Single-page run-through
This reference page contains the basic commands to replicate the primary steps of the walk-through:
- creating the QC+ dataset
- generating the core metrics up to the association analysis step
That is, this does not perform all steps in the primary walk-through, e.g. reviewing data or making plots, etc, and is not a replacement for following the walk-through. Some secondary analyses are dropped, and we do not step through details of outputs, etc: see the primary walk-through for those details. This page is instead intended, as a complement to the walk-through, to show the overall flow of commands more clearly. This assumes a Unix-style command line: Windows users are advised to use a Dockerized version of Luna, in particular one that includes the JupyterLab environment which will allow the commands below to be run directly.
Step 0 : prerequisities
First, we assume the data have already been downloaded and exist in
the current folder as a subfolder called luna-grins/.
We clear any working folders:
rm -rf work
Make working folder structure:
mkdir -p work/data work/harm1 work/harm2 work/clean
Copy data (EDFs, annotations, some auxiliary files) over:
cp -r luna-grins/v2/edfs luna-grins/v2/annots luna-grins/auxiliary work/data/
Step 1 : file QC
Make initial sample list, linking EDFs and annotation files:
luna --build work/data -ext=csv,annot,eannot,xml,tsv > s1.lst
You'll see a message about an annotation file:
Warning: also found 1 annotation files without a matching EDF:
work/data/annots/F04b.annot
Fix the incorrect file name (note: here using Unix-like shell commands: replace with copy etc if using
mv work/data/annots/F04b.annot work/data/annots/F04.annot
Validate files:
luna --validate -o out.db --options slist=s1.lst
This should complain about some remaining files:
problem : [F06] corrupt EDF: expecting 370214400 but observed 370210000 bytes: work/data/edfs/F06.edf
problem : [F08] corrupt EDF, file < header size (256 bytes): work/data/edfs/F08.edf
problem : [M01] did not recognize annotation file extension: work/data/annots/M01.csv
problem : [M02] did not recognize annotation file extension: work/data/annots/M02.csv
problem : [M03] bad format for class-inst pairing: 22:00:00
problem : [M04] bad format for class-inst pairing: 22:00:00
6 of 20 observations scanned had corrupt/missing EDF/annotation files
"Fix" corrupt EDFs for F06 and F08 (by copying the originals):
cp luna-grins/v1/edfs/F06.edf luna-grins/v1/edfs/F08.edf work/data/edfs/
Reformat annotation files for M01 and M02 (commas to tabs) using sed:
sed 's/,/\t/g' < work/data/annots/M01.csv > work/data/annots/M01.tsv
sed 's/,/\t/g' < work/data/annots/M02.csv > work/data/annots/M02.tsv
Reformat annotation files for M03 and M04 (changing column order) using awk:
awk ' { print $2 , $1 , $3 } ' OFS="\t" work/data/annots/M03.tsv > xx
mv xx work/data/annots/M03.tsv
awk ' { print $2 , $1 , $3 } ' OFS="\t" work/data/annots/M04.tsv > xx
mv xx work/data/annots/M04.tsv
Rebuild the sample list:
luna --build work/data/edfs/ work/data/annots -ext=annot,eannot,xml,tsv > s1.lst
And revalidate it:
luna --validate -o out.db --options slist=s1.lst
This should give this message:
all good, no problems detected in 20 observations scanned
Next, review key EDF header information:
luna s1.lst -o out.db -s HEADERS signals
Look at EDF type, number of signals, record size and duration:
destrat out.db +HEADERS -v EDF_TYPE NS TOT_DUR_HMS REC_DUR
ID EDF_TYPE NS REC_DUR TOT_DUR_HMS
F01 EDF+D 59 1 06:58:00
F02 EDF 59 1 07:10:33
F03 EDF 59 1 07:15:24
F04 EDF 59 1 07:18:42
F05 EDF 59 1 07:28:26
F06 EDF 59 1 06:48:30
F07 EDF 59 1 07:52:06
F08 EDF 59 1 07:01:25
F09 EDF 59 1 06:29:53
F10 EDF+D 59 1 07:46:23
M01 EDF 59 1 07:18:05
M02 EDF 59 1 07:22:43
M03 EDF 59 1 07:49:00
M04 EDF 58 1 07:55:32
M05 EDF 59 1 07:46:45
M06 EDF 59 17 07:36:44
M07 EDF 59 1 08:19:20
M08 EDF 59 1 08:11:00
M09 EDF 57 1 08:08:32
M10 EDF+D 59 1 07:33:02
Changle unusual (17s) record size for M06:
luna s1.lst id=M06 -s RECORD-SIZE dur=1 edf-dir=work/data/edfs/ edf=M06-v2
mv work/data/edfs/M06-v2.edf work/data/edfs/M06.edf
Reviewing channel-level outputs and console outputs, we note that M09 already has
linked mastoid channels.
Make harmonized files (except M09 which has different referencing)
using work/data/auxiliary/cmaps which contains mappings, setting all
channels to linked-mastoid referencing and then dropping the mastods
(A1 and A2), and resampling to 128 Hz and ensuring micro-volt
units for all values:
luna s1.lst @work/data/auxiliary/cmaps skip=M09 \
-s ' RESAMPLE sr=128
uV
REFERENCE sig=${eeg} ref=A1,A2
SIGNALS drop=A1,A2
WRITE edf-dir=work/harm1 '
Make harmonized M09 (special case):
luna s1.lst @work/data/auxiliary/cmaps id=M09 -s WRITE edf-dir=work/harm1
Make new annotation files:
luna s1.lst @work/data/auxiliary/amaps -s ' WRITE-ANNOTS file=work/harm1/^.annot '
This should end with 20 new EDFs and 20 matching annotation files in the folder work/harm1/.
To make a new sample list for this revised project:
luna --build work/harm1 > harm1.lst
Check the new samples:
luna --validate --options slist=harm1.lst
all good, no problems detected in 20 observations scanned
Step 2 : signal QC
View general headers to these new files:
luna harm1.lst -o out.db -s ' HEADERS & ANNOTS '
As above, we'd see that three EDFs are discontinuous (EDF+D); we can generate statistics for these (e.g. the number of segments):
luna harm1.lst -o out.db id=F01,F10,M10 -s SEGMENTS
destrat out.db +SEGMENTS
ID NGAPS NSEGS
F01 30 30
F10 2 3
M10 2 2
Attempt to get hypnogram statistics on segments EDFs:
luna harm1.lst -o out.db id=F01,F10,M10 -s HYPNO
*** found 211 epoch(s) of 890 with conflicting spanning annotations
Repeat after aligning epochs to stage annotations, which handles the above issue and runs without warnings:
luna harm1.lst -o out.db id=F01,F10,M10 -s 'EPOCH align & HYPNO'
We'll skip the steps that detected flat/duplicated channels, recordings with unexpected EEG amplitudes, or inverted EEGs. As shown here, we needed to pull the original EDFs for four individuals, which we'll do here, first making a sample list that points to the original EDFs:x
Point to the original data:
luna --build luna-grins/v1/edfs luna-grins/v1/annots > s.lst
We then update the EDFs for four individuals w/ issues, from that original set:
luna s.lst @work/data/auxiliary/cmaps @work/data/auxiliary/amaps id=F01,F05,F10,M10 \
-s ' RESAMPLE sr=128 & uV
REFERENCE sig=${eeg} ref=A1,A2
SIGNALS drop=A1,A2
WRITE edf-dir=work/harm1
WRITE-ANNOTS file=work/harm1/^.annot '
Step 3 : staging
We next summarize the annotations available:
luna harm1.lst -o out.db -s ANNOTS
M01:
destrat out.db +ANNOTS -r ANNOT -i M01
ID ANNOT COUNT DUR
M01 Arousal 132 1534.0
M01 N1 102 3060
M01 N2 592 17760
M01 N3 61 1830
M01 R 42 1260
M01 W 79 2370
We generate hypnogram stats (including epoch-level statistics):
luna harm1.lst -o out.db -s HYPNO epoch
Primary individual-level statistics can be extracted as follows, e.g. for total sleep time and wake after sleep onset:
destrat out.db +HYPNO -v TST WASO
ID TST WASO
F01 341 39.5
F02 388.5 12
F03 284.5 125
F04 368 49.5
F05 356.5 71
F06 389 13
F07 317 145.5
F08 354 33.5
F09 365.5 23.5
F10 369 36.5
M01 398.5 34.5
M02 181 21.5
M03 408 35
M04 353 72
M05 417.5 49
M06 350.5 104.5
M07 317.5 102.5
M08 326 153.5
M09 448 25.5
M10 386.5 49.5
Single-channel SOAP (forcing all components with pc):
luna harm1.lst -o out.db -s ' SOAP sig=C3 pc=0.9 '
F02, F03, F04 and M05.
destrat out.db +SOAP -r CH -v K3
ID CH K3
F01 C3 0.8547
F02 C3 0.4624
F03 C3 0.3803
F04 C3 0.2069
F05 C3 0.8338
F06 C3 0.8238
F07 C3 0.8817
F08 C3 0.7748
F09 C3 0.8529
F10 C3 0.7940
M01 C3 0.7348
M02 C3 0.9150
M03 C3 0.7874
M04 C3 0.8798
M05 C3 0.0000
M06 C3 0.8871
M07 C3 0.8657
M08 C3 0.9132
M09 C3 0.7548
M10 C3 0.8448
For F02, we extract stages in a plain-text file:
luna harm1.lst id=F02 -s STAGE min > stg.txt
And then apply PLACE, This suggests the stage annotations are offset by 22 epochs:
luna harm1.lst id=F02 -o out.db -s PLACE sig=C3 stages=stg.txt out=new.eannot
optimal epoch offset = -22 epochs (kappa = 0.774458)
which spans 839 epochs (of 861 in the EDF, and of 861 in the input stages)
Alternatively, we can run POPS (automated staging) for all individuals, here based on a single EEG channel (C4):
luna s.lst -o out.db -s RUN-POPS sig=C4 ref=A1 path=work/data/auxiliary/pops
destrat out.db +RUN-POPS -r E -p 4 -v PP_N1 PP_N2 PP_N3 PP_R PP_W PRED | head
ID E PP_N1 PP_N2 PP_N3 PP_R PP_W PRED
F01 1 0.0010 0.0019 0.0030 0.0003 0.9938 W
F01 2 0.0011 0.0023 0.0003 0.0003 0.9960 W
F01 3 0.0013 0.0012 0.0001 0.0002 0.9972 W
F01 4 0.0018 0.0014 0.0001 0.0003 0.9964 W
F01 5 0.0010 0.0010 0.0001 0.0002 0.9976 W
F01 6 0.0015 0.0010 0.0001 0.0002 0.9972 W
F01 7 0.0026 0.0018 0.0001 0.0003 0.9952 W
F01 8 0.0030 0.0019 0.0001 0.0003 0.9947 W
F01 9 0.0203 0.0032 0.0001 0.0006 0.9758 W
Step 4 : artifacts
Make a revised project, initially copying all EDFs, then making some adjustments as needed:
cp work/harm1/*edf work/harm2/
We rescale units for F04:
luna harm1.lst id=F04 -s ' SET-HEADERS unit=mV & uV & WRITE edf-dir=work/harm2 '
We flip EEGs for F07 and F09:
luna harm1.lst id=F07,F09 -s ' FLIP & WRITE edf-dir=work/harm2 '
We add (as an empty slot) Cz for M04 (to let it be interpolated below):
luna harm1.lst id=M04 \
-s ' TRANS sig=CZ expr=" CZ = C1 "
WRITE edf-dir=work/harm2 '
We also copy all annotations:
cp luna-grins/v1/annots/* work/harm2/
We can then create a new sample list harm2.lst:
luna --build work/harm2 > harm2.lst
We perform interpolation, using the information in luna-grins/auxiliary/badchs.txt to indicate which
channels should be forced to be interpolated:
luna harm2.lst vars=luna-grins/auxiliary/badchs.txt \
-o int-out.db \
-s ' SIGNALS drop=A1,A2
TAG INTERPOLATE/0
SIGSTATS epoch
TAG .
CHEP-MASK ch-th=3
CHEP bad-channels=${badch} channels=0.3
CHEP-MASK ch-th=2
CHEP bad-channels=${badch} dump
INTERPOLATE
TAG INTERPOLATE/1
SIGSTATS epoch
WRITE edf-dir=work/clean '
We then build a post-interpolation project in work/clean/:
cp work/harm2/*.annot work/clean/
We make the final (clean) sample list c.lst:
luna --build work/clean > c.lst
Step 5a: derive metrics
After generating a cleaned dataset, we then generate metrics in out/ (Luna output databases) and res/ (selected text-based outputs from the databases in out/):
mkdir -p out res
luna c.lst -o out/hypno.db -s HYPNO epoch
destrat out/hypno.db +HYPNO > res/hypno.base
destrat out/hypno.db +HYPNO -r SS/N1,N2,N3,R,W > res/hypno.stage
destrat out/hypno.db +HYPNO -r C/1,2,3,4 > res/hypno.cycle
Using the Welch method for all channels, both spectra and band-summaries for N2 and REM:
luna c.lst -o out/welch.db \
-s ' FREEZE F1
TAG stg/N2
MASK ifnot=N2 & RE
CHEP-MASK sig=${eeg} ep-th=3,3
CHEP sig=${eeg} epochs & RE
PSD sig=${eeg} dB spectrum min=0.5 max=20
THAW F1
TAG stg/R
MASK ifnot=R & RE
CHEP-MASK sig=${eeg} ep-th=3,3
CHEP sig=${eeg} epochs & RE
PSD sig=${eeg} dB spectrum min=0.5 max=20 '
destrat out/welch.db +PSD -r F CH stg/N2 > res/spectral.welch.n2.allchs
destrat out/welch.db +PSD -r F CH stg/R > res/spectral.welch.rem.allchs
destrat out/welch.db +PSD -r B CH stg/N2 > res/spectral.welch.band.n2.allchs
destrat out/welch.db +PSD -r B CH stg/R > res/spectral.welch.band.rem.allchs
luna c.lst -o out/cycles.db \
-s ' ${z=FZ,CZ,PZ,OZ}
HYPNO annot
MASK ifnot=N2,N3 & RE
CHEP-MASK sig=${z} ep-th=3,3
CHEP sig=${z} epochs & RE
MASK ifnot=h_cycle_n1
TAG P/C1
PSD sig=${z} dB
MASK ifnot=h_cycle_n2
TAG P/C2
PSD sig=${z} dB
MASK ifnot=h_cycle_n3
TAG P/C3
PSD sig=${z} dB
MASK ifnot=h_cycle_n4
TAG P/C4
PSD sig=${z} dB '
destrat out/cycles.db +PSD \
-r CH P \
-r B/SLOW,DELTA,THETA,ALPHA,SIGMA,FAST_SIGMA,SLOW_SIGMA,BETA \
-v PSD > res/dynam.cycles
luna c.lst -o out/dynam.db \
-s ' HYPNO
MASK ifnot=N2,N3 & RE
CHEP-MASK sig=CPZ ep-th=3,3
CHEP epochs & RE
PSD sig=CPZ dynam dynam-max-cycle=4 dynam-norm-cycles=F '
destrat out/dynam.db +PSD -r CH VAR B QD > res/dynam.band.1
destrat out/dynam.db +PSD -r CH VAR B Q QD > res/dynam.band.2
luna c.lst -o out/coh-nrem.db order-signals=T \
-s ' MASK ifnot=N2 & RE
CHEP-MASK ep-th=3,3
CHEP epochs & RE
MASK random=50 & RE
PSI sig=${eeg} f-lwr=3 f-upr=19 r=2 w=4 double-entry=F '
luna c.lst -o out/coh-rem.db order-signals=T \
-s ' MASK ifnot=R & RE
CHEP-MASK ep-th=3,3
CHEP epochs & RE
MASK random=50 & RE
PSI sig=${eeg} f-lwr=3 f-upr=19 r=2 w=4 double-entry=F '
destrat out/coh-nrem.db +PSI -r CH F > res/psi1.n2
destrat out/coh-rem.db +PSI -r CH F > res/psi1.rem
destrat out/coh-nrem.db +PSI -r CH1 CH2 F > res/psi2.n2
destrat out/coh-rem.db +PSI -r CH1 CH2 F > res/psi2.rem
NREM transients (spindles/SO):
luna c.lst -o out/spindles1.db \
-s ' MASK ifnot=N2 & RE
CHEP-MASK sig=${eeg} ep-th=3,3
CHEP sig=${eeg} epochs & RE
SPINDLES sig=${eeg} fc=11,15 so mag=2 nreps=1000 '
destrat out/spindles1.db +SPINDLES -r F CH -v DENS AMP DUR FRQ NOSC CHIRP > res/spso.spindles
destrat out/spindles1.db +SPINDLES -r CH > res/spso.slowosc
destrat out/spindles1.db +SPINDLES -r F CH \
-v CDENS UDENS COUPL_OVERLAP_Z COUPL_MAG_Z > res/spso.coupl
luna c.lst vars=work/data/auxiliary/master.txt cen=C3,C4 th=3 \
mpath=work/data/auxiliary/models/ \
-o out/pad.db < work/data/auxiliary/models/m1-adult-age-luna.txt
destrat out/pad.db +PREDICT > res/pad.base
Step 5b: association analysis
We should now have a set of key metrics in res/ from the above steps:
ls res/
hypno.base
hypno.stage
hypno.cycle
dynam.cycles
dynam.band.1
dynam.band.2
psi1.n2
psi1.rem
psi2.n2
psi2.rem
spectral.welch.n2.allchs
spectral.welch.rem.allchs
spectral.welch.band.n2.allchs
spectral.welch.band.rem.allchs
spso.spindles
spso.slowosc
spso.coupl
pad.base
Different files are stratified by different factors, in long-format (i.e. repeated measures on different rows). The GPA module of Luna can combine all these easily and
perform association analysis. First just for N2 power metrics (spectral.welch.n2.allchs) linked with age/sex information (work/data/auxiliary/master.txt).
We first set some variables and make a working folder gpa/:
dvs="res/spectral.welch.n2.allchs|psd|CH|F"
ivs="work/data/auxiliary/master.txt|demo"
mkdir -p gpa
We make the binary file gpa/b.n2.spec with --gpa-prep:
echo " inputs=${dvs},${ivs}
vars=PSD,age,male
dat=gpa/b.n2.spec " | luna --gpa-prep > gpa/manifest.n2.spec
We then run association with --gpa, testing for sex differences controlling for age:
luna --gpa -o out/gpa-n2-spec.db \
--options dat=gpa/b.n2.spec X=male Z=age nreps=10000
1649 (prop = 0.3662) significant at nominal p < 0.05
573 (prop = 0.127249) significant at FDR p < 0.05
59 (prop = 0.0131024) significant after empirical family-wise type I error control p < 0.05
We can see which variables are significant, e.g. with correceted empirical p-values p < 0.01:
destrat out/gpa-n2-spec.db +GPA -r X Y | awk ' $7 < 0.01 ' | wc
destrat out/gpa-n2-spec.db +GPA -r X Y -v STRAT BETA P P_FDR EMPADJ -p 4 | awk ' $4 < 0.02 '
ID X Y EMPADJ P P_FDR STRAT
. male PSD_CH_P7_F_3 0.0165 0.0000 0.0130 CH=P7;F=3
. male PSD_CH_P7_F_3.25 0.0182 0.0001 0.0130 CH=P7;F=3.25
. male PSD_CH_P7_F_14.25 0.0162 0.0000 0.0130 CH=P7;F=14.25
. male PSD_CH_P7_F_14.5 0.0121 0.0000 0.0130 CH=P7;F=14.5
. male PSD_CH_P7_F_14.75 0.0150 0.0000 0.0130 CH=P7;F=14.75
. male PSD_CH_P5_F_14.5 0.0121 0.0000 0.0130 CH=P5;F=14.5
. male PSD_CH_P5_F_14.75 0.0123 0.0000 0.0130 CH=P5;F=14.75
. male PSD_CH_P5_F_15 0.0153 0.0000 0.0130 CH=P5;F=15
. male PSD_CH_P1_F_14.75 0.0176 0.0001 0.0130 CH=P1;F=14.75
. male PSD_CH_P1_F_15 0.0172 0.0001 0.0130 CH=P1;F=15
. male PSD_CH_PO3_F_14.75 0.0165 0.0001 0.0130 CH=PO3;F=14.75
. male PSD_CH_PO3_F_15 0.0165 0.0000 0.0130 CH=PO3;F=15
. male PSD_CH_OZ_F_14.75 0.0154 0.0000 0.0130 CH=OZ;F=14.75
. male PSD_CH_OZ_F_15 0.0141 0.0000 0.0130 CH=OZ;F=15
. male PSD_CH_OZ_F_15.25 0.0163 0.0000 0.0130 CH=OZ;F=15.25
We can alternatively run CPT to perform cluster-based association:
luna --cpt -o out/cpt-n2-spec.db \
--options dv-file=res/spectral.welch.n2.allchs dv=PSD \
iv-file=work/data/auxiliary/master.txt iv=male covar=age \
th-freq=0.5 th-spatial=0.5 th-cluster=3 \
nreps=10000
2 clusters significant at corrected empirical P<0.05
destrat out/cpt-n2-spec.db +CPT -r K
ID K N P SEED
. 1 367 0.0169 P5~14.5~0~PSD
. 2 271 0.0285 P7~3~0~PSD
Finally, we can run GPA for multiple domains, using the pre-defined specs.json file to specify which files/variables/factors to use. We first make
the binary file:
luna --gpa-prep --options dat=gpa/b.dat \
spec=work/data/auxiliary/specs.json > gpa/manifest
preparing inputs...
++ res/dynam.band.1: read 20 indivs, 2 base vars --> 140 expanded vars
++ res/dynam.band.2: read 20 indivs, 1 base vars --> 500 expanded vars
++ res/dynam.cycles: read 20 indivs, 1 base vars --> 128 expanded vars
++ res/hypno.base: read 20 indivs, 11 base vars --> 11 expanded vars
++ res/hypno.cycle: read 20 indivs, 3 base vars --> 12 expanded vars
++ res/hypno.stage: read 20 indivs, 7 base vars --> 35 expanded vars
++ res/pad.base: read 20 indivs, 1 base vars --> 1 expanded vars
++ res/psi1.n2: read 20 indivs, 1 base vars --> 513 expanded vars
++ res/psi1.rem: read 20 indivs, 1 base vars --> 513 expanded vars
++ res/psi2.n2: read 20 indivs, 1 base vars --> 14364 expanded vars
++ res/psi2.rem: read 20 indivs, 1 base vars --> 14364 expanded vars
++ res/spectral.welch.band.n2.allchs: read 20 indivs, 1 base vars --> 570 expanded vars
++ res/spectral.welch.band.rem.allchs: read 20 indivs, 1 base vars --> 570 expanded vars
++ res/spectral.welch.n2.allchs: read 20 indivs, 1 base vars --> 4503 expanded vars
++ res/spectral.welch.rem.allchs: read 20 indivs, 1 base vars --> 4503 expanded vars
++ res/spso.coupl: read 20 indivs, 4 base vars --> 456 expanded vars
++ res/spso.slowosc: read 20 indivs, 8 base vars --> 456 expanded vars
++ res/spso.spindles: read 20 indivs, 6 base vars --> 684 expanded vars
++ work/data/auxiliary/master.txt: read 20 indivs, 2 base vars --> 2 expanded vars
writing binary data (20 obs, 42325 variables) to gpa/b.dat
Second, we perform the actual association analyses, with kNN imputation of missing data:
luna --gpa -o out/gpa-full.db \
--options dat=gpa/b.dat knn=3 X=male Z=age nreps=10000
read 20 individuals and 42325 variables from gpa/b.dat
selected 1 X vars & 1 Z vars, implying 42323 Y vars
6208 (prop = 0.146688) significant at nominal p < 0.05
233 (prop = 0.00550554) significant at FDR p < 0.05
8 (prop = 0.000189031) significant after empirical family-wise type I error control p < 0.05
We can review the "hits", e.g. with empirical p-value less than 0.05:
destrat out/gpa-full.db +GPA -r X Y -p 3 | awk ' NR == 1 || $7 < 0.05 '
ID X Y B BASE EMP EMPADJ GROUP N P P_FDR STRAT T
. male PSI_stg_N2_CH1_AF3_CH2_FPZ_F_13 -1.664 PSI 0.000 0.036 psi 20 0.000 0.014 CH1=AF3;CH2=FPZ;F=13;stg=N2 -6.876
. male PSI_stg_N2_CH1_F3_CH2_Fp2_F_13 -1.693 PSI 0.000 0.034 psi 20 0.000 0.014 CH1=F3;CH2=Fp2;F=13;stg=N2 -6.909
. male PSI_stg_N2_CH1_F5_CH2_FPZ_F_13 -1.746 PSI 0.000 0.004 psi 20 0.000 0.007 CH1=F5;CH2=FPZ;F=13;stg=N2 -8.087
. male PSI_stg_N2_CH1_F5_CH2_Fp2_F_13 -1.725 PSI 0.000 0.014 psi 20 0.000 0.014 CH1=F5;CH2=Fp2;F=13;stg=N2 -7.414
. male PSI_stg_N2_CH1_F7_CH2_FPZ_F_13 -1.755 PSI 0.000 0.005 psi 20 0.000 0.007 CH1=F7;CH2=FPZ;F=13;stg=N2 -8.060
. male PSI_stg_N2_CH1_FC5_CH2_Fp2_F_13 -1.694 PSI 0.000 0.034 psi 20 0.000 0.014 CH1=FC5;CH2=Fp2;F=13;stg=N2 -6.909
. male PSI_stg_N2_CH1_FPZ_CH2_FT7_F_13 1.699 PSI 0.000 0.027 psi 20 0.000 0.014 CH1=FPZ;CH2=FT7;F=13;stg=N2 7.046
. male PSI_stg_N2_CH1_CP1_CH2_F8_F_17 1.679 PSI 0.000 0.036 psi 20 0.000 0.014 CH1=CP1;CH2=F8;F=17;stg=N2 6.873
We're all done as far as this run-through goes. Please (re)visit the original pages of the walk-through if things aren't clear; you can also see additional steps and checks implemented there including visualizing outputs.