from extract_chirps import get_valid_datasets
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from tqdm import tqdm
from IPython import embed
from pandas import read_csv
from modules.logger import makeLogger
from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
from modules.behaviour_handling import (
Behavior, correct_chasing_events, center_chirps)
from modules.plotstyle import PlotStyle
logger = makeLogger(__name__)
ps = PlotStyle()
def bootstrap(data, nresamples, kde_time, kernel_width, event_times, time_before, time_after):
bootstrapped_kdes = []
data = data[data <= 3*60*60] # only night time
# diff_data = np.diff(np.sort(data), prepend=0)
# if len(data) != 0:
# mean_chirprate = (len(data) - 1) / (data[-1] - data[0])
for i in tqdm(range(nresamples)):
# np.random.shuffle(diff_data)
# bootstrapped_data = np.cumsum(diff_data)
bootstrapped_data = data + np.random.randn(len(data)) * 10
bootstrap_data_centered = center_chirps(
bootstrapped_data, event_times, time_before, time_after)
bootstrapped_kde = acausal_kde1d(
bootstrap_data_centered, time=kde_time, width=kernel_width)
# bootstrapped_kdes = list(np.asarray(
# bootstrapped_kdes) / len(event_times))
bootstrapped_kdes.append(bootstrapped_kde)
return bootstrapped_kdes
def jackknife(data, nresamples, subsetsize, kde_time, kernel_width, event_times, time_before, time_after):
jackknife_kdes = []
data = data[data <= 3*60*60] # only night time
subsetsize = int(len(data) * subsetsize)
diff_data = np.diff(np.sort(data), prepend=0)
for i in tqdm(range(nresamples)):
bootstrapped_data = np.random.sample(data, subsetsize, replace=False)
bootstrapped_data = np.cumsum(diff_data)
bootstrap_data_centered = center_chirps(
bootstrapped_data, event_times, time_before, time_after)
bootstrapped_kde = acausal_kde1d(
bootstrap_data_centered, time=kde_time, width=kernel_width)
# bootstrapped_kdes = list(np.asarray(
# bootstrapped_kdes) / len(event_times))
jackknife_kdes.append(bootstrapped_kde)
return jackknife_kdes
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 = Behavior.chirps[Behavior.chirps_ids == winner_fish_id]
chirp_loser = Behavior.chirps[Behavior.chirps_ids == loser_fish_id]
return chirp_winner, chirp_loser
return None, None
def main(dataroot):
foldernames, _ = get_valid_datasets(dataroot)
plot_all = True
time_before = 60
time_after = 60
dt = 0.001
kernel_width = 1
kde_time = np.arange(-time_before, time_after, dt)
nbootstraps = 2
meta_path = (
'/').join(foldernames[0].split('/')[:-2]) + '/order_meta.csv'
meta = pd.read_csv(meta_path)
meta['recording'] = meta['recording'].str[1:-1]
winner_onsets = []
winner_offsets = []
winner_physicals = []
loser_onsets = []
loser_offsets = []
loser_physicals = []
winner_onsets_boot = []
winner_offsets_boot = []
winner_physicals_boot = []
loser_onsets_boot = []
loser_offsets_boot = []
loser_physicals_boot = []
onset_count = 0
offset_count = 0
physical_count = 0
# Iterate over all recordings and save chirp- and event-timestamps
for folder in tqdm(foldernames):
foldername = folder.split('/')[-2]
# logger.info('Loading data from folder: {}'.format(foldername))
broken_folders = ['../data/mount_data/2020-05-12-10_00/']
if folder in broken_folders:
continue
bh = Behavior(folder)
category, timestamps = correct_chasing_events(bh.behavior, bh.start_s)
category = category[timestamps < 3*60*60] # only night time
timestamps = timestamps[timestamps < 3*60*60] # only night time
winner, loser = get_chirp_winner_loser(folder, bh, meta)
if winner is None:
continue
onsets = (timestamps[category == 0])
offsets = (timestamps[category == 1])
physicals = (timestamps[category == 2])
onset_count += len(onsets)
offset_count += len(offsets)
physical_count += len(physicals)
winner_onsets.append(center_chirps(
winner, onsets, time_before, time_after))
winner_offsets.append(center_chirps(
winner, offsets, time_before, time_after))
winner_physicals.append(center_chirps(
winner, physicals, time_before, time_after))
loser_onsets.append(center_chirps(
loser, onsets, time_before, time_after))
loser_offsets.append(center_chirps(
loser, offsets, time_before, time_after))
loser_physicals.append(center_chirps(
loser, physicals, time_before, time_after))
# bootstrap
# chirps = [winner, winner, winner, loser, loser, loser]
winner_onsets_boot.append(bootstrap(
winner,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=onsets,
time_before=time_before,
time_after=time_after))
winner_offsets_boot.append(bootstrap(
winner,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=offsets,
time_before=time_before,
time_after=time_after))
winner_physicals_boot.append(bootstrap(
winner,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=physicals,
time_before=time_before,
time_after=time_after))
loser_onsets_boot.append(bootstrap(
loser,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=onsets,
time_before=time_before,
time_after=time_after))
loser_offsets_boot.append(bootstrap(
loser,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=offsets,
time_before=time_before,
time_after=time_after))
loser_physicals_boot.append(bootstrap(
loser,
nresamples=nbootstraps,
kde_time=kde_time,
kernel_width=kernel_width,
event_times=physicals,
time_before=time_before,
time_after=time_after))
if plot_all:
winner_onsets_conv = acausal_kde1d(
winner_onsets[-1], kde_time, kernel_width)
winner_offsets_conv = acausal_kde1d(
winner_offsets[-1], kde_time, kernel_width)
winner_physicals_conv = acausal_kde1d(
winner_physicals[-1], kde_time, kernel_width)
loser_onsets_conv = acausal_kde1d(
loser_onsets[-1], kde_time, kernel_width)
loser_offsets_conv = acausal_kde1d(
loser_offsets[-1], kde_time, kernel_width)
loser_physicals_conv = acausal_kde1d(
loser_physicals[-1], kde_time, kernel_width)
fig, ax = plt.subplots(2, 3, figsize=(
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
ax[0, 0].set_title(
f"{foldername}, onsets {len(onsets)}, offsets {len(offsets)}, physicals {len(physicals)},winner {len(winner)}, looser {len(loser)} , onsets")
ax[0, 0].plot(kde_time, winner_onsets_conv/len(onsets))
ax[0, 1].plot(kde_time, winner_offsets_conv/len(offsets))
ax[0, 2].plot(kde_time, winner_physicals_conv/len(physicals))
ax[1, 0].plot(kde_time, loser_onsets_conv/len(onsets))
ax[1, 1].plot(kde_time, loser_offsets_conv/len(offsets))
ax[1, 2].plot(kde_time, loser_physicals_conv/len(physicals))
# # plot bootstrap lines
for kde in winner_onsets_boot[-1]:
ax[0, 0].plot(kde_time, kde/len(onsets),
color='gray')
for kde in winner_offsets_boot[-1]:
ax[0, 1].plot(kde_time, kde/len(offsets),
color='gray')
for kde in winner_physicals_boot[-1]:
ax[0, 2].plot(kde_time, kde/len(physicals),
color='gray')
for kde in loser_onsets_boot[-1]:
ax[1, 0].plot(kde_time, kde/len(onsets),
color='gray')
for kde in loser_offsets_boot[-1]:
ax[1, 1].plot(kde_time, kde/len(offsets),
color='gray')
for kde in loser_physicals_boot[-1]:
ax[1, 2].plot(kde_time, kde/len(physicals),
color='gray')
# plot bootstrap percentiles
# ax[0, 0].fill_between(
# kde_time,
# np.percentile(winner_onsets_boot[-1], 5, axis=0),
# np.percentile(winner_onsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[0, 1].fill_between(
# kde_time,
# np.percentile(winner_offsets_boot[-1], 5, axis=0),
# np.percentile(
# winner_offsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[0, 2].fill_between(
# kde_time,
# np.percentile(
# winner_physicals_boot[-1], 5, axis=0),
# np.percentile(
# winner_physicals_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 0].fill_between(
# kde_time,
# np.percentile(loser_onsets_boot[-1], 5, axis=0),
# np.percentile(loser_onsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 1].fill_between(
# kde_time,
# np.percentile(loser_offsets_boot[-1], 5, axis=0),
# np.percentile(loser_offsets_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[1, 2].fill_between(
# kde_time,
# np.percentile(
# loser_physicals_boot[-1], 5, axis=0),
# np.percentile(
# loser_physicals_boot[-1], 95, axis=0),
# color='gray',
# alpha=0.5)
# ax[0, 0].plot(kde_time, np.median(winner_onsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[0, 1].plot(kde_time, np.median(winner_offsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[0, 2].plot(kde_time, np.median(winner_physicals_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 0].plot(kde_time, np.median(loser_onsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 1].plot(kde_time, np.median(loser_offsets_boot[-1], axis=0),
# color='black', linewidth=2)
# ax[1, 2].plot(kde_time, np.median(loser_physicals_boot[-1], axis=0),
# color='black', linewidth=2)
ax[0, 0].set_xlim(-30, 30)
plt.show()
winner_onsets = np.sort(flatten(winner_onsets))
winner_offsets = np.sort(flatten(winner_offsets))
winner_physicals = np.sort(flatten(winner_physicals))
loser_onsets = np.sort(flatten(loser_onsets))
loser_offsets = np.sort(flatten(loser_offsets))
loser_physicals = np.sort(flatten(loser_physicals))
winner_onsets_conv = acausal_kde1d(
winner_onsets, kde_time, kernel_width)
winner_offsets_conv = acausal_kde1d(
winner_offsets, kde_time, kernel_width)
winner_physicals_conv = acausal_kde1d(
winner_physicals, kde_time, kernel_width)
loser_onsets_conv = acausal_kde1d(
loser_onsets, kde_time, kernel_width)
loser_offsets_conv = acausal_kde1d(
loser_offsets, kde_time, kernel_width)
loser_physicals_conv = acausal_kde1d(
loser_physicals, kde_time, kernel_width)
winner_onsets_conv = winner_onsets_conv / onset_count
winner_offsets_conv = winner_offsets_conv / offset_count
winner_physicals_conv = winner_physicals_conv / physical_count
loser_onsets_conv = loser_onsets_conv / onset_count
loser_offsets_conv = loser_offsets_conv / offset_count
loser_physicals_conv = loser_physicals_conv / physical_count
winner_onsets_boot = np.concatenate(
winner_onsets_boot)
winner_offsets_boot = np.concatenate(
winner_offsets_boot)
winner_physicals_boot = np.concatenate(
winner_physicals_boot)
loser_onsets_boot = np.concatenate(
loser_onsets_boot)
loser_offsets_boot = np.concatenate(
loser_offsets_boot)
loser_physicals_boot = np.concatenate(
loser_physicals_boot)
percs = [5, 50, 95]
winner_onsets_boot_quarts = np.percentile(
winner_onsets_boot, percs, axis=0)
winner_offsets_boot_quarts = np.percentile(
winner_offsets_boot, percs, axis=0)
winner_physicals_boot_quarts = np.percentile(
winner_physicals_boot, percs, axis=0)
loser_onsets_boot_quarts = np.percentile(
loser_onsets_boot, percs, axis=0)
loser_offsets_boot_quarts = np.percentile(
loser_offsets_boot, percs, axis=0)
loser_physicals_boot_quarts = np.percentile(
loser_physicals_boot, percs, axis=0)
fig, ax = plt.subplots(2, 3, figsize=(
21*ps.cm, 10*ps.cm), sharey=True, sharex=True)
ax[0, 0].plot(kde_time, winner_onsets_conv)
ax[0, 1].plot(kde_time, winner_offsets_conv)
ax[0, 2].plot(kde_time, winner_physicals_conv)
ax[1, 0].plot(kde_time, loser_onsets_conv)
ax[1, 1].plot(kde_time, loser_offsets_conv)
ax[1, 2].plot(kde_time, loser_physicals_conv)
ax[0, 0].plot(kde_time, winner_onsets_boot_quarts[1], c=ps.black)
ax[0, 1].plot(kde_time, winner_offsets_boot_quarts[1], c=ps.black)
ax[0, 2].plot(kde_time, winner_physicals_boot_quarts[1], c=ps.black)
ax[1, 0].plot(kde_time, loser_onsets_boot_quarts[1], c=ps.black)
ax[1, 1].plot(kde_time, loser_offsets_boot_quarts[1], c=ps.black)
ax[1, 2].plot(kde_time, loser_physicals_boot_quarts[1], c=ps.black)
# for kde in winner_onsets_boot:
# ax[0, 0].plot(kde_time, kde,
# color='gray')
# for kde in winner_offsets_boot:
# ax[0, 1].plot(kde_time, kde,
# color='gray')
# for kde in winner_physicals_boot:
# ax[0, 2].plot(kde_time, kde,
# color='gray')
# for kde in loser_onsets_boot:
# ax[1, 0].plot(kde_time, kde,
# color='gray')
# for kde in loser_offsets_boot:
# ax[1, 1].plot(kde_time, kde,
# color='gray')
# for kde in loser_physicals_boot:
# ax[1, 2].plot(kde_time, kde,
# color='gray')
ax[0, 0].fill_between(kde_time,
winner_onsets_boot_quarts[0],
winner_onsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[0, 1].fill_between(kde_time,
winner_offsets_boot_quarts[0],
winner_offsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[0, 2].fill_between(kde_time,
loser_physicals_boot_quarts[0],
loser_physicals_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 0].fill_between(kde_time,
loser_onsets_boot_quarts[0],
loser_onsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 1].fill_between(kde_time,
loser_offsets_boot_quarts[0],
loser_offsets_boot_quarts[2],
color=ps.gray,
alpha=0.5)
ax[1, 2].fill_between(kde_time,
loser_physicals_boot_quarts[0],
loser_physicals_boot_quarts[2],
color=ps.gray,
alpha=0.5)
plt.show()
if __name__ == '__main__':
main('../data/mount_data/')