wilcoxon test

code/plot_chirp_bodylegth.py

@@ -1,4 +1,5 @@
 import numpy as np
+from extract_chirps import get_valid_datasets
 
 import os
 
@@ -116,6 +117,8 @@ def main(datapath: str):
 
     foldernames = [
         datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+
+    foldernames, _ = get_valid_datasets(datapath)
     path_order_meta = (
         '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
     order_meta_df = read_csv(path_order_meta)
@@ -223,7 +226,8 @@ 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=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
+    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

code/plot_chirp_size.py

@@ -1,10 +1,11 @@
 import numpy as np
+from extract_chirps import get_valid_datasets
 
 import os
 
 import numpy as np
 import matplotlib.pyplot as plt
-from scipy.stats import pearsonr, spearmanr
+from scipy.stats import pearsonr, spearmanr, wilcoxon
 from thunderfish.powerspectrum import decibel
 
 from IPython import embed
@@ -75,10 +76,10 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
             size_diff_bigger = size_fish1 - size_fish2
             size_diff_smaller = size_fish2 - size_fish1
         else:
-            size_diff_bigger =  np.nan
+            size_diff_bigger = np.nan
             size_diff_smaller = np.nan
-            winner_fish_id =    np.nan
+            winner_fish_id = np.nan
-            loser_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_id1'].values[0]
@@ -93,19 +94,19 @@ def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
             size_diff_bigger = size_fish2 - size_fish1
             size_diff_smaller = size_fish1 - size_fish2
         else:
-            size_diff_bigger =  np.nan
+            size_diff_bigger = np.nan
             size_diff_smaller = np.nan
-            winner_fish_id =    np.nan
+            winner_fish_id = np.nan
-            loser_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_bigger =  np.nan
+        size_diff_bigger = np.nan
         size_diff_smaller = np.nan
-        winner_fish_id =    np.nan
+        winner_fish_id = np.nan
-        loser_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(
@@ -182,6 +183,7 @@ def main(datapath: str):
 
     foldernames = [
         datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    foldernames, _ = get_valid_datasets(datapath)
     path_order_meta = (
         '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
     order_meta_df = read_csv(path_order_meta)
@@ -242,24 +244,25 @@ def main(datapath: str):
         size_chirps_winner.append(chirp_winner)
         size_chirps_loser.append(chirp_loser)
 
-
+    size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner)
-    size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner )
+    size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser)
-    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)
+        13*ps.cm, 10*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
     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)
+    print(stat)
 
     bplot1 = ax1.boxplot(chirps_winner, positions=[
-        1], showfliers=False, patch_artist=True)
+        0.9], showfliers=False, patch_artist=True)
 
     bplot2 = ax1.boxplot(chirps_loser,  positions=[
-        2], showfliers=False, patch_artist=True)
+        2.1], showfliers=False, patch_artist=True)
     ax1.scatter(np.ones(len(chirps_winner)) *
                 scatterwinner, chirps_winner, color=ps.red)
     ax1.scatter(np.ones(len(chirps_loser)) *
@@ -270,19 +273,27 @@ def main(datapath: str):
 
     for w, l in zip(chirps_winner, chirps_loser):
         ax1.plot([scatterwinner, scatterloser], [w, l],
-                 color='r', alpha=0.5, linewidth=0.5)
+                 color=ps.white, alpha=1, linewidth=0.5)
-    ax1.set_ylabel('Chirps [n]', color=ps.white)
+    ax1.set_ylabel('chirps [n]', color=ps.white)
+    ax1.set_xlabel('outcome', 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_winner, size_chirps_winner, color=ps.red)
+    ax2.scatter(size_diffs_winner, size_chirps_winner,
-    ax2.scatter(size_diffs_loser, size_chirps_loser, color=ps.orange)
+                color=ps.red, label='winner')
+    ax2.scatter(size_diffs_loser, size_chirps_loser,
+                color=ps.orange, label='loser')
+
+    ax2.set_xlabel('size difference [cm]')
+    # ax2.set_xticks(np.arange(-10, 10.1, 2))
+
+    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)
 
-    ax2.set_xlabel('Size difference [cm]')
-    ax2.set_xticks(np.arange(-10, 10.1, 2))
     # pearson r
     plt.savefig('../poster/figs/chirps_winner_loser.pdf')
     plt.show()

code/plot_event_timeline.py

@@ -12,6 +12,7 @@ from modules.logger import makeLogger
 from modules.plotstyle import PlotStyle
 from modules.behaviour_handling import Behavior, correct_chasing_events
 
+from extract_chirps import get_valid_datasets
 ps = PlotStyle()
 
 logger = makeLogger(__name__)
@@ -19,14 +20,16 @@ logger = makeLogger(__name__)
 
 def main(datapath: str):
 
-    foldernames = [datapath + x + '/'  for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    foldernames, _ = get_valid_datasets(datapath)
     for foldername in foldernames:
-    #foldername = foldernames[0]
+        #foldername = foldernames[0]
         if foldername == '../data/mount_data/2020-05-12-10_00/':
             continue
-        #behabvior is pandas dataframe with all the data
+        # behabvior is pandas dataframe with all the data
         bh = Behavior(foldername)
-        #2020-06-11-10
+        # 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
@@ -34,46 +37,49 @@ def main(datapath: str):
         category, timestamps = correct_chasing_events(category, timestamps)
 
         # split categories
-        chasing_onset = (timestamps[category == 0]/ 60) /60
+        chasing_onset = (timestamps[category == 0] / 60) / 60
-        chasing_offset = (timestamps[category == 1]/ 60) /60
+        chasing_offset = (timestamps[category == 1] / 60) / 60
-        physical_contact = (timestamps[category == 2] / 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
+        fish1 = (bh.chirps[bh.chirps_ids == fish1_id] / 60) / 60
-        fish2 = (bh.chirps[bh.chirps_ids == fish2_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)
+        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[0].scatter(physical_contact, np.ones(
-        ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)), color='green', marker='|', s=s )
+            len(physical_contact)), color='firebrick', marker='|', s=s)
-        ax[2].scatter(fish1, np.ones(len(fish1))-0.25, color=fish1_color, marker='|', s=s)
+        ax[1].scatter(chasing_onset, np.ones(len(chasing_onset)),
-        ax[2].scatter(fish2, np.zeros(len(fish2))+0.25, color=fish2_color, marker='|', s=s)
+                      color='green', marker='|', s=s)
-
+        ax[2].scatter(fish1, np.ones(len(fish1))-0.25,
-
+                      color=fish1_color, marker='|', s=s)
-        freq_temp = bh.freq[bh.ident==fish1_id]
+        ax[2].scatter(fish2, np.zeros(len(fish2))+0.25,
-        time_temp = bh.time[bh.idx[bh.ident==fish1_id]]
+                      color=fish2_color, marker='|', s=s)
-        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish1_color)
+
-
+        freq_temp = bh.freq[bh.ident == fish1_id]
-        freq_temp = bh.freq[bh.ident==fish2_id]
+        time_temp = bh.time[bh.idx[bh.ident == fish1_id]]
-        time_temp = bh.time[bh.idx[bh.ident==fish2_id]]
+        ax[3].plot((time_temp / 60) / 60, freq_temp, color=fish1_color)
-        ax[3].plot((time_temp/ 60) /60, freq_temp, color=fish2_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
+        # 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([])
@@ -86,26 +92,26 @@ def main(datapath: str):
         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[0].set_ylabel('Physical contact', rotation=0,
-        ax[1].set_ylabel('Chasing events', rotation=0, labelpad=labelpad, fontsize=fsize)
+                         labelpad=labelpad, fontsize=fsize)
-        ax[2].set_ylabel('Chirps', 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.savefig('../poster/figs/timeline.pdf')
         plt.show()
 
-
     # plot chirps