from pathlib import Path import numpy as np from IPython import embed from pandas import read_csv class Behavior: """Load behavior data from csv file as class attributes Attributes ---------- 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: stop_s: total_length: """ def __init__(self, datapath: str) -> None: csv_file = str(sorted(Path(datapath).glob('**/*.csv'))[0]) self.dataframe = read_csv(csv_file, delimiter=',') for key in self.dataframe: if ' ' in key: new_key = key.replace(' ', '_') if '(' in new_key: new_key = new_key.replace('(', '') new_key = new_key.replace(')', '') new_key = new_key.lower() setattr(self, new_key, np.array(self.dataframe[key])) """ 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 main(datapath: str): # behabvior is pandas dataframe with all the data behavior = Behavior(datapath) if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/2020-03-13-10_00/' main(datapath)