The process script has now been created by running the afni_proc.py command.
Recall that running 's01.ap.simple' created 'proc.FT', and that proc.FT is the
script to perform single subject analysis of FT's data.

Recall also that afni_proc.py suggested how to run the script:

        tcsh -xef proc.FT |& tee output.proc.FT

Execute that command:

% tcsh -xef proc.FT |& tee output.proc.FT

Note that it can be executed via cut-and-paste.  On a Linux system, use the
left mouse button to highlight the command, then use the right button to paste
it into the the terminal window.  On a mac, again highlight with the left mouse 
button, then use apple-c (copy) and apple-v (vomit? paste?) to paste the text
into the terminal window.

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Once the command is started, a few things should happen:

1. Script output should start appearing in the terminal window.

   Notice that the actual commands appear as well, not just the output.  For
   example, consider these AFNI commands in the script.

        3dcopy FT/FT_anat+orig FT.results/FT_anat
        ...
        3dToutcount -automask -fraction pb00.FT.r01.tcat+orig

   They appear because of the '-x' option to 'tcsh', above.

2. The script output should be written to the file output.proc.FT.

   Open another terminal window and go to the same 'AFNI_data6/FT_analysis'
   directory, and type "ls -l".  The output.proc.FT file will be see, and will
   grow as the processing continues.  Again, this text file contains the same
   output that is displayed on the terminal window.

3. The output directory 'FT.results' is created.

   Another new entry in the current directory is 'FT.results'.  That is where
   all of the input files are copied to, and where all of the processing is
   done.  That directory can be deleted without concern of losing any original
   data.

The script should take 1-10 minutes, depending on the computer being used.

Note that the input directory FT contains about 200 MB of data.  The results
directory ends up containing about 1.5 GB of data (or 1500 MB), and so is 7.5
times the original size.  That is because each pre-processing step duplicates
the data size, as do the all_runs, fitts and errts datasets.