adding plot in chasing

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)), time_precents, color=ps.white)
+    ax.scatter(np.ones(len(chirps_percents))*2, 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)
+
+