adding stuff

code/chirp_sim.py

@@ -4,42 +4,29 @@ import matplotlib.pyplot as plt
 from thunderfish import fakefish
 
 from modules.filters import bandpass_filter
-from chirpdetection import instantaneos_frequency
+from modules.datahandling import instantaneous_frequency
 from modules.simulations import create_chirp
 
-# create chirp
-"""
-time, signal, ampl, freq = create_chirp(
-    chirptimes=[0.05, 0.2501, 0.38734, 0.48332, 0.73434, 0.823424], )
-# filter signal with bandpass_filter
-signal = bandpass_filter(signal, 1/0.00001, 495, 505)
-fig, axs = plt.subplots(2, 1, figsize=(10, 10))
-axs[0].plot(np.arange(len(ampl)), ampl)
-
-# plot instatneous frequency
 
-baseline_freq_time, baseline_freq = instantaneos_frequency(signal, 1/0.00001)
-axs[1].plot(baseline_freq_time[1:], baseline_freq[1:])
-plt.close()
 
-"""
 # 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)
+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)
-data_freq_time, data_freq, true_zero = instantaneos_frequency(data*ampl, samplerate)
+embed()
+data_freq_time, data_freq = instantaneous_frequency(data, samplerate, 5)
 
 
 fig, ax = plt.subplots(4, 1, figsize=(20 / 2.54, 12 / 2.54), sharex=True)
 
 ax[0].plot(np.arange(len(data))/samplerate, data*ampl)
-ax[0].scatter(true_zero, np.zeros_like(true_zero), color='red')
+#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[1:], data_freq[1:])
+ax[3].plot(data_freq_time, data_freq)
 
 
 plt.show()

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:
-            freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
-            freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
-
-        elif chirp_freq_fish1 < chirp_freq_fish2:
-            freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
-            freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
-        else:
-            freq_diff_higher = np.nan
-            freq_diff_lower = np.nan
-            winner_fish_id = np.nan
-            loser_fish_id = np.nan
+        # if chirp_freq_fish1 > chirp_freq_fish2:
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+
+        # elif chirp_freq_fish1 < chirp_freq_fish2:
+        #     freq_diff_higher = chirp_freq_fish1 - chirp_freq_fish2
+        #     freq_diff_lower = chirp_freq_fish2 - chirp_freq_fish1
+        # else:
+        #     freq_diff_higher = np.nan
+        #     freq_diff_lower = np.nan
+        #     winner_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:
-            freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
-            freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
-
-        elif chirp_freq_fish2 < chirp_freq_fish1:
-            freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
-            freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
-        else:
-            freq_diff_higher = np.nan
-            freq_diff_lower = np.nan
-            winner_fish_id = np.nan
-            loser_fish_id = np.nan
+        # if chirp_freq_fish2 > chirp_freq_fish1:
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+
+        # elif chirp_freq_fish2 < chirp_freq_fish1:
+        #     freq_diff_higher = chirp_freq_fish2 - chirp_freq_fish1
+        #     freq_diff_lower = chirp_freq_fish1 - chirp_freq_fish2
+        # else:
+        #     freq_diff_higher = np.nan
+        #     freq_diff_lower = np.nan
+        #     winner_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
-        freq_diff_lower = np.nan
+        winner_fish_freq = 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_lower.append(freq_diff_lower)
+        freq_diffs_higher.append(freq_winner)
+        freq_diffs_lower.append(freq_loser)
         freq_chirps_winner.append(chirp_freq_winner)
         freq_chirps_loser.append(chirp_freq_loser)
 
@@ -247,15 +251,15 @@ 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=(
-        13*ps.cm, 10*ps.cm), sharey=True)
-    plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
+    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(
+        21*ps.cm, 10*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True)
+    plt.subplots_adjust(left=0.11, right=0.948, top=0.905, wspace=0.343, bottom=0.145)
     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)]
 
-    stat = wilcoxon(chirps_winner, chirps_loser) 
+    stat = wilcoxon(chirps_winner, chirps_loser)
     print(stat)
 
     bplot1 = ax1.boxplot(chirps_winner, positions=[
@@ -268,14 +272,15 @@ def main(datapath: str):
     ax1.scatter(np.ones(len(chirps_loser)) *
                 scatterloser, chirps_loser, color=ps.orange)
     ax1.set_xticklabels(['winner', 'loser'])
-    ax1.text(0.1, 0.9, f'n = {len(chirps_winner)}',
-             transform=ax1.transAxes, color=ps.white)
+
+    ax1.text(1, 2000, f'{len(chirps_winner)}',  color='gray')
+    ax1.text(1.8, 2000, f'{len(chirps_loser)}', color='gray')
 
     for w, l in zip(chirps_winner, chirps_loser):
         ax1.plot([scatterwinner, scatterloser], [w, l],
                  color=ps.white, alpha=1, linewidth=0.5)
-    ax1.set_ylabel('chirps [n]', color=ps.white)
-    ax1.set_xlabel('outcome', color=ps.white)
+    ax1.set_ylabel('chirpcount', color=ps.white)
+    ax1.set_xlabel('outcome',    color=ps.white)
 
     colors1 = ps.red
     ps.set_boxplot_color(bplot1, colors1)
@@ -283,17 +288,24 @@ def main(datapath: str):
     ps.set_boxplot_color(bplot2, colors1)
 
     ax2.scatter(size_diffs_winner, size_chirps_winner,
-                color=ps.red, label='winner')
+                color=ps.red, label=f'winner')
     ax2.scatter(size_diffs_loser, size_chirps_loser,
                 color=ps.orange, label='loser')
+    ax2.text(-1, 2000, f'{len(size_chirps_winner)}', color= 'gray')
+    ax2.text(1, 2000, f'{len(size_chirps_loser)}',   color= 'gray')
 
     ax2.set_xlabel('size difference [cm]')
     # ax2.set_xticks(np.arange(-10, 10.1, 2))
 
+    ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=ps.red)
+    ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=ps.orange)
+
+    ax3.text(600, 2000, f'n = {len(freq_chirps_winner)}', color='gray')
+    ax3.text(650, 2000, f'{len(freq_chirps_loser)}',  color='gray')
+
+    ax3.set_xlabel('absolut frequency [Hz]')
     handles, labels = ax2.get_legend_handles_labels()
     fig.legend(handles, labels, loc='upper center', ncol=2)
-    plt.subplots_adjust(left=0.162, right=0.97, top=0.85, bottom=0.176)
-
     # pearson r
     plt.savefig('../poster/figs/chirps_winner_loser.pdf')
     plt.show()

code/plot_chirps_in_chasing.py

@@ -57,7 +57,7 @@ def main(datapath: str):
         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_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)
@@ -69,6 +69,9 @@ def main(datapath: str):
     ax.set_ylabel('Percent')
     ax.scatter(np.ones(len(time_precents))*1.25, time_precents, color=ps.white)
     ax.scatter(np.ones(len(chirps_percents))*1.75, chirps_percents, color=ps.white)
+    for i in range(len(time_precents)):
+        ax.plot([1.25, 1.75], [time_precents[i], chirps_percents[i]], color=ps.white)
+    ax.text(0.99, 0.99, f'{len(time_precents)} fish', transform=ax.transAxes)
     plt.savefig('../poster/figs/chirps_in_chasing.pdf')
     plt.show()
 

poster/main.tex

@@ -54,19 +54,19 @@ blockverticalspace=2mm, colspace=20mm, subcolspace=0mm]{tikzposter} %Default val
     \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}
+
+    \begin{tikzfigure}[]
+        \label{fig:example_b}
+        \includegraphics[width=\linewidth]{figs/chirps_winner_loser.pdf}
+    \end{tikzfigure}
+
+
+    \begin{itemize}
+        \setlength\itemsep{0.5em}
+        \item Fish who won the competition chirped more often than the fish who lost.
+        \item
+    \end{itemize}
+
 }
 
 \myblock[TranspBlock]{Interactions at modulations}{