Matrix condition errors for block design

[qianqian]

Hello AFNI experts,
My experiment is a block design, each block consists of three identical processes (i.e. one second of sound cue, two seconds of touch, and one second of sound cue), so the duration of each block is 12 ((1+2+1) * 3=12), and the blank time is also 12 seconds. The purpose of our study was to see if there were differences in activation in subjects under different experimental conditions. As discussed before, the background information about our experimental design like that, the purpose of our experiment was to investigate how different object positions and object sizes were represented in the haptic condition, so there were two factors in the experiment: object location(left, right), and object size (large, small), and it is worth noting that the subjects grasped only with their right hand during the experiment.What's more, in order to make the grasping process cleaner, we set up another control condition, i.e., the hand was open, which did not present the real object, but only mimicked the grasping action, so this was the third experimental condition (type of grasping: gras, open).

During operation, afni prompts:**** FATAL ERROR: Can’t continue after matrix condition errors!
** you might try -GOFORIT, but be careful! (cf. ‘-help’)**

Is this caused by the subject's head movement?

Here is our script

What would be the cause of this error? And, how do I fix this error?

Thanks,
Qianqian Wu

[ptaylor]

Hi, Qianqian-

To more easily help address this question, could you please post your afni_proc.py in text, preferably within a "triple backtick codeblock" as described here:

typing about code or terminal text: please use backticks (and avoid screenshotting text)

Also, a fun feature of the codeblock backticks is that you can specify the highlighting language within the codeblock. Just put the name of the available script/programming language in the top line, where the opening backticks are: here, I started the codeblock with: ``` bash #!/bin/bash # comment varA=$1 varB=$2 echo "++ The variables are: ${varA} ${varB}" here, I started the codeblock with: ``` python # comment varA = 'some string' varB = '''another string''' print("++ The variables…

? That will make it much easier to help read and interact with your code.

thanks,
pt

[qianqian]

Hi~ pt
Here is our afni_proc.py

#!/bin/tcsh -xef

echo "auto-generated by afni_proc.py, Thu Aug 22 18:33:31 2024"
echo "(version 7.60, August 21, 2023)"
echo "execution started: `date`"

# to execute via tcsh: 
#   tcsh -xef proc.haptical_BLOCK_SSwarper_sub01 |& tee output.proc.haptical_BLOCK_SSwarper_sub01
# to execute via bash: 
#   tcsh -xef proc.haptical_BLOCK_SSwarper_sub01 2>&1 | tee output.proc.haptical_BLOCK_SSwarper_sub01

# =========================== 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 14 Nov 2022
if ( $status ) then
    echo "** this script requires newer AFNI binaries (than 14 Nov 2022)"
    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 = sub01
endif

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

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

# set list of runs
set runs = (`count -digits 2 1 8`)

# create results and stimuli directories
mkdir -p $output_dir
mkdir $output_dir/stimuli

# copy stim files into stimulus directory
cp /media/linux/Z/sub01/Stim_time/OpenLeftSmall.txt    \
    /media/linux/Z/sub01/Stim_time/OpenRightSmall.txt  \
    /media/linux/Z/sub01/Stim_time/OpenLeftLarge.txt   \
    /media/linux/Z/sub01/Stim_time/OpenRightLarge.txt  \
    /media/linux/Z/sub01/Stim_time/GraspLeftSmall.txt  \
    /media/linux/Z/sub01/Stim_time/GraspRightSmall.txt \
    /media/linux/Z/sub01/Stim_time/GraspLeftLarge.txt  \
    /media/linux/Z/sub01/Stim_time/GraspRightLarge.txt $output_dir/stimuli

# copy anatomy to results dir
3dcopy /media/linux/Z/sub01/to3dfile/T1+orig $output_dir/T1

# copy template to results dir (for QC)
3dcopy /home/linux/abin/MNI152_2009_template_SSW.nii.gz \
    $output_dir/MNI152_2009_template_SSW.nii.gz

# copy external -tlrc_NL_warped_dsets datasets
3dcopy /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01.nii      \
    $output_dir/anatQQ.sub01
3dcopy /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01.aff12.1D \
    $output_dir/anatQQ.sub01.aff12.1D
3dcopy /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01_WARP.nii \
    $output_dir/anatQQ.sub01_WARP.nii

# ============================ auto block: tcat ============================
# apply 3dTcat to copy input dsets to results dir,
# while removing the first 0 TRs
3dTcat -prefix $output_dir/pb00.$subj.r01.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run1+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r02.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run2+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r03.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run3+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r04.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run4+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r05.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run5+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r06.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run6+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r07.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run7+orig'[0..$]'
3dTcat -prefix $output_dir/pb00.$subj.r08.tcat \
    /media/linux/Z/sub01/EPI/sub01_haptical_run8+orig'[0..$]'

# and make note of repetitions (TRs) per run
set tr_counts = ( 198 198 198 198 198 198 198 198 )

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


# ---------------------------------------------------------
# QC: look for columns of high variance
find_variance_lines.tcsh -polort 3 -nerode 2   \
       -rdir vlines.pb00.tcat                  \
       pb00.$subj.r*.tcat+orig.HEAD |& tee out.vlines.pb00.tcat.txt

# ========================== auto block: outcount ==========================
# QC: compute outlier fraction for each volume
touch out.pre_ss_warn.txt
foreach run ( $runs )
    3dToutcount -automask -fraction -polort 3 -legendre                     \
                pb00.$subj.r$run.tcat+orig > outcount.r$run.1D

    # outliers at TR 0 might suggest pre-steady state TRs
    if ( `1deval -a outcount.r$run.1D"{0}" -expr "step(a-0.4)"` ) then
        echo "** TR #0 outliers: possible pre-steady state TRs in run $run" \
            >> out.pre_ss_warn.txt
    endif
end

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

# get run number and TR index for minimum outlier volume
set minindex = `3dTstat -argmin -prefix - outcount_rall.1D\'`
set ovals = ( `1d_tool.py -set_run_lengths $tr_counts                       \
                          -index_to_run_tr $minindex` )
# save run and TR indices for extraction of vr_base_min_outlier
set minoutrun = $ovals[1]
set minouttr  = $ovals[2]
echo "min outlier: run $minoutrun, TR $minouttr" | tee out.min_outlier.txt

# ================================= tshift =================================
# time shift data so all slice timing is the same 
foreach run ( $runs )
    3dTshift -tzero 0 -quintic -prefix pb01.$subj.r$run.tshift \
             pb00.$subj.r$run.tcat+orig
end

# --------------------------------
# extract volreg registration base
3dbucket -prefix vr_base_min_outlier                           \
    pb01.$subj.r$minoutrun.tshift+orig"[$minouttr]"

# ================================= align ==================================
# for e2a: compute anat alignment transformation to EPI registration base
# (new anat will be intermediate, stripped, T1_ns+orig)
align_epi_anat.py -anat2epi -anat T1+orig        \
       -save_skullstrip -suffix _al_junk         \
       -epi vr_base_min_outlier+orig -epi_base 0 \
       -epi_strip 3dAutomask                     \
       -giant_move                               \
       -volreg off -tshift off

# ================================== tlrc ==================================

# nothing to do: have external -tlrc_NL_warped_dsets

# warped anat     : anatQQ.sub01+tlrc
# affine xform    : anatQQ.sub01.aff12.1D
# non-linear warp : anatQQ.sub01_WARP.nii

# ================================= volreg =================================
# align each dset to base volume, to anat, warp to tlrc space

# verify that we have a +tlrc warp dataset
if ( ! -f anatQQ.sub01+tlrc.HEAD ) then
    echo "** missing +tlrc warp dataset: anatQQ.sub01+tlrc.HEAD" 
    exit
endif

# register and warp
foreach run ( $runs )
    # register each volume to the base image
    3dvolreg -verbose -zpad 1 -base vr_base_min_outlier+orig            \
             -1Dfile dfile.r$run.1D -prefix rm.epi.volreg.r$run         \
             -cubic                                                     \
             -1Dmatrix_save mat.r$run.vr.aff12.1D                       \
             pb01.$subj.r$run.tshift+orig

    # create an all-1 dataset to mask the extents of the warp
    3dcalc -overwrite -a pb01.$subj.r$run.tshift+orig -expr 1           \
           -prefix rm.epi.all1

    # catenate volreg/epi2anat/tlrc xforms
    cat_matvec -ONELINE                                                 \
               anatQQ.sub01.aff12.1D                                    \
               T1_al_junk_mat.aff12.1D -I                               \
               mat.r$run.vr.aff12.1D > mat.r$run.warp.aff12.1D

    # apply catenated xform: volreg/epi2anat/tlrc/NLtlrc
    # then apply non-linear standard-space warp
    3dNwarpApply -master anatQQ.sub01+tlrc -dxyz 2                      \
                 -source pb01.$subj.r$run.tshift+orig                   \
                 -nwarp "anatQQ.sub01_WARP.nii mat.r$run.warp.aff12.1D" \
                 -prefix rm.epi.nomask.r$run

    # warp the all-1 dataset for extents masking 
    3dNwarpApply -master anatQQ.sub01+tlrc -dxyz 2                      \
                 -source rm.epi.all1+orig                               \
                 -nwarp "anatQQ.sub01_WARP.nii mat.r$run.warp.aff12.1D" \
                 -interp cubic                                          \
                 -ainterp NN -quiet                                     \
                 -prefix rm.epi.1.r$run

    # make an extents intersection mask of this run
    3dTstat -min -prefix rm.epi.min.r$run rm.epi.1.r$run+tlrc
end

# make a single file of registration params
cat dfile.r*.1D > dfile_rall.1D

# ----------------------------------------
# create the extents mask: mask_epi_extents+tlrc
# (this is a mask of voxels that have valid data at every TR)
3dMean -datum short -prefix rm.epi.mean rm.epi.min.r*.HEAD 
3dcalc -a rm.epi.mean+tlrc -expr 'step(a-0.999)' -prefix mask_epi_extents

# and apply the extents mask to the EPI data 
# (delete any time series with missing data)
foreach run ( $runs )
    3dcalc -a rm.epi.nomask.r$run+tlrc -b mask_epi_extents+tlrc         \
           -expr 'a*b' -prefix pb02.$subj.r$run.volreg
end

# warp the volreg base EPI dataset to make a final version
cat_matvec -ONELINE                                                     \
           anatQQ.sub01.aff12.1D                                        \
           T1_al_junk_mat.aff12.1D -I  > mat.basewarp.aff12.1D

3dNwarpApply -master anatQQ.sub01+tlrc -dxyz 2                          \
             -source vr_base_min_outlier+orig                           \
             -nwarp "anatQQ.sub01_WARP.nii mat.basewarp.aff12.1D"       \
             -prefix final_epi_vr_base_min_outlier

# create an anat_final dataset, aligned with stats
3dcopy anatQQ.sub01+tlrc anat_final.$subj

# record final registration costs
3dAllineate -base final_epi_vr_base_min_outlier+tlrc -allcostX          \
            -input anat_final.$subj+tlrc |& tee out.allcostX.txt

# -----------------------------------------
# warp anat follower datasets (non-linear)
# warp follower dataset T1+orig
3dNwarpApply -source T1+orig                                            \
             -master anat_final.$subj+tlrc                              \
             -ainterp wsinc5 -nwarp anatQQ.sub01_WARP.nii               \
             anatQQ.sub01.aff12.1D                                      \
             -prefix anat_w_skull_warped

# ================================== blur ==================================
# blur each volume of each run
foreach run ( $runs )
    3dmerge -1blur_fwhm 4.0 -doall -prefix pb03.$subj.r$run.blur \
            pb02.$subj.r$run.volreg+tlrc
end

# ================================== mask ==================================
# create 'full_mask' dataset (union mask)
foreach run ( $runs )
    3dAutomask -prefix rm.mask_r$run pb03.$subj.r$run.blur+tlrc
end

# create union of inputs, output type is byte
3dmask_tool -inputs rm.mask_r*+tlrc.HEAD -union -prefix full_mask.$subj

# ---- create subject anatomy mask, mask_anat.$subj+tlrc ----
#      (resampled from tlrc anat)
3dresample -master full_mask.$subj+tlrc -input anatQQ.sub01+tlrc      \
           -prefix rm.resam.anat

# convert to binary anat mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.anat+tlrc  \
            -prefix mask_anat.$subj

# compute tighter EPI mask by intersecting with anat mask
3dmask_tool -input full_mask.$subj+tlrc mask_anat.$subj+tlrc          \
            -inter -prefix mask_epi_anat.$subj

# compute overlaps between anat and EPI masks
3dABoverlap -no_automask full_mask.$subj+tlrc mask_anat.$subj+tlrc    \
            |& tee out.mask_ae_overlap.txt

# note Dice coefficient of masks, as well
3ddot -dodice full_mask.$subj+tlrc mask_anat.$subj+tlrc               \
      |& tee out.mask_ae_dice.txt

# ---- create group anatomy mask, mask_group+tlrc ----
#      (resampled from tlrc base anat, MNI152_2009_template_SSW.nii.gz)
3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group      \
           -input /home/linux/abin/MNI152_2009_template_SSW.nii.gz'[0]'

# convert to binary group mask; fill gaps and holes
3dmask_tool -dilate_input 5 -5 -fill_holes -input rm.resam.group+tlrc \
            -prefix mask_group

# note Dice coefficient of anat and template masks
3ddot -dodice mask_anat.$subj+tlrc mask_group+tlrc                    \
      |& tee out.mask_at_dice.txt

# ---- segment anatomy into classes CSF/GM/WM ----
3dSeg -anat anat_final.$subj+tlrc -mask AUTO -classes 'CSF ; GM ; WM'

# copy resulting Classes dataset to current directory
3dcopy Segsy/Classes+tlrc .

# make individual ROI masks for regression (CSF GM WM and CSFe GMe WMe)
foreach class ( CSF GM WM )
   # unitize and resample individual class mask from composite
   3dmask_tool -input Segsy/Classes+tlrc"<$class>"                    \
               -prefix rm.mask_${class}
   3dresample -master pb03.$subj.r01.blur+tlrc -rmode NN              \
              -input rm.mask_${class}+tlrc -prefix mask_${class}_resam
   # also, generate eroded masks
   3dmask_tool -input Segsy/Classes+tlrc"<$class>" -dilate_input -1   \
               -prefix rm.mask_${class}e
   3dresample -master pb03.$subj.r01.blur+tlrc -rmode NN              \
              -input rm.mask_${class}e+tlrc -prefix mask_${class}e_resam
end

# ================================= scale ==================================
# scale each voxel time series to have a mean of 100
# (be sure no negatives creep in)
# (subject to a range of [0,200])
foreach run ( $runs )
    3dTstat -prefix rm.mean_r$run pb03.$subj.r$run.blur+tlrc
    3dcalc -a pb03.$subj.r$run.blur+tlrc -b rm.mean_r$run+tlrc \
           -c mask_epi_extents+tlrc                            \
           -expr 'c * min(200, a/b*100)*step(a)*step(b)'       \
           -prefix pb04.$subj.r$run.scale
end

# ================================ regress =================================

# compute de-meaned motion parameters (for use in regression)
1d_tool.py -infile dfile_rall.1D -set_nruns 8                            \
           -demean -write motion_demean.1D

# compute motion parameter derivatives (just to have)
1d_tool.py -infile dfile_rall.1D -set_nruns 8                            \
           -derivative -demean -write motion_deriv.1D

# convert motion parameters for per-run regression
1d_tool.py -infile motion_demean.1D -set_nruns 8                         \
           -split_into_pad_runs mot_demean

# create censor file motion_${subj}_censor.1D, for censoring motion 
1d_tool.py -infile dfile_rall.1D -set_nruns 8                            \
    -show_censor_count -censor_prev_TR                                   \
    -censor_motion 0.3 motion_${subj}

# note TRs that were not censored
# (apply from a text file, in case of a lot of censoring)
1d_tool.py -infile motion_${subj}_censor.1D                              \
           -show_trs_uncensored space                                    \
           > out.keep_trs_rall.txt
set ktrs = "1dcat out.keep_trs_rall.txt"

# ------------------------------
# run the regression analysis
3dDeconvolve -input pb04.$subj.r*.scale+tlrc.HEAD                        \
    -censor motion_${subj}_censor.1D                                     \
    -ortvec mot_demean.r01.1D mot_demean_r01                             \
    -ortvec mot_demean.r02.1D mot_demean_r02                             \
    -ortvec mot_demean.r03.1D mot_demean_r03                             \
    -ortvec mot_demean.r04.1D mot_demean_r04                             \
    -ortvec mot_demean.r05.1D mot_demean_r05                             \
    -ortvec mot_demean.r06.1D mot_demean_r06                             \
    -ortvec mot_demean.r07.1D mot_demean_r07                             \
    -ortvec mot_demean.r08.1D mot_demean_r08                             \
    -polort 3                                                            \
    -GOFORIT 18                                                          \
    -num_stimts 8                                                        \
    -stim_times_IM 1 stimuli/OpenLeftSmall.txt 'BLOCK(12,1)'                \
    -stim_label 1 OpenLeftSmall                                          \
    -stim_times_IM 2 stimuli/OpenRightSmall.txt 'BLOCK(12,1)'               \
    -stim_label 2 OpenRightSmall                                         \
    -stim_times_IM 3 stimuli/OpenLeftLarge.txt 'BLOCK(12,1)'                \
    -stim_label 3 OpenLeftLarge                                          \
    -stim_times_IM 4 stimuli/OpenRightLarge.txt 'BLOCK(12,1)'               \
    -stim_label 4 OpenRightLarge                                         \
    -stim_times_IM 5 stimuli/GraspLeftSmall.txt 'BLOCK(12,1)'               \
    -stim_label 5 GraspLeftSmall                                         \
    -stim_times_IM 6 stimuli/GraspRightSmall.txt 'BLOCK(12,1)'              \
    -stim_label 6 GraspRightSmall                                        \
    -stim_times_IM 7 stimuli/GraspLeftLarge.txt 'BLOCK(12,1)'               \
    -stim_label 7 GraspLeftLarge                                         \
    -stim_times_IM 8 stimuli/GraspRightLarge.txt 'BLOCK(12,1)'              \
    -stim_label 8 GraspRightLarge                                        \
    -gltsym 'SYM: OpenRightSmall -OpenLeftSmall'                         \
    -glt_label 1 OS_R-L                                                  \
    -gltsym 'SYM: OpenRightLarge -OpenLeftLarge'                         \
    -glt_label 2 OB_R-L                                                  \
    -gltsym 'SYM: GraspRightSmall -GraspLeftSmall'                       \
    -glt_label 3 GS_R-L                                                  \
    -gltsym 'SYM: GraspRightLarge -GraspLeftLarge'                       \
    -glt_label 4 GB_R-L                                                  \
    -gltsym 'SYM: OpenRightSmall  +OpenRightLarge  -OpenLeftLarge        \
    -OpenLeftSmall'                                                      \
    -glt_label 5 O2_R-L                                                  \
    -gltsym 'SYM: GraspRightSmall +GraspRightLarge -GraspLeftLarge       \
    -GraspLeftSmall'                                                     \
    -glt_label 6 G2_R-L                                                  \
    -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall        \
    +GraspRightLarge'                                                    \
    -glt_label 7 Grasp_all                                               \
    -gltsym 'SYM: OpenLeftSmall +OpenLeftLarge +OpenRightSmall           \
    +OpenRightLarge'                                                     \
    -glt_label 8 Open_all                                                \
    -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall        \
    +GraspRightLarge +OpenLeftSmall +OpenLeftLarge +OpenRightSmall       \
    +OpenRightLarge'                                                     \
    -glt_label 9 ALL_all                                                 \
    -fout -tout -x1D X.xmat.1D -xjpeg X.jpg                              \
    -x1D_uncensored X.nocensor.xmat.1D                                   \
    -errts errts.${subj}                                                 \
    -bucket stats.$subj


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


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

# display degrees of freedom info from X-matrix
1d_tool.py -show_df_info -infile X.xmat.1D |& tee out.df_info.txt

# --------------------------------------------------
# ANATICOR: generate local WMe time series averages
# create catenated volreg dataset
3dTcat -prefix rm.all_runs.volreg pb02.$subj.r*.volreg+tlrc.HEAD
3dLocalstat -stat mean -nbhd 'SPHERE(30)' -prefix Local_WMe_rall         \
            -mask mask_WMe_resam+tlrc -use_nonmask                       \
            rm.all_runs.volreg+tlrc

# -- execute the 3dREMLfit script, written by 3dDeconvolve --
# (include ANATICOR regressors via -dsort)
tcsh -x stats.REML_cmd -dsort Local_WMe_rall+tlrc 

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


# create an all_runs dataset to match the fitts, errts, etc.
3dTcat -prefix all_runs.$subj pb04.$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.$subj+tlrc"[$ktrs]"
3dTstat -stdev -prefix rm.noise.all errts.${subj}_REML+tlrc"[$ktrs]"
3dcalc -a rm.signal.all+tlrc                                             \
       -b rm.noise.all+tlrc                                              \
       -expr '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.${subj}_REML+tlrc
3dmaskave -quiet -mask full_mask.$subj+tlrc rm.errts.unit+tlrc           \
          > mean.errts.unit.1D
3dTstat -sos -prefix - mean.errts.unit.1D\' > out.gcor.1D
echo "-- GCOR = `cat out.gcor.1D`"

# ---------------------------------------------------
# compute correlation volume
# (per voxel: correlation with masked brain average)
3dmaskave -quiet -mask full_mask.$subj+tlrc errts.${subj}_REML+tlrc      \
          > mean.errts.1D
3dTcorr1D -prefix corr_brain errts.${subj}_REML+tlrc mean.errts.1D

# create fitts dataset from all_runs and errts
3dcalc -a all_runs.$subj+tlrc -b errts.${subj}+tlrc -expr a-b            \
       -prefix fitts.$subj
# create fitts from REML errts
3dcalc -a all_runs.$subj+tlrc -b errts.${subj}_REML+tlrc -expr a-b       \
       -prefix fitts.$subj\_REML

# create ideal files for fixed response stim types
1dcat X.nocensor.xmat.1D'[32]' > ideal_OpenLeftSmall.1D
1dcat X.nocensor.xmat.1D'[33]' > ideal_OpenRightSmall.1D
1dcat X.nocensor.xmat.1D'[34]' > ideal_OpenLeftLarge.1D
1dcat X.nocensor.xmat.1D'[35]' > ideal_OpenRightLarge.1D
1dcat X.nocensor.xmat.1D'[36]' > ideal_GraspLeftSmall.1D
1dcat X.nocensor.xmat.1D'[37]' > ideal_GraspRightSmall.1D
1dcat X.nocensor.xmat.1D'[38]' > ideal_GraspLeftLarge.1D
1dcat X.nocensor.xmat.1D'[39]' > ideal_GraspRightLarge.1D

# --------------------------------------------------
# extract non-baseline regressors from the X-matrix,
# then compute their sum
1d_tool.py -infile X.nocensor.xmat.1D -write_xstim X.stim.xmat.1D
3dTstat -sum -prefix sum_ideal.1D X.stim.xmat.1D

# ============================ blur estimation =============================
# compute blur estimates
touch blur_est.$subj.1D   # start with empty file

# create directory for ACF curve files
mkdir files_ACF

# -- estimate blur for each run in epits --
touch blur.epits.1D

# restrict to uncensored TRs, per run
foreach run ( $runs )
    set trs = `1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \
                          -show_trs_run $run`
    if ( $trs == "" ) continue
    3dFWHMx -detrend -mask full_mask.$subj+tlrc                          \
            -ACF files_ACF/out.3dFWHMx.ACF.epits.r$run.1D                \
            all_runs.$subj+tlrc"[$trs]" >> blur.epits.1D
end

# compute average FWHM blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.epits.1D'{0..$(2)}'\'` )
echo average epits FWHM blurs: $blurs
echo "$blurs   # epits FWHM blur estimates" >> blur_est.$subj.1D

# compute average ACF blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.epits.1D'{1..$(2)}'\'` )
echo average epits ACF blurs: $blurs
echo "$blurs   # epits ACF blur estimates" >> blur_est.$subj.1D

# -- estimate blur for each run in errts --
touch blur.errts.1D

# restrict to uncensored TRs, per run
foreach run ( $runs )
    set trs = `1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \
                          -show_trs_run $run`
    if ( $trs == "" ) continue
    3dFWHMx -detrend -mask full_mask.$subj+tlrc                          \
            -ACF files_ACF/out.3dFWHMx.ACF.errts.r$run.1D                \
            errts.${subj}+tlrc"[$trs]" >> blur.errts.1D
end

# compute average FWHM blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.errts.1D'{0..$(2)}'\'` )
echo average errts FWHM blurs: $blurs
echo "$blurs   # errts FWHM blur estimates" >> blur_est.$subj.1D

# compute average ACF blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.errts.1D'{1..$(2)}'\'` )
echo average errts ACF blurs: $blurs
echo "$blurs   # errts ACF blur estimates" >> blur_est.$subj.1D

# -- estimate blur for each run in err_reml --
touch blur.err_reml.1D

# restrict to uncensored TRs, per run
foreach run ( $runs )
    set trs = `1d_tool.py -infile X.xmat.1D -show_trs_uncensored encoded \
                          -show_trs_run $run`
    if ( $trs == "" ) continue
    3dFWHMx -detrend -mask full_mask.$subj+tlrc                          \
            -ACF files_ACF/out.3dFWHMx.ACF.err_reml.r$run.1D             \
            errts.${subj}_REML+tlrc"[$trs]" >> blur.err_reml.1D
end

# compute average FWHM blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.err_reml.1D'{0..$(2)}'\'` )
echo average err_reml FWHM blurs: $blurs
echo "$blurs   # err_reml FWHM blur estimates" >> blur_est.$subj.1D

# compute average ACF blur (from every other row) and append
set blurs = ( `3dTstat -mean -prefix - blur.err_reml.1D'{1..$(2)}'\'` )
echo average err_reml ACF blurs: $blurs
echo "$blurs   # err_reml ACF blur estimates" >> blur_est.$subj.1D


# add 3dClustSim results as attributes to any stats dset
mkdir files_ClustSim

# run Monte Carlo simulations using method 'ACF'
set params = ( `grep ACF blur_est.$subj.1D | tail -n 1` )
3dClustSim -both -mask full_mask.$subj+tlrc -acf $params[1-3]            \
           -cmd 3dClustSim.ACF.cmd -prefix files_ClustSim/ClustSim.ACF

# run 3drefit to attach 3dClustSim results to stats
set cmd = ( `cat 3dClustSim.ACF.cmd` )
$cmd stats.$subj+tlrc stats.${subj}_REML+tlrc


# ---------------------------------------------------------
# QC: compute correlations with spherical ~averages
@radial_correlate -nfirst 0 -polort 3 -do_clean yes                      \
                  -rdir radcor.pb05.regress                              \
                  all_runs.$subj+tlrc.HEAD errts.${subj}_REML+tlrc.HEAD

# ========================= auto block: QC_review ==========================
# generate quality control review scripts and HTML report

# generate a review script for the unprocessed EPI data
gen_epi_review.py -script @epi_review.$subj \
    -dsets pb00.$subj.r*.tcat+orig.HEAD

# -------------------------------------------------
# generate scripts to review single subject results
# (try with defaults, but do not allow bad exit status)

# write AP uvars into a simple txt file
cat << EOF > out.ap_uvars.txt
  mot_limit       : 0.3
  copy_anat       : T1+orig.HEAD
  mask_dset       : full_mask.$subj+tlrc.HEAD
  template        : MNI152_2009_template_SSW.nii.gz
  ss_review_dset  : out.ss_review.$subj.txt
  vlines_tcat_dir : vlines.pb00.tcat
EOF

# and convert the txt format to JSON
cat out.ap_uvars.txt | afni_python_wrapper.py -eval "data_file_to_json()" \
  > out.ap_uvars.json

# initialize gen_ss_review_scripts.py with out.ap_uvars.json
gen_ss_review_scripts.py -exit0        \
    -init_uvars_json out.ap_uvars.json \
    -write_uvars_json out.ss_review_uvars.json

# ========================== auto block: finalize ==========================

# remove temporary files
\rm -fr rm.* Segsy

# if the basic subject review script is here, run it
# (want this to be the last text output)
if ( -e @ss_review_basic ) then
    ./@ss_review_basic |& tee out.ss_review.$subj.txt

    # generate html ss review pages
    # (akin to static images from running @ss_review_driver)
    apqc_make_tcsh.py -review_style pythonic -subj_dir . \
        -uvar_json out.ss_review_uvars.json
    apqc_make_html.py -qc_dir QC_$subj

    echo "\nconsider running: \n"
    echo "   afni_open -b $subj.results/QC_$subj/index.html"
    echo ""
endif

# return to parent directory (just in case...)
cd ..

echo "execution finished: `date`"

An interesting thing happened, for another subject collected on the same day-sub02, the same script was able to be run in complete. So we really wanna understand how to solve this problem.

Thanks,
Qianqian Wu

[ptaylor]

Hi, Qianqian-

Thanks for posting this, but this the processing script ("proc" script) that afni_proc.py creates.

It actually usually has a comment at the bottom that is a copy of the afni_proc.py command itself, but that hasn't been pasted here. It would be helpful if you could copy+paste the full afni_proc.py command still.

The "matrix condition errors" means that there is collinearity in the regressors. This can happen from a couple different root causes. Is there a file called "out.cormat_warn.txt" that was created when you ran afni_proc.py? That woudl hold correlation matrix warning info.

--pt

[qianqian]

Hi, pt~
I'm very sorry, here are our afni_proc.py command

#!/bin/bash
# subjNameT=(01)
# for subN in "${subjNameT[*]}"
# do

afni_proc.py -subj_id sub01 -script proc.sub01 -scr_overwrite                                 \
 -blocks tshift align tlrc volreg blur mask scale regress -copy_anat                \
 /media/linux/Z/sub01/to3dfile/T1+orig.HEAD      \
 -dsets                                                                             \
 /media/linux/Z/sub01/EPI/sub01_haptical_run1+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run2+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run3+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run4+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run5+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run6+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run7+orig.HEAD         \
 /media/linux/Z/sub01/EPI/sub01_haptical_run8+orig.HEAD         \
 -tcat_remove_first_trs 0 -align_opts_aea -giant_move -tlrc_base MNI152_2009_template_SSW.nii.gz                          \
 -tlrc_NL_warp                                                                      \
 -tlrc_NL_warped_dsets                                                              \
 /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01.nii        \
 /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01.aff12.1D   \
 /media/linux/Z/sub01/anat/sub01_SSwarper/anatQQ.sub01_WARP.nii   \
 -volreg_align_to MIN_OUTLIER -volreg_align_e2a                                     \
 -volreg_tlrc_warp -blur_size 4.0                                                   \
 -regress_stim_times                                                                \
 /media/linux/Z/sub01/Stim_time/OpenLeftSmall.txt    \
 /media/linux/Z/sub01/Stim_time/OpenRightSmall.txt   \
 /media/linux/Z/sub01/Stim_time/OpenLeftLarge.txt    \
 /media/linux/Z/sub01/Stim_time/OpenRightLarge.txt   \
 /media/linux/Z/sub01/Stim_time/GraspLeftSmall.txt   \
 /media/linux/Z/sub01/Stim_time/GraspRightSmall.txt  \
 /media/linux/Z/sub01/Stim_time/GraspLeftLarge.txt   \
 /media/linux/Z/sub01/Stim_time/GraspRightLarge.txt  \
 -regress_stim_labels OpenLeftSmall  OpenRightSmall  OpenLeftLarge  OpenRightLarge  GraspLeftSmall GraspRightSmall GraspLeftLarge GraspRightLarge \
 -regress_basis 'BLOCK(12,1)'                                                       \
 -regress_anaticor                                                                  \
 -regress_reml_exec                                                                 \
 -regress_censor_motion 0.3                                                         \
 -regress_motion_per_run                                                            \
 -regress_opts_3dD                                                                  \
 -gltsym 'SYM: OpenRightSmall -OpenLeftSmall' -glt_label 1 OS_R-L                   \
 -gltsym 'SYM: OpenRightLarge -OpenLeftLarge' -glt_label 2 OB_R-L                   \
 -gltsym 'SYM: GraspRightSmall -GraspLeftSmall' -glt_label 3 GS_R-L                 \
 -gltsym 'SYM: GraspRightLarge -GraspLeftLarge' -glt_label 4 GB_R-L                 \
 -gltsym 'SYM: OpenRightSmall  +OpenRightLarge  -OpenLeftLarge  -OpenLeftSmall' -glt_label 5 O2_R-L   \
 -gltsym 'SYM: GraspRightSmall +GraspRightLarge -GraspLeftLarge -GraspLeftSmall' -glt_label 6 G2_R-L   \
 -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall +GraspRightLarge' -glt_label 7 Grasp_all   \
 -gltsym 'SYM: OpenLeftSmall +OpenLeftLarge +OpenRightSmall +OpenRightLarge' -glt_label 8 Open_all   \
 -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall +GraspRightLarge +OpenLeftSmall +OpenLeftLarge +OpenRightSmall +OpenRightLarge' -glt_label 9 ALL_all   \
 -regress_compute_fitts                                                             \
 -regress_make_ideal_sum                                                            \
 sum_ideal.1D  -regress_est_blur_epits                                              \
 -regress_est_blur_errts                                                            

and the contents of "out.cormat_warn.txt"

Warnings regarding Correlation Matrix: X.xmat.1D

 severity   correlation   cosine  regressor pair
 --------   -----------   ------  ----------------------------------------
 medium:      -0.486      -0.485  (32 vs. 169)     OpenLeftSmall#0  vs.  mot_demean_r02[3]#0
 medium:       0.475       0.474  (127 vs. 202)  GraspRightSmall#15  vs.  mot_demean_r08[0]#0
 medium:       0.466       0.464  (127 vs. 203)  GraspRightSmall#15  vs.  mot_demean_r08[1]#0
 medium:      -0.459      -0.458  (96 vs. 192)    GraspLeftSmall#0  vs.  mot_demean_r06[2]#0
 medium:      -0.454      -0.453  (103 vs. 194)    GraspLeftSmall#7  vs.  mot_demean_r06[4]#0
 medium:       0.438       0.437  (63 vs. 178)   OpenRightSmall#15  vs.  mot_demean_r04[0]#0
 medium:      -0.436      -0.435  (96 vs. 190)    GraspLeftSmall#0  vs.  mot_demean_r06[0]#0
 medium:      -0.423      -0.422  (96 vs. 195)    GraspLeftSmall#0  vs.  mot_demean_r06[5]#0
 medium:      -0.418      -0.417  (151 vs. 186)   GraspRightLarge#7  vs.  mot_demean_r05[2]#0
 medium:       0.414       0.413  (39 vs. 168)     OpenLeftSmall#7  vs.  mot_demean_r02[2]#0
 medium:       0.411       0.410  (104 vs. 198)    GraspLeftSmall#8  vs.  mot_demean_r07[2]#0

Thanks,
Qianqian Wu

[rickr]

Hi Qianqian,

These X.xmat.1D warnings do not seem to match the afni_proc.py command. For example GraspRightSmall#15 means at least 16 regressors exist for that GraspRightSmall condition. Did your original regression use TENT(), which were then changed to BLOCK(12,1)?

The warnings show that there are at least 167 regressors of interest, aside from motion or polort, while the AP command shows only 8. That suggests the basis function was previously TENT(0,21,22), or something along those lines. What was it?

It's good to know that there are 12s blank time intervals, as well.

• rick

[qianqian]

Hi rickr~
Thank you very much for your answer!
In fact, unlike the slow event related design previously asked, this is a new experimental design (block), so I used the regression function block. Actually, I'm not quite sure where TENT would be used, since I've set up the regress basis=block（12,1) in afni_proc.py. Perhaps I should check which steps I used the TENT function in the previous steps.
thanks
Qianqian Wu

[qianqian]

Hi, pt and rickr
Because we have 16 blocks for each condition, we get the sub-brick as shown in the image below.

But now we want to average the 16 conditions and then get one sub-brick for each condition, how should we do that? Should we using 3dmaskave or 3dmaskdump? Because I’m not familiar with AFNI codes, so I will really appreciate it if you can show me a sample script.
Thanks,
Qianqian Wu

[rickr]

Hi Qianqian,

What you are showing in this image matches the (most recent) warnings, above, but not the afni_proc.py command that you showed above.

The afni_proc.py command that I responded to above should actually give what you are asking for: 8 main betas of interest, all averaged across the trials.

I suggest that you copy the above afni_proc.py script to a new directory, and run it from there (the inputs seem to have full paths, which makes it portable that way). It should produce the overall results that you are asking for.

• rick

[qianqian]

Hi rickr
Thank you for your response.
I'm not sure if my understanding is correct. The reason for the error is that I didn't use the correct regression. I checked my steps today and still don't know where I mixed regression. So according to your suggestion, should I need to change BLOCK (12,1) in my afni_proc.py to TENT (0,12,7)？
I'm very confused about this, so thank you even more for your help~
Qianqian Wu

[ptaylor]

Hi, Qianqian-

The outputs here look like they came from processing with TENT(), but your provided afni_proc.py command above in the current thread has BLOCK(). So, it would first be good to match your afni_proc.py command with the output, to help address what is happening.

In this earlier thread with Gang and Rick, it looks like you moved your afni_proc.py processing to use TENT() (and Gang did mention there might still be potential for a collinearity issue). Is it possible that the outputs shown here came from the results of that processing?

Some things that might help clarify here:

• At the bottom of the utilized proc script, there is a comment containing the afni_proc.py command that created it. In the posted proc script above, it wasn't included, but I do see "BLOCK()" used in 3dDeconvolve.
• in the output stats file label snapshot, having so many Coef+Tstat pairs with the same prefix but then just changing "#0", "#1", "#2", "#3", etc. is a hallmark of running TENTS(), not "BLOCK".
• afni_proc.py will not overwrite preexisting results directories or proc scripts---those have to be removed before rerunning a processing. So, when rerunning, it is good to use another name or move preexisting things, so there is no confusion.

--pt

[qianqian]

Hi pt~
I' m so sorry for pasting the wrong contents of "out.cormat_warn.txt"
Here's the right one.

Warnings regarding Correlation Matrix: X.xmat.1D

  severity   correlation   cosine  regressor pair
  --------   -----------   ------  ----------------------------------------
  high:         1.000       1.000  (143 vs. 159)   GraspLeftLarge#15  vs.  GraspRightLarge#15
  high:         1.000       1.000  (142 vs. 158)   GraspLeftLarge#14  vs.  GraspRightLarge#14
  high:         1.000       1.000  (141 vs. 157)   GraspLeftLarge#13  vs.  GraspRightLarge#13
  high:         1.000       1.000  (140 vs. 156)   GraspLeftLarge#12  vs.  GraspRightLarge#12
  high:         1.000       1.000  (139 vs. 155)   GraspLeftLarge#11  vs.  GraspRightLarge#11
  high:         1.000       1.000  (138 vs. 154)   GraspLeftLarge#10  vs.  GraspRightLarge#10
  high:         1.000       1.000  (137 vs. 153)    GraspLeftLarge#9  vs.  GraspRightLarge#9
  high:         1.000       1.000  (136 vs. 152)    GraspLeftLarge#8  vs.  GraspRightLarge#8
  high:         1.000       1.000  (135 vs. 151)    GraspLeftLarge#7  vs.  GraspRightLarge#7
  high:         1.000       1.000  (134 vs. 150)    GraspLeftLarge#6  vs.  GraspRightLarge#6
  high:         1.000       1.000  (133 vs. 149)    GraspLeftLarge#5  vs.  GraspRightLarge#5
  high:         1.000       1.000  (132 vs. 148)    GraspLeftLarge#4  vs.  GraspRightLarge#4
  high:         1.000       1.000  (131 vs. 147)    GraspLeftLarge#3  vs.  GraspRightLarge#3
  high:         1.000       1.000  (130 vs. 146)    GraspLeftLarge#2  vs.  GraspRightLarge#2
  high:         1.000       1.000  (129 vs. 145)    GraspLeftLarge#1  vs.  GraspRightLarge#1
  high:         1.000       1.000  (128 vs. 144)    GraspLeftLarge#0  vs.  GraspRightLarge#0
  medium:       0.503       0.501  (55 vs. 171)    OpenRightSmall#7  vs.  mot_demean_r02[5]#0
  medium:       0.502       0.500  (73 vs. 182)     OpenLeftLarge#9  vs.  mot_demean_r04[4]#0
  medium:      -0.471      -0.470  (104 vs. 206)    GraspLeftSmall#8  vs.  mot_demean_r08[4]#0
  medium:      -0.465      -0.464  (112 vs. 195)   GraspRightSmall#0  vs.  mot_demean_r06[5]#0

[ptaylor]

Hi, Qianqian-

OK, that does look more in line with the fatal error for processing. Do you have any update on the afni_proc.py command, as well, that has TENT() in it, and so would accompany this processing?

--pt

[qianqian]

Hi pt
Thank you very much for your answer!
I didn't update my afni_proc.py in BLOCK design.The reason for the previous paste error is most likely that I got the wrong folder, not in BLOCK's folder but in the previous TENT's folder of results. What I pasted just now is the result of the BLOCK
here are our afni_proc.py command for BLOCK

#!/bin/bash
# subjNameT=(01)
# for subN in "${subjNameT[*]}"
# do

afni_proc.py -subj_id sub03 -script proc.sub03 -scr_overwrite                                 \
 -blocks tshift align tlrc volreg blur mask scale regress -copy_anat                \
 /media/linux/Z/analy/data/sub03/to3dfile/T1+orig.HEAD      \
 -dsets                                                                             \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run1+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run2+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run3+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run4+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run5+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run6+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run7+orig.HEAD         \
 /media/linux/Z/analy/data/sub03/EPI/sub03_haptical_run8+orig.HEAD         \
 -tcat_remove_first_trs 0 -align_opts_aea -giant_move -tlrc_base MNI152_2009_template_SSW.nii.gz                          \
 -tlrc_NL_warp                                                                      \
 -tlrc_NL_warped_dsets                                                              \
 /media/linux/Z/analy/data/sub03/anat/sub03_SSwarper/anatQQ.sub03.nii        \
 /media/linux/Z/analy/data/sub03/anat/sub03_SSwarper/anatQQ.sub03.aff12.1D   \
 /media/linux/Z/analy/data/sub03/anat/sub03_SSwarper/anatQQ.sub03_WARP.nii   \
 -volreg_align_to MIN_OUTLIER -volreg_align_e2a                                     \
 -volreg_tlrc_warp -blur_size 4.0                                                   \
 -regress_stim_times                                                                \
 /media/linux/Z/analy/data/sub03/Stim_time/OpenLeftSmall.txt    \
 /media/linux/Z/analy/data/sub03/Stim_time/OpenRightSmall.txt   \
 /media/linux/Z/analy/data/sub03/Stim_time/OpenLeftLarge.txt    \
 /media/linux/Z/analy/data/sub03/Stim_time/OpenRightLarge.txt   \
 /media/linux/Z/analy/data/sub03/Stim_time/GraspLeftSmall.txt   \
 /media/linux/Z/analy/data/sub03/Stim_time/GraspRightSmall.txt  \
 /media/linux/Z/analy/data/sub03/Stim_time/GraspLeftLarge.txt   \
 /media/linux/Z/analy/data/sub03/Stim_time/GraspRightLarge.txt  \
 -regress_stim_labels OpenLeftSmall  OpenRightSmall  OpenLeftLarge  OpenRightLarge  GraspLeftSmall GraspRightSmall GraspLeftLarge GraspRightLarge \
 -regress_basis 'BLOCK(12,1)'                                                       \
 -regress_anaticor                                                                  \
 -regress_reml_exec                                                                 \
 -regress_censor_motion 0.3                                                         \
 -regress_motion_per_run                                                            \
 -regress_opts_3dD                                                                  \
 -gltsym 'SYM: GraspRightLarge -GraspRightSmall' -glt_label 1 GR_B-S                   \
 -gltsym 'SYM: GraspLeftLarge -GraspLeftSmall' -glt_label 2 GL_B-S                   \
 -gltsym 'SYM: GraspRightSmall -GraspLeftSmall' -glt_label 3 GS_R-L                 \
 -gltsym 'SYM: GraspRightLarge -GraspLeftLarge' -glt_label 4 GB_R-L                 \
 -gltsym 'SYM: OpenRightSmall  +OpenRightLarge  -OpenLeftLarge  -OpenLeftSmall' -glt_label 5 O2_R-L   \
 -gltsym 'SYM: GraspRightSmall +GraspRightLarge -GraspLeftLarge -GraspLeftSmall' -glt_label 6 G2_R-L   \
 -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall +GraspRightLarge' -glt_label 7 Grasp_all   \
 -gltsym 'SYM: OpenLeftSmall +OpenLeftLarge +OpenRightSmall +OpenRightLarge' -glt_label 8 Open_all   \
 -gltsym 'SYM: GraspLeftSmall +GraspLeftLarge +GraspRightSmall +GraspRightLarge +OpenLeftSmall +OpenLeftLarge +OpenRightSmall +OpenRightLarge' -glt_label 9 ALL_all   \
 -regress_compute_fitts                                                             \
 -regress_make_ideal_sum                                                            \
 sum_ideal.1D  -regress_est_blur_epits                                              \
 -regress_est_blur_errts                                                            

Qianqian Wu