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, 'chirps_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] # 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) longer_array = onset_ids shorter_array = 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) longer_array = offset_ids shorter_array = onset_ids logger.info(f'Offsets are greater than offsets by {len_diff}') elif len(onset_ids) == len(offset_ids): logger.info('Chasing events are equal') return category, timestamps # Correct the wrong chasing events; delete double events wrong_ids = [] for i in range(len(longer_array)-(len_diff+1)): if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]): pass else: wrong_ids.append(longer_array[i]) longer_array = np.delete(longer_array, i) category = np.delete( category, wrong_ids) timestamps = np.delete( timestamps, wrong_ids) 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): # behavior is pandas dataframe with all the data bh = Behavior(datapath) # chirps are not sorted in time (presumably due to prior groupings) # get and sort chirps and corresponding fish_ids of the chirps chirps = bh.chirps[np.argsort(bh.chirps)] chirps_fish_ids = bh.chirps_ids[np.argsort(bh.chirps)] category = bh.behavior timestamps = bh.start_s # 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] chasing_offsets = timestamps[category == 1] physical_contacts = timestamps[category == 2] 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() # Get fish ids fish_ids = np.unique(chirps_fish_ids) # # 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)] # 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=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) ########## !Winner physical strange! ########## 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') 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') 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') 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') 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() 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/2020-05-13-10_00/' main(datapath)