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code/plot_chirp_bodylegth.py

@@ -253,7 +253,7 @@ def main(datapath: str):
     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]')
+    ax2.set_ylabel('Chirps [n]')
 
     ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
     # ax3.scatter(freq_diffs, freq_chirps_diffs, color='r')

code/plot_chirp_size.py

@@ -4,6 +4,7 @@ import os
 
 import numpy as np
 import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr
 from thunderfish.powerspectrum import decibel
 
 from IPython import embed
@@ -12,6 +13,7 @@ from modules.logger import makeLogger
 from modules.plotstyle import PlotStyle
 from modules.behaviour_handling import Behavior, correct_chasing_events
 
+
 ps = PlotStyle()
 
 logger = makeLogger(__name__)
@@ -50,6 +52,7 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
     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)
+    winner = folder_row['winner'].values[0].astype(int)
 
     groub = folder_row['group'].values[0].astype(int)
     size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
@@ -59,26 +62,51 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
 
     size_winners = [size_fish1_row[col].values[0]
                     for col in ['l1', 'l2', 'l3']]
-    mean_size_winner = np.nanmean(size_winners)
+    size_fish1 = 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)
+    size_fish2 = np.nanmean(size_losers)
+    if winner == fish1:
+        if size_fish1 > size_fish2:
+            size_diff_bigger = size_fish1 - size_fish2
+            size_diff_smaller = size_fish2 - size_fish1
+
+        elif size_fish1 < size_fish2:
+            size_diff_bigger = size_fish1 - size_fish2
+            size_diff_smaller = size_fish2 - size_fish1
+        else:
+            size_diff_bigger =  np.nan
+            size_diff_smaller = np.nan
+            winner_fish_id =    np.nan
+            loser_fish_id =     np.nan
+            return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
 
-    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:
+    elif winner == fish2:
-        size_diff_bigger = mean_size_loser - mean_size_winner
+        if size_fish2 > size_fish1:
-        size_diff_smaller = mean_size_winner - mean_size_loser
+            size_diff_bigger = size_fish2 - size_fish1
+            size_diff_smaller = size_fish1 - size_fish2
+
+        elif size_fish2 < size_fish1:
+            size_diff_bigger = size_fish2 - size_fish1
+            size_diff_smaller = size_fish1 - size_fish2
+        else:
+            size_diff_bigger =  np.nan
+            size_diff_smaller = np.nan
+            winner_fish_id =    np.nan
+            loser_fish_id =     np.nan
+            return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
+
         winner_fish_id = folder_row['rec_id2'].values[0]
         loser_fish_id = folder_row['rec_id1'].values[0]
     else:
-        size_diff = np.nan
+        size_diff_bigger =  np.nan
+        size_diff_smaller = np.nan
         winner_fish_id =    np.nan
         loser_fish_id =     np.nan
+        return size_diff_bigger, size_diff_smaller, winner_fish_id, loser_fish_id
 
     chirp_winner = len(
         Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
@@ -92,27 +120,62 @@ 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)
+    fish1 = folder_row['fish1'].values[0].astype(int)
-    fish2 = folder_row['rec_id2'].values[0].astype(int)
+    fish2 = folder_row['fish2'].values[0].astype(int)
+
+    fish1_freq = folder_row['rec_id1'].values[0].astype(int)
+    fish2_freq = folder_row['rec_id2'].values[0].astype(int)
+    winner = folder_row['winner'].values[0].astype(int)
     chirp_freq_fish1 = np.nanmedian(
-        Behavior.freq[Behavior.ident == fish1])
+        Behavior.freq[Behavior.ident == fish1_freq])
     chirp_freq_fish2 = np.nanmedian(
-        Behavior.freq[Behavior.ident == fish2])
+        Behavior.freq[Behavior.ident == fish2_freq])
 
+    if winner == fish1:
         if chirp_freq_fish1 > chirp_freq_fish2:
-        freq_diff = 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]
         loser_fish_id = folder_row['rec_id2'].values[0]
 
-    elif chirp_freq_fish1 < chirp_freq_fish2:
+    elif winner == fish2:
-        freq_diff = chirp_freq_fish2 - chirp_freq_fish1
+        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]
         loser_fish_id = folder_row['rec_id1'].values[0]
+    else:
+        freq_diff_higher = np.nan
+        freq_diff_lower = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
 
-    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+    chirp_winner = len(
+        Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+    chirp_loser = len(
         Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
 
-    return freq_diff, chirp_diff
+    return freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
 
 
 def main(datapath: str):
@@ -128,8 +191,17 @@ def main(datapath: str):
     id_meta_df = read_csv(path_id_meta)
 
     chirps_winner = []
-    size_diffs = []
+
-    size_chirps_diffs = []
+    size_diffs_winner = []
+    size_diffs_loser = []
+    size_chirps_winner = []
+    size_chirps_loser = []
+
+    freq_diffs_higher = []
+    freq_diffs_lower = []
+    freq_chirps_winner = []
+    freq_chirps_loser = []
+
     chirps_loser = []
     freq_diffs = []
     freq_chirps_diffs = []
@@ -151,17 +223,32 @@ def main(datapath: str):
             foldername,  bh, order_meta_df)
         chirps_winner.append(winner_chirp)
         chirps_loser.append(loser_chirp)
-        size_diff, chirp_diff = get_chirp_size(
+
+        size_diff_bigger, chirp_winner,  size_diff_smaller, chirp_loser = 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(
+        freq_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = 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])
+        freq_diffs_higher.append(freq_diff_higher)
+        freq_diffs_lower.append(freq_diff_lower)
+        freq_chirps_winner.append(chirp_freq_winner)
+        freq_chirps_loser.append(chirp_freq_loser)
+
+        if np.isnan(size_diff_bigger):
+            continue
+        size_diffs_winner.append(size_diff_bigger)
+        size_diffs_loser.append(size_diff_smaller)
+        size_chirps_winner.append(chirp_winner)
+        size_chirps_loser.append(chirp_loser)
+
+
+    embed()
+    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=(
+        22*ps.cm, 12*ps.cm), sharey=True)
     plt.subplots_adjust(left=0.098, right=0.945, top=0.94, wspace=0.343)
     scatterwinner = 1.15
     scatterloser = 1.85
@@ -173,9 +260,9 @@ def main(datapath: str):
     bplot2 = ax1.boxplot(chirps_loser,  positions=[
         2], showfliers=False, patch_artist=True)
     ax1.scatter(np.ones(len(chirps_winner)) *
-                scatterwinner, chirps_winner, color='r')
+                scatterwinner, chirps_winner, color=ps.red)
     ax1.scatter(np.ones(len(chirps_loser)) *
-                scatterloser, chirps_loser, color='r')
+                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)
@@ -189,17 +276,13 @@ def main(datapath: str):
     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.scatter(size_diffs_winner, size_chirps_winner, color=ps.red)
-    ax2.set_xlabel('Size difference [mm]')
+    ax2.scatter(size_diffs_loser, size_chirps_loser, color=ps.orange)
-    ax2.set_ylabel('Chirps difference [n]')
 
-    #ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
+    ax2.set_xlabel('Size difference [cm]')
-    # 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')
+    # pearson r
+    plt.savefig('../poster/figs/chirps_winner_loser.pdf')
     plt.show()
 
 

code/plot_chirps_in_chasing.py

@@ -0,0 +1,81 @@
+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.stats import pearsonr, spearmanr
+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
+from modules.datahandling import flatten
+
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    time_precents = []
+    chirps_percents = []
+    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)
+
+        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)
+
+        chasing_onset = timestamps[category == 0]
+        chasing_offset = timestamps[category == 1]
+        if len(chasing_onset) != len(chasing_offset):
+            embed()
+
+        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_chasings.append(chirps_in_chasing)
+
+        try:
+            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_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
+
+        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])
+    ax.set_xticklabels(['Time Chasing', 'Chirps in Chasing'])
+    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)
+    plt.savefig('../poster/figs/chirps_in_chasing.pdf')
+    plt.show()
+
+
+if __name__ == '__main__':
+    # Path to the data
+    datapath = '../data/mount_data/'
+    main(datapath)
+
+