adding new scirpt for chirp vs size

code/plot_chirp_size.py

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+import numpy as np
+
+import os
+
+import numpy as np
+import matplotlib.pyplot as plt
+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
+
+ps = PlotStyle()
+
+logger = makeLogger(__name__)
+
+
+def get_chirp_winner_loser(folder_name, Behavior, order_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    winner_row = order_meta_df[order_meta_df['recording'] == foldername]
+    winner = winner_row['winner'].values[0].astype(int)
+    winner_fish1 = winner_row['fish1'].values[0].astype(int)
+    winner_fish2 = winner_row['fish2'].values[0].astype(int)
+
+    if winner > 0:
+        if winner == winner_fish1:
+            winner_fish_id = winner_row['rec_id1'].values[0]
+            loser_fish_id = winner_row['rec_id2'].values[0]
+
+        elif winner == winner_fish2:
+            winner_fish_id = winner_row['rec_id2'].values[0]
+            loser_fish_id = winner_row['rec_id1'].values[0]
+
+        chirp_winner = len(
+            Behavior.chirps[Behavior.chirps_ids == winner_fish_id])
+        chirp_loser = len(
+            Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+        return chirp_winner, chirp_loser
+    else:
+        return np.nan, np.nan
+
+
+def get_chirp_size(folder_name, Behavior, order_meta_df, id_meta_df):
+
+    foldername = folder_name.split('/')[-2]
+    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)
+
+    groub = folder_row['group'].values[0].astype(int)
+    size_fish1_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish1)]
+    size_fish2_row = id_meta_df[(id_meta_df['group'] == groub) & (
+        id_meta_df['fish'] == fish2)]
+
+    size_winners = [size_fish1_row[col].values[0]
+                    for col in ['l1', 'l2', 'l3']]
+    mean_size_winner = 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)
+
+    if mean_size_winner > mean_size_loser:
+        size_diff = mean_size_winner - mean_size_loser
+        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:
+        size_diff = mean_size_loser - mean_size_winner
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+
+    else:
+        size_diff = np.nan
+        winner_fish_id = np.nan
+        loser_fish_id = np.nan
+
+    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return size_diff, chirp_diff
+
+
+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)
+    fish2 = folder_row['rec_id2'].values[0].astype(int)
+    chirp_freq_fish1 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish1])
+    chirp_freq_fish2 = np.nanmedian(
+        Behavior.freq[Behavior.ident == fish2])
+
+    if chirp_freq_fish1 > chirp_freq_fish2:
+        freq_diff = chirp_freq_fish1 - chirp_freq_fish2
+        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:
+        freq_diff = chirp_freq_fish2 - chirp_freq_fish1
+        winner_fish_id = folder_row['rec_id2'].values[0]
+        loser_fish_id = folder_row['rec_id1'].values[0]
+
+    chirp_diff = len(Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) - len(
+        Behavior.chirps[Behavior.chirps_ids == loser_fish_id])
+
+    return freq_diff, chirp_diff
+
+
+def main(datapath: str):
+
+    foldernames = [
+        datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath+x)]
+    path_order_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
+    order_meta_df = read_csv(path_order_meta)
+    order_meta_df['recording'] = order_meta_df['recording'].str[1:-1]
+    path_id_meta = (
+        '/').join(foldernames[0].split('/')[:-2]) + '/id_meta.csv'
+    id_meta_df = read_csv(path_id_meta)
+
+    chirps_winner = []
+    size_diffs = []
+    size_chirps_diffs = []
+    chirps_loser = []
+    freq_diffs = []
+    freq_chirps_diffs = []
+
+    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)
+        # chirps are not sorted in time (presumably due to prior groupings)
+        # get and sort chirps and corresponding fish_ids of the chirps
+        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)
+
+        winner_chirp, loser_chirp = get_chirp_winner_loser(
+            foldername,  bh, order_meta_df)
+        chirps_winner.append(winner_chirp)
+        chirps_loser.append(loser_chirp)
+        size_diff, chirp_diff = 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(
+            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])
+    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)]
+
+    bplot1 = ax1.boxplot(chirps_winner, positions=[
+        1], showfliers=False, patch_artist=True)
+    bplot2 = ax1.boxplot(chirps_loser,  positions=[
+        2], showfliers=False, patch_artist=True)
+    ax1.scatter(np.ones(len(chirps_winner)) *
+                scatterwinner, chirps_winner, color='r')
+    ax1.scatter(np.ones(len(chirps_loser)) *
+                scatterloser, chirps_loser, color='r')
+    ax1.set_xticklabels(['winner', 'loser'])
+    ax1.text(0.1, 0.9, 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='r', alpha=0.5, linewidth=0.5)
+    ax1.set_ylabel('Chirps [n]', 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, size_chirps_diffs, color='r')
+    ax2.set_xlabel('Size difference [mm]')
+    ax2.set_ylabel('Chirps difference [n]')
+
+    ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
+    # 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')
+    plt.show()
+
+
+if __name__ == '__main__':
+
+    # Path to the data
+    datapath = '../data/mount_data/'
+
+    main(datapath)