Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection

.gitignore

@@ -14,7 +14,7 @@ output
 __pycache__/
 *.py[cod]
 *$py.class
-poster/main.pdf
+/poster/main.pdf
 
 # C extensions
 *.so

code/chirp_sim.py

@@ -1,24 +1,37 @@
-from modules.filters import create_chirp, bandpass_filter
+import numpy as np
-import matplotlib.pyplot as plt 
-from chirpdetection import instantaneos_frequency
 from IPython import embed
-# create chirp
+import matplotlib.pyplot as plt
+from thunderfish import fakefish
 
-time, signal, ampl, freq = create_chirp(chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
+from modules.filters import bandpass_filter
+from modules.datahandling import instantaneous_frequency
+from modules.simulations import create_chirp
 
-# filter signal with bandpass_filter 
 
-signal = bandpass_filter(signal, 1/0.00001, 495, 505)
+
+# trying thunderfish fakefish chirp simulation ---------------------------------
+samplerate = 44100
+freq, ampl = fakefish.chirps(eodf=500, chirp_contrast=0.2)
+data = fakefish.wavefish_eods(fish='Alepto', frequency=freq, phase0=3, samplerate=samplerate)
+
+# filter signal with bandpass_filter
+data_filterd = bandpass_filter(data*ampl+1, samplerate, 0.01, 1.99)
 embed()
-exit()
+data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
-fig, axs = plt.subplots(2, 1, figsize=(10, 10))
-axs[0].plot(time, signal)
 
-# plot instatneous frequency
 
-baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
+fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
-axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
 
+ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
+#ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
+ax[1].plot(np.arange(len(data_filterd))/samplerate, data_filterd)
+ax[2].plot(np.arange(len(freq))/samplerate, freq)
+ax[3].plot(data_freq_time, data_freq)
 
 
 plt.show()
+embed()
+
+
+
+

code/modules/plotstyle.py

@@ -110,7 +110,7 @@ def PlotStyle() -> None:
             plt.setp(bp["boxes"], color=color)
             plt.setp(bp["whiskers"], color=white)
             plt.setp(bp["caps"], color=white)
-            plt.setp(bp["medians"], color=white)
+            plt.setp(bp["medians"], color=black)
 
 
         @classmethod
@@ -255,10 +255,10 @@ def PlotStyle() -> None:
     plt.rcParams["boxplot.boxprops.color"] = gray
     plt.rcParams["boxplot.whiskerprops.color"] = gray
     plt.rcParams["boxplot.capprops.color"] = gray
-    plt.rcParams["boxplot.medianprops.color"] = gray
+    plt.rcParams["boxplot.medianprops.color"] = black
     plt.rcParams["text.color"] = white
     plt.rcParams["axes.facecolor"] = black  # axes background color
-    plt.rcParams["axes.edgecolor"] = gray  # axes edge color
+    plt.rcParams["axes.edgecolor"] = white  # axes edge color
     # plt.rcParams["axes.grid"] = True    # display grid or not
     # plt.rcParams["axes.grid.axis"] = "y"  # which axis the grid is applied to
     plt.rcParams["axes.labelcolor"] = white
@@ -284,9 +284,9 @@ def PlotStyle() -> None:
             "#f5c2e7",
         ],
     )
-    plt.rcParams["xtick.color"] = gray  # color of the ticks
+    plt.rcParams["xtick.color"] = white  # color of the ticks
-    plt.rcParams["ytick.color"] = gray  # color of the ticks
+    plt.rcParams["ytick.color"] = white  # color of the ticks
-    plt.rcParams["grid.color"] = dark_gray  # grid color
+    plt.rcParams["grid.color"] = white  # grid color
     plt.rcParams["figure.facecolor"] = black  # figure face color
     plt.rcParams["figure.edgecolor"] = black  # figure edge color
     plt.rcParams["savefig.facecolor"] = black  # figure face color when saving

code/plot_chirp_size.py

@@ -133,41 +133,45 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
         Behavior.freq[Behavior.ident == fish2_freq])
 
     if winner == fish1:
-        if chirp_freq_fish1 > chirp_freq_fish2:
+        # if chirp_freq_fish1 > chirp_freq_fish2:
-            freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
-            freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
-
+
-        elif chirp_freq_fish1 < chirp_freq_fish2:
+        # elif chirp_freq_fish1 < chirp_freq_fish2:
-            freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
-            freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
-        else:
+        # else:
-            freq_diff_higher = np.nan
+        #     freq_diff_higher = np.nan
-            freq_diff_lower = np.nan
+        #     freq_diff_lower = np.nan
-            winner_fish_id = np.nan
+        #     winner_fish_id = np.nan
-            loser_fish_id = np.nan
+        #     loser_fish_id = np.nan
 
         winner_fish_id = folder_row['rec_id1'].values[0]
+        winner_fish_freq = chirp_freq_fish1
         loser_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_freq = chirp_freq_fish2
 
     elif winner == fish2:
-        if chirp_freq_fish2 > chirp_freq_fish1:
+        # if chirp_freq_fish2 > chirp_freq_fish1:
-            freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
-            freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
-
+
-        elif chirp_freq_fish2 < chirp_freq_fish1:
+        # elif chirp_freq_fish2 < chirp_freq_fish1:
-            freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
-            freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
-        else:
+        # else:
-            freq_diff_higher = np.nan
+        #     freq_diff_higher = np.nan
-            freq_diff_lower = np.nan
+        #     freq_diff_lower = np.nan
-            winner_fish_id = np.nan
+        #     winner_fish_id = np.nan
-            loser_fish_id = np.nan
+        #     loser_fish_id = np.nan
 
         winner_fish_id = folder_row['rec_id2'].values[0]
+        winner_fish_freq = chirp_freq_fish2
         loser_fish_id = folder_row['rec_id1'].values[0]
+        loser_fish_freq = chirp_freq_fish1
     else:
-        freq_diff_higher = np.nan
+        winner_fish_freq = np.nan
-        freq_diff_lower = np.nan
+        loser_fish_freq = np.nan
         winner_fish_id = np.nan
         loser_fish_id = np.nan
 
@@ -176,7 +180,7 @@ def get_chirp_freq(folder_name, Behavior, order_meta_df):
     chirp_loser = len(
         Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
 
-    return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
+    return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser
 
 
 def main(datapath: str):
@@ -229,11 +233,11 @@ def main(datapath: str):
         size_diff_bigger, chirp_winner,  size_diff_smaller, chirp_loser = get_chirp_size(
             foldername, bh, order_meta_df, id_meta_df)
 
-        freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq(
+        freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq(
             foldername, bh, order_meta_df)
 
-        freq_diffs_higher.append(freq_diff_higher)
+        freq_diffs_higher.append(freq_winner)
-        freq_diffs_lower.append(freq_diff_lower)
+        freq_diffs_lower.append(freq_loser)
         freq_chirps_winner.append(chirp_freq_winner)
         freq_chirps_loser.append(chirp_freq_loser)
 
@@ -247,9 +251,10 @@ def main(datapath: str):
     size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
     size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
 
-    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
-        13*ps.cm, 10*ps.cm), sharey=True)
+        21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
-    plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
+    plt.subplots_adjust(left=0.11, right=0.948, top=0.86,
+                        wspace=0.343, bottom=0.198)
     scatterwinner = 1.15
     scatterloser = 1.85
     chirps_winner = np.asarray(chirps_winner)[~np.isnan(chirps_winner)]
@@ -257,43 +262,54 @@ def main(datapath: str):
 
     stat = wilcoxon(chirps_winner, chirps_loser)
     print(stat)
+    winner_color = ps.gblue3
+    loser_color = ps.gblue1
 
     bplot1 = ax1.boxplot(chirps_winner, positions=[
         0.9], showfliers=False, patch_artist=True)
 
     bplot2 = ax1.boxplot(chirps_loser,  positions=[
         2.1], showfliers=False, patch_artist=True)
+
     ax1.scatter(np.ones(len(chirps_winner)) *
-                scatterwinner, chirps_winner, color=ps.red)
+                scatterwinner, chirps_winner, color=winner_color)
     ax1.scatter(np.ones(len(chirps_loser)) *
-                scatterloser, chirps_loser, color=ps.orange)
+                scatterloser, chirps_loser, color=loser_color)
-    ax1.set_xticklabels(['winner', 'loser'])
+    ax1.set_xticklabels(['Winner', 'Loser'])
-    ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
+
+    ax1.text(0.1, 0.95, 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=ps.white, alpha=1, linewidth=0.5)
+                 color=ps.white, alpha=0.6, linewidth=1, zorder=-1)
-    ax1.set_ylabel('chirps [n]', color=ps.white)
+    ax1.set_ylabel('Chirp counts', color=ps.white)
-    ax1.set_xlabel('outcome', color=ps.white)
+    ax1.set_xlabel('Competition outcome',    color=ps.white)
 
-    colors1 = ps.red
+    ps.set_boxplot_color(bplot1, winner_color)
-    ps.set_boxplot_color(bplot1, colors1)
+    ps.set_boxplot_color(bplot2, loser_color)
-    colors1 = ps.orange
-    ps.set_boxplot_color(bplot2, colors1)
 
     ax2.scatter(size_diffs_winner, size_chirps_winner,
-                color=ps.red, label='winner')
+                color=winner_color, label=f'Winner')
     ax2.scatter(size_diffs_loser, size_chirps_loser,
-                color=ps.orange, label='loser')
+                color=loser_color, label='Loser')
+
+    ax2.text(0.05, 0.95, f'n={len(size_chirps_winner)}',
+             transform=ax2.transAxes, color=ps.white)
 
-    ax2.set_xlabel('size difference [cm]')
+    ax2.set_xlabel('Size difference [cm]')
     # ax2.set_xticks(np.arange(-10, 10.1, 2))
 
-    handles, labels = ax2.get_legend_handles_labels()
+    ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color)
-    fig.legend(handles, labels, loc='upper center', ncol=2)
+    ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color)
-    plt.subplots_adjust(left=0.162, right=0.97, top=0.85, bottom=0.176)
+
+    ax3.text(0.1, 0.95, f'n={len(freq_chirps_loser)}',
+             transform=ax3.transAxes, color=ps.white)
 
+    ax3.set_xlabel('EODf [Hz]')
+    handles, labels = ax2.get_legend_handles_labels()
+    fig.legend(handles, labels, loc='upper center',
+               ncol=2, bbox_to_anchor=(0.5, 1.04))
     # pearson r
     plt.savefig('../poster/figs/chirps_winner_loser.pdf')
     plt.show()

code/plot_chirps_in_chasing.py

@@ -45,30 +45,46 @@ def main(datapath: str):
 
         chirps_in_chasings = []
         for onset, offset in zip(chasing_onset, chasing_offset):
-            chirps_in_chasing = [c for c in bh.chirps if (c > onset) & (c < offset)]
+            chirps_in_chasing = [
+                c for c in bh.chirps if (c > onset) & (c < offset)]
             chirps_in_chasings.append(chirps_in_chasing)
 
         try:
-            time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60])
+            time_chasing = np.sum(
+                chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60])
         except:
-            time_chasing = np.sum(chasing_offset[chasing_offset<3*60*60] - chasing_onset[chasing_onset<3*60*60][:-1])
+            time_chasing = np.sum(
-
+                chasing_offset[chasing_offset < 3*60*60] - chasing_onset[chasing_onset < 3*60*60][:-1])
 
         time_chasing_percent = (time_chasing/(3*60*60))*100
         chirps_chasing = np.asarray(flatten(chirps_in_chasings))
-        chirps_chasing_new = chirps_chasing[chirps_chasing<3*60*60]
+        chirps_chasing_new = chirps_chasing[chirps_chasing < 3*60*60]
-        chirps_percent = (len(chirps_chasing_new)/len(bh.chirps))*100
+        chirps_percent = (len(chirps_chasing_new) /
+                          len(bh.chirps[bh.chirps < 3*60*60]))*100
 
         time_precents.append(time_chasing_percent)
         chirps_percents.append(chirps_percent)
-    
-    fig, ax = plt.subplots(1, 1, figsize=(14*ps.cm, 10*ps.cm))
 
-    ax.boxplot([time_precents, chirps_percents])
+    fig, ax = plt.subplots(1, 1, figsize=(7*ps.cm, 7*ps.cm))
-    ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing'])
+    scatter_time = 1.20
+    scatter_chirps = 1.80
+    size = 10
+    bplot1 = ax.boxplot([time_precents, chirps_percents],
+                        showfliers=False, patch_artist=True)
+    ps.set_boxplot_color(bplot1, ps.gray)
+    ax.set_xticklabels(['Time \nChasing', 'Chirps \nin Chasing'])
     ax.set_ylabel('Percent')
-    ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white)
+    ax.scatter(np.ones(len(time_precents))*scatter_time, time_precents,
-    ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white)
+               facecolor=ps.white, s=size)
+    ax.scatter(np.ones(len(chirps_percents))*scatter_chirps, chirps_percents,
+               facecolor=ps.white, s=size)
+
+    for i in range(len(time_precents)):
+        ax.plot([scatter_time, scatter_chirps], [time_precents[i],
+                chirps_percents[i]], alpha=0.6, linewidth=1, color=ps.white)
+
+    ax.text(0.1, 0.9, f'n={len(time_precents)}', transform=ax.transAxes)
+    plt.subplots_adjust(left=0.221, bottom=0.186, right=0.97, top=0.967)
     plt.savefig('../poster/figs/chirps_in_chasing.pdf')
     plt.show()
 
@@ -77,5 +93,3 @@ if __name__ == '__main__':
     # Path to the data
     datapath = '../data/mount_data/'
     main(datapath)
-
-        

code/plot_event_timeline.py

@@ -47,17 +47,17 @@ def main(datapath: str):
         # 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.purple
+        fish1_color = ps.gblue1
-        fish2_color = ps.lavender
+        fish2_color = ps.gblue3
 
         fig, ax = plt.subplots(5, 1, figsize=(
             21*ps.cm, 10*ps.cm), height_ratios=[0.5, 0.5, 0.5, 0.2, 6], sharex=True)
         # marker size
         s = 80
         ax[0].scatter(physical_contact, np.ones(
-            len(physical_contact)), color=ps.maroon, marker='|', s=s)
+            len(physical_contact)), color=ps.red, marker='|', s=s)
         ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)),
-                      color=ps.orange, marker='|', s=s)
+                      color=ps.purple, 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,
@@ -79,7 +79,6 @@ def main(datapath: str):
         ax[0].set_xticks([])
         ax[0].set_yticks([])
         ps.hide_ax(ax[0])
-        ax[0].yaxis.set_label_coords(-0.1, 0.5)
 
         ax[1].grid(False)
         ax[1].set_frame_on(False)
@@ -99,19 +98,23 @@ def main(datapath: str):
 
         labelpad = 30
         fsize = 12
-        ax[0].set_ylabel('contact', rotation=0,
+
+        ax[0].set_ylabel('Contact', rotation=0,
                          labelpad=labelpad, fontsize=fsize)
-        ax[1].set_ylabel('chasing', rotation=0,
+        ax[0].yaxis.set_label_coords(-0.062, -0.08)
+        ax[1].set_ylabel('Chasing', rotation=0,
                          labelpad=labelpad, fontsize=fsize)
-        ax[2].set_ylabel('chirps', rotation=0,
+        ax[1].yaxis.set_label_coords(-0.06, -0.08)
+        ax[2].set_ylabel('Chirps', rotation=0,
                          labelpad=labelpad, fontsize=fsize)
+        ax[2].yaxis.set_label_coords(-0.07, -0.08)
         ax[4].set_ylabel('EODf')
 
-        ax[4].set_xlabel('time [h]')
+        ax[4].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.subplots_adjust(left=0.158, right=0.987, top=0.918, bottom=0.136)
-        # plt.savefig('../poster/figs/timeline.pdf')
+        plt.savefig('../poster/figs/timeline.pdf')
         plt.show()
 
     # plot chirps

poster/main.tex

@@ -22,20 +22,20 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
     weakly electric fish impossible. This profoundly limits our current
     understanding of chirps to experiments
     with single - or physically separated - individuals.
-    % \begin{tikzfigure}[]
+    \begin{tikzfigure}[]
-    %     \label{griddrawing}
+        \label{griddrawing}
-    %     \includegraphics[width=0.8\linewidth]{figs/introplot}
+        \includegraphics[width=0.6\linewidth]{figs/introplot}
-    % \end{tikzfigure}
+    \end{tikzfigure}
 }
 \myblock[TranspBlock]{Chirp detection}{
     \begin{tikzfigure}[]
         \label{fig:alg1}
-        \includegraphics[width=0.9\linewidth]{figs/algorithm1}
+        \includegraphics[width=0.6\linewidth]{figs/algorithm1}
     \end{tikzfigure}
     \vspace{2cm}
     \begin{tikzfigure}[]
         \label{fig:alg2}
-        \includegraphics[width=1\linewidth]{figs/algorithm}
+        \includegraphics[width=0.9\linewidth]{figs/algorithm}
     \end{tikzfigure}
     \vspace{0cm}
 }
@@ -49,34 +49,35 @@ 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}[]
-            \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}{
-    \vspace{-1.2cm}
     \begin{tikzfigure}[]
-        \label{fig:example_c}
+        \label{fig:example_b}
-        \includegraphics[width=0.5\linewidth]{example-image-c}
+        \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
     \end{tikzfigure}
+    \noindent
+    \begin{itemize}
+        \item Fish who lost the competition chirped more often than the fish who lost.
+        \item Size has an effect on the Competition outcome, and the chirp count.
+        \item Frequency of the fish has no effect on the competition outcome.
+    \end{itemize}
+}
 
-
+\myblock[TranspBlock]{Are Chirps coding for onset or offset of physical interaction?}{
+    \vspace{-1.2cm}
+    \begin{minipage}{0.6666\linewidth}
+        \begin{tikzfigure}[]
+            \includegraphics[width=0.3\linewidth]{figs/chirps_in_chasing.pdf}
+        \end{tikzfigure}
+    \end{minipage}
+        \begin{minipage}{0.3333\linewidth}
+            \begin{tikzfigure}[]
+                \includegraphics[width=\linewidth]{figs/chirps_in_chasing.pdf}
+            \end{tikzfigure}
+    \end{minipage}
 }
 
 \myblock[GrayBlock]{Conclusion}{