonlinear
  warping.