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Plot.py
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Plot.py
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import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
from scipy import stats
import scipy.signal as sgn
import os
import seaborn as sn
import mne
import Funciones
import librosa
from statannot import add_stat_annotation
from scipy.stats import wilcoxon
import setup
exp_info = setup.exp_info()
def highlight_cell(x, y, ax=None, **kwargs):
rect = plt.Rectangle((x - .5, y - .5), 1, 1, **kwargs)
ax = ax or plt.gca()
ax.add_patch(rect)
return rect
def lateralized_channels(info, channels_right=None, channels_left=None, path=None, Display=False, Save=True):
if Display:
plt.ion()
else:
plt.ioff()
# Lateralization comparison
if channels_right == None:
channels_right = ['B27', 'B28', 'B29', 'B30', 'C4', 'C5', 'C6', 'C7', 'C9', 'C10', 'B31', 'C3']
if channels_left == None:
channels_left = ['D8', 'D9', 'D10', 'D11', 'D7', 'D6', 'D5', 'D4', 'C31', 'C32', 'D12', 'D3']
# Plot masked channels
mask = [i in channels_right + channels_left for i in info['ch_names']]
plt.ion()
fig = plt.figure()
plt.title('Masked channels for lateralization comparisson', fontsize=19)
mne.viz.plot_topomap(np.zeros(info['nchan']), info, show=Display, sphere=0.07, mask=np.array(mask),
mask_params=dict(marker='o', markerfacecolor='k', markeredgecolor='k', linewidth=0, markersize=12))
fig.tight_layout()
if Save:
path += 'Lateralization/'
os.makedirs(path, exist_ok=True)
fig.savefig(path + f'left_vs_right_chs_{len(channels_right)}.png')
fig.savefig(path + f'left_vs_right_chs_{len(channels_right)}.svg')
def plot_cabezas_canales(channel_names, info, sesion, sujeto, Valores_promedio, Display,
n_canales, name, Save, Run_graficos_path, Canales_sobrevivientes):
if Display:
plt.ion()
else:
plt.ioff()
# Grafico cabezas Correlaciones
if len(Canales_sobrevivientes):
fig, axs = plt.subplots(1, 2)
plt.suptitle("Sesion{} Sujeto{}\n{} = {:.3f} +/- {:.3f}".format(sesion, sujeto, name, Valores_promedio.mean(),
Valores_promedio.std()), fontsize=19)
im = mne.viz.plot_topomap(Valores_promedio, info, axes=axs[0], show=False, sphere=0.07, cmap='Greys',
vmin=Valores_promedio.min(), vmax=Valores_promedio.max())
mask = [i in Canales_sobrevivientes for i in range(n_canales)]
im2 = mne.viz.plot_topomap(np.zeros(n_canales), info, axes=axs[1], show=False, sphere=0.07,
mask=np.array(mask), mask_params=dict(marker='o', markerfacecolor='g',
markeredgecolor='k', linewidth=0,
markersize=4))
plt.colorbar(im[0], ax=[axs[0], axs[1]], shrink=0.85, label=name, orientation='horizontal',
boundaries=np.linspace(Valores_promedio.min().round(decimals=3),
Valores_promedio.max().round(decimals=3), 100),
ticks=np.linspace(Valores_promedio.min(), Valores_promedio.max(), 9).round(decimals=3))
else:
fig, ax = plt.subplots()
plt.suptitle("Sesion{} Sujeto{}\n{} = {:.3f} +/- {:.3f}".format(sesion, sujeto, name, Valores_promedio.mean(),
Valores_promedio.std()), fontsize=19)
im = mne.viz.plot_topomap(Valores_promedio, info, axes=ax, show=False, sphere=0.07, cmap='Greys',
vmin=Valores_promedio.min(), vmax=Valores_promedio.max())
plt.colorbar(im[0], ax=ax, shrink=0.85, label=name, orientation='horizontal',
boundaries=np.linspace(Valores_promedio.min().round(decimals=3),
Valores_promedio.max().round(decimals=3), 100),
ticks=np.linspace(Valores_promedio.min(), Valores_promedio.max(), 9).round(decimals=3))
fig.tight_layout()
if Save:
save_path_cabezas = Run_graficos_path + 'Cabezas_canales/'
os.makedirs(save_path_cabezas, exist_ok=True)
fig.savefig(save_path_cabezas + '{}_Cabeza_Sesion{}_Sujeto{}.png'.format(name, sesion, sujeto))
def corr_sujeto_decoding(sesion, sujeto, Valores_promedio, Display, name, Save, Run_graficos_path):
if Display:
plt.ion()
else:
plt.ioff()
data = pd.DataFrame({name: Valores_promedio})
if Display:
plt.ion()
else:
plt.ioff()
fig, ax = plt.subplots()
sn.violinplot(data=data, ax=ax)
plt.ylim([-0.2, 1])
plt.ylabel(name)
plt.title('{}:{:.3f} +/- {:.3f}'.format(name, np.mean(Valores_promedio), np.std(Valores_promedio), fontsize=19))
if Save:
save_path_cabezas = Run_graficos_path + 'Corr_sujetos/'
try:
os.makedirs(save_path_cabezas)
except:
pass
fig.savefig(save_path_cabezas + '{}_Sesion{}_Sujeto{}.png'.format(name, sesion, sujeto))
def plot_grafico_pesos(Display, sesion, sujeto, best_alpha, Pesos_promedio,
info, times, Corr_promedio, Rmse_promedio, Save,
Run_graficos_path, Len_Estimulos, stim, title=None, ERP=True):
# Defino cosas que voy a graficar
Corr_mejor_canal = Corr_promedio.max()
Rmse_mejor_canal = Rmse_promedio.max()
if Display:
plt.ion()
else:
plt.ioff()
fig = plt.figure()
fig.suptitle('Sesion {} - Sujeto {} - Corr max {:.2f} - Rmse max {:.2f} - '
'alpha: {:.2f}'.format(sesion, sujeto, Corr_mejor_canal, Rmse_mejor_canal, best_alpha))
Stims_Order = stim.split('_')
Cant_Estimulos = len(Len_Estimulos)
for i in range(Cant_Estimulos):
ax = fig.add_subplot(Cant_Estimulos, 1, i + 1)
if Stims_Order[i] == 'Spectrogram':
spectrogram_weights_bands = Pesos_promedio[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
spectrogram_weights_bands = spectrogram_weights_bands.reshape(16, len(times))
if ERP:
# Adapt for ERP
spectrogram_weights_bands = np.flip(spectrogram_weights_bands, axis=1)
im = ax.pcolormesh(times * 1000, np.arange(16), spectrogram_weights_bands, cmap='jet',
vmin=-spectrogram_weights_bands.max(), vmax=spectrogram_weights_bands.max(),
shading='auto')
ax.set(xlabel='Time (ms)', ylabel='Hz')
Bands_center = librosa.mel_frequencies(n_mels=18, fmin=62, fmax=8000)[1:-1]
ticks_positions = np.arange(0, 16, 2)
ticks_labels = [int(Bands_center[i]) for i in np.arange(0, len(Bands_center), 2)]
ax.set_yticks(ticks_positions)
ax.set_yticklabels(ticks_labels)
ax.xaxis.label.set_size(14)
ax.yaxis.label.set_size(14)
ax.tick_params(axis='both', labelsize=14)
cbar = fig.colorbar(im, ax=ax, orientation='vertical')
cbar.set_label('Amplitude (a.u.)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
# spectrogram_weights = Pesos_promedio[:, sum(Len_Estimulos[j] for j in range(i)):sum(
# Len_Estimulos[j] for j in range(i + 1))].mean(0)
# spectrogram_weights = spectrogram_weights.reshape(16, len(times))
#
# im = ax.pcolormesh(times * 1000, np.arange(16), spectrogram_weights, cmap='jet',
# vmin=-spectrogram_weights.max(), vmax=spectrogram_weights.max(), shading='auto')
#
# Bands_center = librosa.mel_frequencies(n_mels=18, fmin=62, fmax=8000)[1:-1]
# ax.set(xlabel='Time (ms)', ylabel='Hz')
# ticks_positions = np.arange(0, 16, 2)
# ticks_labels = [int(Bands_center[i + 1]) for i in np.arange(0, len(Bands_center - 1), 2)]
# ax.set_yticks(ticks_positions)
# ax.set_yticklabels(ticks_labels)
#
# fig.colorbar(im, ax=ax, orientation='vertical')
elif Stims_Order[i] == 'Phonemes' or Stims_Order[i] == 'Phonemes-discrete' or Stims_Order[i] == 'Phonemes-onset':
phonemes_weights = Pesos_promedio[:, sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
phonemes_weights = phonemes_weights.reshape(len(exp_info.ph_labels), len(times))
im = ax.pcolormesh(times * 1000, np.arange(len(exp_info.ph_labels)), phonemes_weights, cmap='jet', shading='auto')
# Bands_center = librosa.mel_frequencies(n_mels=18, fmin=62, fmax=8000)[1:-1]
ax.set(xlabel='Time (ms)', ylabel='Hz')
ticks_positions = np.arange(0, len(exp_info.ph_labels))
ax.set_yticks(ticks_positions)
ax.set_yticklabels(exp_info.ph_labels)
ax.set_yticklabels(exp_info.ph_labels)
fig.colorbar(im, ax=ax, orientation='vertical')
elif Stims_Order[i] == 'Phonemes-manual':
phonemes_weights = Pesos_promedio[:, sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
phonemes_weights = phonemes_weights.reshape(len(exp_info.ph_labels_man), len(times))
im = ax.pcolormesh(times * 1000, np.arange(len(exp_info.ph_labels_man)), phonemes_weights, cmap='jet', shading='auto')
# Bands_center = librosa.mel_frequencies(n_mels=18, fmin=62, fmax=8000)[1:-1]
ax.set(xlabel='Time (ms)', ylabel='Hz')
ticks_positions = np.arange(0, len(exp_info.ph_labels_man))
ax.set_yticks(ticks_positions)
ax.set_yticklabels(exp_info.ph_labels_man)
ax.set_yticklabels(exp_info.ph_labels_man)
fig.colorbar(im, ax=ax, orientation='vertical')
else:
evoked = mne.EvokedArray(
Pesos_promedio[:, sum(Len_Estimulos[j] for j in range(i)):sum(Len_Estimulos[j] for j in range(i + 1))],
info)
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms',
show=False, spatial_colors=True, unit=False, units=dict(eeg='w', grad='fT/cm', mag='fT'),
axes=ax, gfp=False)
ax.plot(times * 1000, evoked._data.mean(0), 'k--', label='Mean', zorder=130, linewidth=2)
ax.xaxis.label.set_size(13)
ax.yaxis.label.set_size(13)
ax.legend(fontsize=13)
ax.grid()
ax.set_title('{}'.format(Stims_Order[i]))
if Save:
if title:
save_path_graficos = Run_graficos_path + 'Individual weights {}/'.format(title)
else:
save_path_graficos = Run_graficos_path + 'Individual weights/'
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + 'Sesion{}_Sujeto{}.png'.format(sesion, sujeto))
def plot_grafico_shadows(Display, sesion, sujeto, best_alpha,
Canales_sobrevivientes_corr, info, sr,
Corr_promedio, Save, Run_graficos_path,
Corr_buenas_ronda_canal, Correlaciones_fake):
# Defino cosas que voy a graficar
Correlaciones_fake_min = Correlaciones_fake.min(1).min(0)
Correlaciones_fake_max = Correlaciones_fake.max(1).max(0)
if Display:
plt.ion()
else:
plt.ioff()
fig, ax = plt.subplots(1, 1, figsize=(10, 7))
fig.suptitle('Session {} - Subject {}'.format(sesion, sujeto))
ax.plot(Corr_promedio, '.', color='C0', label="Mean of correlations among folds (Discarded)")
ax.plot(Canales_sobrevivientes_corr, Corr_promedio[Canales_sobrevivientes_corr], '*',
color='C1', label="Mean of correlations among folds (Test passed)")
ax.fill_between(np.arange(len(Corr_promedio)), Corr_buenas_ronda_canal.min(0),
Corr_buenas_ronda_canal.max(0), alpha=0.5,
label='Correlation distribution (Real data)')
ax.fill_between(np.arange(len(Corr_promedio)), Correlaciones_fake_min,
Correlaciones_fake_max, alpha=0.5,
label='Correlation distribution (Random data)')
ax.set_xlim([-1, 129])
ax.set_xlabel('Channels', fontsize=15)
ax.set_ylabel('Correlation', fontsize=15)
ax.legend(fontsize=13, loc="lower right")
ax.grid()
ax.xaxis.set_tick_params(labelsize=13)
ax.yaxis.set_tick_params(labelsize=13)
fig.tight_layout()
if not len(Canales_sobrevivientes_corr): plt.text(64, np.max(abs(Corr_buenas_ronda_canal)) / 2,
"No surviving channels", size='xx-large', ha='center')
if Save:
save_path_graficos = Run_graficos_path + 'Correlation_shadows/'
try:
os.makedirs(save_path_graficos)
except:
pass
fig.savefig(save_path_graficos + 'Sesion{}_Sujeto{}.png'.format(sesion, sujeto))
def Plot_PSD(sesion, sujeto, Band, situacion, Display, Save, save_path, info, data, fmin=0, fmax=40):
psds_welch_mean, freqs_mean = mne.time_frequency.psd_array_welch(data, info['sfreq'], fmin, fmax)
if Display:
plt.ion()
else:
plt.ioff()
fig, ax = plt.subplots()
fig.suptitle('Sesion {} - Sujeto {} - Situacion {} - Band {}'.format(sesion, sujeto, situacion, Band))
evoked = mne.EvokedArray(psds_welch_mean, info)
evoked.times = freqs_mean
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='s',
show=False, spatial_colors=True, unit=False, units='w', axes=ax)
ax.set_xlabel('Frequency [Hz]')
ax.grid()
if Save:
save_path_graficos = 'gráficos/PSD/Zoom/{}/{}/'.format(save_path, Band)
os.makedirs(save_path_graficos, exist_ok=True)
plt.savefig(save_path_graficos + 'Sesion{} - Sujeto{}.png'.format(sesion, sujeto, Band))
plt.savefig(save_path_graficos + 'Sesion{} - Sujeto{}.svg'.format(sesion, sujeto, Band))
def Cabezas_corr_promedio(Correlaciones_totales_sujetos, info, Display, Save, Run_graficos_path, title, lat_max_chs=12):
Correlaciones_promedio = Correlaciones_totales_sujetos.mean(0)
if Display:
plt.ion()
else:
plt.ioff()
fontsize = 24
fig = plt.figure(figsize=(5, 4))
plt.suptitle('{} = {:.3f} +/- {:.3f}'.format(title, Correlaciones_promedio.mean(), Correlaciones_promedio.std()), fontsize=fontsize)
# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['greenyellow', 'yellow', 'orange', 'red'])
im = mne.viz.plot_topomap(Correlaciones_promedio, info, cmap='OrRd',
vmin=Correlaciones_promedio.min(), vmax=Correlaciones_promedio.max(),
show=False, sphere=0.07)
cb = plt.colorbar(im[0], shrink=0.85, orientation='vertical')
cb.ax.tick_params(labelsize=fontsize)
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + '{}_promedio.svg'.format(title))
fig.savefig(save_path_graficos + '{}_promedio.png'.format(title))
if title == 'Correlation':
# Lateralization comparison
# good_channels_right = ['B27', 'B28', 'B29', 'B30', 'C4', 'C5', 'C6', 'C7', 'C9', 'C10', 'B31', 'C3']
# good_channels_left = ['D8', 'D9', 'D10', 'D11', 'D7', 'D6', 'D5', 'D4', 'C31', 'C32', 'D12', 'D3']
all_channels_right = ['B27', 'B28', 'B29', 'B30', 'B31', 'B32', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8',
'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'C15', 'C16']
all_channels_left = ['D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13',
'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'C30', 'C31', 'C32']
ordered_chs_right = [i for i in info['ch_names'] if i in all_channels_right]
ordered_chs_left = [i for i in info['ch_names'] if i in all_channels_left]
mask_right = [i in all_channels_right for i in info['ch_names']]
mask_left = [i in all_channels_left for i in info['ch_names']]
corr_right = np.sort(Correlaciones_promedio[mask_right])
corr_left = np.sort(Correlaciones_promedio[mask_left])
sorted_chs_right = [x for _, x in sorted(zip(Correlaciones_promedio[mask_right], ordered_chs_right))]
sorted_chs_left = [x for _, x in sorted(zip(Correlaciones_promedio[mask_left], ordered_chs_left))]
if lat_max_chs:
corr_right = np.sort(corr_right)[-lat_max_chs:]
corr_left = np.sort(corr_left)[-lat_max_chs:]
sorted_chs_right = sorted_chs_right[-lat_max_chs:]
sorted_chs_left = sorted_chs_left[-lat_max_chs:]
fontsize = 18
fig = plt.figure(figsize=(6.5, 4))
data = pd.DataFrame({'Left': corr_left, 'Right': corr_right})
ax = sn.boxplot(data=data, width=0.35)
for patch in ax.artists:
r, g, b, a = patch.get_facecolor()
patch.set_facecolor((r, g, b, .8))
sn.swarmplot(data=data, color=".25")
plt.tick_params(labelsize=fontsize)
ax.set_ylabel('Correlation', fontsize=fontsize)
add_stat_annotation(ax, data=data, box_pairs=[('Left', 'Right')],
test='Wilcoxon', text_format='full', loc='outside', fontsize='xx-large')
test_results = wilcoxon(data['Left'], data['Right'])
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path + 'Lateralization/'
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + f'left_vs_right_{title}_{len(sorted_chs_right)}.svg')
fig.savefig(save_path_graficos + f'left_vs_right_{title}_{len(sorted_chs_right)}.png')
lateralized_channels(info, channels_right=sorted_chs_right, channels_left=sorted_chs_left, path=Run_graficos_path,
Display=Display, Save=Save)
else:
test_results = None
return (Correlaciones_promedio.mean(), Correlaciones_promedio.std()), test_results
def violin_plot_decoding(Correlaciones_totales_sujetos, Display, Save, Run_graficos_path, title):
data = pd.DataFrame({title: Correlaciones_totales_sujetos.ravel()})
if Display:
plt.ion()
else:
plt.ioff()
fig, ax = plt.subplots()
sn.violinplot(data=data, ax=ax)
plt.ylim([-0.2, 1])
plt.ylabel(title)
plt.title('{}:{:.3f} +/- {:.3f}'.format(title, np.mean(Correlaciones_totales_sujetos),
np.std(Correlaciones_totales_sujetos), fontsize=19))
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + '{}_promedio.svg'.format(title))
fig.savefig(save_path_graficos + '{}_promedio.png'.format(title))
return Correlaciones_totales_sujetos.mean(), Correlaciones_totales_sujetos.std()
def Cabezas_3d(Correlaciones_totales_sujetos, info, Display, Save, Run_graficos_path, title):
Correlaciones_promedio = Correlaciones_totales_sujetos.mean(0)
if Display:
plt.ion()
else:
plt.ioff()
sample_data_folder = mne.datasets.sample.data_path()
subjects_dir = os.path.join(sample_data_folder, 'subjects')
sample_data_trans_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw-trans.fif')
evoked = mne.EvokedArray(np.array([Correlaciones_promedio,]).transpose(), info)
field_map = mne.make_field_map(evoked, trans=sample_data_trans_file,
subject='sample', subjects_dir=subjects_dir, ch_type='eeg',
meg_surf='head')
fig = evoked.plot_field(field_map, time=0)
xy, im = mne.viz.snapshot_brain_montage(fig, info)
# mne.viz.set_3d_view(figure=fig, azimuth=135, elevation=80)
fig, ax = plt.subplots(figsize=(15, 10))
ax.set_title('Correlation', size='large')
ax.imshow(im)
if Save:
try:
os.makedirs(Run_graficos_path)
except:
pass
fig.savefig(Run_graficos_path + '{}.svg'.format(title))
fig.savefig(Run_graficos_path + '{}.png'.format(title))
return Correlaciones_promedio.mean(), Correlaciones_promedio.std()
def Cabezas_canales_rep(Canales_repetidos_sujetos, info, Display, Save, Run_graficos_path, title):
if Display:
plt.ion()
else:
plt.ioff()
# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['greenyellow', 'yellow', 'orange', 'red'])
fig = plt.figure()
plt.suptitle("Channels passing 5 test per subject - {}".format(title), fontsize=19)
plt.title('Mean: {:.3f} +/- {:.3f}'.format(Canales_repetidos_sujetos.mean(), Canales_repetidos_sujetos.std()),
fontsize=19)
im = mne.viz.plot_topomap(Canales_repetidos_sujetos, info, cmap='OrRd',
vmin=0, vmax=18,
show=False, sphere=0.07)
cb = plt.colorbar(im[0], shrink=0.85, orientation='vertical')
cb.ax.tick_params(labelsize=19)
cb.set_label(label='Number of subjects passed', size=21)
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + 'Canales_repetidos_{}.png'.format(title))
fig.savefig(save_path_graficos + 'Canales_repetidos_{}.svg'.format(title))
def topo_pval(topo_pval, info, Display, Save, Run_graficos_path, title):
if Display:
plt.ion()
else:
plt.ioff()
# cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ['greenyellow', 'yellow', 'orange', 'red'])
fig = plt.figure()
plt.suptitle("Mean p-values - {}".format(title), fontsize=19)
plt.title('Mean: {:.3f} +/- {:.3f}'.format(topo_pval.mean(), topo_pval.std()),
fontsize=19)
im = mne.viz.plot_topomap(topo_pval, info, cmap='OrRd',
vmin=0, vmax=topo_pval.max(),
show=False, sphere=0.07)
cb = plt.colorbar(im[0], shrink=0.85, orientation='vertical')
cb.ax.tick_params(labelsize=19)
cb.set_label(label='p-value', size=21)
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + 'p-value_topo_{}.png'.format(title))
fig.savefig(save_path_graficos + 'p-value_topo_{}.svg'.format(title))
def regression_weights(Pesos_totales_sujetos_todos_canales, info, times, Display, Save, Run_graficos_path,
Len_Estimulos, stim, fontsize=16, ERP=True, title=None, decorrelation_times=None):
# Armo pesos promedio por canal de todos los sujetos que por lo menos tuvieron un buen canal
Pesos_totales_sujetos_todos_canales_copy = Pesos_totales_sujetos_todos_canales.mean(2)
# Ploteo pesos y cabezas
if Display:
plt.ion()
else:
plt.ioff()
Stims_Order = stim.split('_')
Cant_Estimulos = len(Len_Estimulos)
for i in range(Cant_Estimulos):
fig, ax = plt.subplots(figsize=(9, 3))
fig.suptitle('{}'.format(Stims_Order[i]), fontsize=fontsize)
if Stims_Order[i] == 'Spectrogram':
spectrogram_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]. \
reshape(info['nchan'], 16, len(times)).mean(1)
if ERP:
# Adapt for ERP
spectrogram_weights_chanels = np.flip(spectrogram_weights_chanels, axis=1)
evoked = mne.EvokedArray(spectrogram_weights_chanels, info)
elif Stims_Order[i] == 'Phonemes' or Stims_Order[i] == 'Phonemes-discrete' or Stims_Order[i] == 'Phonemes-onset':
phoneme_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]. \
reshape(info['nchan'], len(exp_info.ph_labels), len(times)).mean(1)
if ERP:
# Adapt for ERP
phoneme_weights_chanels = np.flip(phoneme_weights_chanels, axis=1)
evoked = mne.EvokedArray(phoneme_weights_chanels, info)
elif Stims_Order[i] == 'Phonemes-manual':
phoneme_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]. \
reshape(info['nchan'], len(exp_info.ph_labels_man), len(times)).mean(1)
if ERP:
# Adapt for ERP
phoneme_weights_chanels = np.flip(phoneme_weights_chanels, axis=1)
evoked = mne.EvokedArray(phoneme_weights_chanels, info)
else:
mean_coefs = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]
if ERP:
# Adapt for ERP
mean_coefs = np.flip(mean_coefs, axis=1)
evoked = mne.EvokedArray(mean_coefs, info)
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms', titles=dict(eeg=''),
show=False, spatial_colors=True, unit=True, units='mTRF (a.u.)', axes=ax, gfp=False)
# ax.plot(times * 1000, evoked._data.mean(0), "k--", label="Mean", zorder=130, linewidth=2)
if times[0] < 0:
# ax.axvspan(ax.get_xlim()[0], 0, alpha=0.4, color='grey', label='Pre-Stimulus')
ax.axvline(x=0, ymin=0, ymax=1, color='grey')
if decorrelation_times:
# ax.vlines(-np.mean(decorrelation_times), ax.get_ylim()[0], ax.get_ylim()[1], linestyle='dashed',
# color='red', label='Decorrelation time')
ax.axvspan(-np.mean(decorrelation_times), 0, alpha=0.4, color='red', label=' Mean decorrelation time')
# ax.axvspan(-np.mean(decorrelation_times) - np.std(decorrelation_times) / 2,
# -np.mean(decorrelation_times) + np.std(decorrelation_times) / 2,
# alpha=0.4, color='red', label='Decorrelation time std.')
ax.xaxis.label.set_size(fontsize)
ax.yaxis.label.set_size(fontsize)
if Stims_Order[i] == 'Spectrogram':
ax.set_ylim([-0.016, 0.013])
# ax.set_ylim([-0.013, 0.02])
ax.tick_params(axis='both', labelsize=fontsize)
# ax.grid()
ax.legend(fontsize=fontsize)
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
if title:
fig.savefig(save_path_graficos + 'Regression_Weights_{}_{}.svg'.format(title, Stims_Order[i]))
fig.savefig(save_path_graficos + 'Regression_Weights_{}_{}.png'.format(title, Stims_Order[i]))
else:
fig.savefig(save_path_graficos + 'Regression_Weights_{}.svg'.format(Stims_Order[i]))
fig.savefig(save_path_graficos + 'Regression_Weights_{}.png'.format(Stims_Order[i]))
return Pesos_totales_sujetos_todos_canales_copy
def regression_weights_matrix(Pesos_totales_sujetos_todos_canales, info, times, Display,
Save, Run_graficos_path, Len_Estimulos, stim, Band, ERP=True, title=None):
# Armo pesos promedio por canal de todos los sujetos que por lo menos tuvieron un buen canal
Pesos_totales_sujetos_todos_canales_copy = Pesos_totales_sujetos_todos_canales.mean(2)
# Ploteo pesos y cabezas
if Display:
plt.ion()
else:
plt.ioff()
Stims_Order = stim.split('_')
Cant_Estimulos = len(Len_Estimulos)
for i in range(Cant_Estimulos):
if Stims_Order[i] == 'Spectrogram':
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(7, 8), gridspec_kw={'height_ratios': [1, 4]})
fig.suptitle('{} - {}'.format(Stims_Order[i], Band), fontsize=18)
spectrogram_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].\
reshape(info['nchan'], 16, len(times)).mean(1)
spectrogram_weights_bands = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
spectrogram_weights_bands = spectrogram_weights_bands.reshape(16, len(times))
if ERP:
# Adapt for ERP
spectrogram_weights_chanels = np.flip(spectrogram_weights_chanels, axis=1)
spectrogram_weights_bands = np.flip(spectrogram_weights_bands, axis=1)
axs[0].axvline(0, axs[0].get_ylim()[0], axs[0].get_ylim()[1], color='grey')
# axs[0].axhline(0, axs[0].get_xlim()[0], axs[0].get_xlim()[1], color='grey')
evoked = mne.EvokedArray(spectrogram_weights_chanels, info)
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms', titles=dict(eeg=''),
show=False, spatial_colors=True, unit=False, units='w', axes=axs[0], gfp=False)
# axs[0].plot(times * 1000, evoked._data.mean(0), "k--", label="Mean", zorder=130, linewidth=2)
if times[0] < 0:
# axs[0].axvspan(axs[0].get_xlim()[0], 0, alpha=0.4, color='grey', label='Pre-Stimulus')
axs[0].axvline(x=0, ymin=0, ymax=1, color='grey')
axs[0].axis('off')
axs[0].legend(fontsize=10)
im = axs[1].pcolormesh(times * 1000, np.arange(16), spectrogram_weights_bands, cmap='jet',
vmin=-spectrogram_weights_bands.max(), vmax=spectrogram_weights_bands.max(), shading='auto')
axs[1].set(xlabel='Time (ms)', ylabel='Frequency (Hz)')
Bands_center = librosa.mel_frequencies(n_mels=18, fmin=62, fmax=8000)[1:-1]
ticks_positions = np.arange(0, 16, 2)
ticks_labels = [int(Bands_center[i]) for i in np.arange(0, len(Bands_center), 2)]
axs[1].set_yticks(ticks_positions)
axs[1].set_yticklabels(ticks_labels)
axs[1].xaxis.label.set_size(14)
axs[1].yaxis.label.set_size(14)
axs[1].tick_params(axis='both', labelsize=14)
cbar = fig.colorbar(im, ax=axs[1], orientation='vertical')
cbar.set_label('mTRF amplitude (a.u.)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
# Change axis 0 to match axis 1 width after adding colorbar
ax1_box = axs[1].get_position().bounds
ax0_box = axs[0].get_position().bounds
ax0_new_box = (ax0_box[0], ax0_box[1], ax1_box[2], ax0_box[3])
axs[0].set_position(ax0_new_box)
elif Stims_Order[i] == 'Phonemes' or Stims_Order[i] == 'Phonemes-discrete' or Stims_Order[i] == 'Phonemes-onset':
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(6, 8), gridspec_kw={'height_ratios': [1, 4]})
fig.suptitle('{} - {}'.format(Stims_Order[i], Band), fontsize=18)
phoneme_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]. \
reshape(info['nchan'], len(exp_info.ph_labels), len(times)).mean(1)
phoneme_weights_ph = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
phoneme_weights_ph = phoneme_weights_ph.reshape(len(exp_info.ph_labels), len(times))
if ERP:
# Adapt for ERP
phoneme_weights_chanels = np.flip(phoneme_weights_chanels, axis=1)
phoneme_weights_ph = np.flip(phoneme_weights_ph, axis=1)
axs[0].axvline(0, axs[0].get_ylim()[0], axs[0].get_ylim()[1], color='grey')
axs[0].axhline(0, axs[0].get_xlim()[0], axs[0].get_xlim()[1], color='grey')
evoked = mne.EvokedArray(phoneme_weights_chanels, info)
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms', titles=dict(eeg=''),
show=False, spatial_colors=True, unit=False, units='w', axes=axs[0])
axs[0].plot(times * 1000, evoked._data.mean(0), "k--", label="Mean", zorder=130, linewidth=2)
axs[0].axis('off')
axs[0].legend(fontsize=10)
im = axs[1].pcolormesh(times * 1000, np.arange(len(exp_info.ph_labels)), phoneme_weights_ph, cmap='jet',
shading='auto')
axs[1].set(xlabel='Time (ms)', ylabel='Hz')
ticks_positions = np.arange(0, len(exp_info.ph_labels))
axs[1].set_yticks(ticks_positions)
axs[1].set_yticklabels(exp_info.ph_labels)
axs[1].xaxis.label.set_size(14)
axs[1].yaxis.label.set_size(10)
axs[1].tick_params(axis='both', labelsize=14)
cbar = fig.colorbar(im, ax=axs[1], orientation='vertical')
cbar.set_label('Amplitude (a.u.)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
# Change axis 0 to match axis 1 width after adding colorbar
ax1_box = axs[1].get_position().bounds
ax0_box = axs[0].get_position().bounds
ax0_new_box = (ax0_box[0], ax0_box[1], ax1_box[2], ax0_box[3])
axs[0].set_position(ax0_new_box)
elif Stims_Order[i] == 'Phonemes-manual':
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(6, 8), gridspec_kw={'height_ratios': [1, 4]})
fig.suptitle('{} - {}'.format(Stims_Order[i], Band), fontsize=18)
phoneme_weights_chanels = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))]. \
reshape(info['nchan'], len(exp_info.ph_labels_man), len(times)).mean(1)
phoneme_weights_ph = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].mean(0)
phoneme_weights_ph = phoneme_weights_ph.reshape(len(exp_info.ph_labels_man), len(times))
if ERP:
# Adapt for ERP
phoneme_weights_chanels = np.flip(phoneme_weights_chanels, axis=1)
phoneme_weights_ph = np.flip(phoneme_weights_ph, axis=1)
axs[0].axvline(0, axs[0].get_ylim()[0], axs[0].get_ylim()[1], color='grey')
axs[0].axhline(0, axs[0].get_xlim()[0], axs[0].get_xlim()[1], color='grey')
evoked = mne.EvokedArray(phoneme_weights_chanels, info)
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms', titles=dict(eeg=''),
show=False, spatial_colors=True, unit=False, units='w', axes=axs[0])
axs[0].plot(times * 1000, evoked._data.mean(0), "k--", label="Mean", zorder=130, linewidth=2)
axs[0].axis('off')
axs[0].legend(fontsize=10)
im = axs[1].pcolormesh(times * 1000, np.arange(len(exp_info.ph_labels_man)), phoneme_weights_ph, cmap='jet',
shading='auto')
axs[1].set(xlabel='Time (ms)', ylabel='Frequency (Hz)')
ticks_positions = np.arange(0, len(exp_info.ph_labels_man))
axs[1].set_yticks(ticks_positions)
axs[1].set_yticklabels(exp_info.ph_labels_man)
axs[1].xaxis.label.set_size(14)
axs[1].yaxis.label.set_size(10)
axs[1].tick_params(axis='both', labelsize=14)
cbar = fig.colorbar(im, ax=axs[1], orientation='vertical')
cbar.set_label('Amplitude (a.u.)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
# Change axis 0 to match axis 1 width after adding colorbar
ax1_box = axs[1].get_position().bounds
ax0_box = axs[0].get_position().bounds
ax0_new_box = (ax0_box[0], ax0_box[1], ax1_box[2], ax0_box[3])
axs[0].set_position(ax0_new_box)
else:
mean_coefs = Pesos_totales_sujetos_todos_canales_copy[:,
sum(Len_Estimulos[j] for j in range(i)):sum(Len_Estimulos[j] for j in range(i + 1))]
fig, axs = plt.subplots(2, 1, sharex=True, figsize=(6, 8), gridspec_kw={'height_ratios': [1, 3]})
fig.suptitle('{} - {}'.format(Stims_Order[i], Band), fontsize=18)
if ERP:
# Adapt for ERP
mean_coefs = np.flip(mean_coefs, axis=1)
evoked = mne.EvokedArray(mean_coefs, info)
axs[0].axvline(0, axs[0].get_ylim()[0], axs[0].get_ylim()[1], color='grey')
axs[0].axhline(0, axs[0].get_xlim()[0], axs[0].get_xlim()[1], color='grey')
evoked.times = times
evoked.plot(scalings=dict(eeg=1, grad=1, mag=1), zorder='std', time_unit='ms', titles=dict(eeg=''),
show=False, spatial_colors=True, unit=False, units='w', axes=axs[0], gfp=False)
axs[0].plot(times * 1000, evoked._data.mean(0), "k--", label="Mean", zorder=130, linewidth=2)
axs[0].axis('off')
axs[0].legend(fontsize=12)
im = axs[1].pcolormesh(times * 1000, np.arange(info['nchan']), mean_coefs, cmap='jet',
vmin=-(mean_coefs).max(), vmax=(mean_coefs).max(), shading='auto')
axs[1].set(xlabel='Time (ms)', ylabel='Channel')
axs[1].xaxis.label.set_size(14)
axs[1].yaxis.label.set_size(14)
axs[1].tick_params(axis='both', labelsize=14)
cbar = fig.colorbar(im, ax=axs[1], orientation='vertical')
cbar.set_label('Amplitude (a.u.)', fontsize=14)
cbar.ax.tick_params(labelsize=14)
# Change axis 0 to match axis 1 width after adding colorbar
ax1_box = axs[1].get_position().bounds
ax0_box = axs[0].get_position().bounds
ax0_new_box = (ax0_box[0], ax0_box[1], ax1_box[2], ax0_box[3])
axs[0].set_position(ax0_new_box)
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
if title:
fig.savefig(save_path_graficos + 'Regression_Weights_matrix_{}_{}.svg'.format(title, Stims_Order[i]))
fig.savefig(save_path_graficos + 'Regression_Weights_marix_{}_{}.png'.format(title, Stims_Order[i]))
else:
fig.savefig(save_path_graficos + 'Regression_Weights_matrix_{}.svg'.format(Stims_Order[i]))
fig.savefig(save_path_graficos + 'Regression_Weights_marix_{}.png'.format(Stims_Order[i]))
def Plot_cabezas_instantes(Pesos_totales_sujetos_todos_canales, info, Band, stim, times, sr, Display_figure_instantes,
Save_figure_instantes, Run_graficos_path, Len_Estimulos, ERP=True):
# Armo pesos promedio por canal de todos los sujetos que por lo menos tuvieron un buen canal
Pesos_totales_sujetos_todos_canales_copy = Pesos_totales_sujetos_todos_canales.mean(2)
Stims_Order = stim.split('_')
Cant_Estimulos = len(Len_Estimulos)
for i in range(Cant_Estimulos):
if Stims_Order[i] == 'Spectrogram':
Pesos = Pesos_totales_sujetos_todos_canales_copy[:, sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].reshape(info['nchan'], 16, len(times)).mean(1)
if Stims_Order[i] == 'Phonemes':
Pesos = Pesos_totales_sujetos_todos_canales_copy[:, sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].reshape(info['nchan'], len(exp_info.ph_labels), len(times)).mean(1)
if Stims_Order[i] == 'Phonemes-manual':
Pesos = Pesos_totales_sujetos_todos_canales_copy[:, sum(Len_Estimulos[j] for j in range(i)):sum(
Len_Estimulos[j] for j in range(i + 1))].reshape(info['nchan'], len(exp_info.ph_labels_man), len(times)).mean(1)
else:
Pesos = Pesos_totales_sujetos_todos_canales_copy[:, sum(Len_Estimulos[j] for j in range(i)):sum(Len_Estimulos[j] for j in range(i + 1))]
if ERP:
Pesos = np.flip(Pesos, axis=1)
offset = 0
instantes_index = sgn.find_peaks(np.abs(Pesos.mean(0)[offset:]), height=np.abs(Pesos.mean(0)).max() * 0.3)[0] + offset
instantes_de_interes = [i/ sr + times[0] for i in instantes_index if i / sr + times[0] >= 0]
# Ploteo pesos y cabezas
if Display_figure_instantes:
plt.ion()
else:
plt.ioff()
Blues = plt.cm.get_cmap('Blues').reversed()
cmaps = ['Reds' if Pesos.mean(0)[i] > 0 else Blues for i in instantes_index if
i / sr + times[0] >= 0]
fig, axs = plt.subplots(figsize=(10, 5), ncols=len(cmaps))
fig.suptitle('Mean of $w$ among subjects - {} - {} Band'.format(Stims_Order[i], Band))
for j in range(len(instantes_de_interes)):
if len(cmaps)>1:
ax = axs[j]
else:
ax = axs
ax.set_title('{} ms'.format(int(instantes_de_interes[j] * 1000)), fontsize=18)
fig.tight_layout()
im = mne.viz.plot_topomap(Pesos[:, instantes_index[j]].ravel(), info, axes=ax,
show=False,
sphere=0.07, cmap=cmaps[j],
vmin=Pesos[:, instantes_index[j]].min().round(3),
vmax=Pesos[:, instantes_index[j]].max().round(3))
cbar = plt.colorbar(im[0], ax=ax, orientation='vertical', shrink=0.4,
boundaries=np.linspace(
Pesos[:, instantes_index[j]].min(),
Pesos[:, instantes_index[j]].max(), 100).round(3),
ticks=np.linspace(Pesos[:, instantes_index[j]].min(),
Pesos[:, instantes_index[j]].max(), 4).round(3))
cbar.ax.tick_params(labelsize=15)
fig.tight_layout()
if Save_figure_instantes:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + 'Instantes_interes.png')
fig.savefig(save_path_graficos + 'Instantes_interes.svg')
return Pesos.mean(0)
def pearsonr_pval(x, y):
return stats.pearsonr(x, y)[1]
def Matriz_corr_channel_wise(Pesos_totales_sujetos_todos_canales, stim, Len_Estimulos, info, times, sesiones, Display, Save, Run_graficos_path):
Stims_Order = stim.split('_')
Cant_Estimulos = len(Len_Estimulos)
for k in range(Cant_Estimulos):
if Stims_Order[k] == 'Spectrogram':
Pesos_totales_sujetos_todos_canales_reshaped = Pesos_totales_sujetos_todos_canales[:,
sum(Len_Estimulos[j] for j in range(k)):sum(
Len_Estimulos[j] for j in range(k + 1)), :]. \
reshape(info['nchan'], 16, len(times), len(sesiones)*2).mean(1)
elif Stims_Order[k] == 'Phonemes':
Pesos_totales_sujetos_todos_canales_reshaped = Pesos_totales_sujetos_todos_canales[:,
sum(Len_Estimulos[j] for j in range(k)):sum(
Len_Estimulos[j] for j in range(k + 1)), :]. \
reshape(info['nchan'], len(exp_info.ph_labels), len(times), len(sesiones)*2).mean(1)
elif Stims_Order[k] == 'Phonemes-manual':
Pesos_totales_sujetos_todos_canales_reshaped = Pesos_totales_sujetos_todos_canales[:,
sum(Len_Estimulos[j] for j in range(k)):sum(
Len_Estimulos[j] for j in range(k + 1)), :]. \
reshape(info['nchan'], len(exp_info.ph_labels_man), len(times), len(sesiones)*2).mean(1)
else:
Pesos_totales_sujetos_todos_canales_reshaped = Pesos_totales_sujetos_todos_canales
Pesos_totales_sujetos_todos_canales_average = np.dstack(
(Pesos_totales_sujetos_todos_canales_reshaped, Pesos_totales_sujetos_todos_canales_reshaped.mean(2)))
Correlation_matrices = np.zeros((Pesos_totales_sujetos_todos_canales_average.shape[0],
Pesos_totales_sujetos_todos_canales_average.shape[2],
Pesos_totales_sujetos_todos_canales_average.shape[2]))
for channel in range(len(Pesos_totales_sujetos_todos_canales_average)):
Correlation_matrices[channel] = np.array(
pd.DataFrame(Pesos_totales_sujetos_todos_canales_average[channel]).corr(method='pearson'))
# Correlacion por sujeto
Correlation_matrix = Correlation_matrices.mean(0)
for i in range(len(Correlation_matrix)):
Correlation_matrix[i, i] = Correlation_matrix[-1, i]
lista_nombres = [i for i in np.arange(1, Pesos_totales_sujetos_todos_canales_reshaped.shape[-1] + 1)] + ['Promedio']
Correlation_matrix = pd.DataFrame(Correlation_matrix[:-1, :-1])
Correlation_matrix.columns = lista_nombres[:len(Correlation_matrix) - 1] + [lista_nombres[-1]]
if Display:
plt.ion()
else:
plt.ioff()
mask = np.ones_like(Correlation_matrix)
mask[np.tril_indices_from(mask)] = False
# Calculo promedio
corr_values = Correlation_matrices[channel][np.tril_indices(Correlation_matrices[channel].shape[0], k=0)]
Correlation_mean, Correlation_std = np.mean(np.abs(corr_values)), np.std(np.abs(corr_values))
fig, (ax, cax) = plt.subplots(nrows=2, figsize=(15, 13), gridspec_kw={"height_ratios": [1, 0.05]})
fig.suptitle(f'Similarity among subject\'s {Stims_Order[k]} mTRFs', fontsize=26)
ax.set_title('Mean: {:.3f} +/- {:.3f}'.format(Correlation_mean, Correlation_std), fontsize=18)
sn.heatmap(Correlation_matrix, mask=mask, cmap="coolwarm", fmt='.2f', ax=ax,
annot=True, center=0, xticklabels=True, annot_kws={"size": 15},
cbar=False)
ax.set_yticklabels(['Mean of subjects'] + lista_nombres[1:len(Correlation_matrix)], rotation='horizontal',
fontsize=19)
ax.set_xticklabels(lista_nombres[:len(Correlation_matrix) - 1] + ['Mean of subjects'], rotation='horizontal',
ha='left', fontsize=19)
sn.despine(right=True, left=True, bottom=True, top=True)
fig.colorbar(ax.get_children()[0], cax=cax, orientation="horizontal")
cax.yaxis.set_tick_params(labelsize=20)
cax.xaxis.set_tick_params(labelsize=20)
cax.set_xlabel(xlabel= 'Correlation', fontsize=20)
fig.tight_layout()
if Save:
save_path_graficos = Run_graficos_path
os.makedirs(save_path_graficos, exist_ok=True)
fig.savefig(save_path_graficos + 'TRF_correlation_matrix_{}.png'.format(Stims_Order[k]))
fig.savefig(save_path_graficos + 'TRF_correlation_matrix_{}.svg'.format(Stims_Order[k]))
def Channel_wise_correlation_topomap(Pesos_totales_sujetos_todos_canales, info, Display, Save, Run_graficos_path):