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 """ 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 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_onset = timestamps[category == 0] chasing_offset = timestamps[category == 1] physical_contact = timestamps[category == 2] ##### TODO Physical contact-triggered chirps (PTC) mit Rasterplot ##### # Wahrscheinlichkeit von Phys auf Ch und vice versa # Chasing-triggered chirps (CTC) mit Rasterplot # Wahrscheinlichkeit von Chase auf Ch und vice versa # First overview plot fig1, ax1 = plt.subplots() ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps') ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset') ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset') ax1.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact') plt.legend() # plt.show() plt.close() # Get fish ids all_fish_ids = np.unique(chirps_fish_ids) # Associate chirps to inidividual fish fish1 = chirps[chirps_fish_ids == all_fish_ids[0]] fish2 = chirps[chirps_fish_ids == all_fish_ids[1]] fish = [len(fish1), len(fish2)] #### Chirp counts per fish general ##### fig2, ax2 = plt.subplots() x = ['Fish1', 'Fish2'] width = 0.35 ax2.bar(x, fish, width=width) ax2.set_ylabel('Chirp count') # plt.show() plt.close() ##### Count chirps emitted during chasing events and chirps emitted out of chasing events ##### chirps_in_chasings = [] for onset, offset in zip(chasing_onset, chasing_offset): chirps_in_chasing = [c for c in chirps if (c > onset) & (c < offset)] chirps_in_chasings.append(chirps_in_chasing) # chirps out of chasing events counts_chirps_chasings = 0 chasings_without_chirps = 0 for i in chirps_in_chasings: if i: chasings_without_chirps += 1 else: counts_chirps_chasings += 1 # chirps in chasing events fig3 , ax3 = plt.subplots() ax3.bar(['Chirps in chasing events', 'Chasing events without Chirps'], [counts_chirps_chasings, chasings_without_chirps], width=width) plt.ylabel('Count') plt.show() plt.close() # comparison between chasing events with and without chirps ##### Chasing triggered chirps CTC ##### # Evaluate how many chirps were emitted in specific time window around the chasing onset events # Goal: # Plot with Chasing onsets centered at t = 0 on x-axis as a function of event type (0, 1, 2) (or later as a function of recordings) with chirps as rasterplot in background # Chasing onset is defined at the point event 'chasing' # Iterate over chasing onsets (later over fish) # Get chirps which in a time window of -5 to +5 seconds aroung the chasing onset and save them # Set Chasing onset at timepoint 0: Chasing onset timestamp - chasing onset timestamp # Calculate chirp timestamps relative to chasing onset: Chirp timestamp - Chasing onset timestamp # For rasterplot look at plt.eventplot() function # Do the plot # Then same with physical onset events (PTC) embed() if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/2020-05-13-10_00/' main(datapath)