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) # folder_name = foldername.split('/')[-2] # winner_row = order_meta_df[order_meta_df['recording'] == folder_name] # 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) # groub = winner_row['group'].values[0].astype(int) # size_rows = id_meta_df[id_meta_df['group'] == groub] # if winner == winner_fish1: # winner_fish_id = winner_row['rec_id1'].values[0] # loser_fish_id = winner_row['rec_id2'].values[0] # size_winners = [] # for l in ['l1', 'l2', 'l3']: # size_winner = size_rows[size_rows['fish']== winner_fish1][l].values[0] # size_winners.append(size_winner) # mean_size_winner = np.nanmean(size_winners) # size_losers = [] # for l in ['l1', 'l2', 'l3']: # size_loser = size_rows[size_rows['fish']== winner_fish2][l].values[0] # size_losers.append(size_loser) # mean_size_loser = np.nanmean(size_losers) # size_diffs.append(mean_size_winner - mean_size_loser) # elif winner == winner_fish2: # winner_fish_id = winner_row['rec_id2'].values[0] # loser_fish_id = winner_row['rec_id1'].values[0] # size_winners = [] # for l in ['l1', 'l2', 'l3']: # size_winner = size_rows[size_rows['fish']== winner_fish2][l].values[0] # size_winners.append(size_winner) # mean_size_winner = np.nanmean(size_winners) # size_losers = [] # for l in ['l1', 'l2', 'l3']: # size_loser = size_rows[size_rows['fish']== winner_fish1][l].values[0] # size_losers.append(size_loser) # mean_size_loser = np.nanmean(size_losers) # size_diffs.append(mean_size_winner - mean_size_loser) # else: # pass # print(foldername) # all_fish_ids = np.unique(bh.chirps_ids) # chirp_winner = len(bh.chirps[bh.chirps_ids == winner_fish_id]) # chirp_loser = len(bh.chirps[bh.chirps_ids == loser_fish_id]) # freq_winner = np.nanmedian(bh.freq[bh.ident==winner_fish_id]) # freq_loser = np.nanmedian(bh.freq[bh.ident==loser_fish_id]) # chirps_winner.append(chirp_winner) # chirps_loser.append(chirp_loser) # chirps_diffs.append(chirp_winner - chirp_loser) # freq_diffs.append(freq_winner - freq_loser) fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(10, 5)) 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(0.9, 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) colors1 = ps.red ps.set_boxplot_color(bplot1, colors1) colors1 = ps.orange ps.set_boxplot_color(bplot2, colors1) ax1.set_ylabel('Chirpscounts [n]') 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, 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)