.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_paper_results/plot_cluster_results.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_paper_results_plot_cluster_results.py: Reproducing plots of the paper related to the 180,000 simulated MEG data ======================================================================== Code for reproducing the plots that are present in the paper that refer to the 108,000 simulated MEG sensor level recordings .. GENERATED FROM PYTHON SOURCE LINES 11-21 .. code-block:: default import numpy as np import os.path as op import os import pooch import matplotlib.pyplot as plt import matplotlib.pylab as pylab target = '..' .. GENERATED FROM PYTHON SOURCE LINES 22-23 Load results .. GENERATED FROM PYTHON SOURCE LINES 23-54 .. code-block:: default data_path = op.join('..', 'data') if not op.exists(data_path): os.mkdir(data_path) fname = 'data_cluster.npy' if not op.exists(op.join(data_path, fname)): url = 'https://osf.io/download/auvxz/?direct%26mode=render' pooch.retrieve(url=url, known_hash=None, path=data_path, fname=fname) data_cluster = np.load(op.join(data_path, fname), allow_pickle=True).item() features = data_cluster['features'] tested_par = data_cluster['tested_par'] opt_par = data_cluster['opt_par'] N_mod = features['N_mod'] # Number of simulated AR models (with connections) N_act = features['N_act'] # Number of active patches N_loc = features['N_loc'] # Number of different connected pairs of locations T = features['T'] # Number of time points patch_radii = features['patch_radii'] # Patch radius values area = [2, 4, 8] coh_levels = features['coh_levels'] # Intracoherence values bg_noise_levels = features['bg_noise_levels'] # Background SNR values SNR_val = features['SNR_val'] # Sensor SNR values N_snr = len(SNR_val) # Number of sensor SNR levels N_lam = len(tested_par) # Number of tested parameters for connectivity # estimation N_r = len(patch_radii) # Number of radius values N_c = len(coh_levels) # Number of intracoherence values N_gamma = len(bg_noise_levels) # Number of background SNR values .. GENERATED FROM PYTHON SOURCE LINES 55-61 AUC values as function of the tested regularization parameter lam for the four connectivity metrics. In each panel, barplots and corresponding errorbars represent mean and standard deviation of the AUC values across the 108, 000 simultations, while the x axis displays the value of the ratio between lam and the parameter lamX providing the best estimate of the neural activity. The red vertical line highlight when lam = lamX .. GENERATED FROM PYTHON SOURCE LINES 61-127 .. code-block:: default # setting the parameters for the plots params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (9, 7), 'axes.labelsize': 16, 'axes.titlesize': 18, 'xtick.labelsize': 10, 'ytick.labelsize': 10} width = 0.8*tested_par # width of the bars i_snr = [0, 1, 2, 3] # sensor SNR to be considered when averaging axis = (0, 1, 2, 3, 4, 5) # axis along with compute the average fig, ax = plt.subplots(2, 2) mean_cpsd = -opt_par['conn'][:, :, :, :, :, i_snr, 0, :].mean(axis=axis) std_cpsd = -opt_par['conn'][:, :, :, :, :, i_snr, 0, :].std(axis=axis) ax[0, 0].bar(tested_par, mean_cpsd, width=width, yerr=std_cpsd) ax[0, 0].set_ylim([0.4, 1]) ax[0, 0].set_xscale('log') ax[0, 0].set_title('CPS') ax[0, 0].set_xlabel(r'$\lambda/\lambda_\mathbf{x}$') ax[0, 0].set_ylabel('AUC') ax[0, 0].axvline(1, c='r') ax[0, 0].grid() mean_imcoh = -opt_par['conn'][:, :, :, :, :, i_snr, 1, :].mean(axis=axis) std_imcoh = -opt_par['conn'][:, :, :, :, :, i_snr, 1, :].std(axis=axis) ax[0, 1].bar(tested_par, mean_imcoh, width=width, yerr=std_imcoh) ax[0, 1].set_ylim([0.4, 1]) ax[0, 1].set_xscale('log') ax[0, 1].set_title('imCOH') ax[0, 1].set_xlabel(r'$\lambda/\lambda_\mathbf{x}$') ax[0, 1].set_ylabel('AUC') ax[0, 1].axvline(1, c='r') ax[0, 1].grid() mean_ciplv = -opt_par['conn'][:, :, :, :, :, i_snr, 2, :].mean(axis=axis) std_ciplv = -opt_par['conn'][:, :, :, :, :, i_snr, 2, :].std(axis=axis) ax[1, 0].bar(tested_par, mean_ciplv, width=width, yerr=std_ciplv) ax[1, 0].set_ylim([0.4, 1]) ax[1, 0].set_xscale('log') ax[1, 0].set_title('ciPLV') ax[1, 0].set_xlabel(r'$\lambda/\lambda_\mathbf{x}$') ax[1, 0].set_ylabel('AUC') ax[1, 0].axvline(1, c='r') ax[1, 0].grid() mean_wpli = -opt_par['conn'][:, :, :, :, :, i_snr, 3, :].mean(axis=axis) std_wpli = -opt_par['conn'][:, :, :, :, :, i_snr, 3, :].std(axis=axis) ax[1, 1].bar(tested_par, mean_wpli, width=width, yerr=std_wpli) ax[1, 1].set_ylim([0.4, 1]) ax[1, 1].set_xscale('log') ax[1, 1].set_title('wPLI') ax[1, 1].set_xlabel(r'$\lambda/\lambda_\mathbf{x}$') ax[1, 1].set_ylabel('AUC') ax[1, 1].axvline(1, c='r') ax[1, 1].grid() pylab.rcParams.update(params) fig.tight_layout() fig.show() .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_001.png :alt: CPS, imCOH, ciPLV, wPLI :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_001.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 128-133 2D histogram showing the relationship between the optimal regularization parameters for different connectivity metrics. In each panel the x-axis shows the value of the optimal parameter for wPLI in logarithmic scale, while the y-axis refers to CPS, imCOH and ciPLV, respectively. Notice the different scale for the colorbar in each panel. .. GENERATED FROM PYTHON SOURCE LINES 133-194 .. code-block:: default params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (10, 4), 'axes.labelsize': 16, 'axes.titlesize': 18, 'xtick.labelsize': 10, 'ytick.labelsize': 10} pylab.rcParams.update(params) lam_cps = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, :, 0, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, :, 0] lam_imcoh = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, :, 1, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, :, 0] lam_ciplv = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, :, 2, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, :, 0] lam_wpli = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, :, 3, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, :, 0] fig, ax = plt.subplots(1, 3, tight_layout=True) shrink = 0.5 origin = 'lower' cmap = 'jet' hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_cps, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[0].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[0], location='right', shrink=shrink) ax[0].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[0].set_ylabel(r'log$_{10}(\lambda_\mathbf{CPS})$') ax[0].set_xticks([-2, 0, 2, 4]) ax[0].set_yticks([-2, 0, 2, 4]) hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_imcoh, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im2 = ax[1].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im2, ax=ax[1], location='right', shrink=shrink) ax[1].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[1].set_ylabel(r'log$_{10}(\lambda_\mathbf{imCOH})$') ax[1].set_xticks([-2, 0, 2, 4]) ax[1].set_yticks([-2, 0, 2, 4]) hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_ciplv, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im3 = ax[2].imshow(hist, cmap=cmap, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], origin=origin) ax[2].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[2].set_ylabel(r'log$_{10}(\lambda_\mathbf{ciPLV})$') ax[2].set_xticks([-2, 0, 2, 4]) ax[2].set_yticks([-2, 0, 2, 4]) fig.colorbar(im3, ax=ax[2], location='right', shrink=shrink) .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_002.png :alt: plot cluster results :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_002.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out .. code-block:: none .. GENERATED FROM PYTHON SOURCE LINES 195-201 Impact of the measurement noise on the relationship between the optimal regularization parameters for different connectivity metrics. Each row refers to a different signal-to-noise ratio whose value is reported on top of the panels. Each column shows the 2D-histogram for a different pair of connectivity metrics, namely CPS vs wPLI (left column), imCOH vs wPLI (middle column), and ciPLV vs wPLI (right column) .. GENERATED FROM PYTHON SOURCE LINES 201-265 .. code-block:: default params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (10, 14), 'axes.labelsize': 16, 'axes.titlesize': 16, 'xtick.labelsize': 10, 'ytick.labelsize': 10} pylab.rcParams.update(params) fig, ax = plt.subplots(4, 3, tight_layout=True) shrink = 0.47 origin = 'lower' cmap = 'jet' for i_snr in range(N_snr): lam_cps = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, i_snr, 0, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, i_snr, 0] lam_imcoh = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, i_snr, 1, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, i_snr, 0] lam_ciplv = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, i_snr, 2, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, i_snr, 0] lam_wpli = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, :, i_snr, 3, :], axis=-1)]*opt_par['tc'][:, :, :, :, :, i_snr, 0] hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_cps, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_snr, 0].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_snr, 0], location='right', shrink=shrink) ax[i_snr, 0].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_snr, 0].set_title(str(np.round(SNR_val[i_snr], decimals=1))+'dB') ax[i_snr, 0].set_xticks([-2, 0, 2, 4]) ax[i_snr, 0].set_yticks([-2, 0, 2, 4]) ax[i_snr, 0].set_ylabel(r'log$_{10}(\lambda_\mathbf{CPS})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_imcoh, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_snr, 1].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_snr, 1], location='right', shrink=shrink) ax[i_snr, 1].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_snr, 1].set_title(str(np.round(SNR_val[i_snr], decimals=1))+'dB') ax[i_snr, 1].set_xticks([-2, 0, 2, 4]) ax[i_snr, 1].set_yticks([-2, 0, 2, 4]) ax[i_snr, 1].set_ylabel(r'log$_{10}(\lambda_\mathbf{imCOH})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_ciplv, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_snr, 2].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_snr, 2], location='right', shrink=shrink) ax[i_snr, 2].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_snr, 2].set_title(str(np.round(SNR_val[i_snr], decimals=1))+'dB') ax[i_snr, 2].set_xticks([-2, 0, 2, 4]) ax[i_snr, 2].set_yticks([-2, 0, 2, 4]) ax[i_snr, 2].set_ylabel(r'log$_{10}(\lambda_\mathbf{ciPLV})$') .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_003.png :alt: -20.0dB, -20.0dB, -20.0dB, -11.7dB, -11.7dB, -11.7dB, -3.3dB, -3.3dB, -3.3dB, 5.0dB, 5.0dB, 5.0dB :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_003.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 266-267 Impact of patch area .. GENERATED FROM PYTHON SOURCE LINES 267-332 .. code-block:: default params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (10, 9), 'axes.labelsize': 16, 'axes.titlesize': 16, 'xtick.labelsize': 10, 'ytick.labelsize': 10} pylab.rcParams.update(params) fig, ax = plt.subplots(3, 3, tight_layout=True) shrink = 0.78 origin = 'lower' cmap = 'jet' for i_r in range(N_r): lam_cps = tested_par[np.argmax(-opt_par['conn'][:, :, i_r, :, :, :, 0, :], axis=-1)]*opt_par['tc'][:, :, i_r, :, :, :, 0] lam_imcoh = tested_par[np.argmax(-opt_par['conn'][:, :, i_r, :, :, :, 1, :], axis=-1)]*opt_par['tc'][:, :, i_r, :, :, :, 0] lam_ciplv = tested_par[np.argmax(-opt_par['conn'][:, :, i_r, :, :, :, 2, :], axis=-1)]*opt_par['tc'][:, :, i_r, :, :, :, 0] lam_wpli = tested_par[np.argmax(-opt_par['conn'][:, :, i_r, :, :, :, 3, :], axis=-1)]*opt_par['tc'][:, :, i_r, :, :, :, 0] hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_cps, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_r, 0].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_r, 0], location='right', shrink=shrink) ax[i_r, 0].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_r, 0].set_title(str(np.round(area[i_r], decimals=1))+r'cm$^2$') ax[i_r, 0].set_xticks([-2, 0, 2, 4]) ax[i_r, 0].set_yticks([-2, 0, 2, 4]) ax[i_r, 0].set_ylabel(r'log$_{10}(\lambda_\mathbf{CPS})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_imcoh, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_r, 1].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_r, 1], location='right', shrink=shrink) ax[i_r, 1].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_r, 1].set_title(str(np.round(area[i_r], decimals=1))+r'cm$^2$') ax[i_r, 1].set_xticks([-2, 0, 2, 4]) ax[i_r, 1].set_yticks([-2, 0, 2, 4]) ax[i_r, 1].set_ylabel(r'log$_{10}(\lambda_\mathbf{imCOH})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_ciplv, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_r, 2].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_r, 2], location='right', shrink=shrink) ax[i_r, 2].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_r, 2].set_title(str(np.round(area[i_r], decimals=1))+r'cm$^2$') ax[i_r, 2].set_xticks([-2, 0, 2, 4]) ax[i_r, 2].set_yticks([-2, 0, 2, 4]) ax[i_r, 2].set_ylabel(r'log$_{10}(\lambda_\mathbf{ciPLV})$') .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_004.png :alt: 2cm$^2$, 2cm$^2$, 2cm$^2$, 4cm$^2$, 4cm$^2$, 4cm$^2$, 8cm$^2$, 8cm$^2$, 8cm$^2$ :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_004.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 333-334 Impact of intra-patch coherence .. GENERATED FROM PYTHON SOURCE LINES 334-397 .. code-block:: default params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (10, 9), 'axes.labelsize': 16, 'axes.titlesize': 16, 'xtick.labelsize': 10, 'ytick.labelsize': 10} pylab.rcParams.update(params) fig, ax = plt.subplots(3, 3, tight_layout=True) shrink = 0.78 origin = 'lower' cmap = 'jet' for i_c in range(N_c): lam_cps = tested_par[np.argmax(-opt_par['conn'][:, :, :, i_c, :, :, 0, :], axis=-1)]*opt_par['tc'][:, :, :, i_c, :, :, 0] lam_imcoh = tested_par[np.argmax(-opt_par['conn'][:, :, :, i_c, :, :, 1, :], axis=-1)]*opt_par['tc'][:, :, :, i_c, :, :, 0] lam_ciplv = tested_par[np.argmax(-opt_par['conn'][:, :, :, i_c, :, :, 2, :], axis=-1)]*opt_par['tc'][:, :, :, i_c, :, :, 0] lam_wpli = tested_par[np.argmax(-opt_par['conn'][:, :, :, i_c, :, :, 3, :], axis=-1)]*opt_par['tc'][:, :, :, i_c, :, :, 0] hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_cps, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_c, 0].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_c, 0], location='right', shrink=shrink) ax[i_c, 0].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_c, 0].set_title(str(np.round(coh_levels[i_c], decimals=1))) ax[i_c, 0].set_xticks([-2, 0, 2, 4]) ax[i_c, 0].set_yticks([-2, 0, 2, 4]) ax[i_c, 0].set_ylabel(r'log$_{10}(\lambda_\mathbf{CPS})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_imcoh, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_c, 1].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_c, 1], location='right', shrink=shrink) ax[i_c, 1].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_c, 1].set_title(str(np.round(coh_levels[i_c], decimals=1))) ax[i_c, 1].set_xticks([-2, 0, 2, 4]) ax[i_c, 1].set_yticks([-2, 0, 2, 4]) ax[i_c, 1].set_ylabel(r'log$_{10}(\lambda_\mathbf{imCOH})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_ciplv, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_c, 2].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_c, 2], location='right', shrink=shrink) ax[i_c, 2].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_c, 2].set_title(str(np.round(coh_levels[i_c], decimals=1))) ax[i_c, 2].set_xticks([-2, 0, 2, 4]) ax[i_c, 2].set_yticks([-2, 0, 2, 4]) ax[i_c, 2].set_ylabel(r'log$_{10}(\lambda_\mathbf{ciPLV})$') .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_005.png :alt: 1.0, 1.0, 1.0, 0.5, 0.5, 0.5, 0.2, 0.2, 0.2 :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_005.png :class: sphx-glr-single-img .. GENERATED FROM PYTHON SOURCE LINES 398-399 Impact of biological background noise. .. GENERATED FROM PYTHON SOURCE LINES 399-470 .. code-block:: default params = {'legend.fontsize': 14, 'lines.linewidth': 3, 'figure.figsize': (10, 9), 'axes.labelsize': 16, 'axes.titlesize': 16, 'xtick.labelsize': 10, 'ytick.labelsize': 10} pylab.rcParams.update(params) fig, ax = plt.subplots(3, 3, tight_layout=True) shrink = 0.78 origin = 'lower' cmap = 'jet' for i_gamma in range(N_gamma): lam_cps = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, i_gamma, :, 0, :], axis=-1)]*opt_par['tc'][:, :, :, :, i_gamma, :, 0] lam_imcoh = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, i_gamma, :, 1, :], axis=-1)]*opt_par['tc'][:, :, :, :, i_gamma, :, 0] lam_ciplv = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, i_gamma, :, 2, :], axis=-1)]*opt_par['tc'][:, :, :, :, i_gamma, :, 0] lam_wpli = tested_par[np.argmax(-opt_par['conn'][:, :, :, :, i_gamma, :, 3, :], axis=-1)]*opt_par['tc'][:, :, :, :, i_gamma, :, 0] hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_cps, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_gamma, 0].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_gamma, 0], location='right', shrink=shrink) ax[i_gamma, 0].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_gamma, 0].set_title( str(np.round(bg_noise_levels[i_gamma], decimals=1))) ax[i_gamma, 0].set_xticks([-2, 0, 2, 4]) ax[i_gamma, 0].set_yticks([-2, 0, 2, 4]) ax[i_gamma, 0].set_ylabel(r'log$_{10}(\lambda_\mathbf{CPS})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_imcoh, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_gamma, 1].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_gamma, 1], location='right', shrink=shrink) ax[i_gamma, 1].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_gamma, 1].set_title( str(np.round(bg_noise_levels[i_gamma], decimals=1))) ax[i_gamma, 1].set_xticks([-2, 0, 2, 4]) ax[i_gamma, 1].set_yticks([-2, 0, 2, 4]) ax[i_gamma, 1].set_ylabel(r'log$_{10}(\lambda_\mathbf{imCOH})$') hist, ybins, xbins = np.histogram2d(np.log10(np.reshape(lam_ciplv, (-1, ))), np.log10(np.reshape(lam_wpli, (-1, ))), bins=[np.arange(-3, 5.5, 0.4), np.arange(-3, 5.5, 0.4)]) im1 = ax[i_gamma, 2].imshow(hist, extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]], cmap=cmap, origin=origin) fig.colorbar(im1, ax=ax[i_gamma, 2], location='right', shrink=shrink) ax[i_gamma, 2].set_xlabel(r'log$_{10}(\lambda_\mathbf{wPLI})$') ax[i_gamma, 2].set_title( str(np.round(bg_noise_levels[i_gamma], decimals=1))) ax[i_gamma, 2].set_xticks([-2, 0, 2, 4]) ax[i_gamma, 2].set_yticks([-2, 0, 2, 4]) ax[i_gamma, 2].set_ylabel(r'log$_{10}(\lambda_\mathbf{ciPLV})$') .. image-sg:: /auto_paper_results/images/sphx_glr_plot_cluster_results_006.png :alt: 0.1, 0.1, 0.1, 0.5, 0.5, 0.5, 0.9, 0.9, 0.9 :srcset: /auto_paper_results/images/sphx_glr_plot_cluster_results_006.png :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 4.238 seconds) .. _sphx_glr_download_auto_paper_results_plot_cluster_results.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_cluster_results.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_cluster_results.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_