187 lines
6.3 KiB
Python
187 lines
6.3 KiB
Python
import os
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import numpy as np
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import matplotlib.pyplot as plt
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from IPython import embed
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from pandas import read_csv
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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 correct_chasing_events(
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category: np.ndarray,
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timestamps: np.ndarray
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) -> tuple[np.ndarray, np.ndarray]:
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onset_ids = np.arange(
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len(category))[category == 0]
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offset_ids = np.arange(
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len(category))[category == 1]
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# Check whether on- or offset is longer and calculate length difference
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if len(onset_ids) > len(offset_ids):
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len_diff = len(onset_ids) - len(offset_ids)
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longer_array = onset_ids
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shorter_array = offset_ids
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elif len(onset_ids) < len(offset_ids):
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len_diff = len(offset_ids) - len(onset_ids)
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longer_array = offset_ids
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shorter_array = onset_ids
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elif len(onset_ids) == len(offset_ids):
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print('Chasing events are equal')
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return category, timestamps
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# Correct the wrong chasing events; delete double events
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wrong_ids = []
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for i in range(len(longer_array)-len_diff+1):
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if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
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pass
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else:
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wrong_ids.append(longer_array[i])
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longer_array = np.delete(longer_array, i)
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category = np.delete(
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category, wrong_ids)
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timestamps = np.delete(
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timestamps, wrong_ids)
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return category, timestamps
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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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# Correct for
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category, timestamps = correct_chasing_events(category, timestamps)
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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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##### TODO 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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fig1, ax1 = plt.subplots()
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ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps')
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ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset')
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ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset')
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ax1.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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# Get fish ids
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all_fish_ids = np.unique(chirps_ids)
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# Associate chirps to inidividual fish
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fish1 = chirps[chirps_ids == all_fish_ids[0]]
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fish2 = chirps[chirps_ids == all_fish_ids[1]]
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fish = [len(fish1), len(fish2)]
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#### Chirp counts per fish general #####
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fig2, ax2 = plt.subplots()
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x = ['Fish1', 'Fish2']
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width = 0.35
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ax2.bar(x, fish, width=width)
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ax2.set_ylabel('Chirp count')
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# plt.show()
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##### Count chirps emitted during chasing events and chirps emitted out of chasing events #####
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# Check if on- and offset are equal in length to get the right on-/offset pairs
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# Get rid of tracking faults (two onsets or two offsets after another)
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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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