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, wilcoxon 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) 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) & ( 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']] size_fish1 = np.nanmean(size_winners) size_losers = [size_fish2_row[col].values[0] for col in ['l1', 'l2', 'l3']] 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 winner_fish_id = folder_row['rec_id1'].values[0] loser_fish_id = folder_row['rec_id2'].values[0] elif winner == fish2: if size_fish2 > size_fish1: 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_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]) chirp_loser = len( Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) return size_diff_bigger, chirp_winner, size_diff_smaller, chirp_loser 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['fish1'].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_freq]) chirp_freq_fish2 = np.nanmedian( Behavior.freq[Behavior.ident == fish2_freq]) if winner == fish1: # if 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] winner_fish_freq = chirp_freq_fish1 loser_fish_id = folder_row['rec_id2'].values[0] loser_fish_freq = chirp_freq_fish2 elif winner == fish2: # 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] winner_fish_freq = chirp_freq_fish2 loser_fish_id = folder_row['rec_id1'].values[0] loser_fish_freq = chirp_freq_fish1 else: winner_fish_freq = np.nan loser_fish_freq = np.nan winner_fish_id = np.nan loser_fish_id = np.nan chirp_winner = len( Behavior.chirps[Behavior.chirps_ids == winner_fish_id]) chirp_loser = len( Behavior.chirps[Behavior.chirps_ids == loser_fish_id]) return winner_fish_freq, chirp_winner, loser_fish_freq, chirp_loser 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) 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_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 = [] 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_bigger, chirp_winner, size_diff_smaller, chirp_loser = get_chirp_size( foldername, bh, order_meta_df, id_meta_df) freq_winner, chirp_freq_winner, freq_loser, chirp_freq_loser = get_chirp_freq( foldername, bh, order_meta_df) freq_diffs_higher.append(freq_winner) freq_diffs_lower.append(freq_loser) 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) size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner) size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser) fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=( 21*ps.cm, 7*ps.cm), width_ratios=[1, 0.8, 0.8], sharey=True) plt.subplots_adjust(left=0.11, right=0.948, top=0.86, wspace=0.343, bottom=0.198) 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) winner_color = ps.gblue2 loser_color = ps.gblue1 bplot1 = ax1.boxplot(chirps_winner, positions=[ 0.9], showfliers=False, patch_artist=True) bplot2 = ax1.boxplot(chirps_loser, positions=[ 2.1], showfliers=False, patch_artist=True) ax1.scatter(np.ones(len(chirps_winner)) * scatterwinner, chirps_winner, color=winner_color) ax1.scatter(np.ones(len(chirps_loser)) * scatterloser, chirps_loser, color=loser_color) ax1.set_xticklabels(['Winner', 'Loser']) ax1.text(0.1, 0.85, f'n={len(chirps_loser)}', transform=ax1.transAxes, color=ps.white) for w, l in zip(chirps_winner, chirps_loser): ax1.plot([scatterwinner, scatterloser], [w, l], color=ps.white, alpha=0.6, linewidth=1, zorder=-1) ax1.set_ylabel('Chirp counts', color=ps.white) ax1.set_xlabel('Competition outcome', color=ps.white) ps.set_boxplot_color(bplot1, winner_color) ps.set_boxplot_color(bplot2, loser_color) ax2.scatter(size_diffs_winner, size_chirps_winner, color=winner_color, label=f'Winner') ax2.scatter(size_diffs_loser, size_chirps_loser, color=loser_color, label='Loser') ax2.text(0.05, 0.85, f'n={len(size_chirps_loser)}', transform=ax2.transAxes, color=ps.white) ax2.set_xlabel('Size difference [cm]') # ax2.set_xticks(np.arange(-10, 10.1, 2)) ax3.scatter(freq_diffs_higher, freq_chirps_winner, color=winner_color) ax3.scatter(freq_diffs_lower, freq_chirps_loser, color=loser_color) ax3.text(0.1, 0.85, f'n={len(np.asarray(freq_chirps_winner)[~np.isnan(freq_chirps_loser)])}', transform=ax3.transAxes, color=ps.white) ax3.set_xlabel('EODf [Hz]') handles, labels = ax2.get_legend_handles_labels() fig.legend(handles, labels, loc='upper center', ncol=2, bbox_to_anchor=(0.5, 1.04)) # pearson r plt.savefig('../poster/figs/chirps_winner_loser.pdf') plt.show() if __name__ == '__main__': # Path to the data datapath = '../data/mount_data/' main(datapath)