From ea9d5f22f869f12c2054131648210d14b519f563 Mon Sep 17 00:00:00 2001 From: Till Raab Date: Fri, 23 Jun 2023 14:21:28 +0200 Subject: [PATCH] fix Nvidia shit and compute event time correlations that have been added. event time analysis now include analysis on chirpt times/quantity during or relative to chasings. this about this again. --- event_time_analysis.py | 578 +++++++++++++++++++++++-------------- event_time_correlations.py | 10 +- 2 files changed, 366 insertions(+), 222 deletions(-) diff --git a/event_time_analysis.py b/event_time_analysis.py index e5d7fea..ab64dbc 100644 --- a/event_time_analysis.py +++ b/event_time_analysis.py @@ -182,7 +182,301 @@ def relative_rate_progression(all_event_t, title=''): print(f'Progression {title}: pearson-r={r:.2f} p={p:.3f}') +def chase_time_progression(all_ag_on_t, all_ag_off_t): + stop_t = 3*60*60 + snippet_len = 15*60 + + snippet_starts = np.arange(0, stop_t, snippet_len) + all_snippet_chase_dur = [] + for a_on, a_off in zip(all_ag_on_t, all_ag_off_t): + if len(a_on) == 0: + continue + mean_chase_dur = np.mean(a_off - a_on) + snippet_chase_dur = [] + for s0 in snippet_starts: + snippet_mask = (a_on > s0) & (a_on <= s0+snippet_len) + if np.any(snippet_mask): + snippet_chase_dur.append(np.mean(a_off[snippet_mask] - a_on[snippet_mask])) + else: + snippet_chase_dur.append(np.nan) + + all_snippet_chase_dur.append(np.array(snippet_chase_dur) / mean_chase_dur) + all_snippet_chase_dur = np.array(all_snippet_chase_dur) + + fig = plt.figure(figsize=(20/2.54, 12/2.54)) + gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95) + ax = fig.add_subplot(gs[0, 0]) + + plot_t = np.repeat(snippet_starts, 2) + plot_t[1::2] += snippet_len + + for trial_snippet_chase_dur in all_snippet_chase_dur: + plot_ratios = np.repeat(trial_snippet_chase_dur, 2) + ax.plot(plot_t / 3600, plot_ratios, color='grey', lw=1, alpha=0.5) + # ax.plot(snippet_starts + snippet_len/2, event_ratios) + mean_ratio = np.nanmean(all_snippet_chase_dur, axis=0) + plot_mean_ratio = np.repeat(mean_ratio, 2) + ax.plot(plot_t / 3600, plot_mean_ratio, color='k', lw=3) + ax.plot(plot_t / 3600, np.ones_like(plot_t), linestyle='dotted', lw=2, color='k') + + ax.set_xlabel('time [h]', fontsize=12) + ax.set_ylabel('chase duration / mean(chase duration)', fontsize=12) + ax.set_title('progression chase duration ') + ax.tick_params(labelsize=10) + + ax.set_xlim(0, 3) + ax.set_ylim(0, 5) + + x = np.hstack(all_snippet_chase_dur) + y = np.hstack(np.tile(snippet_starts, (all_snippet_chase_dur.shape[0], 1))) + + r, p = scp.pearsonr(x[~np.isnan(x)], y[~np.isnan(x)]) + + print(f'Progression chase duration: pearson-r={r:.2f} p={p:.3f}') + + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', 'chase_duration_progression.png'), dpi=300) + plt.close() + + +def event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name): + print('') + all_pre_chase_event_mask = [] + all_chase_event_mask = [] + all_end_chase_event_mask = [] + all_after_chase_event_mask = [] + all_before_contact_event_mask = [] + all_after_contact_event_mask = [] + all_pre_chase_time = [] + all_chase_time = [] + all_end_chase_time = [] + all_after_chase_time = [] + all_before_contact_time = [] + all_after_contact_time = [] + + video_trial_win_sex = [] + video_trial_lose_sex = [] + + time_tol = 5 + + for enu, contact_t, ag_on_t, ag_off_t, event_times in zip( + np.arange(len(all_contact_t)), all_contact_t, all_ag_on_t, all_ag_off_t, all_event_t): + + if len(ag_on_t) == 0: + continue + + if len(event_times) == 0: + continue + + pre_chase_event_mask = np.zeros_like(event_times) + chase_event_mask = np.zeros_like(event_times) + end_chase_event_mask = np.zeros_like(event_times) + after_chase_event_mask = np.zeros_like(event_times) + + video_trial_win_sex.append(win_sex[enu]) + video_trial_lose_sex.append(lose_sex[enu]) + + for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t): + pre_chase_event_mask[(event_times >= chase_on_t - time_tol) & (event_times < chase_on_t)] = 1 + chase_event_mask[(event_times >= chase_on_t) & (event_times < chase_off_t - time_tol)] = 1 + end_chase_event_mask[(event_times >= chase_off_t - time_tol) & (event_times < chase_off_t)] = 1 + after_chase_event_mask[(event_times >= chase_off_t) & (event_times < chase_off_t + time_tol)] = 1 + + all_pre_chase_event_mask.append(pre_chase_event_mask) + all_chase_event_mask.append(chase_event_mask) + all_end_chase_event_mask.append(end_chase_event_mask) + all_after_chase_event_mask.append(after_chase_event_mask) + + all_pre_chase_time.append(len(ag_on_t) * time_tol) + chasing_dur = (ag_off_t - ag_on_t) - time_tol + chasing_dur[chasing_dur < 0] = 0 + all_chase_time.append(np.sum(chasing_dur)) + all_end_chase_time.append(len(ag_on_t) * time_tol) + all_after_chase_time.append(len(ag_on_t) * time_tol) + + before_countact_event_mask = np.zeros_like(event_times) + after_countact_event_mask = np.zeros_like(event_times) + for ct in contact_t: + before_countact_event_mask[(event_times >= ct - time_tol) & (event_times < ct)] = 1 + after_countact_event_mask[(event_times >= ct) & (event_times < ct + time_tol)] = 1 + all_before_contact_event_mask.append(before_countact_event_mask) + all_after_contact_event_mask.append(after_countact_event_mask) + + all_before_contact_time.append(len(contact_t) * time_tol) + all_after_contact_time.append(len(contact_t) * time_tol) + + all_pre_chase_time = np.array(all_pre_chase_time) + all_chase_time = np.array(all_chase_time) + all_end_chase_time = np.array(all_end_chase_time) + all_after_chase_time = np.array(all_after_chase_time) + all_before_contact_time = np.array(all_before_contact_time) + all_after_contact_time = np.array(all_after_contact_time) + + video_trial_win_sex = np.array(video_trial_win_sex) + video_trial_lose_sex = np.array(video_trial_lose_sex) + + all_pre_chase_time_ratio = all_pre_chase_time / (3 * 60 * 60) + all_chase_time_ratio = all_chase_time / (3 * 60 * 60) + all_end_chase_time_ratio = all_end_chase_time / (3 * 60 * 60) + all_after_chase_time_ratio = all_after_chase_time / (3 * 60 * 60) + all_before_countact_time_ratio = all_before_contact_time / (3 * 60 * 60) + all_after_countact_time_ratio = all_after_contact_time / (3 * 60 * 60) + + all_pre_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_pre_chase_event_mask))) + all_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_chase_event_mask))) + all_end_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_end_chase_event_mask))) + all_after_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_after_chase_event_mask))) + all_before_countact_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_before_contact_event_mask))) + all_after_countact_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_after_contact_event_mask))) + + for x, y, name in [[all_pre_chase_event_ratio, all_pre_chase_time_ratio, 'pre chase'], + [all_chase_event_ratio, all_chase_time_ratio, 'while chase'], + [all_end_chase_event_ratio, all_end_chase_time_ratio, 'end chase'], + [all_after_chase_event_ratio, all_after_chase_time_ratio, 'after chase'], + [all_before_countact_event_ratio, all_before_countact_time_ratio, 'pre contact'], + [all_after_countact_event_ratio, all_after_countact_time_ratio, 'post contact']]: + t, p = scp.ttest_rel(x, y) + print(f'{event_name} {name}: t={t:.2f} p={p:.3f}') + + fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) + gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.15, right=0.95, top=0.9) + ax = fig.add_subplot(gs[0, 0]) + ax_pie = fig.add_subplot(gs[0, 1]) + + ax.boxplot([all_pre_chase_event_ratio / all_pre_chase_time_ratio, + all_chase_event_ratio / all_chase_time_ratio, + all_end_chase_event_ratio / all_end_chase_time_ratio, + all_after_chase_event_ratio / all_after_chase_time_ratio, + all_before_countact_event_ratio / all_before_countact_time_ratio, + all_after_countact_event_ratio / all_after_countact_time_ratio], positions=np.arange(6), sym='', + zorder=2) + + ylim = list(ax.get_ylim()) + ylim[0] = -.1 if ylim[0] < -.1 else ylim[0] + ylim[1] = 1.1 if ylim[1] < 1.1 else ylim[1] + ############################################################################## + for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2): + mec = 'k' if sex_w == sex_l else 'None' + if 'lose' in event_name: + marker = 'o' + c = male_color if sex_l == 'm' else female_color + elif "win" in event_name: + marker = 'p' + c = male_color if sex_w == 'm' else female_color + else: + print('error') + embed() + quit() + values = np.array(all_pre_chase_event_ratio / all_pre_chase_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + values = np.array(all_chase_event_ratio / all_chase_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + values = np.array(all_end_chase_event_ratio / all_end_chase_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + values = np.array(all_after_chase_event_ratio / all_after_chase_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + values = np.array(all_before_countact_event_ratio / all_before_countact_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + values = np.array(all_after_countact_event_ratio / all_after_countact_time_ratio)[ + (video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] + ax.plot(np.ones_like(values) * 5, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, + zorder=1) + + ############################################################################## + + ax.plot(np.arange(7) - 1, np.ones(7), linestyle='dotted', lw=2, color='k') + ax.set_xlim(-0.5, 5.5) + ax.set_ylim(ylim[0], ylim[1]) + + ax.set_ylabel(r'rel. count$_{event}$ / rel. time$_{event}$', fontsize=12) + ax.set_xticks(np.arange(6)) + ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$', + 'contact$_{after}$'], rotation=45) + ax.tick_params(labelsize=10) + fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}') + + ############################################### + flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask) + flat_chase_event_mask = np.hstack(all_chase_event_mask) + flat_end_chase_event_mask = np.hstack(all_end_chase_event_mask) + flat_after_chase_event_mask = np.hstack(all_after_chase_event_mask) + flat_before_countact_event_mask = np.hstack(all_before_contact_event_mask) + flat_after_countact_event_mask = np.hstack(all_after_contact_event_mask) + + flat_pre_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_end_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + flat_after_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 + + event_context_values = [np.sum(flat_pre_chase_event_mask) / len(flat_pre_chase_event_mask), + np.sum(flat_chase_event_mask) / len(flat_chase_event_mask), + np.sum(flat_end_chase_event_mask) / len(flat_end_chase_event_mask), + np.sum(flat_after_chase_event_mask) / len(flat_after_chase_event_mask), + np.sum(flat_before_countact_event_mask) / len(flat_before_countact_event_mask), + np.sum(flat_after_countact_event_mask) / len(flat_after_countact_event_mask)] + + event_context_values.append(1 - np.sum(event_context_values)) + + time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time), + np.sum(all_after_chase_time), np.sum(all_before_contact_time), + np.sum(all_after_contact_time)] + + time_context_values.append(len(all_pre_chase_time) * 3 * 60 * 60 - np.sum(time_context_values)) + time_context_values /= np.sum(time_context_values) + + # fig, ax = plt.subplots(figsize=(12/2.54,12/2.54)) + size = 0.3 + outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k', 'tab:brown', 'tab:grey'] + ax_pie.pie(event_context_values, radius=1, colors=outer_colors, + wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, 1)) + ax_pie.pie(time_context_values, radius=1 - size, colors=outer_colors, + wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, 1)) + + ax_pie.set_title(r'event context') + legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'), + Patch(facecolor='tab:orange', edgecolor='w', + label='%.1f' % (event_context_values[1] * 100) + '%'), + Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'), + Patch(facecolor='tab:green', edgecolor='w', + label='%.1f' % (event_context_values[3] * 100) + '%'), + Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'), + Patch(facecolor='tab:brown', edgecolor='w', + label='%.1f' % (event_context_values[5] * 100) + '%'), + Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[0] * 100) + '%'), + Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[1] * 100) + '%'), + Patch(facecolor='yellow', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[2] * 100) + '%'), + Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[3] * 100) + '%'), + Patch(facecolor='k', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[4] * 100) + '%'), + Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w', + label='%.1f' % (time_context_values[5] * 100) + '%')] + + ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.25), frameon=False, + fontsize=9) + + plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{event_name}_categories.png'), + dpi=300) + plt.close() + # plt.show() def main(base_path): @@ -196,8 +490,8 @@ def main(base_path): trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0) chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0) trial_mask = chirp_notes['good'] == 1 - # trial_summary = trial_summary[chirp_notes['good'] == 1] + ### data processing ####################### all_rise_times_lose = [] all_rise_times_win = [] all_chirp_times_lose = [] @@ -268,11 +562,13 @@ def main(base_path): win_sex = np.array(win_sex) lose_sex = np.array(lose_sex) + ### inter event intervalls ### iei_analysis(all_chirp_times_lose, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{lose}$') iei_analysis(all_chirp_times_win, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{win}$') iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$') iei_analysis(all_rise_times_win, win_sex, lose_sex, kernal_w=50, title=r'rises$_{win}$') + ### event progressions ### print('') relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$') relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$') @@ -282,232 +578,72 @@ def main(base_path): relative_rate_progression(all_contact_t, title=r'contact') relative_rate_progression(all_ag_on_t, title=r'chasing') + chase_time_progression(all_ag_on_t, all_ag_off_t) - ############################################################################# + ### event category signals ### for all_event_t, event_name in zip([all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win], [r'chirps$_{lose}$', r'chirps$_{win}$', r'rises$_{lose}$', r'rises$_{win}$']): - print('') - all_pre_chase_event_mask = [] - all_chase_event_mask = [] - all_end_chase_event_mask = [] - all_after_chase_event_mask = [] - all_before_contact_event_mask = [] - all_after_contact_event_mask = [] - - all_pre_chase_time = [] - all_chase_time = [] - all_end_chase_time = [] - all_after_chase_time = [] - all_before_contact_time = [] - all_after_contact_time = [] - - video_trial_win_sex = [] - video_trial_lose_sex = [] - - time_tol = 5 - - for enu, contact_t, ag_on_t, ag_off_t, event_times in zip( - np.arange(len(all_contact_t)), all_contact_t, all_ag_on_t, all_ag_off_t, all_event_t): - - if len(ag_on_t) == 0: - continue - - if len(event_times) == 0: - continue - - pre_chase_event_mask = np.zeros_like(event_times) - chase_event_mask = np.zeros_like(event_times) - end_chase_event_mask = np.zeros_like(event_times) - after_chase_event_mask = np.zeros_like(event_times) - - video_trial_win_sex.append(win_sex[enu]) - video_trial_lose_sex.append(lose_sex[enu]) - - for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t): - pre_chase_event_mask[(event_times >= chase_on_t - time_tol) & (event_times < chase_on_t)] = 1 - chase_event_mask[(event_times >= chase_on_t) & (event_times < chase_off_t - time_tol)] = 1 - end_chase_event_mask[(event_times >= chase_off_t - time_tol) & (event_times < chase_off_t)] = 1 - after_chase_event_mask[(event_times >= chase_off_t) & (event_times < chase_off_t + time_tol)] = 1 - - all_pre_chase_event_mask.append(pre_chase_event_mask) - all_chase_event_mask.append(chase_event_mask) - all_end_chase_event_mask.append(end_chase_event_mask) - all_after_chase_event_mask.append(after_chase_event_mask) - - all_pre_chase_time.append(len(ag_on_t) * time_tol) - chasing_dur = (ag_off_t - ag_on_t) - time_tol - chasing_dur[chasing_dur < 0 ] = 0 - all_chase_time.append(np.sum(chasing_dur)) - all_end_chase_time.append(len(ag_on_t) * time_tol) - all_after_chase_time.append(len(ag_on_t) * time_tol) - - before_countact_event_mask = np.zeros_like(event_times) - after_countact_event_mask = np.zeros_like(event_times) - for ct in contact_t: - before_countact_event_mask[(event_times >= ct-time_tol) & (event_times < ct)] = 1 - after_countact_event_mask[(event_times >= ct) & (event_times < ct+time_tol)] = 1 - all_before_contact_event_mask.append(before_countact_event_mask) - all_after_contact_event_mask.append(after_countact_event_mask) - - all_before_contact_time.append(len(contact_t) * time_tol) - all_after_contact_time.append(len(contact_t) * time_tol) - - all_pre_chase_time = np.array(all_pre_chase_time) - all_chase_time = np.array(all_chase_time) - all_end_chase_time = np.array(all_end_chase_time) - all_after_chase_time = np.array(all_after_chase_time) - all_before_contact_time = np.array(all_before_contact_time) - all_after_contact_time = np.array(all_after_contact_time) - - video_trial_win_sex = np.array(video_trial_win_sex) - video_trial_lose_sex = np.array(video_trial_lose_sex) - - all_pre_chase_time_ratio = all_pre_chase_time / (3*60*60) - all_chase_time_ratio = all_chase_time / (3*60*60) - all_end_chase_time_ratio = all_end_chase_time / (3*60*60) - all_after_chase_time_ratio = all_after_chase_time / (3*60*60) - all_before_countact_time_ratio = all_before_contact_time / (3*60*60) - all_after_countact_time_ratio = all_after_contact_time / (3*60*60) - - all_pre_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_pre_chase_event_mask))) - all_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_chase_event_mask))) - all_end_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_end_chase_event_mask))) - all_after_chase_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_after_chase_event_mask))) - all_before_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_before_contact_event_mask))) - all_after_countact_event_ratio = np.array(list(map(lambda x: np.sum(x)/len(x), all_after_contact_event_mask))) - - for x, y, name in [[all_pre_chase_event_ratio, all_pre_chase_time_ratio, 'pre chase'], - [all_chase_event_ratio, all_chase_time_ratio, 'while chase'], - [all_end_chase_event_ratio, all_end_chase_time_ratio, 'end chase'], - [all_after_chase_event_ratio, all_after_chase_time_ratio, 'after chase'], - [all_before_countact_event_ratio, all_before_countact_time_ratio, 'pre contact'], - [all_after_countact_event_ratio, all_after_countact_time_ratio, 'post contact']]: - t, p = scp.ttest_rel(x, y) - print(f'{event_name} {name}: t={t:.2f} p={p:.3f}') - - - fig = plt.figure(figsize=(20/2.54, 12/2.54)) - gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.15, right=0.95, top=0.9) - ax = fig.add_subplot(gs[0, 0]) - ax_pie = fig.add_subplot(gs[0, 1]) - - ax.boxplot([all_pre_chase_event_ratio/all_pre_chase_time_ratio, - all_chase_event_ratio/all_chase_time_ratio, - all_end_chase_event_ratio/all_end_chase_time_ratio, - all_after_chase_event_ratio/all_after_chase_time_ratio, - all_before_countact_event_ratio/all_before_countact_time_ratio, - all_after_countact_event_ratio/all_after_countact_time_ratio], positions=np.arange(6), sym='', zorder=2) - - ylim = list(ax.get_ylim()) - ylim[0] = -.1 if ylim[0] < -.1 else ylim[0] - ylim[1] = 1.1 if ylim[1] < 1.1 else ylim[1] - ############################################################################## - for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2): - mec = 'k' if sex_w == sex_l else 'None' - if 'lose' in event_name: - marker='o' - c = male_color if sex_l == 'm' else female_color - elif "win" in event_name: - marker='p' - c = male_color if sex_w == 'm' else female_color + event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name) + + embed() + quit() + + chase_dur = [] + chase_chirp_count = [] + dt_start_first_chirp = [] + dt_end_first_chirp = [] + for ag_on_t, ag_off_t, chirp_times_lose in zip(all_ag_on_t, all_ag_off_t, all_chirp_times_lose): + if len(chirp_times_lose) == 0: + continue + for a_on, a_off in zip(ag_on_t, ag_off_t): + chase_dur.append(a_off - a_on) + chirp_t_oi = chirp_times_lose[(chirp_times_lose > a_on) & (chirp_times_lose <= a_off)] + chase_chirp_count.append(len(chirp_t_oi)) + if len(chirp_t_oi) >= 1: + dt_start_first_chirp.append(chirp_t_oi[0] - a_on) + dt_end_first_chirp.append(a_off - chirp_t_oi[0]) else: - print('error') - embed() - quit() - values = np.array(all_pre_chase_event_ratio/all_pre_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - values = np.array(all_chase_event_ratio/all_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - values = np.array(all_end_chase_event_ratio/all_end_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - values = np.array(all_after_chase_event_ratio/all_after_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - values = np.array(all_before_countact_event_ratio/all_before_countact_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - values = np.array(all_after_countact_event_ratio/all_after_countact_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)] - ax.plot(np.ones_like(values) * 5, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1) - - # ax.plot(np.ones_like(all_pre_chase_event_ratio) * 0, all_pre_chase_event_ratio/all_pre_chase_time_ratio, 'ok') - # ax.plot(np.ones_like(all_chase_event_ratio) * 1, all_chase_event_ratio/all_chase_time_ratio, 'ok') - # ax.plot(np.ones_like(all_end_chase_event_ratio) * 2, all_end_chase_event_ratio/all_end_chase_time_ratio, 'ok') - # ax.plot(np.ones_like(all_after_chase_event_ratio) * 3, all_after_chase_event_ratio/all_after_chase_time_ratio, 'ok') - - # ax.plot(np.ones_like(all_before_countact_event_ratio) * 4, all_before_countact_event_ratio/all_before_countact_time_ratio, 'ok') - ############################################################################## - - ax.plot(np.arange(7)-1, np.ones(7), linestyle='dotted', lw=2, color='k') - ax.set_xlim(-0.5, 5.5) - ax.set_ylim(ylim[0], ylim[1]) - - ax.set_ylabel(r'rel. count$_{event}$ / rel. time$_{event}$', fontsize=12) - ax.set_xticks(np.arange(6)) - ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$', 'contact$_{after}$'], rotation=45) - ax.tick_params(labelsize=10) - # ax.set_title(event_name) - fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}') - # plt.show() - - ############################################### - flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask) - flat_chase_event_mask = np.hstack(all_chase_event_mask) - flat_end_chase_event_mask = np.hstack(all_end_chase_event_mask) - flat_after_chase_event_mask = np.hstack(all_after_chase_event_mask) - flat_before_countact_event_mask = np.hstack(all_before_contact_event_mask) - flat_after_countact_event_mask = np.hstack(all_after_contact_event_mask) - - flat_pre_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 - flat_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 - flat_end_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 - flat_after_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0 - - event_context_values = [np.sum(flat_pre_chase_event_mask) / len(flat_pre_chase_event_mask), - np.sum(flat_chase_event_mask) / len(flat_chase_event_mask), - np.sum(flat_end_chase_event_mask) / len(flat_end_chase_event_mask), - np.sum(flat_after_chase_event_mask) / len(flat_after_chase_event_mask), - np.sum(flat_before_countact_event_mask) / len(flat_before_countact_event_mask), - np.sum(flat_after_countact_event_mask) / len(flat_after_countact_event_mask)] - - event_context_values.append(1 - np.sum(event_context_values)) - - time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time), - np.sum(all_after_chase_time), np.sum(all_before_contact_time), np.sum(all_after_contact_time)] - - time_context_values.append(len(all_pre_chase_time) * 3*60*60 - np.sum(time_context_values)) - time_context_values /= np.sum(time_context_values) - - # fig, ax = plt.subplots(figsize=(12/2.54,12/2.54)) - size = 0.3 - outer_colors = ['tab:red', 'tab:orange', 'yellow', 'tab:green', 'k','tab:brown', 'tab:grey'] - ax_pie.pie(event_context_values, radius=1, colors=outer_colors, - wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, 1)) - ax_pie.pie(time_context_values, radius=1-size, colors=outer_colors, - wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, 1)) + dt_start_first_chirp.append(np.nan) + dt_end_first_chirp.append(np.nan) + + fig = plt.figure(figsize=(21/2.54, 19/2.54)) + gs = gridspec.GridSpec(2, 2, left=.15, bottom=0.1, right=0.95, top=0.95) + ax = [] + ax.append(fig.add_subplot(gs[0, 0])) + ax.append(fig.add_subplot(gs[0, 1], sharex=ax[0])) + ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0])) + ax.append(fig.add_subplot(gs[1, 1], sharex=ax[0])) + + ax[0].plot(chase_dur, chase_chirp_count, '.') + ax[1].plot(chase_dur, np.array(chase_chirp_count) / np.array(chase_dur), '.') + ax[2].plot(chase_dur, dt_start_first_chirp, '.') + ax[3].plot(chase_dur, dt_end_first_chirp, '.') + + ax[2].plot([0, 60], [0, 60], '-k', lw=1) + ax[3].plot([0, 60], [0, 60], '-k', lw=1) + + ax[2].set_xlabel(r'chase$_{duration}$ [s]', fontsize=12) + ax[3].set_xlabel(r'chase$_{duration}$ [s]', fontsize=12) + ax[0].set_ylabel('chirps [n]', fontsize=12) + ax[1].set_ylabel('chirp rate [Hz]', fontsize=12) + ax[2].set_ylabel(r'$\Delta$t chase$_{on}$ - chirp$_{0}$', fontsize=12) + ax[3].set_ylabel(r'$\Delta$t chirp$_{0}$ - chase$_{off}$', fontsize=12) + + + + chase_chirp_count = np.array(chase_chirp_count) + chase_dur = np.array(chase_dur) + + chirp_rate = chase_chirp_count / chase_dur + + r, p = scp.pearsonr(chase_dur[chase_chirp_count >= 3], chase_chirp_count[chase_chirp_count >= 3]) + ax[0].text(1, 1, f'r= {r:.2f} p={p:.3f}', transform=ax[0].transAxes, ha='right', va='bottom') - ax_pie.set_title(r'event context') - legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'), - Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (event_context_values[1] * 100) + '%'), - Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'), - Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (event_context_values[3] * 100) + '%'), - Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'), - Patch(facecolor='tab:brown', edgecolor='w', label='%.1f' % (event_context_values[5] * 100) + '%'), - Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[0] * 100) + '%'), - Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[1] * 100) + '%'), - Patch(facecolor='yellow', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%'), - Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[3] * 100) + '%'), - Patch(facecolor='k', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[4] * 100) + '%'), - Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[5] * 100) + '%')] - - ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.25), frameon=False, fontsize=9) - - plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{event_name}_categories.png'), dpi=300) - plt.close() - # plt.show() + r, p = scp.pearsonr(chase_dur[chase_chirp_count >= 3], chirp_rate[chase_chirp_count >= 3]) + ax[1].text(1, 1, f'r= {r:.2f} p={p:.3f}', transform=ax[1].transAxes, ha='right', va='bottom') + plt.show() + fig, ax = plt.subplots() if __name__ == '__main__': diff --git a/event_time_correlations.py b/event_time_correlations.py index 98ddd09..fa6561f 100644 --- a/event_time_correlations.py +++ b/event_time_correlations.py @@ -279,6 +279,9 @@ def main(base_path): win_rises_centered_on_lose_chirps = [] win_rises_count = [] + ag_off_centered_on_ag_on = [] + ag_count = [] + sex_win = [] sex_lose = [] @@ -362,6 +365,9 @@ def main(base_path): win_rises_centered_on_lose_chirps.append(event_centered_times(chirp_times[1], rise_times[0])) win_rises_count.append(len(rise_times[0])) + ag_off_centered_on_ag_on.append(event_centered_times(ag_on_off_t_GRID[:, 0], ag_on_off_t_GRID[:, 1])) + ag_count.append(len(ag_on_off_t_GRID)) + sex_win.append(trial['sex_win']) sex_lose.append(trial['sex_lose']) @@ -399,7 +405,9 @@ def main(base_path): [win_rises_centered_on_ag_off_t, win_rises_count, r'rise$_{win}$ on chase$_{off}$'], [win_rises_centered_on_ag_on_t, win_rises_count, r'rise$_{win}$ on chase$_{on}$'], [win_rises_centered_on_contact_t, win_rises_count, r'rise$_{win}$ on contact'], - [win_rises_centered_on_lose_chirps, win_rises_count, r'rise$_{win}$ on chirp$_{lose}$']]: + [win_rises_centered_on_lose_chirps, win_rises_count, r'rise$_{win}$ on chirp$_{lose}$'], + + [ag_off_centered_on_ag_on, ag_count, r'chase$_{off}$ on chase$_{on}$']]: save_str = title.replace('$', '').replace('{', '').replace('}', '').replace(' ', '_')