raab/GP2023_chirp_detection
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new plotfile for CTC and PTC

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sprause 2023-01-23 09:49:19 +01:00
parent a8171814dd
commit 5d8c41c899
2 changed files with 253 additions and 22 deletions
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50
code/behavior.py
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@ -131,7 +131,6 @@ def event_triggered_chirps(
)-> tuple[np.ndarray, np.ndarray]: )-> tuple[np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event event_chirps = [] # chirps that are in specified window around event
centered_chirps = [] # timestamps of chirps around event centered on the event timepoint centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
@ -188,36 +187,43 @@ def main(datapath: str):
# Iterate over chasing onsets (later over fish) # Iterate over chasing onsets (later over fish)
time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event
#### Loop crashes at concatenate in function #### #### Loop crashes at concatenate in function ####
for i in range(len(fish_ids)): # for i in range(len(fish_ids)):
fish = fish_ids[i] # fish = fish_ids[i]
chirps = chirps[chirps_fish_ids == fish] # chirps = chirps[chirps_fish_ids == fish]
print(fish) # print(fish)
chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event) chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event)
physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event) physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event)
# Kernel density estimation ??? # Kernel density estimation ???
# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5) # centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5)
# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0 # centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0
offsets = [0.5, 1] offsets = [0.5, 1]
fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True) fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True)
ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r']) ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r'])
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event') ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event')
# ax4.plot(centered_chasing_chirps_convolved) # ax4.plot(centered_chasing_chirps_convolved)
ax4.set_yticks(offsets) ax4.set_yticks(offsets)
ax4.set_yticklabels(['Chasings', 'Physical \n contacts']) ax4.set_yticklabels(['Chasings', 'Physical \n contacts'])
ax4.set_xlabel('Time[s]') ax4.set_xlabel('Time[s]')
ax4.set_ylabel('Type of event') ax4.set_ylabel('Type of event')
plt.show() plt.show()
# Associate chirps to inidividual fish # Associate chirps to inidividual fish
fish1 = chirps[chirps_fish_ids == fish_ids[0]] fish1 = chirps[chirps_fish_ids == fish_ids[0]]
fish2 = chirps[chirps_fish_ids == fish_ids[1]] fish2 = chirps[chirps_fish_ids == fish_ids[1]]
fish = [len(fish1), len(fish2)] fish = [len(fish1), len(fish2)]
### Plots:
# 1. All recordings, all fish, all chirps
# One CTC, one PTC
# 2. All recordings, only winners
# One CTC, one PTC
# 3. All recordings, all losers
# One CTC, one PTC
#### Chirp counts per fish general ##### #### Chirp counts per fish general #####
fig2, ax2 = plt.subplots() fig2, ax2 = plt.subplots()
x = ['Fish1', 'Fish2'] x = ['Fish1', 'Fish2']

225
code/eventchirpsplots.py Normal file
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@ -0,0 +1,225 @@
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
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)
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
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 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 event_triggered_chirps(
event: np.ndarray,
chirps:np.ndarray,
time_before_event: int,
time_after_event: int
)-> tuple[np.ndarray, np.ndarray]:
event_chirps = [] # chirps that are in specified window around event
centered_chirps = [] # timestamps of chirps around event centered on the event timepoint
for event_timestamp in event:
start = event_timestamp - time_before_event # timepoint of window start
stop = event_timestamp + time_after_event # timepoint of window ending
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)] # get chirps that are in a -5 to +5 sec window around event
event_chirps.append(chirps_around_event)
if len(chirps_around_event) == 0:
continue
else:
centered_chirps.append(chirps_around_event - event_timestamp)
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
return event_chirps, centered_chirps
def main(datapath: str):
# 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]
# Get fish ids
fish_ids = np.unique(chirps_fish_ids)
##### Chasing triggered chirps CTC #####
# Evaluate how many chirps were emitted in specific time window around the chasing onset events
# Iterate over chasing onsets (later over fish)
time_around_event = 5 # time window around the event in which chirps are counted, 5 = -5 to +5 sec around event
#### Loop crashes at concatenate in function ####
# for i in range(len(fish_ids)):
# fish = fish_ids[i]
# chirps = chirps[chirps_fish_ids == fish]
# print(fish)
chasing_chirps, centered_chasing_chirps = event_triggered_chirps(chasing_onset, chirps, time_around_event, time_around_event)
physical_chirps, centered_physical_chirps = event_triggered_chirps(physical_contact, chirps, time_around_event, time_around_event)
# Kernel density estimation ???
# centered_chasing_chirps_convolved = gaussian_filter1d(centered_chasing_chirps, 5)
# centered_chasing = chasing_onset[0] - chasing_onset[0] ## get the 0 timepoint for plotting; set one chasing event to 0
offsets = [0.5, 1]
fig4, ax4 = plt.subplots(figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True)
ax4.eventplot(np.array([centered_chasing_chirps, centered_physical_chirps]), lineoffsets=offsets, linelengths=0.25, colors=['g', 'r'])
ax4.vlines(0, 0, 1.5, 'tab:grey', 'dashed', 'Timepoint of event')
# ax4.plot(centered_chasing_chirps_convolved)
ax4.set_yticks(offsets)
ax4.set_yticklabels(['Chasings', 'Physical \n contacts'])
ax4.set_xlabel('Time[s]')
ax4.set_ylabel('Type of event')
plt.show()
# Associate chirps to inidividual fish
fish1 = chirps[chirps_fish_ids == fish_ids[0]]
fish2 = chirps[chirps_fish_ids == fish_ids[1]]
fish = [len(fish1), len(fish2)]
### Plots:
# 1. All recordings, all fish, all chirps
# One CTC, one PTC
# 2. All recordings, only winners
# One CTC, one PTC
# 3. All recordings, all losers
# One CTC, one PTC
embed()
exit()
if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/2020-05-13-10_00/'
main(datapath)