join(poke_folder, 'ti_decreasing_neurons_on_non_trial_pokes.df')) # ------------------------------------------------- # <editor-fold desc="LOOK AT ALL THE NEURONS AROUND THE POKE EVENT"> smooth_time = 0.5 smooth_frames = smooth_time * 120 t = binning.rolling_window_with_step(avg_firing_rate_around_suc_trials, np.mean, smooth_frames, int(smooth_frames / 3)) tn = preproc.normalize(t, norm='l1', axis=0) tn = np.asarray(t) for i in np.arange(len(t)): tn[i, :] = binning.scale(t[i], 0, 1) y_positions = template_info['position Y'].values position_sorted_indices = np.argsort(y_positions) regions_pos = list(const.BRAIN_REGIONS.values()) region_lines = [] for rp in regions_pos: region_lines.append( sync_funcs.find_nearest( y_positions[position_sorted_indices] * const.POSITION_MULT, rp)[0]) region_lines = np.array(region_lines) tns = tn[position_sorted_indices] plt.imshow(np.flipud(tns), aspect='auto')
trial_type = 'r' events = { 's': 'Successful', 'ns': 'Not successful', 'tb': 'Ball Touch', 'r': 'Random' } avg_muae_around_event = np.load( join(results_folder, 'Lfp', 'Averages', 'Muaes_around_{}.npy'.format(trial_type))) regions_pos = np.array(list(const.BRAIN_REGIONS.values())) pos_to_elect_factor = const_comm.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE / 8100 region_lines = binning.scale(regions_pos, np.min(regions_pos) * pos_to_elect_factor, np.max(regions_pos) * pos_to_elect_factor) muae_smooth = binning.rolling_window_with_step(avg_muae_around_event, np.mean, 40, 40) muae_smooth = (muae_smooth - muae_smooth.min()) / (muae_smooth.max() - muae_smooth.min()) _ = plt.figure(1) plt.imshow(np.flipud(muae_smooth), aspect='auto', extent=[-8, 8, len(muae_smooth), 0]) plt.vlines(x=0, ymin=0, ymax=muae_smooth.shape[0] - 1) plt.hlines(y=muae_smooth.shape[0] - region_lines, xmin=-8, xmax=8, linewidth=3,
# ------------------------------------------------- # <editor-fold desc="LOOK AT ALL THE NEURONS AROUND THE POKE EVENT"> smooth_time = 0.5 smooth_frames = smooth_time * 120 #data = avg_firing_rate_around_random data = avg_firing_rate_around_suc_trials t = binning.rolling_window_with_step(data, np.mean, smooth_frames, int(smooth_frames / 3)) tn = preproc.normalize(t, norm='l1', axis=0) tn = np.asarray(t) for i in np.arange(len(t)): tn[i, :] = binning.scale(t[i], 0, 1) y_positions = template_info['position Y'].values position_sorted_indices = np.argsort(y_positions) regions_pos = list(const.BRAIN_REGIONS.values()) region_lines = [] for rp in regions_pos: region_lines.append( sync_funcs.find_nearest( y_positions[position_sorted_indices] * const.POSITION_MULT, rp)[0]) region_lines = np.array(region_lines) tns = tn[position_sorted_indices] # hack to remove dead neurons
high_pass_cutoff=3000, rectify=True, low_pass_cutoff=400, avg_reref=True, keep_trials=False) np.save(join(results_folder, 'Lfp', 'Averages', 'Muaes_around_tp.npy'), avg_muae_around_tp) np.save(join(results_folder, 'Lfp', 'Averages', 'Muaes_around_ntp.npy'), avg_muae_around_ntp) # Normalise avg_muae_around_tp = np.load(join(results_folder, 'Lfp', 'Averages', 'Muaes_around_tp.npy')) avg_muae_around_ntp = np.load(join(results_folder, 'Lfp', 'Averages', 'Muaes_around_ntp.npy')) regions_pos = np.array(list(const.BRAIN_REGIONS.values())) pos_to_elect_factor = const.NUMBER_OF_AP_CHANNELS_IN_BINARY_FILE / 8100 region_lines = binning.scale(regions_pos, np.min(regions_pos) * pos_to_elect_factor, np.max(regions_pos) * pos_to_elect_factor) _ = plt.figure(1) plt.imshow(np.flipud(avg_muae_around_tp), aspect='auto') plt.vlines(x=avg_muae_around_tp.shape[1] / 2, ymin=0, ymax=avg_muae_around_tp.shape[0] - 1) plt.hlines(y=avg_muae_around_tp.shape[0] - region_lines, xmin=0, xmax=avg_muae_around_tp.shape[1]-1, linewidth=3, color='w') tp_n = np.empty((avg_muae_around_ntp.shape)) for i in np.arange(len(avg_muae_around_ntp)): tp_n[i, :] = binning.scale(avg_muae_around_ntp[i], 0, 1) _= plt.figure(2) plt.imshow(np.flipud(tp_n), aspect='auto') plt.vlines(x=tp_n.shape[1] / 2, ymin=0, ymax=tp_n.shape[0] - 1) plt.hlines(y=tp_n.shape[0] - region_lines, xmin=0, xmax=tp_n.shape[1]-1, linewidth=1, color='w')
smooth_time = 0.5 smooth_frames = smooth_time * 120 data = avg_firing_rates[trial_type] events = { 's': 'Successful', 'ns': 'Not successful', 'tb': 'Ball Touch', 'r': 'Random' } frs = binning.rolling_window_with_step(data, np.mean, smooth_frames, int(smooth_frames / 3)) frs_norm = np.asarray(frs) for i in np.arange(len(frs)): frs_norm[i, :] = binning.scale(frs[i], 0, 1) y_positions = template_info['position Y'].values position_sorted_indices = np.argsort(y_positions) regions_pos = list(const.BRAIN_REGIONS.values()) region_lines = [] for rp in regions_pos: region_lines.append( sync_funcs.find_nearest( y_positions[position_sorted_indices] * const_comm.POSITION_MULT, rp)[0]) region_lines = np.array(region_lines) frs_norm_sorted = frs_norm[position_sorted_indices] '''