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daisyburr

September 26, 2017 01:01PM

Registered: 7 years ago
Posts: 44

Actually i fixed this, I just needed to execute it in tcsh.

So the output of sorting:
sort -n stim_results/NSD_sums | head -1
0.247900 = 0.0822 + 0.0820 + 0.0837 : iteration 0076, seed 1234643

Does this mean iteration 76 had the highest efficiency?

Output of:
timing_tool.py -multi_stim_dur 5 5 5 -multi_timing stimes.0001_*.1D -multi_show_isi_stats
is this:
ISI statistics (3 elements) :

total per run
------ ------------------------------
total time 2190.4 725.9 735.2 729.3
total time: stim 675.0 225.0 225.0 225.0
total time: rest 1515.4 500.9 510.2 504.3

rest: total isi 1439.6 477.9 481.1 480.6
rest: pre stim 75.8 23.0 29.1 23.7
rest: post stim 0.0 0.0 0.0 0.0

num stimuli 135 45 45 45

min mean max stdev
------- ------- ------- -------
rest: pre-stim 23.000 25.267 29.100 3.338
rest: post-stim 0.000 0.000 0.000 0.000

rest: run #0 ISI 2.000 10.861 45.600 8.890
rest: run #1 ISI 2.000 10.934 58.200 10.152
rest: run #2 ISI 2.000 10.923 43.800 8.539

all runs: ISI 2.000 10.906 58.200 9.149
all runs: stimuli 5.000 5.000 5.000 0.000

and the output of:
timing_tool.py -multi_stim_dur 5 5 5 -multi_timing stimes.0001_*.1D -multi_timing_to_event_list GE:ALL -
# have 45 events in run 1
is this:
# have 45 events in run 1
# class prev_class event_time timediff duration stim_file
3 INIT 23.000 23.000 5.000 stimes.0001_03_neu.1D
1 3 32.800 4.800 5.000 stimes.0001_01_pos.1D
2 1 48.800 11.000 5.000 stimes.0001_02_neg.1D
2 2 59.000 5.200 5.000 stimes.0001_02_neg.1D
2 2 66.600 2.600 5.000 stimes.0001_02_neg.1D
3 2 73.600 2.000 5.000 stimes.0001_03_neu.1D
1 3 83.300 4.700 5.000 stimes.0001_01_pos.1D
2 1 95.200 6.900 5.000 stimes.0001_02_neg.1D
1 2 108.000 7.800 5.000 stimes.0001_01_pos.1D
1 1 122.700 9.700 5.000 stimes.0001_01_pos.1D
2 1 130.900 3.200 5.000 stimes.0001_02_neg.1D
2 2 139.800 3.900 5.000 stimes.0001_02_neg.1D
2 2 166.900 22.100 5.000 stimes.0001_02_neg.1D
1 2 176.100 4.200 5.000 stimes.0001_01_pos.1D
2 1 183.700 2.600 5.000 stimes.0001_02_neg.1D
3 2 199.900 11.200 5.000 stimes.0001_03_neu.1D
2 3 219.200 14.300 5.000 stimes.0001_02_neg.1D
1 2 226.700 2.500 5.000 stimes.0001_01_pos.1D
1 1 238.000 6.300 5.000 stimes.0001_01_pos.1D
1 1 247.100 4.100 5.000 stimes.0001_01_pos.1D
3 1 272.200 20.100 5.000 stimes.0001_03_neu.1D
3 3 296.300 19.100 5.000 stimes.0001_03_neu.1D
2 3 304.600 3.300 5.000 stimes.0001_02_neg.1D
3 2 323.100 13.500 5.000 stimes.0001_03_neu.1D
3 3 330.100 2.000 5.000 stimes.0001_03_neu.1D
3 3 349.700 14.600 5.000 stimes.0001_03_neu.1D
2 3 362.500 7.800 5.000 stimes.0001_02_neg.1D
3 2 399.200 31.700 5.000 stimes.0001_03_neu.1D
1 3 449.800 45.600 5.000 stimes.0001_01_pos.1D
3 1 467.700 12.900 5.000 stimes.0001_03_neu.1D
1 3 482.200 9.500 5.000 stimes.0001_01_pos.1D
3 1 490.600 3.400 5.000 stimes.0001_03_neu.1D
1 3 502.900 7.300 5.000 stimes.0001_01_pos.1D
2 1 521.500 13.600 5.000 stimes.0001_02_neg.1D
2 2 541.400 14.900 5.000 stimes.0001_02_neg.1D
2 2 557.100 10.700 5.000 stimes.0001_02_neg.1D
3 2 575.800 13.700 5.000 stimes.0001_03_neu.1D
3 3 593.300 12.500 5.000 stimes.0001_03_neu.1D
3 3 604.900 6.600 5.000 stimes.0001_03_neu.1D
1 3 633.300 23.400 5.000 stimes.0001_01_pos.1D
3 1 643.900 5.600 5.000 stimes.0001_03_neu.1D
1 3 651.500 2.600 5.000 stimes.0001_01_pos.1D
1 1 669.300 12.800 5.000 stimes.0001_01_pos.1D
2 1 688.700 14.400 5.000 stimes.0001_02_neg.1D
1 2 720.900 27.200 5.000 stimes.0001_01_pos.1D

# have 45 events in run 2
# class prev_class event_time timediff duration stim_file
1 INIT 29.100 29.100 5.000 stimes.0001_01_pos.1D
2 1 36.700 2.600 5.000 stimes.0001_02_neg.1D
1 2 45.200 3.500 5.000 stimes.0001_01_pos.1D
3 1 58.300 8.100 5.000 stimes.0001_03_neu.1D
2 3 76.500 13.200 5.000 stimes.0001_02_neg.1D
1 2 93.100 11.600 5.000 stimes.0001_01_pos.1D
2 1 100.900 2.800 5.000 stimes.0001_02_neg.1D
3 2 115.900 10.000 5.000 stimes.0001_03_neu.1D
2 3 136.000 15.100 5.000 stimes.0001_02_neg.1D
3 2 146.900 5.900 5.000 stimes.0001_03_neu.1D
1 3 169.100 17.200 5.000 stimes.0001_01_pos.1D
3 1 178.800 4.700 5.000 stimes.0001_03_neu.1D
2 3 186.700 2.900 5.000 stimes.0001_02_neg.1D
3 2 208.900 17.200 5.000 stimes.0001_03_neu.1D
1 3 231.800 17.900 5.000 stimes.0001_01_pos.1D
2 1 295.000 58.200 5.000 stimes.0001_02_neg.1D
1 2 302.600 2.600 5.000 stimes.0001_01_pos.1D
1 1 322.100 14.500 5.000 stimes.0001_01_pos.1D
3 1 351.600 24.500 5.000 stimes.0001_03_neu.1D
3 3 379.000 22.400 5.000 stimes.0001_03_neu.1D
3 3 388.900 4.900 5.000 stimes.0001_03_neu.1D
2 3 399.500 5.600 5.000 stimes.0001_02_neg.1D
2 2 413.700 9.200 5.000 stimes.0001_02_neg.1D
3 2 422.100 3.400 5.000 stimes.0001_03_neu.1D
3 3 437.100 10.000 5.000 stimes.0001_03_neu.1D
1 3 446.500 4.400 5.000 stimes.0001_01_pos.1D
3 1 455.400 3.900 5.000 stimes.0001_03_neu.1D
1 3 465.400 5.000 5.000 stimes.0001_01_pos.1D
2 1 475.800 5.400 5.000 stimes.0001_02_neg.1D
2 2 504.300 23.500 5.000 stimes.0001_02_neg.1D
3 2 525.400 16.100 5.000 stimes.0001_03_neu.1D
2 3 538.600 8.200 5.000 stimes.0001_02_neg.1D
1 2 553.900 10.300 5.000 stimes.0001_01_pos.1D
3 1 589.200 30.300 5.000 stimes.0001_03_neu.1D
2 3 608.700 14.500 5.000 stimes.0001_02_neg.1D
2 2 615.700 2.000 5.000 stimes.0001_02_neg.1D
2 2 627.000 6.300 5.000 stimes.0001_02_neg.1D
1 2 637.100 5.100 5.000 stimes.0001_01_pos.1D
3 1 654.000 11.900 5.000 stimes.0001_03_neu.1D
1 3 663.700 4.700 5.000 stimes.0001_01_pos.1D
1 1 671.200 2.500 5.000 stimes.0001_01_pos.1D
2 1 697.400 21.200 5.000 stimes.0001_02_neg.1D
3 2 707.800 5.400 5.000 stimes.0001_03_neu.1D
1 3 715.100 2.300 5.000 stimes.0001_01_pos.1D
1 1 730.200 10.100 5.000 stimes.0001_01_pos.1D

# have 45 events in run 3
# class prev_class event_time timediff duration stim_file
3 INIT 23.700 23.700 5.000 stimes.0001_03_neu.1D
2 3 46.000 17.300 5.000 stimes.0001_02_neg.1D
2 2 55.000 4.000 5.000 stimes.0001_02_neg.1D
2 2 64.800 4.800 5.000 stimes.0001_02_neg.1D
2 2 75.400 5.600 5.000 stimes.0001_02_neg.1D
3 2 124.200 43.800 5.000 stimes.0001_03_neu.1D
3 3 134.100 4.900 5.000 stimes.0001_03_neu.1D
2 3 150.900 11.800 5.000 stimes.0001_02_neg.1D
3 2 162.800 6.900 5.000 stimes.0001_03_neu.1D
1 3 175.800 8.000 5.000 stimes.0001_01_pos.1D
3 1 187.100 6.300 5.000 stimes.0001_03_neu.1D
1 3 203.400 11.300 5.000 stimes.0001_01_pos.1D
3 1 220.100 11.700 5.000 stimes.0001_03_neu.1D
3 3 237.700 12.600 5.000 stimes.0001_03_neu.1D
2 3 265.000 22.300 5.000 stimes.0001_02_neg.1D
1 2 280.100 10.100 5.000 stimes.0001_01_pos.1D
2 1 292.400 7.300 5.000 stimes.0001_02_neg.1D
2 2 330.600 33.200 5.000 stimes.0001_02_neg.1D
1 2 348.800 13.200 5.000 stimes.0001_01_pos.1D
1 1 367.300 13.500 5.000 stimes.0001_01_pos.1D
3 1 380.800 8.500 5.000 stimes.0001_03_neu.1D
3 3 388.400 2.600 5.000 stimes.0001_03_neu.1D
2 3 403.000 9.600 5.000 stimes.0001_02_neg.1D
2 2 421.200 13.200 5.000 stimes.0001_02_neg.1D
3 2 437.700 11.500 5.000 stimes.0001_03_neu.1D
1 3 453.600 10.900 5.000 stimes.0001_01_pos.1D
1 1 463.100 4.500 5.000 stimes.0001_01_pos.1D
1 1 487.400 19.300 5.000 stimes.0001_01_pos.1D
1 1 494.400 2.000 5.000 stimes.0001_01_pos.1D
3 1 508.700 9.300 5.000 stimes.0001_03_neu.1D
1 3 541.900 28.200 5.000 stimes.0001_01_pos.1D
3 1 552.600 5.700 5.000 stimes.0001_03_neu.1D
1 3 561.000 3.400 5.000 stimes.0001_01_pos.1D
3 1 571.700 5.700 5.000 stimes.0001_03_neu.1D
1 3 579.100 2.400 5.000 stimes.0001_01_pos.1D
3 1 589.000 4.900 5.000 stimes.0001_03_neu.1D
1 3 612.300 18.300 5.000 stimes.0001_01_pos.1D
2 1 621.800 4.500 5.000 stimes.0001_02_neg.1D
2 2 642.500 15.700 5.000 stimes.0001_02_neg.1D
2 2 653.200 5.700 5.000 stimes.0001_02_neg.1D
1 2 660.700 2.500 5.000 stimes.0001_01_pos.1D
2 1 685.300 19.600 5.000 stimes.0001_02_neg.1D
1 2 703.700 13.400 5.000 stimes.0001_01_pos.1D
2 1 712.300 3.600 5.000 stimes.0001_02_neg.1D
3 2 724.300 7.000 5.000 stimes.0001_03_neu.1D

Output of:
1d_tool.py -cormat_cutoff 0.1 -show_cormat_warnings -infile X.xmat.1D
is this:

Warnings regarding Correlation Matrix: X.xmat.1D

severity correlation cosine regressor pair
-------- ----------- ------ ----------------------------------------
medium: -0.210 0.007 (21 vs. 22) pos#0 vs. neg#0
medium: -0.197 0.016 (21 vs. 23) pos#0 vs. neu#0
medium: -0.190 0.022 (22 vs. 23) neg#0 vs. neu#0
medium: -0.150 -0.136 (15 vs. 22) Run#3Pol#1 vs. neg#0
medium: -0.123 -0.111 ( 6 vs. 21) Run#1Pol#6 vs. pos#0
medium: -0.119 -0.108 ( 2 vs. 22) Run#1Pol#2 vs. neg#0
medium: -0.103 -0.094 (19 vs. 23) Run#3Pol#5 vs. neu#0

Would you be able to provide any guidance on how to interpret these?
How should I be interpreting the NSD sums? Is iteration 76 the high efficiency? NSD sums are linear contrast sums, right?

My goal here was to identify the most efficient ITI timing and I want to compare exponential versus uniform jitters and I don't know how to set those different parameters. I also am having trouble finding the "@DesginSearch: Output" script referred to in various handouts.

Thanks!

Edited 1 time(s). Last edit at 09/26/2017 01:36PM by daisyburr.

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Subject	Author	Posted
@make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 25, 2017 04:59PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 26, 2017 09:47AM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 26, 2017 12:08PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 26, 2017 01:01PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 26, 2017 04:55PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 26, 2017 06:39PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 27, 2017 10:39AM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 27, 2017 01:47PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 27, 2017 04:20PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 28, 2017 10:43PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 29, 2017 11:41AM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 29, 2017 11:41AM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 29, 2017 12:09PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 29, 2017 12:27PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 29, 2017 12:32PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 29, 2017 12:51PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	daisyburr	September 29, 2017 01:04PM
Re: @make_random timing.py and 3dDeconvolve -nodata for ITI efficiency	rick reynolds	September 29, 2017 02:17PM