def test_map_t_real_data2():
import os
pth = os.path.join(os.path.dirname(__file__), 's_beamtf1_avg.mat')
if not os.path.exists(pth):
assert False, 'did not find data file'
return
mdict, mbeam, dof = su.split_combo_tfstats_matfile(pth)
p_corr_pos = mdict['p val pos (corr)']
p_corr_neg = mdict['p val neg (corr)']
tt = mdict['T test']
n = 2048
# create the adapted results
s_res = adapt_mlab_tf_snpm_stats(pth)
alpha = 0.05
while not (p_corr_neg.s <= alpha).any():
alpha += 0.05
print 'testing negative tail at significance level', alpha
tc, alpha = s_res.threshold(alpha, 'neg')
m = tt.s <= tc
yield nt.assert_false, (p_corr_neg.s[m] > alpha).any()
alpha = 0.05
while not (p_corr_pos.s <= alpha).any():
alpha += 0.05
print 'testing positive tail at significance level', alpha
tc, alpha = s_res.threshold(alpha, 'pos')
m = tt.s >= tc
yield nt.assert_false, (p_corr_pos.s[m] > alpha).any()
def adapt_mlab_tf_snpm_stats(combo_beam, avg_beam=None):
mdict, mbeam, dof = su.split_combo_tfstats_matfile(combo_beam)
stat = mdict['T test'].s
ranks = mdict['p val neg (uncorr)'].s
p_scores_neg = mdict['p val neg (corr)'].s
p_scores_pos = mdict['p val pos (corr)'].s
if avg_beam is None:
avg_beam = mbeam
return AdaptedTimeFreqSnPMResults(
stat, avg_beam.voxel_indices, ranks, p_scores_pos, p_scores_neg
)
def test_map_t_real_data():
import os
pth = os.path.join(os.path.dirname(__file__), 's_beamtf1_avg.mat')
if not os.path.exists(pth):
assert False, 'did not find data file'
return
mdict, mbeam, dof = su.split_combo_tfstats_matfile(pth)
p_corr_pos = mdict['p val pos (corr)']
p_corr_neg = mdict['p val neg (corr)']
tt = mdict['T test']
n = 2048
nt, nf = tt.s.shape[1:]
max_t_maps = np.empty((n, nt, nf))
min_t_maps = np.empty((n, nt, nf))
for t in xrange(nt):
for f in xrange(nf):
edges, _ = su.map_t(tt.s[:,t,f], p_corr_neg.s[:,t,f], 1.0/n)
min_t_maps[:,t,f] = edges
edges, _ = su.map_t(-tt.s[:,t,f], p_corr_pos.s[:,t,f], 1.0/n)
max_t_maps[:,t,f] = -edges
min_t_maps = np.sort(min_t_maps, axis=0)
alpha = 0.05
while not (p_corr_neg.s <= alpha).any():
alpha += 0.05
print 'testing negative tail at significance level', alpha
# highest k index satisfying t <= tc
k_mn = int(alpha * n)
tc = min_t_maps[k_mn]
m = tt.s <= tc
yield assert_false, (p_corr_neg.s[m] > alpha).any()
max_t_maps = np.sort(max_t_maps, axis=0)
alpha = 0.05
while not (p_corr_pos.s <= alpha).any():
alpha += 0.05
print 'testing positive tail at significance level', alpha
# lowest k index in max_t_maps satisfying significant t >= tc
k_mx = int((1-alpha) * n + 0.5)
tc = max_t_maps[k_mx]
m = tt.s >= tc
print m.sum()
yield assert_false, (p_corr_pos.s[m] > alpha).any()
