From b1b9d9d3f18a31c80c50df7f1f0b2fe851ae65ea Mon Sep 17 00:00:00 2001
From: sprause <sprause95@gmail.com>
Date: Mon, 23 Jan 2023 15:58:22 +0100
Subject: [PATCH] implemented bootstrapping, q5 of physical still weird
---
code/eventchirpsplots.py | 133 +++++++++++++++++++++++++--------------
1 file changed, 85 insertions(+), 48 deletions(-)
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()