diff --git a/code/CTCPTC.py b/code/CTCPTC.py
deleted file mode 100644
index 1935ba2..0000000
--- a/code/CTCPTC.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-
-from tqdm import tqdm
-from IPython import embed
-from pandas import read_csv
-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
-
-logger = makeLogger(__name__)
-ps = PlotStyle()
-
-#### Goal: CTC & PTC for each winner and loser and for all winners and loser ####
diff --git a/code/eventchirpsplots.py b/code/eventchirpsplots.py
index 9117743..4ebaa66 100644
--- a/code/eventchirpsplots.py
+++ b/code/eventchirpsplots.py
@@ -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)
@@ -330,7 +345,7 @@ def main(datapath: str):
np.random.shuffle(diff_physical)
shuffled_physical = np.cumsum(diff_physical)
- kde_onset = (acausal_kde1d(shuffled_onset, time, width))/(27*100)
+ kde_onset (acausal_kde1d(shuffled_onset, time, width))/(27*100)
kde_offset = (acausal_kde1d(shuffled_offset, time, width))/(27*100)
kde_physical = (acausal_kde1d(shuffled_physical, time, width))/(27*100)
@@ -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/'
diff --git a/code/extract_chirps.py b/code/extract_chirps.py
index 5d5580e..77e3e8d 100644
--- a/code/extract_chirps.py
+++ b/code/extract_chirps.py
@@ -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
diff --git a/code/modules/behaviour_handling.py b/code/modules/behaviour_handling.py
index 29499b6..94a0ca1 100644
--- a/code/modules/behaviour_handling.py
+++ b/code/modules/behaviour_handling.py
@@ -1,14 +1,10 @@
import numpy as np
-
-import os
-
-import numpy as np
+import os
from IPython import embed
-
from pandas import read_csv
from modules.logger import makeLogger
-from modules.datahandling import causal_kde1d, acausal_kde1d
+from modules.datahandling import causal_kde1d, acausal_kde1d, flatten
logger = makeLogger(__name__)
@@ -19,46 +15,60 @@ 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 = [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))
+ csv_filename = os.path.split(folder_path[:-1])[-1]
+ csv_filename = '-'.join(csv_filename.split('-')[:-1]) + '.csv'
+ # embed()
- 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)
+ # 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.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 +78,23 @@ 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 category[category != 2][-1] == 0:
+ wrong_bh = np.append(
+ wrong_bh,
+ np.arange(len(category))[category != 2][-1])
+
if onset_ids[0] > offset_ids[0]:
offset_ids = np.delete(offset_ids, 0)
help_index = offset_ids[0]
@@ -92,49 +109,61 @@ def correct_chasing_events(
len(category))[category == 1]
# Check whether on- or offset is longer and calculate length difference
+
if len(new_onset_ids) > len(new_offset_ids):
- len_diff = len(onset_ids) - len(offset_ids)
- logger.info(f'Onsets are greater than offsets by {len_diff}')
+ embed()
+ logger.warning('Onsets are greater than offsets')
elif len(new_onset_ids) < len(new_offset_ids):
- len_diff = len(offset_ids) - len(onset_ids)
- logger.info(f'Offsets are greater than onsets by {len_diff}')
+ logger.warning('Offsets are greater than onsets')
elif len(new_onset_ids) == len(new_offset_ids):
- logger.info('Chasing events are equal')
-
+ # logger.info('Chasing events are equal')
+ pass
+
return category, timestamps
-def event_triggered_chirps(
- event: np.ndarray,
- chirps:np.ndarray,
+def center_chirps(
+ events: np.ndarray,
+ chirps: np.ndarray,
time_before_event: int,
time_after_event: int,
- dt: float,
- width: float,
- )-> tuple[np.ndarray, np.ndarray, np.ndarray]:
+ # dt: float,
+ # width: float,
+) -> 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 events:
- for event_timestamp in event:
start = event_timestamp - time_before_event
stop = event_timestamp + time_after_event
chirps_around_event = [c for c in chirps if (c >= start) & (c <= stop)]
- event_chirps.append(chirps_around_event)
+
if len(chirps_around_event) == 0:
continue
- 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)
-
- return event_chirps, centered_chirps, centered_chirps_convolved
+ centered_chirps.append(chirps_around_event - event_timestamp)
+ event_chirps.append(chirps_around_event)
+
+ centered_chirps = np.sort(flatten(centered_chirps))
+ event_chirps = np.sort(flatten(event_chirps))
+
+ if len(centered_chirps) != len(event_chirps):
+ raise ValueError(
+ 'Non centered chirps and centered chirps are not equal')
+
+ # 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:
+ # # 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 centered_chirps
diff --git a/code/plot_kdes.py b/code/plot_kdes.py
new file mode 100644
index 0000000..03c6621
--- /dev/null
+++ b/code/plot_kdes.py
@@ -0,0 +1,432 @@
+from extract_chirps import get_valid_datasets
+import os
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+from tqdm import tqdm
+from IPython import embed
+from pandas import read_csv
+from modules.logger import makeLogger
+from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
+from modules.behaviour_handling import (
+ Behavior, correct_chasing_events, center_chirps)
+from modules.plotstyle import PlotStyle
+
+logger = makeLogger(__name__)
+ps = PlotStyle()
+
+
+def jackknife(data, nresamples, subsetsize, kde_time, kernel_width):
+
+ if len(data) == 0:
+ return []
+
+ jackknifed_kdes = []
+ data = np.sort(data)
+ subsetsize = int(np.round(len(data)*subsetsize))
+
+ for n in range(nresamples):
+
+ subset = np.random.choice(data, subsetsize, replace=False)
+ subset_kde = acausal_kde1d(subset, time=kde_time, width=kernel_width)
+ jackknifed_kdes.append(subset_kde)
+
+ return jackknifed_kdes
+
+
+def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
+
+ bootstrapped_kdes = []
+ data = data[data <= 3*60*60] # only night time
+
+ if len(data) == 0:
+ logger.info('No data for bootstrap, added zeros')
+ return [np.zeros_like(kde_time) for i in range(nresamples)]
+
+ diff_data = np.diff(np.sort(data), prepend=np.sort(data)[0])
+
+ for i in tqdm(range(nresamples)):
+
+ np.random.shuffle(diff_data)
+ bootstrapped_data = np.cumsum(diff_data)
+ bootstrap_data_centered = center_chirps(
+ bootstrapped_data, event_times, time_before, time_after)
+ bootstrapped_kde = acausal_kde1d(
+ bootstrap_data_centered, time=kde_time, width=kernel_width)
+
+ bootstrapped_kdes.append(bootstrapped_kde)
+
+ return bootstrapped_kdes
+
+
+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
+ return None, None
+
+
+def main(dataroot):
+
+ foldernames, _ = get_valid_datasets(dataroot)
+ plot_all = False
+ time_before = 60
+ time_after = 60
+ dt = 0.001
+ kernel_width = 1
+ kde_time = np.arange(-time_before, time_after, dt)
+ nbootstraps = 2
+
+ meta_path = (
+ '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+ meta = pd.read_csv(meta_path)
+ meta['recording'] = meta['recording'].str[1:-1]
+
+ winner_onsets = []
+ winner_offsets = []
+ winner_physicals = []
+
+ loser_onsets = []
+ loser_offsets = []
+ loser_physicals = []
+
+ winner_onsets_boot = []
+ winner_offsets_boot = []
+ winner_physicals_boot = []
+
+ loser_onsets_boot = []
+ loser_offsets_boot = []
+ loser_physicals_boot = []
+
+ onset_count = 0
+ offset_count = 0
+ physical_count = 0
+
+ # Iterate over all recordings and save chirp- and event-timestamps
+ for folder in tqdm(foldernames):
+
+ foldername = folder.split('/')[-2]
+ # logger.info('Loading data from folder: {}'.format(foldername))
+
+ broken_folders = ['../data/mount_data/2020-05-12-10_00/']
+ if folder in broken_folders:
+ continue
+
+ bh = Behavior(folder)
+ category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
+
+ winner, loser = get_chirp_winner_loser(folder, bh, meta)
+
+ if winner is None:
+ continue
+
+ onsets = (timestamps[category == 0])
+ offsets = (timestamps[category == 1])
+ physicals = (timestamps[category == 2])
+
+ onset_count += len(onsets)
+ offset_count += len(offsets)
+ physical_count += len(physicals)
+
+ winner_onsets.append(center_chirps(
+ winner, onsets, time_before, time_after))
+ winner_offsets.append(center_chirps(
+ winner, offsets, time_before, time_after))
+ winner_physicals.append(center_chirps(
+ winner, physicals, time_before, time_after))
+
+ loser_onsets.append(center_chirps(
+ loser, onsets, time_before, time_after))
+ loser_offsets.append(center_chirps(
+ loser, offsets, time_before, time_after))
+ loser_physicals.append(center_chirps(
+ loser, physicals, time_before, time_after))
+
+ # bootstrap
+ winner_onsets_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=onsets,
+ time_before=time_before,
+ time_after=time_after))
+ winner_offsets_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=offsets,
+ time_before=time_before,
+ time_after=time_after))
+ winner_physicals_boot.append(bootstrap(
+ winner,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=physicals,
+ time_before=time_before,
+ time_after=time_after))
+
+ loser_onsets_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=onsets,
+ time_before=time_before,
+ time_after=time_after))
+ loser_offsets_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=offsets,
+ time_before=time_before,
+ time_after=time_after))
+ loser_physicals_boot.append(bootstrap(
+ loser,
+ nresamples=nbootstraps,
+ kde_time=kde_time,
+ kernel_width=kernel_width,
+ event_times=physicals,
+ time_before=time_before,
+ time_after=time_after))
+
+ if plot_all:
+
+ winner_onsets_conv = acausal_kde1d(
+ winner_onsets[-1], kde_time, kernel_width)
+ winner_offsets_conv = acausal_kde1d(
+ winner_offsets[-1], kde_time, kernel_width)
+ winner_physicals_conv = acausal_kde1d(
+ winner_physicals[-1], kde_time, kernel_width)
+
+ loser_onsets_conv = acausal_kde1d(
+ loser_onsets[-1], kde_time, kernel_width)
+ loser_offsets_conv = acausal_kde1d(
+ loser_offsets[-1], kde_time, kernel_width)
+ loser_physicals_conv = acausal_kde1d(
+ loser_physicals[-1], kde_time, kernel_width)
+
+ fig, ax = plt.subplots(2, 3, figsize=(
+ 21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
+ ax[0, 0].set_title(
+ f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
+ ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
+ ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
+ ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
+ ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
+ ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
+ ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
+
+ # # plot bootstrap lines
+ # for kde in winner_onsets_boot[-1]:
+ # ax[0, 0].plot(kde_time, kde/len(offsets),
+ # color='gray')
+ # for kde in winner_offsets_boot[-1]:
+ # ax[0, 1].plot(kde_time, kde/len(offsets),
+ # color='gray')
+ # for kde in winner_physicals_boot[-1]:
+ # ax[0, 2].plot(kde_time, kde/len(offsets),
+ # color='gray')
+ # for kde in loser_onsets_boot[-1]:
+ # ax[1, 0].plot(kde_time, kde/len(offsets),
+ # color='gray')
+ # for kde in loser_offsets_boot[-1]:
+ # ax[1, 1].plot(kde_time, kde/len(offsets),
+ # color='gray')
+ # for kde in loser_physicals_boot[-1]:
+ # ax[1, 2].plot(kde_time, kde/len(offsets),
+ # color='gray')
+
+ # plot bootstrap percentiles
+ ax[0, 0].fill_between(
+ kde_time,
+ np.percentile(winner_onsets_boot[-1], 5, axis=0)/len(onsets),
+ np.percentile(winner_onsets_boot[-1], 95, axis=0)/len(onsets),
+ color='gray',
+ alpha=0.5)
+ ax[0, 1].fill_between(
+ kde_time,
+ np.percentile(winner_offsets_boot[-1], 5, axis=0)/len(offsets),
+ np.percentile(
+ winner_offsets_boot[-1], 95, axis=0)/len(offsets),
+ color='gray',
+ alpha=0.5)
+ ax[0, 2].fill_between(
+ kde_time,
+ np.percentile(
+ winner_physicals_boot[-1], 5, axis=0)/len(physicals),
+ np.percentile(
+ winner_physicals_boot[-1], 95, axis=0)/len(physicals),
+ color='gray',
+ alpha=0.5)
+ ax[1, 0].fill_between(
+ kde_time,
+ np.percentile(loser_onsets_boot[-1], 5, axis=0)/len(onsets),
+ np.percentile(loser_onsets_boot[-1], 95, axis=0)/len(onsets),
+ color='gray',
+ alpha=0.5)
+ ax[1, 1].fill_between(
+ kde_time,
+ np.percentile(loser_offsets_boot[-1], 5, axis=0)/len(offsets),
+ np.percentile(loser_offsets_boot[-1], 95, axis=0)/len(offsets),
+ color='gray',
+ alpha=0.5)
+ ax[1, 2].fill_between(
+ kde_time,
+ np.percentile(
+ loser_physicals_boot[-1], 5, axis=0)/len(physicals),
+ np.percentile(
+ loser_physicals_boot[-1], 95, axis=0)/len(physicals),
+ color='gray',
+ alpha=0.5)
+
+ ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0)/len(onsets),
+ color='black', linewidth=2)
+ ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0)/len(offsets),
+ color='black', linewidth=2)
+ ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0)/len(physicals),
+ color='black', linewidth=2)
+ ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0)/len(onsets),
+ color='black', linewidth=2)
+ ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0)/len(offsets),
+ color='black', linewidth=2)
+ ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0)/len(physicals),
+ color='black', linewidth=2)
+
+ ax[0, 0].set_xlim(-30, 30)
+ plt.show()
+
+ winner_onsets = np.sort(flatten(winner_onsets))
+ winner_offsets = np.sort(flatten(winner_offsets))
+ winner_physicals = np.sort(flatten(winner_physicals))
+ loser_onsets = np.sort(flatten(loser_onsets))
+ loser_offsets = np.sort(flatten(loser_offsets))
+ loser_physicals = np.sort(flatten(loser_physicals))
+
+ winner_onsets_conv = acausal_kde1d(
+ winner_onsets, kde_time, kernel_width)
+ winner_offsets_conv = acausal_kde1d(
+ winner_offsets, kde_time, kernel_width)
+ winner_physicals_conv = acausal_kde1d(
+ winner_physicals, kde_time, kernel_width)
+ loser_onsets_conv = acausal_kde1d(
+ loser_onsets, kde_time, kernel_width)
+ loser_offsets_conv = acausal_kde1d(
+ loser_offsets, kde_time, kernel_width)
+ loser_physicals_conv = acausal_kde1d(
+ loser_physicals, kde_time, kernel_width)
+
+ winner_onsets_conv = winner_onsets_conv / onset_count
+ winner_offsets_conv = winner_offsets_conv / offset_count
+ winner_physicals_conv = winner_physicals_conv / physical_count
+ loser_onsets_conv = loser_onsets_conv / onset_count
+ loser_offsets_conv = loser_offsets_conv / offset_count
+ loser_physicals_conv = loser_physicals_conv / physical_count
+
+ embed()
+
+ winner_onsets_boot = np.concatenate(
+ winner_onsets_boot) / onset_count
+ winner_offsets_boot = np.concatenate(
+ winner_offsets_boot) / offset_count
+ winner_physicals_boot = np.concatenate(
+ winner_physicals_boot) / physical_count
+ loser_onsets_boot = np.concatenate(
+ loser_onsets_boot) / onset_count
+ loser_offsets_boot = np.concatenate(
+ loser_offsets_boot) / offset_count
+ loser_physicals_boot = np.concatenate(
+ loser_physicals_boot) / physical_count
+
+ percs = [5, 50, 95]
+ winner_onsets_boot_quarts = np.percentile(
+ winner_onsets_boot, percs, axis=0)
+ winner_offsets_boot_quarts = np.percentile(
+ winner_offsets_boot, percs, axis=0)
+ winner_physicals_boot_quarts = np.percentile(
+ winner_physicals_boot, percs, axis=0)
+ loser_onsets_boot_quarts = np.percentile(
+ loser_onsets_boot, percs, axis=0)
+ loser_offsets_boot_quarts = np.percentile(
+ loser_offsets_boot, percs, axis=0)
+ loser_physicals_boot_quarts = np.percentile(
+ loser_physicals_boot, percs, axis=0)
+
+ fig, ax = plt.subplots(2, 3, figsize=(
+ 21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
+
+ ax[0, 0].plot(kde_time, winner_onsets_conv)
+ ax[0, 0].fill_between(kde_time,
+ winner_onsets_boot_quarts[0],
+ winner_onsets_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
+
+ ax[0, 1].plot(kde_time, winner_offsets_conv)
+ ax[0, 1].fill_between(kde_time,
+ winner_offsets_boot_quarts[0],
+ winner_offsets_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
+
+ ax[0, 2].plot(kde_time, winner_physicals_conv)
+ ax[0, 2].fill_between(kde_time,
+ loser_physicals_boot_quarts[0],
+ loser_physicals_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
+
+ ax[1, 0].plot(kde_time, loser_onsets_conv)
+ ax[1, 0].fill_between(kde_time,
+ loser_onsets_boot_quarts[0],
+ loser_onsets_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
+
+ ax[1, 1].plot(kde_time, loser_offsets_conv)
+ ax[1, 1].fill_between(kde_time,
+ loser_offsets_boot_quarts[0],
+ loser_offsets_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
+
+ ax[1, 2].plot(kde_time, loser_physicals_conv)
+ ax[1, 2].fill_between(kde_time,
+ loser_physicals_boot_quarts[0],
+ loser_physicals_boot_quarts[2],
+ color=ps.gray,
+ alpha=0.5)
+ ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
+
+ plt.show()
+
+
+if __name__ == '__main__':
+ main('../data/mount_data/')
diff --git a/poster/main.pdf b/poster/main.pdf
index fcef087..a1d28be 100644
Binary files a/poster/main.pdf and b/poster/main.pdf differ
diff --git a/poster/main.tex b/poster/main.tex
index 937aad0..b746206 100644
--- a/poster/main.tex
+++ b/poster/main.tex
@@ -23,7 +23,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
with single - or physically separated - individuals.
\begin{tikzfigure}[]
\label{griddrawing}
+<<<<<<< HEAD
+=======
\includegraphics[width=0.8\linewidth]{figs/introplot}
+>>>>>>> cdcf9564df07914cf57225de5a8bdaa642fbad0e
\end{tikzfigure}
}
\myblock[TranspBlock]{Chirp detection}{
@@ -43,10 +46,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\noindent
\begin{itemize}
\setlength\itemsep{0.5em}
- \item Two fish compete for one hidding place in one tank,
+ \item Two fish compete for one hidding place in one tank,
\item Experiment had a 3 hour long darkphase and a 3 hour long light phase.
\end{itemize}
-
+
\noindent
\begin{minipage}[c]{0.7\linewidth}
\begin{tikzfigure}[]
@@ -57,8 +60,8 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\begin{minipage}[c]{0.2\linewidth}
\begin{itemize}
\setlength\itemsep{0.5em}
- \item Fish who won the competition chirped more often than the fish who lost.
- \item
+ \item Fish who won the competition chirped more often than the fish who lost.
+ \item
\end{itemize}
\end{minipage}
}