new plotfile for CTC and PTC
This commit is contained in:
parent
c6340e5a4d
commit
c5b9d648e8
@ -131,7 +131,6 @@ def event_triggered_chirps(
|
||||
)-> 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
|
||||
|
||||
@ -188,36 +187,43 @@ def main(datapath: str):
|
||||
|
||||
# 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)
|
||||
# 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)
|
||||
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()
|
||||
# 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
|
||||
|
||||
#### Chirp counts per fish general #####
|
||||
fig2, ax2 = plt.subplots()
|
||||
x = ['Fish1', 'Fish2']
|
||||
|
||||
225
code/eventchirpsplots.py
Normal file
225
code/eventchirpsplots.py
Normal file
@ -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)
|
||||
Loading…
Reference in New Issue
Block a user