import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
logger = makeLogger(__name__)
ps = PlotStyle()
class Behavior:
"""Load behavior data from csv file as class attributes
Attributes
----------
behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
behavior_type:
behavioral_category:
comment_start:
comment_stop:
dataframe: pandas dataframe with all the data
duration_s:
media_file:
observation_date:
observation_id:
start_s: start time of the event in seconds
stop_s: stop time of the event in seconds
total_length:
"""
def __init__(self, folder_path: str) -> None:
print(f'{folder_path}')
LED_on_time_BORIS = np.load(os.path.join(
folder_path, 'LED_on_time.npy'), allow_pickle=True)
self.time = np.load(os.path.join(
folder_path, "times.npy"), allow_pickle=True)
csv_filename = [f for f in os.listdir(folder_path) if f.endswith(
'.csv')][0] # check if there are more than one csv file
self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
self.chirps = np.load(os.path.join(
folder_path, 'chirps.npy'), allow_pickle=True)
self.chirps_ids = np.load(os.path.join(
folder_path, 'chirp_ids.npy'), allow_pickle=True)
for k, key in enumerate(self.dataframe.keys()):
key = key.lower()
if ' ' in key:
key = key.replace(' ', '_')
if '(' in key:
key = key.replace('(', '')
key = key.replace(')', '')
setattr(self, key, np.array(
self.dataframe[self.dataframe.keys()[k]]))
last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = self.time[-1] - self.time[0]
factor = 1.034141
shift = last_LED_t_BORIS - real_time_range * factor
self.start_s = (self.start_s - shift) / factor
self.stop_s = (self.stop_s - shift) / factor
"""
1 - chasing onset
2 - chasing offset
3 - physical contact event
temporal encpding needs to be corrected ... not exactly 25FPS.
### correspinding python code ###
factor = 1.034141
LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
last_LED_t_BORIS = LED_on_time_BORIS[-1]
real_time_range = times[-1] - times[0]
shift = last_LED_t_BORIS - real_time_range * factor
data = pd.read_csv(os.path.join(folder_path, file[1:-7] + '.csv'))
boris_times = data['Start (s)']
data_times = []
for Cevent_t in boris_times:
Cevent_boris_times = (Cevent_t - shift) / factor
data_times.append(Cevent_boris_times)
data_times = np.array(data_times)
behavior = data['Behavior']
"""
def correct_chasing_events(
category: np.ndarray,
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange(
len(category))[category == 0]
offset_ids = np.arange(
len(category))[category == 1]
wrong_bh = np.arange(len(category))[
category != 2][:-1][np.diff(category[category != 2]) == 0]
if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0]
wrong_bh = np.append(wrong_bh[help_index])
category = np.delete(category, wrong_bh)
timestamps = np.delete(timestamps, wrong_bh)
# Check whether on- or offset is longer and calculate length difference
if len(onset_ids) > len(offset_ids):
len_diff = len(onset_ids) - len(offset_ids)
logger.info(f'Onsets are greater than offsets by {len_diff}')
elif len(onset_ids) < len(offset_ids):
len_diff = len(offset_ids) - len(onset_ids)
logger.info(f'Offsets are greater than onsets by {len_diff}')
elif len(onset_ids) == len(offset_ids):
logger.info('Chasing events are equal')
return category, timestamps
def event_triggered_chirps(
event: np.ndarray,
chirps: np.ndarray,
time_before_event: int,
time_after_event: int,
dt: float,
width: float,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event
# timestamps of chirps around event centered on the event timepoint
centered_chirps = []
for event_timestamp in event:
start = event_timestamp - time_before_event
stop = event_timestamp + time_after_event
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
event_chirps.append(chirps_around_event)
if len(chirps_around_event) == 0:
continue
else:
centered_chirps.append(chirps_around_event - event_timestamp)
time = np.arange(-time_before_event, time_after_event, dt)
# Kernel density estimation with some if's
if len(centered_chirps) == 0:
centered_chirps = np.array([])
centered_chirps_convolved = np.zeros(len(time))
else:
# convert list of arrays to one array for plotting
centered_chirps = np.concatenate(centered_chirps, axis=0)
centered_chirps_convolved = (acausal_kde1d(
centered_chirps, time, width)) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved
def main(datapath: str):
foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
nrecording_chirps = []
nrecording_chirps_fish_ids = []
nrecording_chasing_onsets = []
nrecording_chasing_offsets = []
nrecording_physicals = []
# Iterate over all recordings and save chirp- and event-timestamps
for folder in foldernames:
# exclude folder with empty LED_on_time.npy
if folder == '../data/mount_data/2020-05-12-10_00/':
continue
bh = Behavior(folder)
# Chirps are already sorted
category = bh.behavior
timestamps = bh.start_s
chirps = bh.chirps
nrecording_chirps.append(chirps)
chirps_fish_ids = bh.chirps_ids
nrecording_chirps_fish_ids.append(chirps_fish_ids)
fish_ids = np.unique(chirps_fish_ids)
# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
# Get rid of tracking faults (two onsets or two offsets after another)
category, timestamps = correct_chasing_events(category, timestamps)
# Split categories
chasing_onsets = timestamps[category == 0]
nrecording_chasing_onsets.append(chasing_onsets)
chasing_offsets = timestamps[category == 1]
nrecording_chasing_offsets.append(chasing_offsets)
physical_contacts = timestamps[category == 2]
nrecording_physicals.append(physical_contacts)
# Define time window for chirps around event analysis
time_before_event = 30
time_after_event = 60
dt = 0.01
width = 1.5 # width of kernel for all recordings, currently gaussian kernel
recording_width = 2 # width of kernel for each recording
time = np.arange(-time_before_event, time_after_event, dt)
##### Chirps around events, all fish, all recordings #####
# Centered chirps per event type
nrecording_centered_onset_chirps = []
nrecording_centered_offset_chirps = []
nrecording_centered_physical_chirps = []
# Bootstrapped chirps per recording and per event: 27[1000[n]] 27 recs, 1000 shuffles, n chirps
nrecording_shuffled_convolved_onset_chirps = []
nrecording_shuffled_convolved_offset_chirps = []
nrecording_shuffled_convolved_physical_chirps = []
nbootstrapping = 100
for i in range(len(nrecording_chirps)):
chirps = nrecording_chirps[i]
chasing_onsets = nrecording_chasing_onsets[i]
chasing_offsets = nrecording_chasing_offsets[i]
physical_contacts = nrecording_physicals[i]
# 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, recording_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, recording_width)
# Chirps around physical contacts
_, 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)
nrecording_centered_physical_chirps.append(centered_physical_chirps)
## Shuffled chirps ##
nshuffled_onset_chirps = []
nshuffled_offset_chirps = []
nshuffled_physical_chirps = []
# 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, 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, 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, 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([])
# ######## median - q5, median + q95
# 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)
# nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
#### New shuffle approach ####
bootstrap_onset = []
bootstrap_offset = []
bootstrap_physical = []
# New bootstrapping approach
for n in range(nbootstrapping):
diff_onset = np.diff(
np.sort(flatten(nrecording_centered_onset_chirps)))
diff_offset = np.diff(
np.sort(flatten(nrecording_centered_offset_chirps)))
diff_physical = np.diff(
np.sort(flatten(nrecording_centered_physical_chirps)))
np.random.shuffle(diff_onset)
shuffled_onset = np.cumsum(diff_onset)
np.random.shuffle(diff_offset)
shuffled_offset = np.cumsum(diff_offset)
np.random.shuffle(diff_physical)
shuffled_physical = np.cumsum(diff_physical)
kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100)
kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100)
kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100)
bootstrap_onset.append(kde_onset)
bootstrap_offset.append(kde_offset)
bootstrap_physical.append(kde_physical)
# New shuffle approach q5, q50, q95
onset_q5, onset_median, onset_q95 = np.percentile(
bootstrap_onset, [5, 50, 95], axis=0)
offset_q5, offset_median, offset_q95 = np.percentile(
bootstrap_offset, [5, 50, 95], axis=0)
physical_q5, physical_median, physical_q95 = np.percentile(
bootstrap_physical, [5, 50, 95], axis=0)
# vstack um 1. Dim zu cutten
# nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
# nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
# nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
# shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
# nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
# shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
# nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0)
# shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
# nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
# Flatten all chirps
all_chirps = np.concatenate(nrecording_chirps).ravel() # not centered
# Flatten event timestamps
all_onsets = np.concatenate(
nrecording_chasing_onsets).ravel() # not centered
all_offsets = np.concatenate(
nrecording_chasing_offsets).ravel() # not centered
all_physicals = np.concatenate(
nrecording_physicals).ravel() # not centered
# Flatten all chirps around events
all_onset_chirps = np.concatenate(
nrecording_centered_onset_chirps).ravel() # centered
all_offset_chirps = np.concatenate(
nrecording_centered_offset_chirps).ravel() # centered
all_physical_chirps = np.concatenate(
nrecording_centered_physical_chirps).ravel() # centered
# Convolute all chirps
# Divide by total number of each event over all recordings
all_onset_chirps_convolved = (acausal_kde1d(
all_onset_chirps, time, width)) / len(all_onsets)
all_offset_chirps_convolved = (acausal_kde1d(
all_offset_chirps, time, width)) / len(all_offsets)
all_physical_chirps_convolved = (acausal_kde1d(
all_physical_chirps, time, width)) / len(all_physicals)
# Plot all events with all shuffled
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.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)
ax[0].fill_between(time, onset_q5, onset_q95, color=ps.gray, alpha=0.5)
ax[0].plot(time, onset_median, 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.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)
ax[1].fill_between(time, offset_q5, offset_q95, color=ps.gray, alpha=0.5)
ax[1].plot(time, offset_median, 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.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)
ax[2].fill_between(time, physical_q5, physical_q95,
color=ps.gray, alpha=0.5)
ax[2].plot(time, physical_median, ps.black)
fig.suptitle('All recordings')
plt.show()
plt.close()
embed()
# chasing_durations = []
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
# for onset, offset in zip(chasing_onsets, chasing_offsets):
# duration = offset - onset
# chasing_durations.append(duration)
# fig, ax = plt.subplots()
# ax.boxplot(chasing_durations)
# plt.show()
# plt.close()
# # Associate chirps to individual fish
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# fish = [len(fish1), len(fish2)]
# Convolution over all recordings
# Rasterplot for each recording
# #### 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)
# ########## Winner VS Loser plot ##########
# fig, ax = plt.subplots(2, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='row')
# offset = [1.35]
# 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')
# ax[0][0].set_zorder(ax0.get_zorder()+1)
# ax[0][0].patch.set_visible(False)
# ax0.set_yticklabels([])
# ax0.set_yticks([])
# # 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')
# ax[0][1].set_zorder(ax1.get_zorder()+1)
# ax[0][1].patch.set_visible(False)
# ax1.set_yticklabels([])
# ax1.set_yticks([])
# # 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')
# ax[0][2].set_zorder(ax2.get_zorder()+1)
# ax[0][2].patch.set_visible(False)
# ax2.set_yticklabels([])
# ax2.set_yticks([])
# # 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')
# ax[1][0].set_zorder(ax3.get_zorder()+1)
# ax[1][0].patch.set_visible(False)
# 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')
# ax[1][1].set_zorder(ax4.get_zorder()+1)
# ax[1][1].patch.set_visible(False)
# 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')
# ax[1][2].set_zorder(ax5.get_zorder()+1)
# ax[1][2].patch.set_visible(False)
# ax5.set_yticklabels([])
# ax5.set_yticks([])
# plt.show()
# plt.close()
# for i in range(len(fish_ids)):
# fish = fish_ids[i]
# chirps_temp = chirps[chirps_fish_ids == fish]
# print(fish)
#### Chirps around events, only losers, one recording ####
if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/'
main(datapath)