analysis_demo.py

cfg = read_study_file(op.join(path, 'study.json'))

# select conditions
conditions = cfg.EVENTS.CONDITIONS.sm_sanity_button

cov_type = 'eroom'
# organize averages along subjects for each conditions
stcs = OrderedDict([(k, {}) for k in conditions._asdict()])
for sub in subjects:
    stcnames = [f for f in os.listdir(sub) if '.stc' in f and 'fsaverage' in f]
    for cond, stcs_ in stcs.items():
        for name in stcnames:
            if all([k in name for k in cond, cov_type, '-lh']):
                fname = op.join(path, sub, name)
                stc = read_source_estimate(fname)
                stc.comment = op.split(fname)[-1][:-4]
                stc.crop(0, stc.times[-1])
                stcs_.update({sub: stc})

fsave_vertices = [np.arange(10242)] * 2

X = make_group_matrix(stcs)  # custom function to construct data matrix from stc dict
X = np.abs(X)
X = X[:, :, :, 0] - X[:, :, :, 1]
X = np.transpose(X, [2, 1, 0])

# adapted example code starts here.

#    Now let's actually do the clustering. This can take a long time...
#    Here we set the threshold quite high to reduce computation.
connectivity = spatial_tris_connectivity(grade_to_tris(5))
# p_threshold = cfg.STATS.p_threshold
p_threshold = 0.05
df = len(subjects) - 1
t_threshold = -stats.distributions.t.ppf(p_threshold / 2., df)
print 'Clustering.'
T_obs, clusters, cluster_p_values, H0 = \
    spatio_temporal_cluster_1samp_test(X, connectivity=connectivity,
                                       n_jobs=-1,
                                       threshold=t_threshold)
#    Now select the clusters that are sig. at p < 0.05 (note that this value
#    is multiple-comparisons corrected).
good_cluster_inds = np.where(cluster_p_values < 0.05)[0]