import os import numpy as np import pandas as pd import matplotlib.pyplot as plt from IPython import embed from pandas import read_csv from modules.logger import makeLogger from modules.datahandling import causal_kde1d, acausal_kde1d logger = makeLogger(__name__) 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: 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 centered_chirps = [] # timestamps of chirps around event centered on the event timepoint 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) centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting # Kernel density estimation time = np.arange(-time_before_event, time_after_event, dt) 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)] all_chirps = [] all_chirps_fish_ids = [] all_chasing_onsets = [] all_chasing_offsets = [] all_physicals = [] 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 all_chirps.append(chirps) chirps_fish_ids = bh.chirps_ids all_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] all_chasing_onsets.append(chasing_onsets) chasing_offsets = timestamps[category == 1] all_chasing_offsets.append(chasing_offsets) physical_contacts = timestamps[category == 2] all_physicals.append(physical_contacts) 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)] # Concolution over all recordings # Rasterplot for each recording # 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) # 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=2) ax0 = ax[0].twinx() ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=1) ax0.vlines(0, 0, 1.5, 'tab:grey', '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='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=2) ax1 = ax[1].twinx() ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=1) ax1.vlines(0, 0, 1.5, 'tab:grey', '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='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=2) ax2 = ax[2].twinx() ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=1) ax2.vlines(0, 0, 1.5, 'tab:grey', '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='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) # ########## 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() embed() exit() 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)