# --------------------------------------------------
# just a reminder of where we should be
cd AFNI_demos/AFNI_pamenc/AFNI_02_pamenc

# --------------------------------------------------
# peek at some single subject results
#
#   ** this subject gets dropped due to motion **

cd sub-60078
cat run.afni_proc.txt
cat out.run.afni_proc.txt
afni_open -e proc.sub-60078

ls -l sub-60078.results
cat sub-60078.results/out.ss_review*.txt
afni_open -b sub-60078.results/QC_sub-60078/index.html
cd ..

# --------------------------------------------------
# post-analysis QC and group prep

# have already generated the spreadsheet, but can do it again
# (optional)
gen_ss_review_table.py -tablefile new.ss_review_table.xls \
        -infiles sub*/s*.results/out.ss*.txt

# maybe view QC/ss_review_table.xls (as a spreadsheet), e.g.
#       open        new.ss_review_table.xls
#       libreoffice new.ss_review_table.xls

# note dropped subjects
gen_ss_review_table.py -outlier_sep space                       \
        -report_outliers 'censor fraction' GE 0.15              \
        -report_outliers 'average censored motion' GE 0.1       \
        -report_outliers 'max censored displacement' GE 8       \
        -infiles sub*/s*.results/out.ss*.txt

# --------------------------------------------------
# run group analysis (note subjects in each group)
awk '/CONTROL/ {print $1}' QC/participants.short.tsv
awk '/BIPOLAR/ {print $1}' QC/participants.short.tsv
cat QC/outliers.c.drop.subs.txt

# display the (simplified) generated script on the screen
# (e.g. paired t-test of TASK-CONTROL using all subjects)
# (could add -dset_sid_omit_list $subs_drop after each $dsets opt)
gen_group_command.py -command 3dttest++                 \
   -dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD      \
   -dsets sub-*/*.results/stats.sub*REML+tlrc.HEAD      \
   -subs_betas "TASK#0_Coef" "CONTROL#0_Coef"           \
   -subj_prefix sub-                                    \
   -options -paired

# view (in some editor) and run full script
# (restrict to BIPOLAR group, and omit dropped subjects)
afni_open -e run.ggc.9.bipolar.T-C
tcsh run.ggc.9.bipolar.T-C

# --------------------------------------------------
# before looking at results, note clustering criteria

# get ACF (blur) means for Monte Carlo cluster simulations
grep -h ACF sub*/*.results/out.ss*.txt | awk -F: '{print $2}'   \
        | 3dTstat -mean -prefix - 1D:stdin\'

# ---------------
# save time: group mask has already been created (in QC dir)
# 3dmask_tool -input sub*/*.results/mask_epi_anat*.HEAD \
#             -prefix QC/group_mask.7 -frac 0.7
# ---------------
# save time: simulations have already been run (in QC dir)
# mkdir files_ClustSim
# set params = ( `cat out.ACF.means.1D` )
# 3dClustSim -both -mask group_mask.7+tlrc -acf $params \
#    -cmd 3dClustSim.ACF.cmd -prefix files_ClustSim/ClustSim.ACF
# ---------------

cat QC/files_ClustSim/ClustSim.ACF.NN1_bisided.1D

1d_tool.py -csim_show_clustsize                         \
    -infile QC/files_ClustSim/ClustSim.ACF.NN1_bisided.1D

# add: -verb 0

# how many degrees of freedom do we have?
3dinfo group_analysis.1*/ttest*.HEAD |& grep statpar
3dinfo group_analysis.2*/ttest*.HEAD |& grep statpar

# check p=0.001 t-stat threshold (symmetric, pos/neg)
cdf -p2t fitt 0.001 24

# --------------------------------------------------
# look at group results
cd group_analysis.9.g_bipolar.paired.T-C
ls -l

# can run afni, p=0.001, cluterize bi-sided (NN1, 17), rpt, 3dclust
# (this is not yet masked)
afni

# --------------------------------------------------
# correct to get cluster-level significance
# (|t| > 3.7454, nvox >= 17)

3dClusterize -mask group_mask.7+tlrc -inset ttest++_result+tlrc \
   -idat 0 -ithr 1 -NN 1 -clust_nvox 17 -bisided -3.7454 3.7454

3dcalc -a ttest++_result+tlrc -b group_mask.7+tlrc -expr 'a*b'  \
       -prefix ttr_masked

3dClusterize -mask group_mask.7+tlrc -inset ttest++_result+tlrc \
   -idat 0 -ithr 1 -NN 1 -clust_nvox 17 -bisided -3.7454 3.7454 \
   -pref_map ttr_clust_rois

# --------------------------------------------------
# what regions do these clusters overlap?
# (e.g. in MNI_caez_ml_18+tlrc)
grep -B 1 MNI_caez_ml_18 ~/abin/AFNI_atlas_spaces.niml

whereami -omask ttr_clust_rois+tlrc -atlas CA_ML_18_MNI

3dROIstats -mask ttr_clust_rois+tlrc ttest++_result+tlrc
3dROIstats -mask ttr_clust_rois+tlrc'<1,2,3>' -nzvoxels ttest++_result+tlrc

cd ..

# ============================================================
# could also consider other group results

# consider viewing 3dttest++ -Clustim or -ETAC results
cd group_analysis.5.g_control.paried.T-C.ETAC