""" =========================== Script for visualization of ALL P300 datasets =========================== This script will download ALL P300 datasets and create descriptive plots for every single session. Total downloaded size will be (as of now) 120GB. .. versionadded:: 0.4.5 """ import warnings # Authors: Jan Sosulski # # License: BSD (3-clause) from pathlib import Path import matplotlib import mne import numpy as np import seaborn as sns from matplotlib import pyplot as plt from moabb.paradigms import P300 matplotlib.use("agg") sns.set_style("whitegrid") mne.set_log_level("WARNING") def create_plot_overview(epo, plot_opts=None, path=None, description=""): # Butterflyplot suptitle = f"{description} ({epo_summary(epo)[1]})" epo_t = epo["Target"] epo_nt = epo["NonTarget"] evkd_t = epo_t.average() evkd_nt = epo_nt.average() ix_t = epo.events[:, 2] == epo.event_id["Target"] ix_nt = epo.events[:, 2] == epo.event_id["NonTarget"] fig0, ax = plt.subplots(1, 1, figsize=(10, 3), sharey="all", sharex="all") ax.scatter( epo.events[ix_t, 0], np.ones((np.sum(ix_t),)), color="r", marker="|", label="Target", ) ax.scatter( epo.events[ix_nt, 0], np.zeros((np.sum(ix_nt),)), color="b", marker="|", label="NonTarget", ) ax.legend() ax.set_title("Event timeline") fig0.suptitle(suptitle) fig0.tight_layout() fig0.savefig(path / f"event_timeline.{plot_opts['format']}", dpi=plot_opts["dpi"]) fig1, axes = plt.subplots(2, 1, figsize=(6, 6), sharey="all", sharex="all") evkd_t.plot(spatial_colors=True, show=False, axes=axes[0]) axes[0].set_title("Target response") evkd_nt.plot(spatial_colors=True, show=False, axes=axes[1]) axes[1].set_title("NonTarget response") fig1.suptitle(suptitle) with warnings.catch_warnings(): warnings.simplefilter("ignore") fig1.tight_layout() fig1.savefig( path / f"target_nontarget_erps.{plot_opts['format']}", dpi=plot_opts["dpi"] ) # topomap tp = plot_opts["topo"]["timepoints"] tmin, tmax = plot_opts["topo"]["tmin"], plot_opts["topo"]["tmax"] times = np.linspace(tmin, tmax, tp) fig2 = evkd_t.plot_topomap(times=times, colorbar=True, show=False) fig2.suptitle(suptitle) fig2.savefig( path / f"target_topomap_{tp}_timepoints.{plot_opts['format']}", dpi=plot_opts["dpi"], ) # jointmap fig3 = evkd_t.plot_joint(show=False) fig3.suptitle(suptitle) fig3.savefig(path / f"target_erp_topo.{plot_opts['format']}", dpi=plot_opts["dpi"]) # sensorplot fig4 = mne.viz.plot_compare_evokeds( [evkd_t.crop(0, 0.6), evkd_nt.crop(0, 0.6)], axes="topo", show=False ) fig4[0].suptitle(suptitle) fig4[0].savefig(path / f"sensorplot.{plot_opts['format']}", dpi=plot_opts["dpi"]) fig5, ax = plt.subplots(2, 1, figsize=(8, 6), sharex="all", sharey="all") t_data = epo_t.get_data() * 1e6 nt_data = epo_nt.get_data() * 1e6 data = epo.get_data() * 1e6 minmax = np.max(data, axis=2) - np.min(data, axis=2) per_channel = np.mean(minmax, axis=0) worst_ch = np.argsort(per_channel) worst_ch = worst_ch[max(-8, -len(epo.ch_names)) :] minmax_t = np.max(t_data, axis=2) - np.min(t_data, axis=2) minmax_nt = np.max(nt_data, axis=2) - np.min(nt_data, axis=2) ch = epo_t.ch_names for i in range(minmax_nt.shape[1]): lab = ch[i] if i in worst_ch else None sns.kdeplot(minmax_t[:, i], ax=ax[0], label=lab, clip=(0, 300)) sns.kdeplot(minmax_nt[:, i], ax=ax[1], label=lab, clip=(0, 300)) ax[0].set_xlim(0, 200) ax[0].set_title("Target minmax") ax[1].set_title("NonTarget minmax") ax[1].set_xlabel("Minmax in $\\mu$V") ax[1].legend(title="Worst channels") fig5.suptitle(suptitle) fig5.tight_layout() fig5.savefig(path / f"minmax.{plot_opts['format']}", dpi=plot_opts["dpi"]) with warnings.catch_warnings(): warnings.simplefilter("ignore") fig6 = epo.plot_psd(0, 20, bandwidth=1) fig6.suptitle(suptitle) fig6.tight_layout() fig6.savefig(path / f"spectrum.{plot_opts['format']}", dpi=plot_opts["dpi"]) plt.close("all") def epo_summary(epos): summary = {} summary["mne_string"] = repr(epos) summary["n_channels"] = len(epos.ch_names) summary["n_target"] = len(epos["Target"]) summary["n_nontarget"] = len(epos["NonTarget"]) info_str = ( f"Ch:{len(epos.ch_names)},T:{len(epos['Target'])},NT:{len(epos['NonTarget'])}" ) return summary, info_str if __name__ == "__main__": FIGURES_PATH = Path.home() / "moabb_figures" / "erps" # Changing this to False re-generates all plots even if they exist. Use with caution. cache_plots = True baseline = None highpass = 0.5 lowpass = 16 sampling_rate = 100 paradigm = P300( resample=sampling_rate, fmin=highpass, fmax=lowpass, baseline=baseline ) ival = [-0.3, 1] plot_opts = { "dpi": 120, "topo": {"timepoints": 10, "tmin": 0, "tmax": 0.6}, "format": "png", } plt.ioff() # dsets = P300_DSETS dsets = paradigm.datasets for dset in dsets: dset.interval = ival dset_name = dset.__class__.__name__ print(f"Processing dataset: {dset_name}") data_path = FIGURES_PATH / dset_name # path of the dataset folder data_path.mkdir(exist_ok=True) all_subjects_cached = True for subject in dset.subject_list: subject_path = data_path / f"subject_{subject}" if cache_plots and subject_path.exists(): continue all_subjects_cached = False print(f" Processing subject: {subject}") subject_path.mkdir(parents=True, exist_ok=True) try: epos, labels, meta = paradigm.get_data( dset, [subject], return_epochs=True ) except Exception: # catch all, dont stop processing pls print(f"Failed to get data for {dset_name}-{subject}") (subject_path / "processing_error").touch() continue description = f"Dset: {dset_name}, Sub: {subject}, Ses: all" create_plot_overview( epos, plot_opts=plot_opts, path=subject_path, description=description ) if len(meta["session"].unique()) > 1: for session in meta["session"].unique(): session_path = subject_path / f"session_{session}" session_path.mkdir(parents=True, exist_ok=True) ix = meta.session == session description = f"Dset: {dset_name}, Sub: {subject}, Ses: {session}" create_plot_overview( epos[ix], plot_opts=plot_opts, path=session_path, description=description, ) if all_subjects_cached: print(" No plots necessary, every subject has output folder.") print("All datasets processed.")