Dear AFNI-community,
I’m having trouble getting 3dDeconvolve to run. It seems to hang after the “polort-only matrix condition” (see output below). The 3dDeconvolve command was created using afni_proc.py. Here is what that command looks like:
afni_proc.py -verb 2 -subj_id ${lc}_${session} -dsets ./rundata/${lc}_${session}_run*_ss+orig.HEAD \
-blocks tcat despike volreg blur mask scale regress \
-test_stim_files yes \
-volreg_interp -quintic \
-volreg_zpad 4 \
-blur_size 3 \
-mask_dilate 3 \
-mask_apply epi \
-regress_censor_motion 0.3 \
-regress_censor_outliers 0.15 \
-regress_bandpass 0.01 0.1 \
-regress_apply_mask \
-regress_apply_mot_types demean deriv \
-regress_stim_labels faces places objects scrambled \
-regress_stim_files ./stimfiles/${monkey_uc}_${session}_stim1_Faces.txt \
./stimfiles/${monkey_uc}_${session}_stim2_Places.txt \
./stimfiles/${monkey_uc}_${session}_stim3_Objects.txt \
./stimfiles/${monkey_uc}_${session}_stim4_Scrambled.txt \
-regress_opts_3dD \
-jobs 2 \
-num_glt 4 \
-gltsym 'SYM: +faces +places +objects +scrambled' -glt_label 1 all_conds \
-gltsym 'SYM: +faces -places' -glt_label 2 faces-places \
-gltsym 'SYM: +faces -objects' -glt_label 3 faces-objects \
-gltsym 'SYM: +faces -scrambled' -glt_label 4 faces-scrambled \
-progress 500 \
-regress_polort 2
It represents a single session of 1200 TRs.
I am using the most up-to-date AFNI version, on a macbook pro (late 2013) with 16GB of RAM
"afni -ver => Precompiled binary macosx_10.7_Intel_64: Jul 22 2016 (Version AFNI_16.2.05)"
I have tried the following:
1) using different platforms (different computers, linux version) - no luck. same error
2) running only the most basic options with 3dDeconvolve
3) I have tried 3dZcutup/3dZcat - and it worked once; but hasn’t worked since. Same error.
4) using fewer TRs / analysing different sessions
5) using fewer/more "-jobs"
Below is what I get (this is for running non-3dZcutup'd data. Obviously the memory requirements for the sliced data are much less)
Any thoughts? Could this be a memory issue? Are there perhaps too many (baseline) parameters?
Many thanks for any advice/tips/shared experiences!
Andrew
++ Number of time points: 1200 (before censor) ; 1194 (after)
+ Number of parameters: 784 [780 baseline ; 4 signal]
++ total shared memory needed = 2,295,718,400 bytes (about 2.3 billion [giga])
++ mmap() memory allocated: 2,295,718,400 bytes (about 2.3 billion [giga])
++ Memory required for output bricks = 2,295,718,400 bytes (about 2.3 billion [giga])
++ Wrote matrix image to file X.jpg
++ Wrote matrix values to file X.xmat.1D
++ ========= Things you can do with the matrix file =========
++ (a) Linear regression with ARMA(1,1) modeling of serial correlation:
3dREMLfit -matrix X.xmat.1D \
-input "pb04.ranger_MI00937.r01.scale+orig.HEAD pb04.ranger_MI00937.r02.scale+orig.HEAD pb04.ranger_MI00937.r03.scale+orig.HEAD pb04.ranger_MI00937.r04.scale+orig.HEAD pb04.ranger_MI00937.r05.scale+orig.HEAD pb04.ranger_MI00937.r06.scale+orig.HEAD pb04.ranger_MI00937.r07.scale+orig.HEAD pb04.ranger_MI00937.r08.scale+orig.HEAD pb04.ranger_MI00937.r09.scale+orig.HEAD pb04.ranger_MI00937.r10.scale+orig.HEAD pb04.ranger_MI00937.r11.scale+orig.HEAD pb04.ranger_MI00937.r12.scale+orig.HEAD" \
-mask full_mask.ranger_MI00937+orig -fout -tout \
-Rbuck stats.ranger_MI00937_REML -Rvar stats.ranger_MI00937_REMLvar \
-Rfitts fitts.ranger_MI00937_REML -Rerrts errts.ranger_MI00937_REML -verb
++ N.B.: 3dREMLfit command above written to file stats.REML_cmd
++ (b) Visualization/analysis of the matrix via ExamineXmat.R
++ (c) Synthesis of sub-model datasets using 3dSynthesize
++ ==========================================================
++ Wrote matrix values to file X.nocensor.xmat.1D
++ ----- Signal+Baseline matrix condition [X] (1194x784): 3.64199 ++ VERY GOOD ++
++ ----- Signal-only matrix condition [X] (1194x4): 1 ++ VERY GOOD ++
++ ----- Baseline-only matrix condition [X] (1194x780): 3.54591 ++ VERY GOOD ++
++ ----- stim_base-only matrix condition [X] (1194x744): 3.26556 ++ VERY GOOD ++
++ ----- polort-only matrix condition [X] (1194x36): 1.02839 ++ VERY GOOD ++:
