raab/GP2023_chirp_detection
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weygoldt 2023-01-25 17:34:33 +01:00
commit bbff7fd80c
6 changed files with 335 additions and 112 deletions
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23
code/plot_chirp_bodylegth.py
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@ -173,13 +173,15 @@ def main(datapath: str):
size_winners = []
for l in ['l1', 'l2', 'l3']:
size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0]
size_winner = size_rows[size_rows['fish']
== winner_fish1][l].values[0]
size_winners.append(size_winner)
mean_size_winner = np.nanmean(size_winners)
size_losers = []
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0]
size_loser = size_rows[size_rows['fish']
== winner_fish2][l].values[0]
size_losers.append(size_loser)
mean_size_loser = np.nanmean(size_losers)
@ -191,13 +193,15 @@ def main(datapath: str):
size_winners = []
for l in ['l1', 'l2', 'l3']:
size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0]
size_winner = size_rows[size_rows['fish']
== winner_fish2][l].values[0]
size_winners.append(size_winner)
mean_size_winner = np.nanmean(size_winners)
size_losers = []
for l in ['l1', 'l2', 'l3']:
size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0]
size_loser = size_rows[size_rows['fish']
== winner_fish1][l].values[0]
size_losers.append(size_loser)
mean_size_loser = np.nanmean(size_losers)
@ -219,7 +223,8 @@ def main(datapath: str):
size_chirps_diffs.append(chirp_winner - chirp_loser)
freq_diffs.append(freq_winner - freq_loser)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5))
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
scatterwinner = 1.15
scatterloser = 1.85
chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
@ -234,24 +239,24 @@ def main(datapath: str):
ax1.scatter(np.ones(len(chirps_loser)) *
scatterloser, chirps_loser, color='r')
ax1.set_xticklabels(['winner', 'loser'])
ax1.text(0.9, 0.9, f'n = {len(chirps_winner)}',
ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
transform=ax1.transAxes, color=ps.white)
for w, l in zip(chirps_winner, chirps_loser):
ax1.plot([scatterwinner, scatterloser], [w, l],
color='r', alpha=0.5, linewidth=0.5)
ax1.set_ylabel('Chirps [n]', color=ps.white)
colors1 = ps.red
ps.set_boxplot_color(bplot1, colors1)
colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1)
ax1.set_ylabel('Chirpscounts [n]')
embed()
ax2.scatter(size_diffs, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]')
ax2.set_ylabel('Chirps difference [n]')
ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
# ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
ax3.set_xlabel('Frequency difference [Hz]')
ax3.set_yticklabels([])
ax3.set

211
code/plot_chirp_size.py Normal file
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@ -0,0 +1,211 @@
import numpy as np
import os
import numpy as np
import matplotlib.pyplot as plt
from thunderfish.powerspectrum import decibel
from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
from modules.plotstyle import PlotStyle
from modules.behaviour_handling import Behavior, correct_chasing_events
ps = PlotStyle()
logger = makeLogger(__name__)
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 = len(
Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return chirp_winner, chirp_loser
else:
return np.nan, np.nan
def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
foldername = folder_name.split('/')[-2]
folder_row = order_meta_df[order_meta_df['recording'] == foldername]
fish1 = folder_row['fish1'].values[0].astype(int)
fish2 = folder_row['fish2'].values[0].astype(int)
groub = folder_row['group'].values[0].astype(int)
size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
id_meta_df['fish'] == fish1)]
size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
id_meta_df['fish'] == fish2)]
size_winners = [size_fish1_row[col].values[0]
for col in ['l1', 'l2', 'l3']]
mean_size_winner = np.nanmean(size_winners)
size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']]
mean_size_loser = np.nanmean(size_losers)
if mean_size_winner > mean_size_loser:
size_diff_bigger = mean_size_winner - mean_size_loser
size_diff_smaller = mean_size_loser - mean_size_winner
winner_fish_id = folder_row['rec_id1'].values[0]
loser_fish_id = folder_row['rec_id2'].values[0]
elif mean_size_winner < mean_size_loser:
size_diff_bigger = mean_size_loser - mean_size_winner
size_diff_smaller = mean_size_winner - mean_size_loser
winner_fish_id = folder_row['rec_id2'].values[0]
loser_fish_id = folder_row['rec_id1'].values[0]
else:
size_diff = np.nan
winner_fish_id = np.nan
loser_fish_id = np.nan
chirp_winner = len(
Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
chirp_loser = len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser
def get_chirp_freq(folder_name, Behavior, order_meta_df):
foldername = folder_name.split('/')[-2]
folder_row = order_meta_df[order_meta_df['recording'] == foldername]
fish1 = folder_row['rec_id1'].values[0].astype(int)
fish2 = folder_row['rec_id2'].values[0].astype(int)
chirp_freq_fish1 = np.nanmedian(
Behavior.freq[Behavior.ident == fish1])
chirp_freq_fish2 = np.nanmedian(
Behavior.freq[Behavior.ident == fish2])
if chirp_freq_fish1 > chirp_freq_fish2:
freq_diff = chirp_freq_fish1 - chirp_freq_fish2
winner_fish_id = folder_row['rec_id1'].values[0]
loser_fish_id = folder_row['rec_id2'].values[0]
elif chirp_freq_fish1 < chirp_freq_fish2:
freq_diff = chirp_freq_fish2 - chirp_freq_fish1
winner_fish_id = folder_row['rec_id2'].values[0]
loser_fish_id = folder_row['rec_id1'].values[0]
chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
return freq_diff, chirp_diff
def main(datapath: str):
foldernames = [
datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
path_order_meta = (
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
order_meta_df = read_csv(path_order_meta)
order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
path_id_meta = (
'/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
id_meta_df = read_csv(path_id_meta)
chirps_winner = []
size_diffs = []
size_chirps_diffs = []
chirps_loser = []
freq_diffs = []
freq_chirps_diffs = []
for foldername in foldernames:
# behabvior is pandas dataframe with all the data
if foldername == '../data/mount_data/2020-05-12-10_00/':
continue
bh = Behavior(foldername)
# chirps are not sorted in time (presumably due to prior groupings)
# get and sort chirps and corresponding fish_ids of the 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)
winner_chirp, loser_chirp = get_chirp_winner_loser(
foldername, bh, order_meta_df)
chirps_winner.append(winner_chirp)
chirps_loser.append(loser_chirp)
size_diff, chirp_diff = get_chirp_size(
foldername, bh, order_meta_df, id_meta_df)
size_diffs.append(size_diff)
size_chirps_diffs.append(chirp_diff)
freq_diff, freq_chirps_diff = get_chirp_freq(
foldername, bh, order_meta_df)
freq_diffs.append(freq_diff)
freq_chirps_diffs.append(freq_chirps_diff)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
scatterwinner = 1.15
scatterloser = 1.85
chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
chirps_loser = np.asarray(chirps_loser)[~np.isnan(chirps_loser)]
bplot1 = ax1.boxplot(chirps_winner, positions=[
1], showfliers=False, patch_artist=True)
bplot2 = ax1.boxplot(chirps_loser, positions=[
2], showfliers=False, patch_artist=True)
ax1.scatter(np.ones(len(chirps_winner)) *
scatterwinner, chirps_winner, color='r')
ax1.scatter(np.ones(len(chirps_loser)) *
scatterloser, chirps_loser, color='r')
ax1.set_xticklabels(['winner', 'loser'])
ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
transform=ax1.transAxes, color=ps.white)
for w, l in zip(chirps_winner, chirps_loser):
ax1.plot([scatterwinner, scatterloser], [w, l],
color='r', alpha=0.5, linewidth=0.5)
ax1.set_ylabel('Chirps [n]', color=ps.white)
colors1 = ps.red
ps.set_boxplot_color(bplot1, colors1)
colors1 = ps.orange
ps.set_boxplot_color(bplot2, colors1)
ax2.scatter(size_diffs, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]')
ax2.set_ylabel('Chirps difference [n]')
#ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
# ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')
ax3.set_xlabel('Frequency difference [Hz]')
ax3.set_yticklabels([])
ax3.set
#plt.savefig('../poster/figs/chirps_winner_loser.pdf')
plt.show()
if __name__ == '__main__':
# Path to the data
datapath = '../data/mount_data/'
main(datapath)

25
code/plot_event_timeline.py
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@ -20,13 +20,13 @@ logger = makeLogger(__name__)
def main(datapath: str):
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
#for foldername in foldernames:
foldername = foldernames[0]
# if foldername == '../data/mount_data/2020-05-12-10_00/':
# continue
for foldername in foldernames:
#foldername = foldernames[0]
if foldername == '../data/mount_data/2020-05-12-10_00/':
continue
#behabvior is pandas dataframe with all the data
bh = Behavior(foldername)
#2020-06-11-10
category = bh.behavior
timestamps = bh.start_s
# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
@ -47,7 +47,7 @@ def main(datapath: str):
fish1_color = ps.red
fish2_color = ps.orange
fig, ax = plt.subplots(4, 1, figsize=(28*ps.cm, 13*ps.cm), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
fig, ax = plt.subplots(4, 1, figsize=(21*ps.cm, 13*ps.cm), height_ratios=[0.5, 0.5, 0.5, 6], sharex=True)
# marker size
s = 200
ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
@ -92,16 +92,17 @@ def main(datapath: str):
ax[3].set_xticks(np.arange(0, 6.1, 0.5))
labelpad = 40
ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad)
ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad)
ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad)
fsize = 12
ax[0].set_ylabel('Physical contact', rotation=0, labelpad=labelpad, fontsize=fsize)
ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad, fontsize=fsize)
ax[2].set_ylabel('Chirps', rotation=0, labelpad=labelpad, fontsize=fsize)
ax[3].set_ylabel('EODf')
ax[3].set_xlabel('Time [h]')
#ax[0].set_title(foldername.split('/')[-2])
ax[0].set_title(foldername.split('/')[-2])
# 2020-03-31-9_59
plt.subplots_adjust(left=0.13, right=0.987, top=0.97)
plt.savefig('../poster/figs/timeline.pdf')
plt.subplots_adjust(left=0.158, right=0.987, top=0.918)
#plt.savefig('../poster/figs/timeline.pdf')
plt.show()

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poster/figs/chirps_winner_loser.pdf
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poster/figs/timeline.pdf
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36
poster/main.tex
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@ -23,7 +23,10 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
with single - or physically separated - individuals.
\begin{tikzfigure}[]
\label{griddrawing}
\includegraphics[width=1\linewidth]{figs/introplot}
<<<<<<< HEAD
=======
\includegraphics[width=0.8\linewidth]{figs/introplot}
>>>>>>> cdcf9564df07914cf57225de5a8bdaa642fbad0e
\end{tikzfigure}
}
\myblock[TranspBlock]{Chirp detection}{
@ -41,11 +44,26 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\includegraphics[width=\linewidth]{figs/timeline.pdf}
\end{tikzfigure}
\noindent
\begin{itemize}
\setlength\itemsep{0.5em}
\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}[]
\label{fig:example_b}
\includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
\end{tikzfigure}
\noindent
\end{minipage} % no space if you would like to put them side by side
\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
\end{itemize}
\end{minipage}
}
\myblock[TranspBlock]{Interactions at modulations}{
@ -55,19 +73,7 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\includegraphics[width=0.5\linewidth]{example-image-c}
\end{tikzfigure}
\begin{multicols}{2}
\begin{itemize}
\setlength\itemsep{0.5em}
\item $\Delta$EOD$f$ does not appear to decrease during synchronous modulations ().
\item Individuals that rise their EOD$f$ first appear to rise their frequency higher compared to reactors (\textbf{B}).
\vfill
\null
\columnbreak
\item Synchronized fish keep distances below 1 m (\textbf{C}) but distances over 3 m also occur (see \textbf{movie}).
\item Spatial interactions increase \textbf{after} the start of a synchronous modulation (\textbf{D}).
\end{itemize}
\end{multicols}
\vspace{-1cm}
}
\myblock[GrayBlock]{Conclusion}{