genTaskTime

• Parses a domain specific language describing a rapid event related fmri task
• Create stimulus onset files.

Designed for optimizing task timing with 3dDeconvolve -nodata as an alternative to optseq, RSFgen, and make_random_timing.py

See

• https://afni.nimh.nih.gov/pub/dist/HOWTO/howto/ht03_stim/html/stim_background.html
• http://andysbrainblog.blogspot.com/2012/06/design-efficiency.html

Install

pip3 install --user git+https://github.com/LabNeuroCogDevel/genTaskTime

# add pip python install scripts to path if not already there
which genTaskTime || { echo 'export PATH=$PATH:$HOME/.local/bin' >> ~/.bashrc && source ~/.bashrc }

# N.B. 
# - OS X might have resource file ~/.profile instead of ~/.bashrc
# - your install might be somewhere else. see:
#     python3 -m site --user-site
#     pip3 show genTaskTime --files

Usage

genTaskTime -h
genTaskTime -i 1 -o stims '<20/4> cue=[1.5](A,B); dly=[3x 3, 1x 6]; end=[1.5]'

Grammar

<totaltime/number_trials> event_name=[duration]; 
<totaltime/number_trials> event_name=[duration]; another_event_that_follows_first=[duration2]
<totaltime/number_trials> event_name=[duration]( event_type1, event_type2 * mutation1, mutation2 )
<totaltime/number_trials> event_name=[duration]( event_type1, event_type2 * mutation1, mutation2 )

Cookbook

Create cue_A and cue_B timing files with an onset (once each b/c total 2 trials) within a 20 second duration run.

<20/2> cue=[1.5](A, B)

Create stimulus onset timing files start and end. end always follows 1.5s after start. The next start is at least 3s after the previous end

<20/1> start=[1.5]; end=[3]

<60/4> cue=[1.5]( Left, Right x Near, Far )

TODO: uneven event subtypes, catch trials, nested events

<20/1> start=[1.5](3x A, 2x B)
<20/1> start=[1.5]{.3}; end=[1.5]
<20/1> start=[1.5]; dly=(short=[1.5],long=[3]); end=[1.5]

Notes

This is very much not complete and very ugly

Data structures and algorithms

1. The grammar is parsed into events cue=[1](A,B); end
2. events are parsed into a tree where each event/stimulus is a node

• building the tree returns the last leaves of each unique branch
• e.g cue->a->end; cue->b->end;
• events without duration go into the parents names (later for output: cue->a will be file cue_a)

4. the tree is fit for the number of trials we have (fit_tree)

• nodes acquire the property total_reps

5. we pull out "unique nodes" (by name only) by backtracking through the branches

• calculate how many times the node will be seen (nrep)
• distribute durations based on nrep
• cue,a,b,end

6. calculate the number and total duration of our events (+ min iti), add itis (with duration=stepdur) to consume remaining time

• n_rep_branch is how many times we should run each branch (== ntrials/n_perms)
• n_perms is number of branches with multipliers 1xA + 2xB == 3

7. shuffle for number of iterations specified (default 1000). and write out files in directories named after the random seed