def map_of_clusters_and_labels(stats, tail, tf, pcrit):
"""
Generate a statistics map and a list of clusters based on the
presence of clusters that exceed a critical p-value.
Parameters
----------
stats: a TimeFreqSnPMClusters object
tail: str {'pos', 'neg'}
tf: tuple (time,freq) index
pcrit: float in (0.0,1.0)
Returns
-------
stats_image, clusters
The `stats_image` is the image of the statistical test values.
`clusters` is a list of the significant clusters. If there
are no cluster scores exceeding pcrit, then clusters returns as None.
"""
g, m = calc_grid_and_map(stats.vox_idx)
l_img = np.zeros(g)
t_img = np.zeros(g)
t, f = tf
np.put(t_img, m, stats.t[:,t,f])
scores = stats.pscore_clusters(tail)
if tail.lower() == 'pos':
print 'doing pos tail'
clusters = stats.ptail_clusters
else:
print 'doing neg tail'
clusters = stats.ntail_clusters
if not clusters[t][f]:
print 'no clusters!'
return t_img, None, 0
crit_scores = (scores[t][f] < pcrit)
if not crit_scores.any():
print 'no significant clusters!'
return t_img, None, 0
else:
tfclusters = clusters[t][f]
idx = np.argwhere(crit_scores).reshape(-1)
critical_clusters = [clusters[t][f][i] for i in idx]
return t_img, critical_clusters
def quick_plot_top_n(stats, tail, n=3):
scores = stats.pscore_clusters(tail)
clusters = stats.ptail_clusters if tail.lower()=='pos' \
else stats.ntail_clusters
# flatten score into a flat list of cluster scores
flattened_scores = []
# flatten clusters into a ntime x nfreq list of cluster sublists
flattened_clusters = []
for crow, srow in zip(clusters, scores):
flattened_scores = np.r_[flattened_scores,
reduce(lambda x, y: x+y,
[ list(e) for e in srow ])]
flattened_clusters += crow
tf_map = []
cluster_lookup = dict()
nc = 0
for i, cl in enumerate(flattened_clusters):
nt, nf = stats.t.shape[1:]
t, f = i / nf, i % nf
tf_map += [ (t,f) ] * len(cl)
cluster_lookup.update( dict( zip(xrange(nc, nc+len(cl)), cl) ) )
nc += len(cl)
n_idx = flattened_scores.argsort()[:n]
g, m = calc_grid_and_map(stats.vox_idx)
imgs = []
tf_pts = []
nclusters = []
for i in n_idx:
clst = cluster_lookup[i]
tf_pts.append('(t,f) = %s, %d pts, p = %1.2f'%(str(tf_map[i]),
clst.size,
flattened_scores[i]))
nclusters.append(cluster_lookup[i])
t_img = np.zeros(g)
t, f = tf_map[i]
np.put(t_img, m, stats.t[:,t,f])
imgs.append(t_img)
quick_plot_clusters(imgs, nclusters, titles=tf_pts)