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)