diff --git a/code/plot_chirp_bodylegth.py b/code/plot_chirp_bodylegth.py index 6d5c782..3c34953 100644 --- a/code/plot_chirp_bodylegth.py +++ b/code/plot_chirp_bodylegth.py @@ -17,11 +17,106 @@ 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 = ( + 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] @@ -30,11 +125,11 @@ def main(datapath: str): id_meta_df = read_csv(path_id_meta) chirps_winner = [] - size_diff = [] - chirps_diff = [] + size_diffs = [] + size_chirps_diffs = [] chirps_loser = [] - freq_diff = [] - + freq_diffs = [] + freq_chirps_diffs = [] for foldername in foldernames: # behabvior is pandas dataframe with all the data @@ -49,89 +144,102 @@ def main(datapath: str): # Get rid of tracking faults (two onsets or two offsets after another) category, timestamps = correct_chasing_events(category, timestamps) - 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_diff.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_diff.append(mean_size_winner - mean_size_loser) - else: - continue - - 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_diff.append(chirp_winner - chirp_loser) - freq_diff.append(freq_winner - freq_loser) - - fish1_id = all_fish_ids[0] - fish2_id = all_fish_ids[1] - print(winner_fish_id) - print(all_fish_ids) - - fig, (ax1, ax2, ax3) = plt.subplots(1,3, figsize=(10,5)) + 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) + 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') + 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) + 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) + ax1.plot([scatterwinner, scatterloser], [w, l], + color='r', alpha=0.5, linewidth=0.5) colors1 = ps.red ps.set_boxplot_color(bplot1, colors1) @@ -139,11 +247,11 @@ def main(datapath: str): ps.set_boxplot_color(bplot2, colors1) ax1.set_ylabel('Chirpscounts [n]') - ax2.scatter(size_diff, chirps_diff, color='r') + ax2.scatter(size_diffs, size_chirps_diffs, color='r') ax2.set_xlabel('Size difference [mm]') ax2.set_ylabel('Chirps difference [n]') - ax3.scatter(freq_diff, chirps_diff, color='r') + ax3.scatter(freq_diffs, freq_chirps_diffs, color='r') ax3.set_xlabel('Frequency difference [Hz]') ax3.set_yticklabels([]) ax3.set diff --git a/poster/figs/chirps_winner_loser.pdf b/poster/figs/chirps_winner_loser.pdf index 8bf9686..7d8d1cd 100644 Binary files a/poster/figs/chirps_winner_loser.pdf and b/poster/figs/chirps_winner_loser.pdf differ