np.mean(timebined_sorted_special_normalised_frs_around_random, axis=0)),
aspect='auto')
plt.hlines(y=neurons - region_lines,
xmin=0,
xmax=timebins - 1,
linewidth=3,
color='w')
plt.vlines(x=int(timebins / 2), ymin=0, ymax=neurons - 1)
timebined_sorted_frs_around_suc_trials_tr = np.transpose(
timebined_sorted_normalised_frs_around_suc_trials, [1, 2, 0])
timebined_sorted_frs_around_random_tr = np.transpose(
timebined_sorted_normalised_frs_around_random, [1, 2, 0])
p_values_pokes_vs_random, cluster_labels_poke_vs_random = \
cl_per.monte_carlo_significance_probability(timebined_sorted_frs_around_suc_trials_tr, timebined_sorted_frs_around_random_tr,
num_permutations=5000, min_area=4, cluster_alpha=0.05,
monte_carlo_alpha=0.05, sample_statistic='independent',
cluster_statistic='maxarea')
data = avg_timebined_sorted_normalised_frs_around_suc_trials
cluster_labels = cluster_labels_poke_vs_random
plf.show_significant_clusters_on_data(data,
cluster_labels,
region_lines,
np.arange(neurons),
window_time=8,
colormap='binary',
markers='o',
alpha=0.6,
marker_color='b')
plt.vlines(x=0, ymin=0, ymax=neurons - 1)
plt.title(const.rat_folder)
# </editor-fold>
lfps_around_tp_smooth = np.load(
join(lfp_average_data_folder, 'lfps_around_tp_smooth.npy'))
lfps_around_ntp_smooth = np.load(
join(lfp_average_data_folder, 'lfps_around_ntp_smooth.npy'))
lfps_around_tp_smooth_left = lfps_around_tp_smooth[np.arange(0, 72, 2)]
lfps_around_tp_smooth_right = lfps_around_tp_smooth[np.arange(1, 72, 2)]
lfps_around_ntp_smooth_left = lfps_around_ntp_smooth[np.arange(0, 72, 2)]
lfps_around_ntp_smooth_right = lfps_around_ntp_smooth[np.arange(1, 72, 2)]
p_values_right, cluster_labels_under_alpha_right = \
cl_per.monte_carlo_significance_probability(lfps_around_tp_smooth_right, lfps_around_ntp_smooth_right,
num_permutations=1000, min_area=10, cluster_alpha=0.05,
monte_carlo_alpha=0.01, sample_statistic='independent',
cluster_statistic='maxsum')
p_values_left, cluster_labels_under_alpha_left = \
cl_per.monte_carlo_significance_probability(lfps_around_tp_smooth_left, lfps_around_ntp_smooth_left,
num_permutations=1000, min_area=10, cluster_alpha=0.05,
monte_carlo_alpha=0.01, sample_statistic='independent',
cluster_statistic='maxsum')
pos = list(const.BRAIN_REGIONS.values())
data = lfps_around_tp_smooth_left.mean(-1)
cluster_labels = cluster_labels_under_alpha_left
plf.show_significant_clusters_on_data(data,
cluster_labels,
pos,
lfp_probe_positions,