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.
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@ -182,110 +182,63 @@ def relative_rate_progression(all_event_t, title=''):
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print(f'Progression {title}: pearson-r={r:.2f} p={p:.3f}')
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def chase_time_progression(all_ag_on_t, all_ag_off_t):
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stop_t = 3*60*60
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snippet_len = 15*60
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def main(base_path):
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if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta'))
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if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')):
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os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr'))
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trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
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chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
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trial_mask = chirp_notes['good'] == 1
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# trial_summary = trial_summary[chirp_notes['good'] == 1]
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all_rise_times_lose = []
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all_rise_times_win = []
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all_chirp_times_lose = []
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all_chirp_times_win = []
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win_sex = []
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lose_sex = []
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all_contact_t = []
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all_ag_on_t = []
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all_ag_off_t = []
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for index, trial in trial_summary.iterrows():
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print(index, len(trial_summary))
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got_boris = False
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trial_path = os.path.join(base_path, trial['recording'])
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if trial['group'] < 3:
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snippet_starts = np.arange(0, stop_t, snippet_len)
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all_snippet_chase_dur = []
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for a_on, a_off in zip(all_ag_on_t, all_ag_off_t):
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if len(a_on) == 0:
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continue
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if trial['draw'] == 1:
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continue
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if os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
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got_boris = True
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if os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
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got_boris = True
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ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
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times = np.load(os.path.join(trial_path, 'times.npy'))
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sorter = -1 if trial['win_ID'] != ids[0] else 1
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### event times --> BORIS behavior
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if got_boris:
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contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
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load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
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all_contact_t.append(contact_t_GRID)
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all_ag_on_t.append(ag_on_off_t_GRID[:, 0])
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all_ag_off_t.append(ag_on_off_t_GRID[:, 1])
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mean_chase_dur = np.mean(a_off - a_on)
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snippet_chase_dur = []
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for s0 in snippet_starts:
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snippet_mask = (a_on > s0) & (a_on <= s0+snippet_len)
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if np.any(snippet_mask):
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snippet_chase_dur.append(np.mean(a_off[snippet_mask] - a_on[snippet_mask]))
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else:
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all_contact_t.append(np.array([]))
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all_ag_on_t.append(np.array([]))
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all_ag_off_t.append(np.array([]))
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snippet_chase_dur.append(np.nan)
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### communication
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if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
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continue
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chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
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chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
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chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
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all_snippet_chase_dur.append(np.array(snippet_chase_dur) / mean_chase_dur)
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all_snippet_chase_dur = np.array(all_snippet_chase_dur)
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fig = plt.figure(figsize=(20/2.54, 12/2.54))
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gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95)
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ax = fig.add_subplot(gs[0, 0])
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plot_t = np.repeat(snippet_starts, 2)
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plot_t[1::2] += snippet_len
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for trial_snippet_chase_dur in all_snippet_chase_dur:
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plot_ratios = np.repeat(trial_snippet_chase_dur, 2)
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ax.plot(plot_t / 3600, plot_ratios, color='grey', lw=1, alpha=0.5)
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# ax.plot(snippet_starts + snippet_len/2, event_ratios)
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mean_ratio = np.nanmean(all_snippet_chase_dur, axis=0)
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plot_mean_ratio = np.repeat(mean_ratio, 2)
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ax.plot(plot_t / 3600, plot_mean_ratio, color='k', lw=3)
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ax.plot(plot_t / 3600, np.ones_like(plot_t), linestyle='dotted', lw=2, color='k')
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ax.set_xlabel('time [h]', fontsize=12)
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ax.set_ylabel('chase duration / mean(chase duration)', fontsize=12)
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ax.set_title('progression chase duration ')
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ax.tick_params(labelsize=10)
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ax.set_xlim(0, 3)
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ax.set_ylim(0, 5)
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x = np.hstack(all_snippet_chase_dur)
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y = np.hstack(np.tile(snippet_starts, (all_snippet_chase_dur.shape[0], 1)))
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r, p = scp.pearsonr(x[~np.isnan(x)], y[~np.isnan(x)])
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print(f'Progression chase duration: pearson-r={r:.2f} p={p:.3f}')
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plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', 'chase_duration_progression.png'), dpi=300)
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plt.close()
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rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
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rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
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rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
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all_rise_times_lose.append(rise_times[1])
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all_rise_times_win.append(rise_times[0])
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if trial_mask[index]:
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all_chirp_times_lose.append(chirp_times[1])
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all_chirp_times_win.append(chirp_times[0])
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else:
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all_chirp_times_lose.append(np.array([]))
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all_chirp_times_win.append(np.array([]))
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win_sex.append(trial['sex_win'])
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lose_sex.append(trial['sex_lose'])
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win_sex = np.array(win_sex)
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lose_sex = np.array(lose_sex)
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iei_analysis(all_chirp_times_lose, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{lose}$')
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iei_analysis(all_chirp_times_win, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{win}$')
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iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$')
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iei_analysis(all_rise_times_win, win_sex, lose_sex, kernal_w=50, title=r'rises$_{win}$')
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print('')
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relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
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relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$')
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relative_rate_progression(all_rise_times_lose, title=r'rises$_{lose}$')
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relative_rate_progression(all_rise_times_win, title=r'rises$_{win}$')
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relative_rate_progression(all_contact_t, title=r'contact')
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relative_rate_progression(all_ag_on_t, title=r'chasing')
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#############################################################################
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for all_event_t, event_name in zip([all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win],
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[r'chirps$_{lose}$', r'chirps$_{win}$', r'rises$_{lose}$', r'rises$_{win}$']):
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def event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name):
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print('')
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all_pre_chase_event_mask = []
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all_chase_event_mask = []
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@ -385,7 +338,6 @@ def main(base_path):
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t, p = scp.ttest_rel(x, y)
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print(f'{event_name} {name}: t={t:.2f} p={p:.3f}')
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fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
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gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.15, right=0.95, top=0.9)
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ax = fig.add_subplot(gs[0, 0])
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@ -396,7 +348,8 @@ def main(base_path):
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all_end_chase_event_ratio / all_end_chase_time_ratio,
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all_after_chase_event_ratio / all_after_chase_time_ratio,
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all_before_countact_event_ratio / all_before_countact_time_ratio,
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all_after_countact_event_ratio/all_after_countact_time_ratio], positions=np.arange(6), sym='', zorder=2)
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all_after_countact_event_ratio / all_after_countact_time_ratio], positions=np.arange(6), sym='',
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zorder=2)
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ylim = list(ax.get_ylim())
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ylim[0] = -.1 if ylim[0] < -.1 else ylim[0]
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@ -414,30 +367,36 @@ def main(base_path):
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print('error')
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embed()
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quit()
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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)]
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ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_pre_chase_event_ratio / all_pre_chase_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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values = np.array(all_chase_event_ratio/all_chase_time_ratio)[(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_chase_event_ratio / all_chase_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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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)]
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ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_end_chase_event_ratio / all_end_chase_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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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)]
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ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_after_chase_event_ratio / all_after_chase_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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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)]
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ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_before_countact_event_ratio / all_before_countact_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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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)]
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ax.plot(np.ones_like(values) * 5, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8, zorder=1)
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values = np.array(all_after_countact_event_ratio / all_after_countact_time_ratio)[
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(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
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ax.plot(np.ones_like(values) * 5, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
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zorder=1)
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# ax.plot(np.ones_like(all_pre_chase_event_ratio) * 0, all_pre_chase_event_ratio/all_pre_chase_time_ratio, 'ok')
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# ax.plot(np.ones_like(all_chase_event_ratio) * 1, all_chase_event_ratio/all_chase_time_ratio, 'ok')
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# ax.plot(np.ones_like(all_end_chase_event_ratio) * 2, all_end_chase_event_ratio/all_end_chase_time_ratio, 'ok')
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# ax.plot(np.ones_like(all_after_chase_event_ratio) * 3, all_after_chase_event_ratio/all_after_chase_time_ratio, 'ok')
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# ax.plot(np.ones_like(all_before_countact_event_ratio) * 4, all_before_countact_event_ratio/all_before_countact_time_ratio, 'ok')
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##############################################################################
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ax.plot(np.arange(7) - 1, np.ones(7), linestyle='dotted', lw=2, color='k')
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@ -446,11 +405,10 @@ def main(base_path):
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ax.set_ylabel(r'rel. count$_{event}$ / rel. time$_{event}$', fontsize=12)
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ax.set_xticks(np.arange(6))
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ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$', 'contact$_{after}$'], rotation=45)
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ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$',
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'contact$_{after}$'], rotation=45)
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ax.tick_params(labelsize=10)
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# ax.set_title(event_name)
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fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}')
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# plt.show()
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###############################################
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flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask)
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@ -475,7 +433,8 @@ def main(base_path):
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event_context_values.append(1 - np.sum(event_context_values))
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time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time),
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np.sum(all_after_chase_time), np.sum(all_before_contact_time), np.sum(all_after_contact_time)]
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np.sum(all_after_chase_time), np.sum(all_before_contact_time),
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np.sum(all_after_contact_time)]
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time_context_values.append(len(all_pre_chase_time) * 3 * 60 * 60 - np.sum(time_context_values))
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time_context_values /= np.sum(time_context_values)
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@ -490,25 +449,202 @@ def main(base_path):
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ax_pie.set_title(r'event context')
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legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'),
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Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (event_context_values[1] * 100) + '%'),
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Patch(facecolor='tab:orange', edgecolor='w',
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label='%.1f' % (event_context_values[1] * 100) + '%'),
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Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'),
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Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (event_context_values[3] * 100) + '%'),
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Patch(facecolor='tab:green', edgecolor='w',
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label='%.1f' % (event_context_values[3] * 100) + '%'),
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Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'),
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Patch(facecolor='tab:brown', edgecolor='w', label='%.1f' % (event_context_values[5] * 100) + '%'),
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Patch(facecolor='tab:red', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[0] * 100) + '%'),
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Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[1] * 100) + '%'),
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Patch(facecolor='yellow', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%'),
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Patch(facecolor='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[3] * 100) + '%'),
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Patch(facecolor='k', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[4] * 100) + '%'),
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Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[5] * 100) + '%')]
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Patch(facecolor='tab:brown', edgecolor='w',
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label='%.1f' % (event_context_values[5] * 100) + '%'),
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Patch(facecolor='tab:red', alpha=0.6, edgecolor='w',
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label='%.1f' % (time_context_values[0] * 100) + '%'),
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Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w',
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label='%.1f' % (time_context_values[1] * 100) + '%'),
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Patch(facecolor='yellow', alpha=0.6, edgecolor='w',
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label='%.1f' % (time_context_values[2] * 100) + '%'),
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Patch(facecolor='tab:green', alpha=0.6, edgecolor='w',
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label='%.1f' % (time_context_values[3] * 100) + '%'),
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Patch(facecolor='k', alpha=0.6, edgecolor='w',
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label='%.1f' % (time_context_values[4] * 100) + '%'),
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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)
|
||||
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.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 ###
|
||||
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$_{win}$')
|
||||
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)
|
||||
|
||||
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:
|
||||
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')
|
||||
|
||||
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__':
|
||||
main(sys.argv[1])
|
||||
|
||||
@ -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(' ', '_')
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user