if stat == 'beta':
if 'vs' in contrast:
dat2use = deepcopy(data)
else:
dat2use = deepcopy(data_baselined)
elif stat == 'tstat':
if 'vs' in contrast:
dat2use = deepcopy(data_t)
else:
dat2use = deepcopy(data_baselined_t)
t_cope, clu_cope, clupv_cope, _ = runclustertest_tfr(
data=dat2use,
contrast_name=contrast,
channels=['FCz'],
contra_channels=None,
ipsi_channels=None,
tmin=tmin,
tmax=tmax,
out_type='mask',
n_permutations=5000)
masks_cope = np.asarray(clu_cope)[clupv_cope <= 0.05]
clutimes = deepcopy(dat2use[contrast][0]).crop(tmin=tmin, tmax=tmax).times
plotdata = mne.grand_average(deepcopy(dat2use[contrast]))
if stat == 'tstat':
plot_t = True
vmin = -3
vmax = 3
else:
plot_t = False
vmin = -5e-11
# vmax = topovmin['tstat']*-1))
#run cluster test
#get data into dataframe first, for the channels we want
if 'right' in contrast or contrast == 'crvsn':
contrachans = visleftchans
ipsichans = visrightchans
else:
contrachans = visrightchans
ipsichans = visleftchans
t_cope, clu_cope, clupv_cope, _ = runclustertest_tfr(
data=dat2use,
contrast_name=contrast,
channels=None, #because lateralised analysis
contra_channels=contrachans,
ipsi_channels=ipsichans,
tmin=tmin,
tmax=tmax,
out_type='mask',
n_permutations=5000)
masks_cope = np.asarray(clu_cope)[clupv_cope <= 0.05]
clutimes = deepcopy(data[contrast][0]).crop(tmin=tmin, tmax=tmax).times
lvsrdata = np.empty(shape=(subs.size, allfreqs.size, alltimes.size))
for i in range(subs.size):
tmp = deepcopy(dat2plot[contrast][i])
tmp_c = deepcopy(tmp).pick_channels(contrachans).data
tmp_i = deepcopy(tmp).pick_channels(ipsichans).data
tmp_c = np.nanmean(tmp_c, axis=0)
tmp_i = np.nanmean(tmp_i, axis=0)
tmp_cvsi = np.subtract(tmp_c, tmp_i)
extent=(np.min(alltimes), np.max(alltimes), np.min(allfreqs),
np.max(allfreqs)))
axes[0].set_xlabel('time rel. 2 cue onset')
axes[0].set_ylabel('Frequency (Hz)')
axes[0].vlines([0, 0.25, 1.75],
lw=1,
linestyles='dashed',
color='#000000',
ymin=1,
ymax=40)
if contrast == 'dt_clvsr' and not baselined:
t_, clu, clupv, _ = runclustertest_tfr(data=data,
contrast_name=contrast,
contra_channels=contrachans,
ipsi_channels=ipsichans,
tmin=.25,
tmax=1.5,
n_permutations=5000)
clutimes = deepcopy(gave).crop(tmin=0, tmax=1.5).times
masks = np.asarray(clu)[clupv <= 0.05]
#%%
stat = 'tstat'
baselined = False
contrast = 'clvsr'
if stat == 'beta' and not baselined:
dat2use = deepcopy(data[contrast])
elif stat == 'beta' and baselined:
dat2use = deepcopy(data_baselined[contrast])
