import numpy as np
import os
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import pearsonr, spearmanr
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]
loser_fish_id = folder_row['rec_id2'].values[0]
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]
loser_fish_id = folder_row['rec_id1'].values[0]
else:
freq_diff_higher = np.nan
freq_diff_lower = 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 freq_diff_higher, chirp_winner, freq_diff_lower, chirp_loser
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_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_diff_higher, chirp_freq_winner, freq_diff_lower, chirp_freq_loser = get_chirp_freq(
foldername, bh, order_meta_df)
freq_diffs_higher.append(freq_diff_higher)
freq_diffs_lower.append(freq_diff_lower)
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)
embed()
size_winner_pearsonr = pearsonr(size_diffs_winner, size_chirps_winner )
size_loser_pearsonr = pearsonr(size_diffs_loser, size_chirps_loser )
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(
22*ps.cm, 12*ps.cm), sharey=True)
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_winner, size_chirps_winner, color=ps.red)
ax2.scatter(size_diffs_loser, size_chirps_loser, color=ps.orange)
ax2.set_xlabel('Size difference [cm]')
# 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)