GP2023_chirp_detection/code/behavior.py
2023-01-16 10:47:34 +01:00

76 lines
2.1 KiB
Python

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)