raab/GP2023_chirp_detection
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weygoldt 2023-01-25 10:19:19 +01:00
parent 0b109e8c5e
commit a6b7ed2c6c
6 changed files with 271 additions and 138 deletions
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86
code/CTCPTC.py
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@ -1,8 +1,8 @@
import os
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython import embed
@ -11,8 +11,88 @@ from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
from modules.datahandling import flatten
from modules.behaviour_handling import Behavior, correct_chasing_events, event_triggered_chirps
from extract_chirps import get_valid_datasets
logger = makeLogger(__name__)
ps = PlotStyle()
#### Goal: CTC & PTC for each winner and loser and for all winners and loser ####
def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
foldername = folder_name.split('/')[-2]
winner_row = order_meta_df[order_meta_df['recording'] == foldername]
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 > 0:
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]
chirp_winner = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
return chirp_winner, chirp_loser
else:
return None, None
def main(dataroot):
foldernames, _ = get_valid_datasets(dataroot)
meta_path = (
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
meta = pd.read_csv(meta_path)
meta['recording'] = meta['recording'].str[1:-1]
winner_chirps = []
loser_chirps = []
onsets = []
offsets = []
physicals = []
# Iterate over all recordings and save chirp- and event-timestamps
for folder in foldernames:
logger.info('Loading data from folder: {}'.format(folder))
time_before = 30
time_after = 60
dt = 0.1
kernel_width = 2
kde_time = np.arange(-time_before, time_after, dt)
broken_folders = ['../data/mount_data/2020-05-12-10_00/']
if folder in broken_folders:
continue
bh = Behavior(folder)
winner, loser = get_chirp_winner_loser(folder, bh, meta)
if winner is None:
continue
# Chirps are already sorted
winner_chirps.append(bh.chirps)
loser_chirps.append(bh.chirps)
# 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(bh.behavior, bh.start_s)
# Split categories
onsets.append(timestamps[category == 0])
offsets.append(timestamps[category == 1])
physicals.append(timestamps[category == 2])
# center chirps around events
if __name__ == '__main__':
main('../data/mount_data/')

188
code/eventchirpsplots.py
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@ -1,8 +1,8 @@
import os
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython import embed
@ -14,42 +14,49 @@ from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
logger = makeLogger(__name__)
ps = PlotStyle()
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:
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:
print(f'{folder_path}')
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
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, 'chirp_ids.npy'), allow_pickle=True)
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)
for k, key in enumerate(self.dataframe.keys()):
key = key.lower()
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]]))
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]
@ -58,6 +65,7 @@ class Behavior:
self.start_s = (self.start_s - shift) / factor
self.stop_s = (self.stop_s - shift) / factor
"""
1 - chasing onset
2 - chasing offset
@ -87,16 +95,17 @@ temporal encpding needs to be corrected ... not exactly 25FPS.
def correct_chasing_events(
category: np.ndarray,
category: np.ndarray,
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange(
len(category))[category == 0]
offset_ids = np.arange(
len(category))[category == 1]
wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
wrong_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]
@ -105,7 +114,6 @@ def correct_chasing_events(
category = np.delete(category, wrong_bh)
timestamps = np.delete(timestamps, wrong_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)
@ -115,21 +123,22 @@ def correct_chasing_events(
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 event_triggered_chirps(
event: np.ndarray,
chirps:np.ndarray,
event: np.ndarray,
chirps: np.ndarray,
time_before_event: int,
time_after_event: int,
dt: float,
width: float,
)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
) -> tuple[np.ndarray, 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
# timestamps of chirps around event centered on the event timepoint
centered_chirps = []
for event_timestamp in event:
start = event_timestamp - time_before_event
@ -138,25 +147,28 @@ def event_triggered_chirps(
event_chirps.append(chirps_around_event)
if len(chirps_around_event) == 0:
continue
else:
else:
centered_chirps.append(chirps_around_event - event_timestamp)
time = np.arange(-time_before_event, time_after_event, dt)
# Kernel density estimation with some if's
if len(centered_chirps) == 0:
centered_chirps = np.array([])
centered_chirps_convolved = np.zeros(len(time))
else:
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
# convert list of arrays to one array for plotting
centered_chirps = np.concatenate(centered_chirps, axis=0)
centered_chirps_convolved = (acausal_kde1d(
centered_chirps, time, width)) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved
def main(datapath: str):
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
nrecording_chirps = []
nrecording_chirps_fish_ids = []
@ -171,7 +183,7 @@ def main(datapath: str):
continue
bh = Behavior(folder)
# Chirps are already sorted
category = bh.behavior
timestamps = bh.start_s
@ -193,14 +205,12 @@ def main(datapath: str):
physical_contacts = timestamps[category == 2]
nrecording_physicals.append(physical_contacts)
# Define time window for chirps around event analysis
time_before_event = 30
time_after_event = 60
dt = 0.01
width = 1.5 # width of kernel for all recordings, currently gaussian kernel
recording_width = 2 # width of kernel for each recording
recording_width = 2 # width of kernel for each recording
time = np.arange(-time_before_event, time_after_event, dt)
##### Chirps around events, all fish, all recordings #####
@ -222,14 +232,18 @@ def main(datapath: str):
physical_contacts = nrecording_physicals[i]
# Chirps around chasing onsets
_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(
chasing_onsets, chirps, time_before_event, time_after_event, dt, recording_width)
# Chirps around chasing offsets
_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(
chasing_offsets, chirps, time_before_event, time_after_event, dt, recording_width)
# Chirps around physical contacts
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(
physical_contacts, chirps, time_before_event, time_after_event, dt, recording_width)
nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
nrecording_centered_offset_chirps.append(
centered_chasing_offset_chirps)
nrecording_centered_physical_chirps.append(centered_physical_chirps)
## Shuffled chirps ##
@ -252,7 +266,6 @@ def main(datapath: str):
# _, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, recording_width)
# nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
# rec_shuffled_q5_onset, rec_shuffled_median_onset, rec_shuffled_q95_onset = np.percentile(
# nshuffled_onset_chirps, (5, 50, 95), axis=0)
# rec_shuffled_q5_offset, rec_shuffled_median_offset, rec_shuffled_q95_offset = np.percentile(
@ -260,7 +273,6 @@ def main(datapath: str):
# rec_shuffled_q5_physical, rec_shuffled_median_physical, rec_shuffled_q95_physical = np.percentile(
# nshuffled_physical_chirps, (5, 50, 95), axis=0)
# #### Recording plots ####
# fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
# ax[0].set_xlabel('Time[s]')
@ -307,7 +319,7 @@ def main(datapath: str):
# fig.suptitle(f'Recording: {i}')
# # plt.show()
# plt.close()
# nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
# nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
# nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
@ -319,9 +331,12 @@ def main(datapath: str):
# New bootstrapping approach
for n in range(nbootstrapping):
diff_onset = np.diff(np.sort(flatten(nrecording_centered_onset_chirps)))
diff_offset = np.diff(np.sort(flatten(nrecording_centered_offset_chirps)))
diff_physical = np.diff(np.sort(flatten(nrecording_centered_physical_chirps)))
diff_onset = np.diff(
np.sort(flatten(nrecording_centered_onset_chirps)))
diff_offset = np.diff(
np.sort(flatten(nrecording_centered_offset_chirps)))
diff_physical = np.diff(
np.sort(flatten(nrecording_centered_physical_chirps)))
np.random.shuffle(diff_onset)
shuffled_onset = np.cumsum(diff_onset)
@ -339,16 +354,18 @@ def main(datapath: str):
bootstrap_physical.append(kde_physical)
# New shuffle approach q5, q50, q95
onset_q5, onset_median, onset_q95 = np.percentile(bootstrap_onset, [5, 50, 95], axis=0)
offset_q5, offset_median, offset_q95 = np.percentile(bootstrap_offset, [5, 50, 95], axis=0)
physical_q5, physical_median, physical_q95 = np.percentile(bootstrap_physical, [5, 50, 95], axis=0)
onset_q5, onset_median, onset_q95 = np.percentile(
bootstrap_onset, [5, 50, 95], axis=0)
offset_q5, offset_median, offset_q95 = np.percentile(
bootstrap_offset, [5, 50, 95], axis=0)
physical_q5, physical_median, physical_q95 = np.percentile(
bootstrap_physical, [5, 50, 95], axis=0)
# vstack um 1. Dim zu cutten
# nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
# nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
# nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
# shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
# nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
# shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
@ -356,27 +373,37 @@ def main(datapath: str):
# shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
# nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
# Flatten all chirps
# Flatten all chirps
all_chirps = np.concatenate(nrecording_chirps).ravel() # not centered
# Flatten event timestamps
all_onsets = np.concatenate(nrecording_chasing_onsets).ravel() # not centered
all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered
all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
all_onsets = np.concatenate(
nrecording_chasing_onsets).ravel() # not centered
all_offsets = np.concatenate(
nrecording_chasing_offsets).ravel() # not centered
all_physicals = np.concatenate(
nrecording_physicals).ravel() # not centered
# Flatten all chirps around events
all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel() # centered
all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered
all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered
all_onset_chirps = np.concatenate(
nrecording_centered_onset_chirps).ravel() # centered
all_offset_chirps = np.concatenate(
nrecording_centered_offset_chirps).ravel() # centered
all_physical_chirps = np.concatenate(
nrecording_centered_physical_chirps).ravel() # centered
# Convolute all chirps
# Divide by total number of each event over all recordings
all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets)
all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets)
all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals)
all_onset_chirps_convolved = (acausal_kde1d(
all_onset_chirps, time, width)) / len(all_onsets)
all_offset_chirps_convolved = (acausal_kde1d(
all_offset_chirps, time, width)) / len(all_offsets)
all_physical_chirps_convolved = (acausal_kde1d(
all_physical_chirps, time, width)) / len(all_physicals)
# Plot all events with all shuffled
fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
fig, ax = plt.subplots(1, 3, figsize=(
28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
# offsets = np.arange(1,28,1)
ax[0].set_xlabel('Time[s]')
@ -384,8 +411,10 @@ def main(datapath: str):
ax[0].set_ylabel('Chirp rate [Hz]')
ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
ax0 = ax[0].twinx()
nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
nrecording_centered_onset_chirps = np.asarray(
nrecording_centered_onset_chirps, dtype=object)
ax0.eventplot(np.array(nrecording_centered_onset_chirps),
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax0.vlines(0, 0, 1.5, ps.white, 'dashed')
ax[0].set_zorder(ax0.get_zorder()+1)
ax[0].patch.set_visible(False)
@ -400,8 +429,10 @@ def main(datapath: str):
ax[1].set_xlabel('Time[s]')
ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
ax1 = ax[1].twinx()
nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
nrecording_centered_offset_chirps = np.asarray(
nrecording_centered_offset_chirps, dtype=object)
ax1.eventplot(np.array(nrecording_centered_offset_chirps),
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax1.vlines(0, 0, 1.5, ps.white, 'dashed')
ax[1].set_zorder(ax1.get_zorder()+1)
ax[1].patch.set_visible(False)
@ -416,8 +447,10 @@ def main(datapath: str):
ax[2].set_xlabel('Time[s]')
ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
ax2 = ax[2].twinx()
nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
nrecording_centered_physical_chirps = np.asarray(
nrecording_centered_physical_chirps, dtype=object)
ax2.eventplot(np.array(nrecording_centered_physical_chirps),
linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax2.vlines(0, 0, 1.5, ps.white, 'dashed')
ax[2].set_zorder(ax2.get_zorder()+1)
ax[2].patch.set_visible(False)
@ -425,14 +458,15 @@ def main(datapath: str):
ax2.set_yticks([])
# ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
# ax[2].plot(time, shuffled_median_physical, ps.black)
ax[2].fill_between(time, physical_q5, physical_q95, color=ps.gray, alpha=0.5)
ax[2].fill_between(time, physical_q5, physical_q95,
color=ps.gray, alpha=0.5)
ax[2].plot(time, physical_median, ps.black)
fig.suptitle('All recordings')
plt.show()
plt.close()
embed()
# chasing_durations = []
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
# for onset, offset in zip(chasing_onsets, chasing_offsets):
@ -444,7 +478,6 @@ def main(datapath: str):
# plt.show()
# plt.close()
# # Associate chirps to individual fish
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
@ -453,7 +486,6 @@ def main(datapath: str):
# Convolution over all recordings
# Rasterplot for each recording
# #### Chirps around events, winner VS loser, one recording ####
# # Load file with fish ids and winner/loser info
# meta = pd.read_csv('../data/mount_data/order_meta.csv')
@ -462,7 +494,7 @@ def main(datapath: str):
# fish2 = current_recording['rec_id2'].values
# # Implement check if fish_ids from meta and chirp detection are the same???
# winner = current_recording['winner'].values
# if winner == fish1:
# loser = fish2
# elif winner == fish2:
@ -546,7 +578,6 @@ def main(datapath: str):
# ax5.set_yticks([])
# plt.show()
# plt.close()
# for i in range(len(fish_ids)):
# fish = fish_ids[i]
@ -556,7 +587,6 @@ def main(datapath: str):
#### Chirps around events, only losers, one recording ####
if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/'

29
code/extract_chirps.py
View File

@ -7,21 +7,12 @@ from IPython import embed
# check rec ../data/mount_data/2020-03-25-10_00/ starting at 3175
def main(datapaths):
for path in datapaths:
chirpdetection(path, plot='show')
if __name__ == '__main__':
dataroot = '../data/mount_data/'
def get_valid_datasets(dataroot):
datasets = sorted([name for name in os.listdir(dataroot) if os.path.isdir(
os.path.join(dataroot, name))])
valid_datasets = []
for dataset in datasets:
path = os.path.join(dataroot, dataset)
@ -43,9 +34,25 @@ if __name__ == '__main__':
datapaths = [os.path.join(dataroot, dataset) +
'/' for dataset in valid_datasets]
return datapaths, valid_datasets
def main(datapaths):
for path in datapaths:
chirpdetection(path, plot='show')
if __name__ == '__main__':
dataroot = '../data/mount_data/'
datapaths, valid_datasets= get_valid_datasets(dataroot)
recs = pd.DataFrame(columns=['recording'], data=valid_datasets)
recs.to_csv('../recs.csv', index=False)
datapaths = ['../data/mount_data/2020-03-25-10_00/']
# datapaths = ['../data/mount_data/2020-03-25-10_00/']
main(datapaths)
# window 1524 + 244 in dataset index 4 is nice example

98
code/modules/behaviour_handling.py
View File

@ -1,8 +1,7 @@
import numpy as np
import os
import os
import numpy as np
from IPython import embed
@ -19,46 +18,60 @@ class Behavior:
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:
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)
LED_on_time_BORIS = np.load(os.path.join(
folder_path, 'LED_on_time.npy'), allow_pickle=True)
csv_filename = os.path.split(folder_path[:-1])[-1]
csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv'
# embed()
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0]
# 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)
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()
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]]))
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
@ -68,16 +81,17 @@ class Behavior:
def correct_chasing_events(
category: np.ndarray,
category: np.ndarray,
timestamps: np.ndarray
) -> tuple[np.ndarray, np.ndarray]:
) -> tuple[np.ndarray, np.ndarray]:
onset_ids = np.arange(
len(category))[category == 0]
offset_ids = np.arange(
len(category))[category == 1]
wrong_bh = np.arange(len(category))[category!=2][:-1][np.diff(category[category!=2])==0]
wrong_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]
@ -95,21 +109,22 @@ def correct_chasing_events(
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 event_triggered_chirps(
event: np.ndarray,
chirps:np.ndarray,
event: np.ndarray,
chirps: np.ndarray,
time_before_event: int,
time_after_event: int,
dt: float,
width: float,
)-> tuple[np.ndarray, np.ndarray, np.ndarray]:
) -> tuple[np.ndarray, 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
# timestamps of chirps around event centered on the event timepoint
centered_chirps = []
for event_timestamp in event:
start = event_timestamp - time_before_event
@ -118,18 +133,19 @@ def event_triggered_chirps(
event_chirps.append(chirps_around_event)
if len(chirps_around_event) == 0:
continue
else:
else:
centered_chirps.append(chirps_around_event - event_timestamp)
time = np.arange(-time_before_event, time_after_event, dt)
# Kernel density estimation with some if's
if len(centered_chirps) == 0:
centered_chirps = np.array([])
centered_chirps_convolved = np.zeros(len(time))
else:
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
# convert list of arrays to one array for plotting
centered_chirps = np.concatenate(centered_chirps, axis=0)
centered_chirps_convolved = (acausal_kde1d(
centered_chirps, time, width)) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved

BIN
poster/main.pdf
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8
poster/main.tex
View File

@ -21,10 +21,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
sender identification of freely interacting individuals impossible.
This profoundly limits our current understanding of chirps to experiments
with single - or physically separated - individuals.
% \begin{tikzfigure}[]
% \label{griddrawing}
% \includegraphics[width=1\linewidth]{figs/introplot}
% \end{tikzfigure}
\begin{tikzfigure}[]
\label{griddrawing}
\includegraphics[width=1\linewidth]{figs/introplot}
\end{tikzfigure}
}
\myblock[TranspBlock]{Chirp detection}{
\begin{tikzfigure}[]