gv.data_type = 'rates' for gv.mouse in [gv.mice[2]]: data.get_sessions_mouse() data.get_stimuli_times() data.get_delays_times() for gv.session in [gv.sessions[-1]]: X, y = data.get_fluo_data() print('mouse', gv.mouse, 'session', gv.session, 'data X', X.shape, 'y', y.shape) data.get_delays_times() data.get_frame_rate() data.get_bins(t_start=.5) gv.duration = X.shape[2] / gv.frame_rate gv.time = np.linspace(0, gv.duration, X.shape[2]) trial_averages = [] for i, gv.trial in enumerate(gv.trials): X_S1, X_S2 = data.get_S1_S2_trials(X, y) data.get_trial_types(X_S1) print('X_S1', X_S1.shape, 'X_S2', X_S2.shape) X_S1 = np.mean(X_S1[:, :, gv.bins_ED], axis=2) X_S2 = np.mean(X_S2[:, :, gv.bins_ED], axis=2) X_S1 = np.mean(X_S1, axis=0)
gv.epochs = ['all'] for gv.mouse in [gv.mice[0]]: data.get_sessions_mouse() data.get_stimuli_times() data.get_delays_times() for gv.session in [gv.sessions[-1]]: X_data, y_labels = data.get_fluo_data() print('mouse', gv.mouse, 'session', gv.session, 'data X', X_data.shape, 'y', y_labels.shape) data.get_delays_times() data.get_frame_rate() data.get_bins(t_start=0) gv.duration = X_data.shape[2] / gv.frame_rate time = np.linspace(0, gv.duration, X_data.shape[2]) for gv.trial in [gv.trials[-1]]: X_S1_trials, X_S2_trials = data.get_S1_S2_trials( X_data, y_labels) X_trials, y_trials = data.get_X_y_epochs( X_S1_trials, X_S2_trials) print('trial:', gv.trial, 'X', X_trials.shape, 'y', y_trials.shape) X_avg = np.mean(X_trials, axis=1)
gv.data_type = 'rates' for gv.mouse in [gv.mice[2]]: data.get_sessions_mouse() data.get_stimuli_times() data.get_delays_times() for gv.session in [gv.sessions[0]]: X, y = data.get_fluo_data() print('mouse', gv.mouse, 'session', gv.session, 'data X', X.shape, 'y', y.shape) data.get_delays_times() data.get_frame_rate() data.get_bins(t_start=1) gv.duration = X.shape[2] / gv.frame_rate gv.time = np.linspace(0, gv.duration, X.shape[2]) trial_averages = [] for i, gv.trial in enumerate(gv.trials): X_S1, X_S2 = data.get_S1_S2_trials(X, y) data.get_trial_types(X_S1) print('X_S1', X_S1.shape, 'X_S2', X_S2.shape) X_S1_ED = np.mean(X_S1[:, :, gv.bins_ED], axis=2) X_S2_ED = np.mean(X_S2[:, :, gv.bins_ED], axis=2) X_S1_LD = np.mean(X_S1[:, :, gv.bins_LD], axis=2)