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afni_align_epi_anat results in very data size

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January 10, 2017 11:19AM
Dear experts,

My alignment process align_epi_anat.py results in very large data size (upto 2.8GB per volume, from the original size of ~300MB), which would crash the subsequent 3dDeconvolve process. Any suggestions are appreciated, or please point me to the right post (if this problem has been reported). Thank you in advance.

The "cmd" script:
# run afni_proc.py to create a single subject processing script
afni_proc.py -subj_id $subj \
-script proc.$subj -scr_overwrite \
-blocks tshift align volreg blur mask scale regress \
-copy_anat $top_dir/T1.nii \
-tcat_remove_first_trs 0 \
-dsets \
$top_dir/session1_raw.nii \
$top_dir/session2_raw.nii \
$top_dir/session3_raw.nii \
$top_dir/session4_raw.nii \
-align_opts_aea -giant_move \
-volreg_align_to third \
-volreg_align_e2a \
-blur_size 4.0 \
-regress_stim_times \
$top_dir/stim.negEmoReg.1D \
$top_dir/stim.negative.1D \
$top_dir/stim.neutral.1D \
-regress_stim_labels \
gEmoReg gative utral \
-regress_basis 'BLOCK(8,1)' \
-regress_censor_motion 0.3 \
-regress_opts_3dD \
-jobs 4 \
-gltsym 'SYM: gEmoReg -gative' -glt_label 1 gEmoReg-gative \
-gltsym 'SYM: gEmoReg -utral' -glt_label 2 gEmoReg-utral \
-gltsym 'SYM: gative -utral' -glt_label 3 gative-utral \
-gltsym 'SYM: 0.333*gEmoReg +0.333*gative +0.333*utral' \
-glt_label 4 mean.GGU \
-regress_make_ideal_sum sum_ideal.1D \
-regress_est_blur_epits \
-regress_est_blur_errts

3dDeconvolve output:
3dDeconvolve -input pb04.Subj01_JiaoDing.r01.scale+orig.HEAD pb04.Subj01_JiaoDing.r02.scale+orig.HEAD pb04.Subj01_JiaoDing.r03.scale+orig.HEAD pb04.Subj01_JiaoDing.r04.scale+orig.HEAD -censor motion_Subj01_JiaoDing_censor.1D -polort 4 -float -num_stimts 9 -stim_times 1 stimuli/stim.negEmoReg.1D BLOCK(8,1) -stim_label 1 gEmoReg -stim_times 2 stimuli/stim.negative.1D BLOCK(8,1) -stim_label 2 gative -stim_times 3 stimuli/stim.neutral.1D BLOCK(8,1) -stim_label 3 utral -stim_file 4 motion_demean.1D[0] -stim_base 4 -stim_label 4 roll -stim_file 5 motion_demean.1D[1] -stim_base 5 -stim_label 5 pitch -stim_file 6 motion_demean.1D[2] -stim_base 6 -stim_label 6 yaw -stim_file 7 motion_demean.1D[3] -stim_base 7 -stim_label 7 dS -stim_file 8 motion_demean.1D[4] -stim_base 8 -stim_label 8 dL -stim_file 9 motion_demean.1D[5] -stim_base 9 -stim_label 9 dP -jobs 4 -gltsym SYM: gEmoReg -gative -glt_label 1 gEmoReg-gative -gltsym SYM: gEmoReg -utral -glt_label 2 gEmoReg-utral -gltsym SYM: gative -utral -glt_label 3 gative-utral -gltsym SYM: 0.333*gEmoReg +0.333*gative +0.333*utral -glt_label 4 mean.GGU -fout -tout -x1D X.xmat.1D -xjpeg X.jpg -x1D_uncensored X.nocensor.xmat.1D -fitts fitts.Subj01_JiaoDing -errts errts.Subj01_JiaoDing -bucket stats.Subj01_JiaoDing
++ 3dDeconvolve: AFNI version=AFNI_16.0.19 (Mar 29 2016) [64-bit]
++ Authored by: B. Douglas Ward, et al.
++ current memory malloc-ated = 292,936 bytes (about 293 thousand [kilo])
++ loading dataset pb04.Subj01_JiaoDing.r01.scale+orig.HEAD pb04.Subj01_JiaoDing.r02.scale+orig.HEAD pb04.Subj01_JiaoDing.r03.scale+orig.HEAD pb04.Subj01_JiaoDing.r04.scale+orig.HEAD
++ current memory malloc-ated = 11,951,219,248 bytes (about 12 billion [giga])
++ Auto-catenated input datasets treated as multiple imaging runs
++ Auto-catenated datasets start at: 0 240 480 720
++ STAT automask has 766571 voxels (out of 3112136 = 24.6%)
++ Skipping check for initial transients
++ Input polort=4; Longest run=480.0 s; Recommended minimum polort=4 ++ OK ++
++ -stim_times using TR=2 s for stimulus timing conversion
++ -stim_times using TR=2 s for any -iresp output datasets
++ [you can alter the -iresp TR via the -TR_times option]
++ ** -stim_times NOTE ** guessing GLOBAL times if 1 time per line; LOCAL otherwise
++ ** GUESSED ** -stim_times 1 using LOCAL times
++ ** GUESSED ** -stim_times 2 using LOCAL times
++ ** GUESSED ** -stim_times 3 using LOCAL times
------------------------------------------------------------
GLT matrix from 'SYM: gEmoReg -gative':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0 0
------------------------------------------------------------
GLT matrix from 'SYM: gEmoReg -utral':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 -1 0 0 0 0 0 0
------------------------------------------------------------
GLT matrix from 'SYM: gative -utral':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 -1 0 0 0 0 0 0
------------------------------------------------------------
GLT matrix from 'SYM: 0.333*gEmoReg +0.333*gative +0.333*utral':
0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.333 0.333 0.333 0 0 0 0 0 0
++ Number of time points: 960 (before censor) ; 910 (after)
+ Number of parameters: 29 [26 baseline ; 3 signal]
++ total shared memory needed = 24,175,072,448 bytes (about 24 billion [giga])
++ current memory malloc-ated = 11,954,394,714 bytes (about 12 billion [giga])
++ mmap() memory allocated: 24,175,072,448 bytes (about 24 billion [giga])
Killed

Please let me know which process output is needed to resolve this issue, I will post in subsequent comment, this post is too long already.



Edited 1 time(s). Last edit at 01/11/2017 12:33PM by ttngu207.
Subject Author Posted

afni_align_epi_anat results in very data size

ttngu207 January 10, 2017 11:19AM

Re: afni_align_epi_anat results in very data size

rick reynolds January 10, 2017 12:10PM

Re: afni_align_epi_anat results in very data size

ttngu207 January 10, 2017 12:33PM

Re: afni_align_epi_anat results in very data size

ttngu207 January 11, 2017 01:49PM

Re: afni_align_epi_anat results in very data size

Bob Cox January 11, 2017 03:19PM

Re: afni_align_epi_anat results in very data size

rick reynolds January 12, 2017 09:06AM

Re: afni_align_epi_anat results in very data size

ttngu207 January 12, 2017 01:08PM

Re: afni_align_epi_anat results in very data size

rick reynolds January 12, 2017 01:53PM



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