afni.nimh.nih.gov

Hello AFNI team,

I'm currently running an analysis and I have ran into a problem. During registration afni_proc executes 3dAllineate looking for a file called warp.all.anat.aff12.1D. Unfortunately this file has not been created so the afni_proc script crashes. I was wondering if anyone knows what preceding part of the afni_proc generates this file and why it may not have been generated in my analysis?

Thanks,

Steve

Function that is called with error.

3dAllineate -source anat.uni+orig -master anat_final.AH740+tlrc -final wsinc5 -1Dmatrix_apply warp.all.anat.aff12.1D -prefix anat_w_skull_warped
++ 3dAllineate: AFNI version=AFNI_2011_12_21_1014 (Apr 15 2015) [64-bit]
++ Authored by: Zhark the Registrator
** FATAL ERROR: Can't read -1Dmatrix_apply 'warp.all.anat.aff12.1D'
** Program compile date = Apr 15 2015

AFNI_Proc I am running

#!/bin/tcsh -xef

echo "auto-generated by afni_proc.py, Tue Apr 21 10:30:14 2015"
echo "(version 4.37, April 9, 2015)"
echo "execution started: `date`"

# execute via :
# tcsh -xef afniProc.sh |& tee output.afniProc.sh

# =========================== 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 1 Apr 2015
if ( $status ) then
echo "** this script requires newer AFNI binaries (than 1 Apr 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 = AH740
endif

# assign output directory name
set output_dir = /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/MIDTask//subAverage/

# 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 2`)

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

# copy stim files into stimulus directory
cp \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/GainHi.txt \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/GainLow.txt \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/LoseHi.txt \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/LoseLow.txt \
$output_dir/stimuli

# copy anatomy to results dir
3dcopy \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/anat.uni+orig \
$output_dir/anat.uni

# copy over the external volreg base
3dbucket -prefix $output_dir/external_volreg_base \
'/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig.HEAD[0]'

# copy over the external align_epi_anat.py EPI volume
3dbucket -prefix $output_dir/ext_align_epi \
'/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig.HEAD[0]'

# ============================ auto block: tcat ============================
# apply 3dTcat to copy input dsets to results dir, while
# removing the first 4 TRs
3dTcat -prefix $output_dir/pb00.$subj.r01.tcat \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig'[4..$]'
3dTcat -prefix $output_dir/pb00.$subj.r02.tcat \
/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r2+orig'[4..$]'

# and make note of repetitions (TRs) per run
set tr_counts = ( 221 221 )

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


# ========================== auto block: outcount ==========================
# data check: 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

# censor outlier TRs per run, ignoring the first 0 TRs
# - censor when more than 0.05 of automask voxels are outliers
# - step() defines which TRs to remove via censoring
1deval -a outcount.r$run.1D -expr "1-step(a-0.05)" > rm.out.cen.r$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

# catenate outlier censor files into a single time series
cat rm.out.cen.r*.1D > outcount_${subj}_censor.1D

# ================================= 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

# ================================= align ==================================
# a2e: align anatomy to EPI registration base
# (new anat will be aligned and stripped, anat.uni_al_keep+orig)
align_epi_anat.py -anat2epi -anat anat.uni+orig \
-suffix _al_keep \
-epi ext_align_epi+orig -epi_base 0 \
-epi_strip 3dAutomask \
-volreg off -tshift off

# ================================== tlrc ==================================
# warp anatomy to standard space
@auto_tlrc -base TT_N27+tlrc -input anat.uni_al_keep+orig -no_ss

# store forward transformation matrix in a text file
cat_matvec anat.uni_al_keep+tlrc::WARP_DATA -I > warp.anat.Xat.1D

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

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

# register and warp
foreach run ( $runs )
# register each volume to the base
3dvolreg -verbose -zpad 1 -base external_volreg_base+orig \
-1Dfile dfile.r$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 and tlrc transformations
cat_matvec -ONELINE \
anat.uni_al_keep+tlrc::WARP_DATA -I \
mat.r$run.vr.aff12.1D > mat.r$run.warp.aff12.1D

# apply catenated xform : volreg and tlrc
3dAllineate -base anat.uni_al_keep+tlrc \
-input pb01.$subj.r$run.tshift+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 1.75 \
-prefix rm.epi.nomask.r$run

# warp the all-1 dataset for extents masking
3dAllineate -base anat.uni_al_keep+tlrc \
-input rm.epi.all1+orig \
-1Dmatrix_apply mat.r$run.warp.aff12.1D \
-mast_dxyz 1.75 -final NN -quiet \
-prefix rm.epi.1.r$run

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

# if there was an error, exit so user can see
if ( $status ) exit

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

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

# -----------------------------------------
# warp anat follower datasets (affine)
#3dAllineate -source anat.uni+orig -master anat_final.$subj+tlrc \
# -final wsinc5 -1Dmatrix_apply warp.all.anat.aff12.1D\
# -prefix anat_w_skull_warped

# ================================== blur ==================================
# blur each volume of each run
foreach run ( $runs )
3dmerge -1blur_fwhm 6 -doall -prefix pb03.$subj.r$run.blur \
pb02.$subj.r$run.volreg+tlrc
end

# ================================== mask ==================================
# create 'full_mask' dataset (union mask)
foreach run ( $runs )
3dAutomask -dilate 1 -prefix rm.mask_r$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 anat.uni_al_keep+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 overlaps between anat and EPI masks
3dABoverlap -no_automask full_mask.$subj+tlrc mask_anat.$subj+tlrc \
|& tee out.mask_ae_overlap.txt

# note Pearson correlation of masks, as well
3ddot -demean full_mask.$subj+tlrc mask_anat.$subj+tlrc \
|& tee out.mask_ae_corr.txt

# ---- create group anatomy mask, mask_group+tlrc ----
# (resampled from tlrc base anat, TT_N27+tlrc)
3dresample -master full_mask.$subj+tlrc -prefix ./rm.resam.group \
-input /opt/abin/TT_N27+tlrc

# 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

# ================================= 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 2 \
-demean -write motion_demean.1D

# compute motion parameter derivatives (just to have)
1d_tool.py -infile dfile_rall.1D -set_nruns 2 \
-derivative -demean -write motion_deriv.1D

# create censor file motion_${subj}_censor.1D, for censoring motion
1d_tool.py -infile dfile_rall.1D -set_nruns 2 \
-show_censor_count -censor_prev_TR \
-censor_motion 0.3 motion_${subj}

# combine multiple censor files
1deval -a motion_${subj}_censor.1D -b outcount_${subj}_censor.1D \
-expr "a*b" > censor_${subj}_combined_2.1D

# ------------------------------
# run the regression analysis
3dDeconvolve -input pb04.$subj.r*.scale+tlrc.HEAD \
-censor censor_${subj}_combined_2.1D \
-polort 3 \
-num_stimts 10 \
-stim_times 1 stimuli/GainHi.txt 'BLOCK(2)' \
-stim_label 1 GainHi \
-stim_times 2 stimuli/GainLow.txt 'BLOCK(2)' \
-stim_label 2 GainLow \
-stim_times 3 stimuli/LoseHi.txt 'BLOCK(2)' \
-stim_label 3 LoseHi \
-stim_times 4 stimuli/LoseLow.txt 'BLOCK(2)' \
-stim_label 4 LoseLow \
-stim_file 5 motion_demean.1D'[0]' -stim_base 5 -stim_label 5 roll \
-stim_file 6 motion_demean.1D'[1]' -stim_base 6 -stim_label 6 pitch \
-stim_file 7 motion_demean.1D'[2]' -stim_base 7 -stim_label 7 yaw \
-stim_file 8 motion_demean.1D'[3]' -stim_base 8 -stim_label 8 dS \
-stim_file 9 motion_demean.1D'[4]' -stim_base 9 -stim_label 9 dL \
-stim_file 10 motion_demean.1D'[5]' -stim_base 10 -stim_label 10 dP \
-jobs 12 \
-num_glt 4 \
-gltsym 'SYM: +GainHi' \
-glt_label 1 GainHi \
-gltsym 'SYM: +GainLow' \
-glt_label 1 GainLo \
-gltsym 'SYM: +LoseHi' \
-glt_label 1 LoseHi \
-gltsym 'SYM: +LoseLow' \
-glt_label 1 LoseLo \
-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 pariwise 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.$subj pb04.$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.$subj+tlrc
3dTstat -stdev -prefix rm.noise.all errts.${subj}+tlrc
3dcalc -a rm.signal.all+tlrc \
-b rm.noise.all+tlrc \
-c full_mask.$subj+tlrc \
-expr 'c*a/b' -prefix TSNR.$subj

# ---------------------------------------------------
# compute and store GCOR (global correlation average)
# (sum of squares of global mean of unit errts)
3dTnorm -norm2 -prefix rm.errts.unit errts.${subj}+tlrc
3dmaskave -quiet -mask full_mask.$subj+tlrc rm.errts.unit+tlrc > \
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+tlrc -b gmean.errts.unit.1D -expr 'a*b' -prefix rm.DP
3dTstat -sum -prefix corr_brain rm.DP+tlrc

# 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 ideal files for fixed response stim types
1dcat X.nocensor.xmat.1D'[8]' > ideal_GainHi.1D
1dcat X.nocensor.xmat.1D'[9]' > ideal_GainLow.1D
1dcat X.nocensor.xmat.1D'[10]' > ideal_LoseHi.1D
1dcat X.nocensor.xmat.1D'[11]' > ideal_LoseLow.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

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

# -- 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 \
all_runs.$subj+tlrc"[$trs]" >> blur.epits.1D
end

# compute average blur and append
set blurs = ( `3dTstat -mean -prefix - blur.epits.1D\'` )
echo average epits blurs: $blurs
echo "$blurs # epits 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 \
errts.${subj}+tlrc"[$trs]" >> blur.errts.1D
end

# compute average blur and append
set blurs = ( `3dTstat -mean -prefix - blur.errts.1D\'` )
echo average errts blurs: $blurs
echo "$blurs # errts blur estimates" >> blur_est.$subj.1D


# add 3dClustSim results as attributes to any stats dset
set fxyz = ( `tail -1 blur_est.$subj.1D` )
3dClustSim -both -mask full_mask.$subj+tlrc -fwhmxyz $fxyz[1-3] \
-prefix ClustSim
set cmd = ( `cat 3dClustSim.cmd` )
$cmd stats.$subj+tlrc


# ================== auto block: generate review scripts ===================

# 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)
gen_ss_review_scripts.py -mot_limit 0.3 -out_limit 0.05 -exit0

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

# remove temporary files
\rm -f rm.*

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

# return to parent directory
cd ..

echo "execution finished: `date`"




# ==========================================================================
# script generated by the command:
#
# afni_proc.py -blocks tcat tshift align tlrc volreg blur mask scale regress \
# -subj_id AH740 -script afniProc.sh -scr_overwrite -out_dir \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/MIDTask//subAverage/ \
# -dsets \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig.HEAD \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r2+orig.HEAD \
# -copy_anat \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/anat.uni+orig.HEAD \
# -tcat_remove_first_trs 4 -align_epi_ext_dset \
# '/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig.HEAD[0]' \
# -volreg_base_dset \
# '/home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Data/Imaging/RawData/v1.0/1stSession/AH740/imagingFiles/func.MID.r1+orig.HEAD[0]' \
# -volreg_tlrc_warp -blur_size 6 -regress_stim_times \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/GainHi.txt \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/GainLow.txt \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/LoseHi.txt \
# /home/LIBRAD/sgreen/storage/labs/jfeinstein/Green/Experiments/Float.01/Analysis/v1.0/1stSession/AH740/Onsets/MIDTask/LoseLow.txt \
# -regress_stim_labels GainHi GainLow LoseHi LoseLow -regress_basis_multi \
# 'BLOCK(2)' 'BLOCK(2)' 'BLOCK(2)' 'BLOCK(2)' -regress_compute_fitts \
# -regress_censor_motion 0.3 -regress_censor_outliers 0.05 \
# -regress_opts_3dD -jobs 12 -num_glt 4 -gltsym 'SYM: +GainHi' -glt_label \
# 1 GainHi -gltsym 'SYM: +GainLow' -glt_label 1 GainLo -gltsym 'SYM: \
# +LoseHi' -glt_label 1 LoseHi -gltsym 'SYM: +LoseLow' -glt_label 1 \
# LoseLo -regress_make_ideal_sum sum_ideal.1D -regress_est_blur_epits \
# -regress_est_blur_errts