raab/GP2023_chirp_detection
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adding subplots cm

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This commit is contained in:
wendtalexander 2023-01-25 13:44:14 +01:00
parent 798884fd01
commit 08c7913c95
6 changed files with 124 additions and 115 deletions
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27
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)
@ -210,8 +214,8 @@ def main(datapath: str):
chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id])
chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id])
freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id])
freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id])
freq_winner = np.nanmedian(bh.freq[bh.ident == winner_fish_id])
freq_loser = np.nanmedian(bh.freq[bh.ident == loser_fish_id])
chirps_winner.append(chirp_winner)
chirps_loser.append(chirp_loser)
@ -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

167
code/plot_event_timeline.py
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@ -20,89 +20,90 @@ 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
# behabvior is pandas dataframe with all the data
bh = Behavior(foldername)
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]/ 60) /60
chasing_offset = (timestamps[category == 1]/ 60) /60
physical_contact = (timestamps[category == 2] / 60) /60
all_fish_ids = np.unique(bh.chirps_ids)
fish1_id = all_fish_ids[0]
fish2_id = all_fish_ids[1]
# Associate chirps to inidividual fish
fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
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)
# marker size
s = 200
ax[0].scatter(physical_contact, np.ones(len(physical_contact)), color='firebrick', marker='|', s=s)
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
freq_temp = bh.freq[bh.ident==fish1_id]
time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
freq_temp = bh.freq[bh.ident==fish2_id]
time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
#ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
# Hide grid lines
ax[0].grid(False)
ax[0].set_frame_on(False)
ax[0].set_xticks([])
ax[0].set_yticks([])
ps.hide_ax(ax[0])
ax[1].grid(False)
ax[1].set_frame_on(False)
ax[1].set_xticks([])
ax[1].set_yticks([])
ps.hide_ax(ax[1])
ax[2].grid(False)
ax[2].set_frame_on(False)
ax[2].set_yticks([])
ax[2].set_xticks([])
ps.hide_ax(ax[2])
ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
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)
ax[3].set_ylabel('EODf')
ax[3].set_xlabel('Time [h]')
#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.show()
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
# 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]/ 60) /60
chasing_offset = (timestamps[category == 1]/ 60) /60
physical_contact = (timestamps[category == 2] / 60) /60
all_fish_ids = np.unique(bh.chirps_ids)
fish1_id = all_fish_ids[0]
fish2_id = all_fish_ids[1]
# Associate chirps to inidividual fish
fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) /60
fish2 = (bh.chirps[bh.chirps_ids == fish2_id] / 60) /60
fish1_color = ps.red
fish2_color = ps.orange
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)
ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
freq_temp = bh.freq[bh.ident==fish1_id]
time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
freq_temp = bh.freq[bh.ident==fish2_id]
time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_color)
#ax[3].imshow(decibel(bh.spec), extent=[bh.time[0]/60/60, bh.time[-1]/60/60, 0, 2000], aspect='auto', origin='lower')
# Hide grid lines
ax[0].grid(False)
ax[0].set_frame_on(False)
ax[0].set_xticks([])
ax[0].set_yticks([])
ps.hide_ax(ax[0])
ax[1].grid(False)
ax[1].set_frame_on(False)
ax[1].set_xticks([])
ax[1].set_yticks([])
ps.hide_ax(ax[1])
ax[2].grid(False)
ax[2].set_frame_on(False)
ax[2].set_yticks([])
ax[2].set_xticks([])
ps.hide_ax(ax[2])
ax[3].axvspan(3, 6, 0, 5, facecolor='grey', alpha=0.5)
ax[3].set_xticks(np.arange(0, 6.1, 0.5))
labelpad = 40
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])
# 2020-03-31-9_59
plt.subplots_adjust(left=0.158, right=0.987, top=0.918)
#plt.savefig('../poster/figs/timeline.pdf')
plt.show()
# plot chirps

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poster/figs/chirps_winner_loser.pdf
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poster/figs/timeline.pdf
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poster/main.pdf
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45
poster/main.tex
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@ -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=0.8\linewidth]{figs/introplot}
\end{tikzfigure}
}
\myblock[TranspBlock]{Chirp detection}{
\begin{tikzfigure}[]
@ -41,11 +41,26 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
\includegraphics[width=\linewidth]{figs/timeline.pdf}
\end{tikzfigure}
\noindent
\begin{tikzfigure}[]
\label{fig:example_b}
\includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
\end{tikzfigure}
\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}
\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 +70,7 @@ 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}{