diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py index 7162387..0627f2d 100644 --- a/code/eventchirpsplots.py +++ b/code/eventchirpsplots.py @@ -195,64 +195,101 @@ def main(datapath: str): # fish2 = chirps[chirps_fish_ids == fish_ids[1]] # fish = [len(fish1), len(fish2)] - # Define time window for chirp around event analysis + # Define time window for chirps around event analysis time_before_event = 30 time_after_event = 60 dt = 0.01 width = 1 + time = np.arange(-time_before_event, time_after_event, dt) + + + ##### Chirps around events, all fish, one recording ##### + # Chirps around chasing onsets + _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around chasing offsets + _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width) + # Chirps around physical contacts + _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width) + + ## Shuffled chirps ## + nbootstrapping = 1000 + nshuffled_chirps_onset = [] + nshuffled_chirps_offset = [] + nshuffled_chirps_physical = [] + + for i in range(nbootstrapping): + # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths + interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) + np.random.shuffle(interchirp_intervals) + shuffled_chirps = np.cumsum(interchirp_intervals) + # Shuffled chasing onset chirps + _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) + # Shuffled chasing offset chirps + _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) + # Shuffled physical contact chirps + _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width) + nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) + + shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0) + shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0) + shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0) + + embed() + + # Plot all events with all shuffled + fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') + offset = [1.35] + ax[0].set_xlabel('Time[s]') + # Plot chasing onsets + ax[0].set_ylabel('Chirp rate [Hz]') + ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) + ax0 = ax[0].twinx() + ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) + ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax0.set_yticklabels([]) + ax0.set_yticks([]) + ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) + ax[0].plot(time, shuffled_median_onset, color='k') + # Plot chasing offets + ax[1].set_xlabel('Time[s]') + ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=100) + ax1 = ax[1].twinx() + ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100) + ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax1.set_yticklabels([]) + ax1.set_yticks([]) + ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) + ax[1].plot(time, shuffled_median_offset, color='k') + # Plot physical contacts + ax[2].set_xlabel('Time[s]') + ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=100) + ax2 = ax[2].twinx() + ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100) + ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') + ax2.set_yticklabels([]) + ax2.set_yticks([]) + ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) + ax[2].plot(time, shuffled_median_physical, color='k') + plt.show() + + embed() + exit() + + + + + + - #### Loop crashes at concatenate in function #### + #### Chirps around events, only winners, one recording #### for i in range(len(fish_ids)): fish = fish_ids[i] chirps_temp = chirps[chirps_fish_ids == fish] print(fish) - ##### Chirps around events ##### - time = np.arange(-time_before_event, time_after_event, dt) - - # Chirps around chasing onsets - _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps_temp, time_before_event, time_after_event, dt, width) - # Chirps around chasing offsets - _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps_temp, time_before_event, time_after_event, dt, width) - # Chirps around physical contacts - _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps_temp, time_before_event, time_after_event, dt, width) - - fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') - offset = [0.25] - ax[0].set_xlabel('Time[s]') - # Plot chasing onsets - ax[0].set_ylabel('Chirp rate [Hz]') - ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue') - ax0 = ax[0].twinx() - ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green']) - ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax0.set_yticklabels([]) - ax0.set_yticks([]) - # Plot chasing offets - ax[1].set_xlabel('Time[s]') - ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue') - ax1 = ax[1].twinx() - ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple']) - ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax1.set_yticklabels([]) - ax1.set_yticks([]) - # Plot physical contacts - ax[2].set_xlabel('Time[s]') - ax[2].plot(time, cc_physical_chirps, color='tab:blue') - ax2 = ax[2].twinx() - ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red']) - ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed') - ax2.set_yticklabels([]) - ax2.set_yticks([]) - plt.show() - - ### Plots: - # 1. All recordings, all fish, all chirps - # One CTC, one PTC - # 2. All recordings, only winners - # One CTC, one PTC - # 3. All recordings, all losers - # One CTC, one PTC + #### Chirps around events, only losers, one recording #### embed()