plt.close() else: weights = pd.DataFrame(weights.reshape(-1, len(weights)), columns=areas) visualize.plot_connectivity(weights) plt.title(part + " " + mode) pdf.savefig() plt.close() X_part_1 = LinearDiscriminantAnalysis(n_components=1).fit( X_part, label_part).transform(X_part) data_one = pd.DataFrame( np.transpose(np.vstack((X_part_1.flatten(), label_part)))) data_one.columns = ['LDA_1D', 'label'] oneD.append(data_one) for i, p in enumerate(participants): data = oneD[i] a = sns.displot(data=data, x="LDA_1D", hue="label", kind="kde", fill=True) a._legend.set_title(p) pdf.savefig() plt.close() toplot = pd.DataFrame() toplot['accuracies'] = acc toplot['ID'] = participants toplot['outcome'] = outcome