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:
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)
size_chirps_diffs.append(chirp_winner - chirp_loser)
freq_diffs.append(freq_winner - freq_loser)
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(22*ps.cm, 12*ps.cm), width_ratios=[1.5, 1,1])
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, size_chirps_diffs, color='r')
ax2.set_xlabel('Size difference [mm]')
ax2.set_ylabel('Chirps [n]')
ax3.scatter(freq_diffs, size_chirps_diffs, color='r')
# 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)