diff --git a/code/plot_event_timeline.py b/code/plot_event_timeline.py deleted file mode 100644 index 239dcaa..0000000 --- a/code/plot_event_timeline.py +++ /dev/null @@ -1,183 +0,0 @@ -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)