implemented individual recording plots
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@ -198,7 +198,8 @@ def main(datapath: str):
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time_before_event = 30
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time_after_event = 60
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dt = 0.01
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width = 1.5 # width of kernel, currently gaussian kernel
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width = 1.5 # width of kernel for all recordings, currently gaussian kernel
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recording_width = 1 # width of kernel for each recording
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time = np.arange(-time_before_event, time_after_event, dt)
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##### Chirps around events, all fish, all recordings #####
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@ -220,11 +221,11 @@ def main(datapath: str):
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physical_contacts = nrecording_physicals[i]
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# Chirps around chasing onsets
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_, centered_chasing_onset_chirps, _ = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
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_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
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# Chirps around chasing offsets
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_, centered_chasing_offset_chirps, _ = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
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_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
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# Chirps around physical contacts
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_, centered_physical_chirps, _ = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
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_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
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nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
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nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
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@ -235,20 +236,73 @@ def main(datapath: str):
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nshuffled_offset_chirps = []
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nshuffled_physical_chirps = []
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for i in tqdm(range(nbootstrapping)):
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for j in tqdm(range(nbootstrapping)):
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# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
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interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
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np.random.shuffle(interchirp_intervals)
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shuffled_chirps = np.cumsum(interchirp_intervals)
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# Shuffled chasing onset chirps
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_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
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_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
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nshuffled_onset_chirps.append(cc_shuffled_onset_chirps)
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# Shuffled chasing offset chirps
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_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
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_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
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nshuffled_offset_chirps.append(cc_shuffled_offset_chirps)
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# Shuffled physical contact chirps
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_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width)
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_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
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nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
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rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
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nshuffled_onset_chirps, (5, 50, 95), axis=0)
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rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
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nshuffled_offset_chirps, (5, 50, 95), axis=0)
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rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
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nshuffled_physical_chirps, (5, 50, 95), axis=0)
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#### Recording plots ####
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fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
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ax[0].set_xlabel('Time[s]')
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# Plot chasing onsets
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ax[0].set_ylabel('Chirp rate [Hz]')
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ax[0].plot(time, cc_chasing_onset_chirps, color=ps.yellow, zorder=2)
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ax0 = ax[0].twinx()
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ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[0].set_zorder(ax0.get_zorder()+1)
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ax[0].patch.set_visible(False)
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ax0.set_yticklabels([])
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ax0.set_yticks([])
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ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5)
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ax[0].plot(time, rec_shuffled_median_onset, color=ps.black)
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# Plot chasing offets
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ax[1].set_xlabel('Time[s]')
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ax[1].plot(time, cc_chasing_offset_chirps, color=ps.orange, zorder=2)
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ax1 = ax[1].twinx()
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ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[1].set_zorder(ax1.get_zorder()+1)
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ax[1].patch.set_visible(False)
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ax1.set_yticklabels([])
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ax1.set_yticks([])
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ax[1].fill_between(time, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5)
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ax[1].plot(time, rec_shuffled_median_offset, color=ps.black)
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# Plot physical contacts
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ax[2].set_xlabel('Time[s]')
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ax[2].plot(time, cc_physical_chirps, color=ps.maroon, zorder=2)
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ax2 = ax[2].twinx()
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ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
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ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[2].set_zorder(ax2.get_zorder()+1)
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ax[2].patch.set_visible(False)
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ax2.set_yticklabels([])
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ax2.set_yticks([])
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ax[2].fill_between(time, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5)
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ax[2].plot(time, rec_shuffled_median_physical, ps.black)
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fig.suptitle(f'Recording: {i}')
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plt.show()
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# plt.close()
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nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
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nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
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@ -290,45 +344,49 @@ def main(datapath: str):
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fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
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# offsets = np.arange(1,28,1)
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ax[0].set_xlabel('Time[s]')
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# Plot chasing onsets
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ax[0].set_ylabel('Chirp rate [Hz]')
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ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
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ax0 = ax[0].twinx()
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nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
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ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
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ax0.vlines(0, 0, 1.5, ps.black, 'dashed')
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ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[0].set_zorder(ax0.get_zorder()+1)
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ax[0].patch.set_visible(False)
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ax0.set_yticklabels([])
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ax0.set_yticks([])
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ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
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ax[0].plot(time, shuffled_median_onset, color=ps.black)
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# Plot chasing offets
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ax[1].set_xlabel('Time[s]')
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ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
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ax1 = ax[1].twinx()
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nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
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ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
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ax1.vlines(0, 0, 1.5, ps.black, 'dashed')
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ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[1].set_zorder(ax1.get_zorder()+1)
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ax[1].patch.set_visible(False)
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ax1.set_yticklabels([])
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ax1.set_yticks([])
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ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
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ax[1].plot(time, shuffled_median_offset, color=ps.black)
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# Plot physical contacts
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ax[2].set_xlabel('Time[s]')
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ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
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ax2 = ax[2].twinx()
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nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
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ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
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ax2.vlines(0, 0, 1.5, ps.black, 'dashed')
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ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
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ax[2].set_zorder(ax2.get_zorder()+1)
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ax[2].patch.set_visible(False)
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ax2.set_yticklabels([])
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ax2.set_yticks([])
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ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
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ax[2].plot(time, shuffled_median_physical, ps.black)
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fig.suptitle('All recordings')
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plt.show()
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# plt.close()
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@ -351,7 +409,7 @@ def main(datapath: str):
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# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
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# fish = [len(fish1), len(fish2)]
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# Concolution over all recordings
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# Convolution over all recordings
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# Rasterplot for each recording
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