Hi,

I am currently receiving the following error and I am unsure of the cause or how to fix it:
** FATAL ERROR: '-stim_times 1' file 'final/stimuli/0_Stay.txt' has 1 auxiliary values per time point [nopt=16]

The timing file of reference (0_Stay.txt) looks like this:
-although this also happens for all of my event files, no matter the participant or the condition (including ones that contain exactly two runs of data)

55.0*2.00257205963 97.5*1.41961884499 110.0*2.65587878227 127.5*2.08984804153 145.0*2.47264695168 155.0*1.80546998978 167.5*2.10321688652 190.0*1.86910700798 195.0*1.88350510597 207.5*1.32351708412 215.0*1.520327489 235.0*1.68914914131 290.0*1.47039198875 305.0*2.05382609367
30.0*1.27251815796 65.0*1.65395784378 77.5*1.62255883217 100.0*2.17324495316 127.5*1.45616602898 137.5*1.29013109207 160.0*1.26953887939 172.5*1.28880596161 177.5*1.40289998055 210.0*1.63748216629 267.5*1.65583705902 285.0*1.520788908
*
*
*
*



The relevant part of the tcsh script that is causing this error is (and particularly in the 3dDeconvolve block):

# execute via :
# tcsh -xef new_glm_draft.sh |& tee output.proc.final

# =========================== auto block: setup ============================
# script setup

# take note of the AFNI version
afni -ver

# check that the current AFNI version is recent enough
afni_history -check_date 28 Oct 2015
if ( $status ) then
echo "** this script requires newer AFNI binaries (than 28 Oct 2015)"
echo " (consider: @update.afni.binaries -defaults)"
exit
endif

# the user may specify a single subject to run with
if ( $#argv > 0 ) then
set subj = $argv[1]
else
set subj = vc$VCnumber
endif

# label of GLM - and directory name
set glm_dir='final'

# assign output directory name
set subj_dir = $subj.results

# copy glm.ALLS file into directory
# cp glm.$glm_dir GLMS/glm.$glm_dir

# verify that the results directory does not yet exist
if ( -d $subj_dir/$glm_dir ) then
echo output dir "$glm_dir" already exists
exit
endif

# set list of runs
set runs = (`count -digits 2 1 6`)

# create results and stimuli directories
mkdir $subj_dir/$glm_dir
mkdir $subj_dir/$glm_dir/stimuli

# copy stim files into stimulus directory
cp \
$default_dir/ParticipantNEWTEST/vc$VCnumber/0_Stay.txt \
$default_dir/ParticipantNEWTEST/vc$VCnumber/1_Switch.txt \
$subj_dir/$glm_dir/stimuli

# copy anatomy to results dir
3dcopy $default_dir/NIfTI/vc$VCnumber/Mpragevc$VCnumber.nii \
$subj_dir/$glm_dir/Mpragevc$VCnumber


# -------------------------------------------------------
# enter the results directory (can begin processing data)
cd $subj_dir

set rundata = "pb04.$subj.r*.scale+tlrc.HEAD"
cat dfile.r*.1D > $glm_dir/dfile_rall.1D # make a single file of registration params


# catenate outlier counts into a single time series
cat outcount.r*.1D > outcount_rall.1D

# create 'full_mask' dataset (union mask)
cp full_mask* $glm_dir

# create an anat_final dataset, aligned with stats
# 3dcopy Mpragevc$VCnumber+orig anat_final.$subj+tlrc.HEAD


# ================================ regress =================================
cd $glm_dir

# compute de-meaned motion parameters (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6 \
-demean -write motion_demean.1D

# compute motion parameter derivatives (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 6 \
-derivative -demean -write motion_deriv.1D

# create censor file motion_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 6 \
-show_censor_count -censor_prev_TR \
-censor_motion 0.3 motion_${subj}

# note TRs that were not censored
set ktrs = `1d_tool.py -infile motion_${subj}_censor.1D \
-show_trs_uncensored encoded`

cd ..


# run the regression analysis
3dDeconvolve -input pb04.$subj.r*.scale+tlrc.HEAD \
-censor $glm_dir/motion_${subj}_censor.1D \
-polort 3 \
-num_stimts 8 \
-stim_times 1 $glm_dir/stimuli/0_Stay.txt 'GAM' \
-stim_label 1 Stay \
-stim_times 2 $glm_dir/stimuli/1_Switch.txt 'GAM' \
-stim_label 2 Switch \
-stim_file 3 $glm_dir/motion_demean.1D'[0]' -stim_base 3 -stim_label 3 roll \
-stim_file 4 $glm_dir/motion_demean.1D'[1]' -stim_base 4 -stim_label 4 pitch \
-stim_file 5 $glm_dir/motion_demean.1D'[2]' -stim_base 5 -stim_label 5 yaw \
-stim_file 6 $glm_dir/motion_demean.1D'[3]' -stim_base 6 -stim_label 6 dS \
-stim_file 7 $glm_dir/motion_demean.1D'[4]' -stim_base 7 -stim_label 7 dL \
-stim_file 8 $glm_dir/motion_demean.1D'[5]' -stim_base 8 -stim_label 8 dP \
-gltsym 'SYM: Stay -Switch' \
-glt_label 1 S-S \
-gltsym 'SYM: 0.5*Stay +0.5*Switch' \
-glt_label 2 mean.SS \
-fout -tout -x1D $glm_dir/X.xmat.1D -xjpeg X.jpg \
-x1D_uncensored $glm_dir/X.nocensor.xmat.1D \
-fitts $glm_dir/fitts.$subj \
-errts $glm_dir/errts.${subj} \
-bucket $glm_dir/stats.$subj


# if 3dDeconvolve fails, terminate the script
if ( $status != 0 ) then
echo '---------------------------------------'
echo '** 3dDeconvolve error, failing...'
echo ' (consider the file 3dDeconvolve.err)'
exit
endif

cd $glm_dir

# display any large pairwise correlations from the X-matrix
1d_tool.py -show_cormat_warnings -infile X.xmat.1D |& tee out.cormat_warn.txt

# create an all_runs dataset to match the fitts, errts, etc.
3dTcat -prefix all_runs.$subj $glm_dir/pb04.$subj.r*.scale+tlrc.HEAD

# --------------------------------------------------
# create a temporal signal to noise ratio dataset
# signal: if 'scale' block, mean should be 100
# noise : compute standard deviation of errts
3dTstat -mean -prefix rm.signal.all all_runs.$subj+orig"[$ktrs]"
3dTstat -stdev -prefix rm.noise.all errts.${subj}+orig"[$ktrs]"
3dcalc -a rm.signal.all+orig \
-b rm.noise.all+orig \
-c full_mask.$subj+orig \
-expr 'c*a/b' -prefix TSNR.$subj

# ---------------------------------------------------
# compute and store GCOR (global correlation average)
# (sum of squares of global mean of unit errts)
3dTnorm -norm2 -prefix rm.errts.unit errts.${subj}+orig
3dmaskave -quiet -mask full_mask.$subj+orig rm.errts.unit+orig \
> gmean.errts.unit.1D
3dTstat -sos -prefix - gmean.errts.unit.1D\' > out.gcor.1D
echo "-- GCOR = `cat out.gcor.1D`"

# ---------------------------------------------------
# compute correlation volume
# (per voxel: average correlation across masked brain)
# (now just dot product with average unit time series)
3dcalc -a rm.errts.unit+orig -b gmean.errts.unit.1D -expr 'a*b' -prefix rm.DP
3dTstat -sum -prefix corr_brain rm.DP+orig

# create ideal files for fixed response stim types
1dcat X.nocensor.xmat.1D'[24]' > ideal_Stay.1D
1dcat X.nocensor.xmat.1D'[25]' > ideal_Switch.1D

# --------------------------------------------------------
# compute sum of non-baseline regressors from the X-matrix
# (use 1d_tool.py to get list of regressor colums)
set reg_cols = `1d_tool.py -infile X.nocensor.xmat.1D -show_indices_interest`
3dTstat -sum -prefix sum_ideal.1D X.nocensor.xmat.1D"[$reg_cols]"

# also, create a stimulus-only X-matrix, for easy review
1dcat X.nocensor.xmat.1D"[$reg_cols]" > X.stim.xmat.1D


It is important to note that this is part 2 of two scripts with part 1 running all of the necessary preprocessing steps and part 2 running the GLMs.
Finally, I have read through some previous forums on this site and have adjusted my timing files accordingly to be married with a colon instead of an asterisk and also changed the asterisks that represented no data for that run to be "-1:0" (as suggested here: [afni.nimh.nih.gov]). Neither of these changes seemed to solve the Fatal Error but it is possible that I implemented these changes wrong.

Thank you in advance for your help!