raab/GP2023_chirp_detection
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winner vs loser plot, winner physical strange

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sprause 2023-01-23 17:49:07 +01:00
parent e7b9d239d2
commit 1bdb7e721a
1 changed files with 130 additions and 56 deletions
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code/eventchirpsplots.py
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@ -1,6 +1,7 @@
import os import os
import numpy as np import numpy as np
import pandas as pd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
from IPython import embed from IPython import embed
@ -182,10 +183,10 @@ def main(datapath: str):
duration = offset - onset duration = offset - onset
chasing_durations.append(duration) chasing_durations.append(duration)
fig, ax = plt.subplots() # fig, ax = plt.subplots()
ax.boxplot(chasing_durations) # ax.boxplot(chasing_durations)
plt.show() # plt.show()
plt.close() # plt.close()
# Get fish ids # Get fish ids
fish_ids = np.unique(chirps_fish_ids) fish_ids = np.unique(chirps_fish_ids)
@ -217,73 +218,150 @@ def main(datapath: str):
nshuffled_chirps_offset = [] nshuffled_chirps_offset = []
nshuffled_chirps_physical = [] nshuffled_chirps_physical = []
for i in range(nbootstrapping): # for i in range(nbootstrapping):
# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths # # Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps)) # interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
np.random.shuffle(interchirp_intervals) # np.random.shuffle(interchirp_intervals)
shuffled_chirps = np.cumsum(interchirp_intervals) # shuffled_chirps = np.cumsum(interchirp_intervals)
# Shuffled chasing onset chirps # # 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, width)
nshuffled_chirps_onset.append(cc_shuffled_onset_chirps) # nshuffled_chirps_onset.append(cc_shuffled_onset_chirps)
# Shuffled chasing offset 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, width)
nshuffled_chirps_offset.append(cc_shuffled_offset_chirps) # nshuffled_chirps_offset.append(cc_shuffled_offset_chirps)
# Shuffled physical contact 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, width)
nshuffled_chirps_physical.append(cc_shuffled_physical_chirps) # 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_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_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) # 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 # Plot all events with all shuffled
fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all') # 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()
# plt.close()
#### Chirps around events, winner VS loser, one recording ####
# Load file with fish ids and winner/loser info
meta = pd.read_csv('../data/mount_data/order_meta.csv')
current_recording = meta[meta.index == 43]
fish1 = current_recording['rec_id1'].values
fish2 = current_recording['rec_id2'].values
# Implement check if fish_ids from meta and chirp detection are the same???
winner = current_recording['winner'].values
if winner == fish1:
loser = fish2
elif winner == fish2:
loser = fish1
winner_chirps = chirps[chirps_fish_ids == winner]
loser_chirps = chirps[chirps_fish_ids == loser]
# Event triggered winner chirps
_, winner_centered_onset, winner_cc_onset = event_triggered_chirps(chasing_onsets, winner_chirps, time_before_event, time_after_event, dt, width)
_, winner_centered_offset, winner_cc_offset = event_triggered_chirps(chasing_offsets, winner_chirps, time_before_event, time_after_event, dt, width)
_, winner_centered_physical, winner_cc_physical = event_triggered_chirps(physical_contacts, winner_chirps, time_before_event, time_after_event, dt, width)
# Event triggered loser chirps
_, loser_centered_onset, loser_cc_onset = event_triggered_chirps(chasing_onsets, loser_chirps, time_before_event, time_after_event, dt, width)
_, loser_centered_offset, loser_cc_offset = event_triggered_chirps(chasing_offsets, loser_chirps, time_before_event, time_after_event, dt, width)
_, loser_centered_physical, loser_cc_physical = event_triggered_chirps(physical_contacts, loser_chirps, time_before_event, time_after_event, dt, width)
fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
offset = [1.35] offset = [1.35]
ax[0].set_xlabel('Time[s]') ax[1][0].set_xlabel('Time[s]')
# Plot chasing onsets ax[1][1].set_xlabel('Time[s]')
ax[0].set_ylabel('Chirp rate [Hz]') ax[1][2].set_xlabel('Time[s]')
ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=100) # Plot winner chasing onsets
ax0 = ax[0].twinx() ax[0][0].set_ylabel('Chirp rate [Hz]')
ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100) ax[0][0].plot(time, winner_cc_onset, color='tab:blue', zorder=100)
ax0 = ax[0][0].twinx()
ax0.eventplot(np.array([winner_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
ax0.set_ylabel('Event')
ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed') ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax0.set_yticklabels([]) ax0.set_yticklabels([])
ax0.set_yticks([]) ax0.set_yticks([])
ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5) # Plot winner chasing offets
ax[0].plot(time, shuffled_median_onset, color='k') ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
# Plot chasing offets ax1 = ax[0][1].twinx()
ax[1].set_xlabel('Time[s]') ax1.eventplot(np.array([winner_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
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.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax1.set_yticklabels([]) ax1.set_yticklabels([])
ax1.set_yticks([]) ax1.set_yticks([])
ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5) # Plot winner physical contacts
ax[1].plot(time, shuffled_median_offset, color='k') ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
# Plot physical contacts ax2 = ax[0][2].twinx()
ax[2].set_xlabel('Time[s]') ax2.eventplot(np.array([winner_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
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.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax2.set_yticklabels([]) ax2.set_yticklabels([])
ax2.set_yticks([]) ax2.set_yticks([])
ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5) # Plot loser chasing onsets
ax[2].plot(time, shuffled_median_physical, color='k') ax[1][0].set_ylabel('Chirp rate [Hz]')
ax[1][0].plot(time, loser_cc_onset, color='tab:blue', zorder=100)
ax3 = ax[1][0].twinx()
ax3.eventplot(np.array([loser_centered_onset]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=-100)
ax3.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax3.set_yticklabels([])
ax3.set_yticks([])
# Plot loser chasing offsets
ax[1][1].plot(time, loser_cc_offset, color='tab:blue', zorder=100)
ax4 = ax[1][1].twinx()
ax4.eventplot(np.array([loser_centered_offset]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=-100)
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax4.set_yticklabels([])
ax4.set_yticks([])
# Plot loser physical contacts
ax[1][2].plot(time, loser_cc_physical, color='tab:blue', zorder=100)
ax5 = ax[1][2].twinx()
ax5.eventplot(np.array([loser_centered_physical]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=-100)
ax5.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
ax5.set_yticklabels([])
ax5.set_yticks([])
plt.show() plt.show()
plt.close()
embed() embed()
exit() exit()
#### Chirps around events, only winners, one recording ####
for i in range(len(fish_ids)): for i in range(len(fish_ids)):
fish = fish_ids[i] fish = fish_ids[i]
chirps_temp = chirps[chirps_fish_ids == fish] chirps_temp = chirps[chirps_fish_ids == fish]
@ -292,10 +370,6 @@ def main(datapath: str):
#### Chirps around events, only losers, one recording #### #### Chirps around events, only losers, one recording ####
embed()
exit()
if __name__ == '__main__': if __name__ == '__main__':
# Path to the data # Path to the data