lengths_buffer = [] for j, y in enumerate(mask_copy): if y: lengths_buffer.append(x[j]) elif len(lengths_buffer) > 0: lengths.append(np.mean(lengths_buffer)) lengths_buffer = [] print(lengths) return np.array(lengths) with h5py.File('{}\\{}\\{}'.format(dir_, experiment, 'experiment_data.h5')) as f: fs, channels, p_names = get_info(f, settings['drop_channels']) if reject: rejections = load_rejections(f, reject_alpha=True)[0] else: rejections = None spatial = f['protocol15/signals_stats/left/spatial_filter'][:] plot_topomap(spatial, ch_names_to_2d_pos(channels), axes=plt.gca(), show=False) plt.savefig('alphaS{}_Day{}_spatial_filter'.format(subj, day+1)) mu_band = f['protocol15/signals_stats/left/bandpass'][:] #mu_band = (12, 13) max_gap = 1 / min(mu_band) * 2 min_sate_duration = max_gap * 2 raw = OrderedDict() signal = OrderedDict() for j, name in enumerate(p_names): x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejections) raw = add_data(raw, name, x, j) signal = add_data(signal, name, f['protocol{}/signals_data'.format(j + 1)][:], j)
from vendor.nfb.pynfb.protocols.ssd.topomap_selector_ica import ICADialog from PyQt5 import QtGui, QtWidgets a = QtWidgets.QApplication([]) fig1, axes1 = plt.subplots(ncols=3) for subj in range(3, 4): for day in range(2, 5): experiments = settings['subjects'][subj] experiment = experiments[day] reject = True with h5py.File('{}\\{}\\{}'.format(settings['dir'], experiment, 'experiment_data.h5')) as f: fs, channels, p_names = get_info(f, settings['drop_channels']) rejection, alpha, ica = load_rejections(f, reject_alpha=False) raw_before = OrderedDict() raw_after = OrderedDict() for j, name in enumerate(p_names): if j < 3: x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None) raw_before = add_data_simple(raw_before, name, x) elif j > len(p_names) - 3: x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None) print(x.shape) raw_after = add_data_simple(raw_after, name, x) xx = np.concatenate([ raw_before['Opened'],
subj = 3 experiments = settings['subjects'][subj] experiment = experiments[2] def preproc(x, fs, rej=None): x = dc_blocker(x) x = fft_filter(x, fs, band=(0, 45)) if rej is not None: x = np.dot(x, rej) return x reject = False with h5py.File('{}\\{}\\{}'.format(settings['dir'], experiment, 'experiment_data.h5')) as f: fs, channels, p_names = get_info(f, settings['drop_channels']) rejection, alpha, ica = load_rejections(f, reject_alpha=True) raw_before = OrderedDict() raw_after = OrderedDict() for j, name in enumerate(p_names): if j < 5: x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None) raw_before = add_data_simple(raw_before, name, x) elif j > len(p_names) - 3: x = preproc(f['protocol{}/raw_data'.format(j + 1)][:], fs, rejection if reject else None) print(x.shape) raw_after = add_data_simple(raw_after, name, x) # plot raw data ch_plot = ['C3', 'C4', 'P3', 'P4']#, 'Pz', 'Fp1']
def plot_results(pilot_dir, subj, channel, alpha_band=(9, 14), theta_band=(3, 6), drop_channels=None, dc=False, reject_alpha=True, normalize_by='opened'): drop_channels = drop_channels or [] cm = get_colors() fg = plt.figure(figsize=(30, 6)) for j_s, experiment in enumerate(subj): with h5py.File('{}\\{}\\{}'.format(pilot_dir, experiment, 'experiment_data.h5')) as f: rejections, top_alpha, top_ica = load_rejections(f, reject_alpha=reject_alpha) fs, channels, p_names = get_info(f, drop_channels) ch = channels.index(channel) #plt.plot(fft_filter(f['protocol6/raw_data'][:, ch], fs, band=(3, 35))) #plt.plot(fft_filter(np.dot(f['protocol6/raw_data'], rejections)[:, ch], fs, band=(3, 35))) #plt.show() #from scipy.signal import welch #plt.plot(*welch(f['protocol1/raw_data'][:60*500//2, channels.index('C3')], fs, nperseg=1000)) #plt.plot(*welch(f['protocol1/raw_data'][60*500//2:, channels.index('C3')], fs, nperseg=1000)) #plt.plot(*welch(f['protocol2/raw_data'][:30*500//2, channels.index('C3')], fs, nperseg=1000)) #plt.plot(*welch(f['protocol2/raw_data'][30*500//2:, channels.index('C3')], fs, nperseg=1000)) #plt.legend(['Close', 'Open', 'Left', 'Right']) #plt.show() # collect powers powers = OrderedDict() raw = OrderedDict() alpha = OrderedDict() pow_theta = [] for j, name in enumerate(p_names): pow, alpha_x, x = get_protocol_power(f, j, fs, rejections, ch, alpha_band, dc=dc) if 'FB' in name: pow_theta.append(get_protocol_power(f, j, fs, rejections, ch, theta_band, dc=dc)[0].mean()) powers = add_data(powers, name, pow, j) raw = add_data(raw, name, x, j) alpha = add_data(alpha, name, alpha_x, j) # plot rejections n_tops = top_ica.shape[1] + top_alpha.shape[1] for j_t in range(top_ica.shape[1]): ax = fg.add_subplot(4, n_tops * len(subj), n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1) ax.set_xlabel('ICA{}'.format(j_t + 1)) labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0]) channels = [label for label in labels if label not in drop_channels] pos = ch_names_to_2d_pos(channels) plot_topomap(data=top_ica[:, j_t], pos=pos, axes=ax, show=False) for j_t in range(top_alpha.shape[1]): ax = fg.add_subplot(4, n_tops * len(subj), n_tops * len(subj) * 3 + n_tops * j_s + j_t + 1 + top_ica.shape[1]) ax.set_xlabel('CSP{}'.format(j_t + 1)) labels, fs = get_lsl_info_from_xml(f['stream_info.xml'][0]) channels = [label for label in labels if label not in drop_channels] pos = ch_names_to_2d_pos(channels) plot_topomap(data=top_alpha[:, j_t], pos=pos, axes=ax, show=False) # plot powers if normalize_by == 'opened': norm = powers['1. Opened'].mean() elif normalize_by == 'beta': norm = np.mean(pow_theta) else: print('WARNING: norm = 1') print('norm', norm) ax1 = fg.add_subplot(3, len(subj), j_s + 1) ax = fg.add_subplot(3, len(subj), j_s + len(subj) + 1) t = 0 for j_p, ((name, pow), (name, x)) in enumerate(zip(powers.items(), raw.items())): if name == '2228. FB': from scipy.signal import periodogram fff = plt.figure() fff.gca().plot(*periodogram(x, fs, nfft=fs * 3), c=cm[name.split()[1]]) plt.xlim(0, 80) plt.ylim(0, 3e-11) plt.show() print(name) time = np.arange(t, t + len(x)) / fs color = cm[''.join([i for i in name.split()[1] if not i.isdigit()])] ax1.plot(time, fft_filter(x, fs, (2, 45)), c=color, alpha=0.4) ax1.plot(time, alpha[name], c=color) t += len(x) ax.plot([j_p], [pow.mean() / norm], 'o', c=color, markersize=10) ax.errorbar([j_p], [pow.mean() / norm], yerr=pow.std() / norm, c=color, ecolor=color) fb_x = np.hstack([[j] * len(pows) for j, (key, pows) in enumerate(powers.items()) if 'FB' in key]) fb_y = np.hstack([pows for key, pows in powers.items() if 'FB' in key]) / norm sns.regplot(x=fb_x, y=fb_y, ax=ax, color=cm['FB'], scatter=False, truncate=True) ax1.set_xlim(0, t / fs) ax1.set_ylim(-40, 40) plt.setp(ax.xaxis.get_majorticklabels(), rotation=70) ax.set_xticks(range(len(powers))) ax.set_xticklabels(powers.keys()) ax.set_ylim(0, 3) ax.set_xlim(-1, len(powers)) ax1.set_title('Day {}'.format(j_s + 1)) return fg