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])