diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py
index 986910a..e1de80d 100644
--- a/code/eventchirpsplots.py
+++ b/code/eventchirpsplots.py
@@ -198,7 +198,8 @@ def main(datapath: str):
     time_before_event = 30
     time_after_event = 60
     dt = 0.01
-    width = 1.5   # width of kernel, currently gaussian kernel
+    width = 1.5   # width of kernel for all recordings, currently gaussian kernel
+    recording_width = 1 # width of kernel for each recording
     time = np.arange(-time_before_event, time_after_event, dt)
 
     ##### Chirps around events, all fish, all recordings #####
@@ -220,11 +221,11 @@ def main(datapath: str):
         physical_contacts = nrecording_physicals[i]
 
         # Chirps around chasing onsets
-        _, centered_chasing_onset_chirps, _ = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
+        _, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around chasing offsets
-        _, centered_chasing_offset_chirps, _ = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
+        _, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
         # Chirps around physical contacts
-        _, centered_physical_chirps, _ = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
+        _, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
 
         nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
         nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
@@ -235,20 +236,73 @@ def main(datapath: str):
         nshuffled_offset_chirps = []
         nshuffled_physical_chirps = []
 
-        for i in tqdm(range(nbootstrapping)):
+        for j in tqdm(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)
+            _, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
             nshuffled_onset_chirps.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)
+            _, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
             nshuffled_offset_chirps.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)
+            _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
             nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
+
+        rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
+        nshuffled_onset_chirps, (5, 50, 95), axis=0)
+        rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
+        nshuffled_offset_chirps, (5, 50, 95), axis=0)
+        rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
+        nshuffled_physical_chirps, (5, 50, 95), axis=0)
+
+        #### Recording plots ####
+        fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
+        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=ps.yellow, zorder=2)
+        ax0 = ax[0].twinx()
+        ax0.eventplot(centered_chasing_onset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
+        ax[0].set_zorder(ax0.get_zorder()+1)
+        ax[0].patch.set_visible(False)
+        ax0.set_yticklabels([])
+        ax0.set_yticks([])
+        ax[0].fill_between(time, rec_shuffled_q5_onset, rec_shuffled_q95_onset, color=ps.gray, alpha=0.5)
+        ax[0].plot(time, rec_shuffled_median_onset, color=ps.black)
+
+        # Plot chasing offets
+        ax[1].set_xlabel('Time[s]')
+        ax[1].plot(time, cc_chasing_offset_chirps, color=ps.orange, zorder=2)
+        ax1 = ax[1].twinx()
+        ax1.eventplot(centered_chasing_offset_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
+        ax[1].set_zorder(ax1.get_zorder()+1)
+        ax[1].patch.set_visible(False)
+        ax1.set_yticklabels([])
+        ax1.set_yticks([])
+        ax[1].fill_between(time, rec_shuffled_q5_offset, rec_shuffled_q95_offset, color=ps.gray, alpha=0.5)
+        ax[1].plot(time, rec_shuffled_median_offset, color=ps.black)
+
+        # Plot physical contacts
+        ax[2].set_xlabel('Time[s]')
+        ax[2].plot(time, cc_physical_chirps, color=ps.maroon, zorder=2)
+        ax2 = ax[2].twinx()
+        ax2.eventplot(centered_physical_chirps, linelengths=0.2, colors=ps.gray, alpha=0.25, zorder=1)
+        ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
+        ax[2].set_zorder(ax2.get_zorder()+1)
+        ax[2].patch.set_visible(False)
+        ax2.set_yticklabels([])
+        ax2.set_yticks([])
+        ax[2].fill_between(time, rec_shuffled_q5_physical, rec_shuffled_q95_physical, color=ps.gray, alpha=0.5)
+        ax[2].plot(time, rec_shuffled_median_physical, ps.black)
+        fig.suptitle(f'Recording: {i}')
+        plt.show()
+        # plt.close()
         
         nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
         nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
@@ -290,45 +344,49 @@ def main(datapath: str):
     fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
     # offsets = np.arange(1,28,1)
     ax[0].set_xlabel('Time[s]')
+
     # Plot chasing onsets
     ax[0].set_ylabel('Chirp rate [Hz]')
     ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
     ax0 = ax[0].twinx()
     nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
     ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
-    ax0.vlines(0, 0, 1.5, ps.black, 'dashed')
+    ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[0].set_zorder(ax0.get_zorder()+1)
     ax[0].patch.set_visible(False)
     ax0.set_yticklabels([])
     ax0.set_yticks([])
     ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
     ax[0].plot(time, shuffled_median_onset, color=ps.black)
+
     # Plot chasing offets
     ax[1].set_xlabel('Time[s]')
     ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
     ax1 = ax[1].twinx()
     nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
     ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
-    ax1.vlines(0, 0, 1.5, ps.black, 'dashed')
+    ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[1].set_zorder(ax1.get_zorder()+1)
     ax[1].patch.set_visible(False)
     ax1.set_yticklabels([])
     ax1.set_yticks([])
     ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
     ax[1].plot(time, shuffled_median_offset, color=ps.black)
+
     # Plot physical contacts
     ax[2].set_xlabel('Time[s]')
     ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
     ax2 = ax[2].twinx()
     nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
     ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
-    ax2.vlines(0, 0, 1.5, ps.black, 'dashed')
+    ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
     ax[2].set_zorder(ax2.get_zorder()+1)
     ax[2].patch.set_visible(False)
     ax2.set_yticklabels([])
     ax2.set_yticks([])
     ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
     ax[2].plot(time, shuffled_median_physical, ps.black)
+    fig.suptitle('All recordings')
     plt.show()
     # plt.close()
 
@@ -351,7 +409,7 @@ def main(datapath: str):
     # fish2 = chirps[chirps_fish_ids == fish_ids[1]]
     # fish = [len(fish1), len(fish2)]
 
-    # Concolution over all recordings
+    # Convolution over all recordings
     # Rasterplot for each recording