[Mne_analysis] Spatio-temporal clusters permutation test

[Eric Larson]

Have you tried doing just two time points? This should only take about
twice as long as the spatial case. Then try three, four, etc. -- it could
be that you have far too many times points (e.g. if n_times=1000, then 15
minutes * 1000 time points is more than 10 days). If that's the case, you
could benefit from choosing a stricter initial t-value threshold, smaller
source space, or temporally downsampling.

Also, you should probably use the function `ttest_1samp_no_p` -- it is
quite a bit faster than the `scipy` equivalent because our version does not
calculate the corresponding p values (which is very slow).

Eric


On Mon, Sep 15, 2014 at 10:42 AM, Stillerman, Benjamin <
BSTILLERMAN at partners.org> wrote:

>  Hello,
>
> I'm trying to run the non-parametric permutation test using the
> *spatio_temporal_cluster_1samp_test* function. I'm first setting the
> connectivity with:
> *connectivity = spatio_temporal_tris_connectivity(grade_to_tris(5),
> n_times)*
> The call is to the stats function is:
> *T_obs, clusters, cluster_pv, H0 =
> mem.cache(spatio_temporal_cluster_1samp_test,*
> *                                                ignore=['n_jobs'])(X1 -
> X2,*
> *                                                threshold=t,*
> *
> n_permutations=n_permutations,*
> *                                                tail=0,*
> *                                                stat_fun=stat_fun,*
> *
> connectivity=connectivity,*
> *                                                n_jobs=n_jobs, seed=0)*
> (Where stat_fun is the scipy t-test: *scipy.ttest_1samp*.)
>
> When this line is encountered, it appears to stall forever. It's been
> stuck on this line for several days. When I run the analogous script for
> spatial clusters only (averaging over a time window), it works and executes
> in about 15 minutes. Any ideas?
>
> Thank you,
>  Ben
>
>
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