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.gblue3
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, 0.95, 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=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, 0.95, f'n={len(size_chirps_winner)}',
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, 0.95, f'n={len(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)