import os import sys import itertools import numpy as np import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from matplotlib.patches import Patch import pandas as pd import scipy.stats as scp from IPython import embed from event_time_correlations import load_and_converete_boris_events, kde, gauss female_color, male_color = '#e74c3c', '#3498db' def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''): iei = [] weighted_mean_iei = [] median_iei = [] for i in range(len(event_times)): night_iei = np.diff(event_times[i][event_times[i] <= 3600*3]) iei.append(np.diff(event_times[i])) if len(night_iei) == 0: weighted_mean_iei.append(np.nan) median_iei.append(np.nan) else: weighted_mean_iei.append(np.sum((night_iei) * night_iei) / np.sum(night_iei)) median_iei.append(np.median(night_iei)) weighted_mean_iei = np.array(weighted_mean_iei) median_iei = np.array(median_iei) fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54)) gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.2, right=0.95, top=0.9, width_ratios=[5, 1], wspace=.3) ax = [] ax.append(fig.add_subplot(gs[0, 0])) ax.append(fig.add_subplot(gs[0, 1])) for i in range(len(iei)): if win_sex[i] == 'm': if lose_sex[i] == 'm': color, linestyle = male_color, '-' sp = 0 else: color, linestyle = male_color, '--' sp = 1 else: if lose_sex[i] == 'm': color, linestyle = female_color, '--' sp = 2 else: color, linestyle = female_color, '-' sp = 3 conv_y = np.arange(0, np.percentile(np.hstack(iei), 80), .5) kde_array = kde(iei[i], conv_y, kernal_w=kernal_w, kernal_h=1) # kde_array /= np.sum(kde_array) ax[0].plot(conv_y, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2) ax[1].boxplot([weighted_mean_iei[~np.isnan(weighted_mean_iei)], median_iei[~np.isnan(median_iei)]], positions=[0, 1], sym='', widths=0.5) ax[0].set_xlim(conv_y[0], conv_y[-1]) ax[0].set_ylabel('KDE', fontsize=12) ax[0].set_xlabel('inter event interval [s]', fontsize=12) fig.suptitle(title, fontsize=12) for a in ax: a.tick_params(labelsize=10) ax[1].set_xticks(np.arange(2)) ax[1].set_xticklabels([r'weighted$_{time}$', 'median'], rotation=45) ax[1].set_ylabel('inter event interval [s]', fontsize=12) # ax[0] # plt.setp(ax[1].get_yticklabels(), visible=False) # plt.setp(ax[3].get_yticklabels(), visible=False) # # plt.setp(ax[0].get_xticklabels(), visible=False) # plt.setp(ax[1].get_xticklabels(), visible=False) plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_iei.png'), dpi=300) plt.close() # all_r = [] # all_p = [] # for lag in np.arange(1, 6): # 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_x = [] # plot_y = [] # for trial_iei in iei: # plot_x.extend(trial_iei[:-lag]) # plot_y.extend(trial_iei[lag:]) # ax.plot(plot_x, plot_y, '.', color='k') # r, p = scp.pearsonr(plot_x, plot_y) # all_r.append(r) # all_p.append(p) # # ax.set_xlim(-1, 120) # ax.set_ylim(-1, 120) # plt.show() # plt.show() return iei def relative_rate_progression(all_event_t, title=''): stop_t = 3*60*60 snippet_len = 15*60 snippet_starts = np.arange(0, stop_t, snippet_len) all_snippet_ratio = [] for event_t in all_event_t: if len(event_t) == 0: continue expected_snippet_count = len(event_t[event_t <= stop_t]) / (stop_t / snippet_len) snippet_ratio = [] for s0 in snippet_starts: snippet_count = len(event_t[(event_t >= s0) & (event_t < s0 + snippet_len)]) snippet_ratio.append(snippet_count/expected_snippet_count) all_snippet_ratio.append(snippet_ratio) all_snippet_ratio = np.array(all_snippet_ratio) 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 event_ratios in all_snippet_ratio: plot_ratios = np.repeat(event_ratios, 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.median(all_snippet_ratio, 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('norm. event rate', fontsize=12) ax.set_title(title) ax.tick_params(labelsize=10) ax.set_xlim(0, 3) ax.set_ylim(0, 5) plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_progression.png'), dpi=300) plt.close() # plt.show() x = np.hstack(all_snippet_ratio) y = np.hstack(np.tile(snippet_starts, (all_snippet_ratio.shape[0], 1))) r, p = scp.pearsonr(x, y) 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): if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')): os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')) if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')): os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')) 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 ### data processing ####################### all_rise_times_lose = [] all_rise_times_win = [] all_chirp_times_lose = [] all_chirp_times_win = [] win_sex = [] lose_sex = [] all_contact_t = [] all_ag_on_t = [] all_ag_off_t = [] for index, trial in trial_summary.iterrows(): print(index, len(trial_summary)) got_boris = False trial_path = os.path.join(base_path, trial['recording']) if trial['group'] < 3: continue if trial['draw'] == 1: continue if os.path.exists(os.path.join(trial_path, 'led_idxs.csv')): got_boris = True if os.path.exists(os.path.join(trial_path, 'LED_frames.npy')): got_boris = True ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy')) times = np.load(os.path.join(trial_path, 'times.npy')) sorter = -1 if trial['win_ID'] != ids[0] else 1 ### event times --> BORIS behavior if got_boris: contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \ load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000) all_contact_t.append(contact_t_GRID) all_ag_on_t.append(ag_on_off_t_GRID[:, 0]) all_ag_off_t.append(ag_on_off_t_GRID[:, 1]) else: all_contact_t.append(np.array([])) all_ag_on_t.append(np.array([])) all_ag_off_t.append(np.array([])) ### communication if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')): continue chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy')) chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy')) chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]] rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter] rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))] rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]] all_rise_times_lose.append(rise_times[1]) all_rise_times_win.append(rise_times[0]) if trial_mask[index]: all_chirp_times_lose.append(chirp_times[1]) all_chirp_times_win.append(chirp_times[0]) else: all_chirp_times_lose.append(np.array([])) all_chirp_times_win.append(np.array([])) win_sex.append(trial['sex_win']) lose_sex.append(trial['sex_lose']) win_sex = np.array(win_sex) lose_sex = np.array(lose_sex) ### inter event intervalls ### inter_chirp_interval_lose = 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$_{win}$') fig, ax = plt.subplots() n, bin_edges = np.histogram(np.hstack(inter_chirp_interval_lose), bins = np.arange(0, 20, 0.05)) ax.bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0])/2, n/np.sum(n)/(bin_edges[1] - bin_edges[0]), width=(bin_edges[1] - bin_edges[0])) ylim = ax.get_ylim() med_ici = np.nanmedian(np.hstack(inter_chirp_interval_lose)) ax.plot([med_ici, med_ici], [ylim[0], ylim[1]], '-k', lw=2) plt.show() chirp_dt_burst_th = med_ici print(np.nanpercentile(np.hstack(inter_chirp_interval_lose), (50, 75, 95))) # chirp_dt_burst_th = bin_edges[np.argmax(n)] - (bin_edges[1] - bin_edges[0]) / 2 burst_chirp_mask = [] for enu, ici in enumerate(inter_chirp_interval_lose): if len(ici) >= 1: trial_burst_chirp_mask = np.zeros_like(ici) trial_burst_chirp_mask[ici < chirp_dt_burst_th] = 1 trial_burst_chirp_mask[1:][(ici[:-1] < chirp_dt_burst_th) & (ici[1:] >= chirp_dt_burst_th)] = 2 last = 2 if trial_burst_chirp_mask[-1] == 1 else 0 trial_burst_chirp_mask = np.append(trial_burst_chirp_mask, np.array(last)) burst_chirp_mask.append(trial_burst_chirp_mask) else: burst_chirp_mask.append(np.array([])) fig = plt.figure(figsize=(21/2.54, 19/2.54)) gs = gridspec.GridSpec(1, 2, left=0.1, 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])) all_chirps_in_burst_distro = [] for i in range(len(burst_chirp_mask)): ax[0].plot(all_chirp_times_lose[i], np.ones_like(all_chirp_times_lose[i]) * i, '|', markersize=12, color='grey') if len(burst_chirp_mask[i]) == 0: continue chirp_idx_burst_start = np.arange(len(all_chirp_times_lose[i])-1)[(burst_chirp_mask[i][:-1] != 1) & (burst_chirp_mask[i][1:] == 1)] + 1 if burst_chirp_mask[i][0] == 1: chirp_idx_burst_start = np.append(0, chirp_idx_burst_start) chirp_idx_burst_end = np.arange(len(all_chirp_times_lose[i]))[(burst_chirp_mask[i] == 2)] chirp_idx_burst_start = np.array(chirp_idx_burst_start, dtype=int) chirp_idx_burst_end = np.array(chirp_idx_burst_end, dtype=int) chirps_in_burst = chirp_idx_burst_end - chirp_idx_burst_start + 1 if len(chirps_in_burst) == 0: continue chirps_in_burst_distro = np.zeros(np.max(chirps_in_burst)) for j in range(np.max(chirps_in_burst)): if j == 0: chirps_in_burst_distro[j] = len(burst_chirp_mask[i][burst_chirp_mask[i] == 0]) else: chirps_in_burst_distro[j] = len(chirps_in_burst[chirps_in_burst == j + 1]) for cbs, cbe in zip(all_chirp_times_lose[i][chirp_idx_burst_start], all_chirp_times_lose[i][chirp_idx_burst_end]): ax[0].plot([cbs, cbe], [i, i], '-k', lw=2) all_chirps_in_burst_distro.append(chirps_in_burst_distro) max_chirps_in_burst = np.max(list(map(lambda x: len(x), all_chirps_in_burst_distro))) collective_chirps_in_burst = np.zeros((len(all_chirps_in_burst_distro), max_chirps_in_burst)) for trial in range(len(all_chirps_in_burst_distro)): collective_chirps_in_burst[trial, :len(all_chirps_in_burst_distro[trial])] = all_chirps_in_burst_distro[trial] ax[1].bar(np.arange(collective_chirps_in_burst.shape[1])+1, collective_chirps_in_burst.sum(0)) ax[1].plot(np.arange(collective_chirps_in_burst.shape[1])+1, collective_chirps_in_burst.sum(0) * (np.arange(collective_chirps_in_burst.shape[1])+1), color='firebrick', lw=2) plt.show() ### event progressions ### print('') relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$') relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$') relative_rate_progression(all_rise_times_lose, title=r'rises$_{lose}$') relative_rate_progression(all_rise_times_win, title=r'rises$_{win}$') 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$_{win}$']): event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name) ################################# chase_dur = [] chase_chirp_count = [] dt_start_first_chirp = [] dt_end_first_chirp = [] dt_start_all_chirp = [] dt_end_all_chirp = [] all_chirp_mask = [] chase_dur_all_chirp = [] for ag_on_t, ag_off_t, chirp_times_lose, trial_chirp_burst_mask in \ zip(all_ag_on_t, all_ag_off_t, all_chirp_times_lose, burst_chirp_mask): 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)] chirp_t_oi_mask = trial_chirp_burst_mask[(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]) dt_start_all_chirp.extend(chirp_t_oi - a_on) dt_end_all_chirp.extend(a_off - chirp_t_oi) all_chirp_mask.extend(chirp_t_oi_mask) chase_dur_all_chirp.extend(np.ones_like(chirp_t_oi) * (a_off - a_on)) else: dt_start_first_chirp.append(np.nan) dt_end_first_chirp.append(np.nan) dt_start_first_chirp = np.array(dt_start_first_chirp) dt_end_first_chirp = np.array(dt_end_first_chirp) dt_start_all_chirp = np.array(dt_start_all_chirp) dt_end_all_chirp = np.array(dt_end_all_chirp) all_chirp_mask = np.array(all_chirp_mask) chase_dur_all_chirp = np.array(chase_dur_all_chirp) chase_chirp_count = np.array(chase_chirp_count) chase_dur = np.array(chase_dur) chirp_rate = chase_chirp_count / chase_dur chase_dur_per_chirp_count = [] positions = np.arange(np.max(chase_chirp_count)+1) for cc in positions: chase_dur_per_chirp_count.append([]) chase_dur_per_chirp_count[-1].extend(chase_dur[chase_chirp_count == cc]) ################################################ chase_dur_pct99 = np.percentile(chase_dur, 99) chase_dur_bins = np.arange(0, chase_dur_pct99+1, 2.5) chase_dur_count_above_th = np.zeros_like(chase_dur_bins[:-1]) for enu, chase_dur_th in enumerate(chase_dur_bins[:-1]): chase_dur_count_above_th[enu] = len(chase_dur[chase_dur >= chase_dur_th]) ################################################ fig = plt.figure(figsize=(21/2.54, 28/2.54)) gs = gridspec.GridSpec(3, 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.append(fig.add_subplot(gs[2, 0], sharex=ax[0])) ax.append(fig.add_subplot(gs[2, 1], sharex=ax[0])) ax[0].plot(chase_dur, chase_chirp_count, '.') ax[0].boxplot(chase_dur_per_chirp_count, positions=positions, vert=False, sym='') 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) n, _ = np.histogram(dt_start_first_chirp, bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[2].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0]) / 2, n, height=(chase_dur_bins[1] - chase_dur_bins[0]) * 0.8, color='firebrick', alpha=0.5, zorder=2) n, _ = np.histogram(dt_end_first_chirp, bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[3].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0]) / 2, n, height=(chase_dur_bins[1] - chase_dur_bins[0]) * 0.8, color='firebrick', alpha=0.5, zorder=2) ax[3].invert_yaxis() ax[2].set_xlim(right=chase_dur_pct99 + 2) ax[2].set_ylim(top=chase_dur_pct99 + 2) ax[3].set_xlim(right=chase_dur_pct99 + 2) ax[3].set_ylim(bottom=chase_dur_pct99 + 2) ax[4].plot(chase_dur_all_chirp[all_chirp_mask == 0], dt_start_all_chirp[all_chirp_mask == 0], '.', color='cornflowerblue', alpha = 0.5) ax[4].plot(chase_dur_all_chirp[all_chirp_mask != 0], dt_start_all_chirp[all_chirp_mask != 0], '.', color='k', alpha = 0.5) ax[5].plot(chase_dur_all_chirp[all_chirp_mask == 0], dt_end_all_chirp[all_chirp_mask == 0], '.', color='cornflowerblue', alpha = 0.5) ax[5].plot(chase_dur_all_chirp[all_chirp_mask != 0], dt_end_all_chirp[all_chirp_mask != 0], '.', color='k', alpha = 0.5) ax[4].plot([0, 60], [0, 60], '-k', lw=1) ax[5].plot([0, 60], [0, 60], '-k', lw=1) n, _ = np.histogram(dt_start_all_chirp, bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[4].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='firebrick', alpha=0.52, zorder=2) n, _ = np.histogram(dt_start_all_chirp[all_chirp_mask != 0], bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[4].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4*3, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='k', alpha=0.52, zorder=2) n, _ = np.histogram(dt_end_all_chirp, bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[5].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='firebrick', alpha=0.5, zorder=2) n, _ = np.histogram(dt_end_all_chirp[all_chirp_mask != 0], bins=chase_dur_bins) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / chase_dur_count_above_th n = n / np.max(n) * chase_dur_pct99 ax[5].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4*3, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='k', alpha=0.5, zorder=2) ax[5].invert_yaxis() ax[4].set_xlim(right=chase_dur_pct99+2) ax[4].set_ylim(top=chase_dur_pct99+2) ax[5].set_xlim(right=chase_dur_pct99+2) ax[5].set_ylim(bottom=chase_dur_pct99+2) ax[4].set_xlabel(r'chase$_{duration}$ [s]', fontsize=12) ax[5].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) ax[4].set_ylabel(r'$\Delta$t chase$_{on}$ - chirps', fontsize=12) ax[5].set_ylabel(r'$\Delta$t chirps - chase$_{off}$', fontsize=12) plt.show() embed() quit() if __name__ == '__main__': main(sys.argv[1])