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 b1b9d9d3f1
commit a96a638c31
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 numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from IPython import embed
@ -182,10 +183,10 @@ def main(datapath: str):
duration = offset - onset
chasing_durations.append(duration)
fig, ax = plt.subplots()
ax.boxplot(chasing_durations)
plt.show()
plt.close()
# fig, ax = plt.subplots()
# ax.boxplot(chasing_durations)
# plt.show()
# plt.close()
# Get fish ids
fish_ids = np.unique(chirps_fish_ids)
@ -217,73 +218,150 @@ def main(datapath: str):
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()
# 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)
# 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]
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)
ax[1][0].set_xlabel('Time[s]')
ax[1][1].set_xlabel('Time[s]')
ax[1][2].set_xlabel('Time[s]')
# Plot winner chasing onsets
ax[0][0].set_ylabel('Chirp rate [Hz]')
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.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)
# Plot winner chasing offets
ax[0][1].plot(time, winner_cc_offset, color='tab:blue', zorder=100)
ax1 = ax[0][1].twinx()
ax1.eventplot(np.array([winner_centered_offset]), 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)
# Plot winner physical contacts
ax[0][2].plot(time, winner_cc_physical, color='tab:blue', zorder=100)
ax2 = ax[0][2].twinx()
ax2.eventplot(np.array([winner_centered_physical]), 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')
# Plot loser chasing onsets
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.close()
embed()
exit()
#### 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]
@ -292,10 +370,6 @@ def main(datapath: str):
#### Chirps around events, only losers, one recording ####
embed()
exit()
if __name__ == '__main__':
# Path to the data