177 lines
6.0 KiB
Python
177 lines
6.0 KiB
Python
import numpy as np
|
|
|
|
import os
|
|
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
from thunderfish.powerspectrum import decibel
|
|
|
|
from IPython import embed
|
|
from pandas import read_csv
|
|
from modules.logger import makeLogger
|
|
from modules.plotstyle import PlotStyle
|
|
|
|
ps = PlotStyle()
|
|
|
|
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)
|
|
|
|
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
|
|
logger.info(f'CSV file: {csv_filename}')
|
|
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, 'chirp_ids.npy'), allow_pickle=True)
|
|
|
|
self.ident = np.load(os.path.join(folder_path, 'ident_v.npy'), allow_pickle=True)
|
|
self.idx = np.load(os.path.join(folder_path, 'idx_v.npy'), allow_pickle=True)
|
|
self.freq = np.load(os.path.join(folder_path, 'fund_v.npy'), allow_pickle=True)
|
|
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
|
|
self.spec = np.load(os.path.join(folder_path, "spec.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]
|
|
|
|
woring_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
|
|
if onset_ids[0] > offset_ids[0]:
|
|
offset_ids = np.delete(offset_ids, 0)
|
|
help_index = offset_ids[0]
|
|
woring_bh = np.append(woring_bh, help_index)
|
|
|
|
category = np.delete(category, woring_bh)
|
|
timestamps = np.delete(timestamps, woring_bh)
|
|
|
|
# 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)
|
|
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)
|
|
logger.info(f'Offsets are greater than onsets by {len_diff}')
|
|
elif len(onset_ids) == len(offset_ids):
|
|
logger.info('Chasing events are equal')
|
|
|
|
|
|
return category, timestamps
|
|
|
|
|
|
|
|
def main(datapath: str):
|
|
|
|
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
|
|
path_to_csv = ('/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
|
|
meta_id = read_csv(path_to_csv)
|
|
meta_id['recording'] = meta_id['recording'].str[1:-1]
|
|
|
|
chirps_winner = []
|
|
chirps_loser = []
|
|
|
|
for foldername in foldernames:
|
|
# behabvior is pandas dataframe with all the data
|
|
if foldername == '../data/mount_data/2020-05-12-10_00/':
|
|
continue
|
|
bh = Behavior(foldername)
|
|
# chirps are not sorted in time (presumably due to prior groupings)
|
|
# get and sort chirps and corresponding fish_ids of the 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)
|
|
|
|
folder_name = foldername.split('/')[-2]
|
|
winner_row = meta_id[meta_id['recording'] == folder_name]
|
|
winner = winner_row['winner'].values[0].astype(int)
|
|
winner_fish1 = winner_row['fish1'].values[0].astype(int)
|
|
winner_fish2 = winner_row['fish2'].values[0].astype(int)
|
|
if winner == winner_fish1:
|
|
winner_fish_id = winner_row['rec_id1'].values[0]
|
|
loser_fish_id = winner_row['rec_id2'].values[0]
|
|
elif winner == winner_fish2:
|
|
winner_fish_id = winner_row['rec_id2'].values[0]
|
|
loser_fish_id = winner_row['rec_id1'].values[0]
|
|
else:
|
|
continue
|
|
|
|
print(foldername)
|
|
all_fish_ids = np.unique(bh.chirps_ids)
|
|
chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
|
|
chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
|
|
chirps_winner.append(chirp_winner)
|
|
chirps_loser.append(chirp_loser)
|
|
|
|
|
|
fish1_id = all_fish_ids[0]
|
|
fish2_id = all_fish_ids[1]
|
|
print(winner_fish_id)
|
|
print(all_fish_ids)
|
|
|
|
|
|
fig, ax = plt.subplots()
|
|
ax.boxplot([chirps_winner, chirps_loser])
|
|
|
|
ax.set_xticklabels(['winner', 'loser'])
|
|
ax.set_ylabel('Chirpscount per trial')
|
|
plt.show()
|
|
|
|
|
|
|
|
embed()
|
|
exit()
|
|
|
|
|
|
|
|
if __name__ == '__main__':
|
|
|
|
# Path to the data
|
|
datapath = '../data/mount_data/'
|
|
|
|
main(datapath) |