Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection
This commit is contained in:
weygoldt
commit
369f26123a
180
code/behavior.py
180
code/behavior.py
@ -1,16 +1,19 @@
|
||||
from pathlib import Path
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from IPython import embed
|
||||
from pandas import read_csv
|
||||
from modules.logger import makeLogger
|
||||
|
||||
|
||||
|
||||
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:
|
||||
@ -20,23 +23,37 @@ class Behavior:
|
||||
media_file:
|
||||
observation_date:
|
||||
observation_id:
|
||||
start_s:
|
||||
stop_s:
|
||||
start_s: start time of the event in seconds
|
||||
stop_s: stop time of the event in seconds
|
||||
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]))
|
||||
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)
|
||||
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
|
||||
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
|
||||
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, 'chirps_ids.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
|
||||
|
||||
"""
|
||||
1 - chasing onset
|
||||
2 - chasing offset
|
||||
@ -64,12 +81,137 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
|
||||
behavior = data['Behavior']
|
||||
"""
|
||||
|
||||
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]
|
||||
|
||||
# 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)
|
||||
longer_array = onset_ids
|
||||
shorter_array = 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)
|
||||
longer_array = offset_ids
|
||||
shorter_array = onset_ids
|
||||
logger.info(f'Offsets are greater than offsets by {len_diff}')
|
||||
elif len(onset_ids) == len(offset_ids):
|
||||
logger.info('Chasing events are equal')
|
||||
return category, timestamps
|
||||
|
||||
|
||||
# Correct the wrong chasing events; delete double events
|
||||
wrong_ids = []
|
||||
for i in range(len(longer_array)-(len_diff+1)):
|
||||
if (shorter_array[i] > longer_array[i]) & (shorter_array[i] < longer_array[i+1]):
|
||||
pass
|
||||
else:
|
||||
wrong_ids.append(longer_array[i])
|
||||
longer_array = np.delete(longer_array, i)
|
||||
|
||||
category = np.delete(
|
||||
category, wrong_ids)
|
||||
timestamps = np.delete(
|
||||
timestamps, wrong_ids)
|
||||
return category, timestamps
|
||||
|
||||
|
||||
def main(datapath: str):
|
||||
# behabvior is pandas dataframe with all the data
|
||||
behavior = Behavior(datapath)
|
||||
|
||||
# behavior is pandas dataframe with all the data
|
||||
bh = Behavior(datapath)
|
||||
|
||||
# chirps are not sorted in time (presumably due to prior groupings)
|
||||
# get and sort chirps and corresponding fish_ids of the chirps
|
||||
chirps = bh.chirps[np.argsort(bh.chirps)]
|
||||
chirps_fish_ids = bh.chirps_ids[np.argsort(bh.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)
|
||||
|
||||
# split categories
|
||||
chasing_onset = timestamps[category == 0]
|
||||
chasing_offset = timestamps[category == 1]
|
||||
physical_contact = timestamps[category == 2]
|
||||
|
||||
##### TODO Physical contact-triggered chirps (PTC) mit Rasterplot #####
|
||||
# Wahrscheinlichkeit von Phys auf Ch und vice versa
|
||||
# Chasing-triggered chirps (CTC) mit Rasterplot
|
||||
# Wahrscheinlichkeit von Chase auf Ch und vice versa
|
||||
|
||||
# First overview plot
|
||||
fig1, ax1 = plt.subplots()
|
||||
ax1.scatter(chirps, np.ones_like(chirps), marker='*', color='royalblue', label='Chirps')
|
||||
ax1.scatter(chasing_onset, np.ones_like(chasing_onset)*2, marker='.', color='forestgreen', label='Chasing onset')
|
||||
ax1.scatter(chasing_offset, np.ones_like(chasing_offset)*2.5, marker='.', color='firebrick', label='Chasing offset')
|
||||
ax1.scatter(physical_contact, np.ones_like(physical_contact)*3, marker='x', color='black', label='Physical contact')
|
||||
plt.legend()
|
||||
# plt.show()
|
||||
plt.close()
|
||||
|
||||
# Get fish ids
|
||||
all_fish_ids = np.unique(chirps_fish_ids)
|
||||
|
||||
# Associate chirps to inidividual fish
|
||||
fish1 = chirps[chirps_fish_ids == all_fish_ids[0]]
|
||||
fish2 = chirps[chirps_fish_ids == all_fish_ids[1]]
|
||||
fish = [len(fish1), len(fish2)]
|
||||
|
||||
#### Chirp counts per fish general #####
|
||||
fig2, ax2 = plt.subplots()
|
||||
x = ['Fish1', 'Fish2']
|
||||
width = 0.35
|
||||
ax2.bar(x, fish, width=width)
|
||||
ax2.set_ylabel('Chirp count')
|
||||
# plt.show()
|
||||
plt.close()
|
||||
|
||||
|
||||
##### Count chirps emitted during chasing events and chirps emitted out of chasing events #####
|
||||
chirps_in_chasings = []
|
||||
for onset, offset in zip(chasing_onset, chasing_offset):
|
||||
chirps_in_chasing = [c for c in chirps if (c > onset) & (c < offset)]
|
||||
chirps_in_chasings.append(chirps_in_chasing)
|
||||
|
||||
# chirps out of chasing events
|
||||
counts_chirps_chasings = 0
|
||||
chasings_without_chirps = 0
|
||||
for i in chirps_in_chasings:
|
||||
if i:
|
||||
chasings_without_chirps += 1
|
||||
else:
|
||||
counts_chirps_chasings += 1
|
||||
|
||||
# chirps in chasing events
|
||||
fig3 , ax3 = plt.subplots()
|
||||
ax3.bar(['Chirps in chasing events', 'Chasing events without Chirps'], [counts_chirps_chasings, chasings_without_chirps], width=width)
|
||||
plt.ylabel('Count')
|
||||
plt.show()
|
||||
plt.close()
|
||||
|
||||
# comparison between chasing events with and without chirps
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
embed()
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# Path to the data
|
||||
datapath = '../data/mount_data/2020-03-13-10_00/'
|
||||
datapath = '../data/mount_data/2020-05-13-10_00/'
|
||||
main(datapath)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user