import numpy as np import os import numpy as np import matplotlib.pyplot as plt from IPython import embed from pandas import read_csv from modules.logger import makeLogger 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 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 main(datapath: str): # behabvior 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_onset = (timestamps[category == 0]/ 60) /60 chasing_offset = (timestamps[category == 1]/ 60) /60 physical_contact = (timestamps[category == 2] / 60) /60 all_fish_ids = np.unique(chirps_fish_ids) # Associate chirps to inidividual fish fish1 = (chirps[chirps_fish_ids == all_fish_ids[0]] / 60) /60 fish2 = (chirps[chirps_fish_ids == all_fish_ids[1]] / 60) /60 fig, ax = plt.subplots(4, 1, figsize=(10, 5), height_ratios=[0.5, 0.5, 0.5, 6]) # marker size s = 200 ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='red', marker='|', s=s) ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='blue', marker='|', s=s ) ax[1].scatter(chasing_offset, np.ones(len(chasing_offset)), color='green', marker='|', s=s) ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color='blue', marker='|', s=s) ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s) ax[3].scatter(fish2, np.zeros(len(fish2))+0.25, color='green', marker='|', s=s) # Hide grid lines ax[0].grid(False) ax[0].set_frame_on(False) ax[0].set_xticks([]) ax[0].set_yticks([]) ax[1].grid(False) ax[1].set_frame_on(False) ax[1].set_xticks([]) ax[1].set_yticks([]) ax[2].grid(False) ax[2].set_frame_on(False) ax[2].set_yticks([]) ax[2].set_xticks([]) ax[3].axvspan(0, 3, 0, 5, facecolor='grey', alpha=0.5) labelpad = 40 ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad) ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad) ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad) ax[3].set_ylabel('EODf') ax[3].set_xlabel('Time [h]') plt.show() # plot chirps """ for track_id in np.unique(ident): # window_index for time array in time window window_index = np.arange(len(idx))[(ident == track_id) & (time[idx] >= t0) & (time[idx] <= (t0+dt))] freq_temp = freq[window_index] time_temp = time[idx[window_index]] #mean_freq = np.mean(freq_temp) #fdata = bandpass_filter(data_oi[:, track_id], data.samplerate, mean_freq-5, mean_freq+200) ax.plot(time_temp - t0, freq_temp) """ if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/2020-05-13-10_00/' main(datapath)