122 lines
4.2 KiB
Python
122 lines
4.2 KiB
Python
import os
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import numpy as np
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from IPython import embed
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from pandas import read_csv
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import matplotlib.pyplot as plt
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class Behavior:
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"""Load behavior data from csv file as class attributes
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Attributes
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----------
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behavior: 0: chasing onset, 1: chasing offset, 2: physical contact
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behavior_type:
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behavioral_category:
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comment_start:
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comment_stop:
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dataframe: pandas dataframe with all the data
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duration_s:
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media_file:
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observation_date:
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observation_id:
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start_s: start time of the event in seconds
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stop_s: stop time of the event in seconds
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total_length:
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"""
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def __init__(self, folder_path: str) -> None:
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
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csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
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self.dataframe = read_csv(os.path.join(folder_path, csv_filename))
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self.chirps = np.load(os.path.join(folder_path, 'chirps.npy'), allow_pickle=True)
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self.chirps_ids = np.load(os.path.join(folder_path, 'chirps_ids.npy'), allow_pickle=True)
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for k, key in enumerate(self.dataframe.keys()):
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key = key.lower()
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if ' ' in key:
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key = key.replace(' ', '_')
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if '(' in key:
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key = key.replace('(', '')
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key = key.replace(')', '')
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setattr(self, key, np.array(self.dataframe[self.dataframe.keys()[k]]))
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = self.time[-1] - self.time[0]
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factor = 1.034141
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shift = last_LED_t_BORIS - real_time_range * factor
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self.start_s = (self.start_s - shift) / factor
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self.stop_s = (self.stop_s - shift) / factor
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"""
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1 - chasing onset
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2 - chasing offset
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3 - physical contact event
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temporal encpding needs to be corrected ... not exactly 25FPS.
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### correspinding python code ###
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factor = 1.034141
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LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
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last_LED_t_BORIS = LED_on_time_BORIS[-1]
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real_time_range = times[-1] - times[0]
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shift = last_LED_t_BORIS - real_time_range * factor
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data = pd.read_csv(os.path.join(folder_path, file[1:-7] + '.csv'))
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boris_times = data['Start (s)']
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data_times = []
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for Cevent_t in boris_times:
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Cevent_boris_times = (Cevent_t - shift) / factor
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data_times.append(Cevent_boris_times)
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data_times = np.array(data_times)
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behavior = data['Behavior']
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"""
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def main(datapath: str):
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# behavior is pandas dataframe with all the data
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bh = Behavior(datapath)
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# chirps are not sorted in time (presumably due to prior groupings)
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# get and sort chirps and corresponding fish_ids of the chirps
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chirps = bh.chirps[np.argsort(bh.chirps)]
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chirps_ids = bh.chirps_ids[np.argsort(bh.chirps)]
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category = bh.behavior
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timestamps = bh.start_s
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# split categories
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chasing_onset = timestamps[category == 0]
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chasing_offset = timestamps[category == 1]
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physical_contact = timestamps[category == 2]
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# Physical contact-triggered chirps (PTC) mit Rasterplot
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# Wahrscheinlichkeit von Phys auf Ch und vice versa
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# Chasing-triggered chirps (CTC) mit Rasterplot
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# Wahrscheinlichkeit von Chase auf Ch und vice versa
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# First overview plot
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fig, ax = plt.subplots()
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ax.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps')
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ax.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset')
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ax.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset')
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ax.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact')
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plt.legend()
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plt.show()
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embed()
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exit()
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if __name__ == '__main__':
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# Path to the data
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datapath = '../data/mount_data/2020-05-13-10_00/'
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main(datapath)
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