from modules.plotstyle import PlotStyle
from modules.behaviour_handling import (
Behavior,
correct_chasing_events,
center_chirps,
)
from modules.datahandling import flatten, causal_kde1d, acausal_kde1d
from modules.logger import makeLogger
from pandas import read_csv
from IPython import embed
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os
from extract_chirps import get_valid_datasets
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_kde = list(np.asarray(bootstrapped_kde) / 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)):
jackknifed_data = np.random.choice(diff_data, subsetsize, replace=False)
jackknifed_data = np.cumsum(jackknifed_data)
jackknifed_data_centered = center_chirps(
jackknifed_data, event_times, time_before, time_after
)
jackknifed_kde = acausal_kde1d(
jackknifed_data_centered, time=kde_time, width=kernel_width
)
jackknifed_kde = list(np.asarray(jackknifed_kde) / len(event_times))
jackknife_kdes.append(jackknifed_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, _ = np.asarray(get_valid_datasets(dataroot))
plot_all = True
time_before = 90
time_after = 90
dt = 0.001
kernel_width = 2
kde_time = np.arange(-time_before, time_after, dt)
nbootstraps = 50
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
# winner_count = 0
# loser_count = 0
# winner_onset_chirpcount = 0
# winner_offset_chirpcount = 0
# winner_physical_chirpcount = 0
# loser_onset_chirpcount = 0
# loser_offset_chirpcount = 0
# loser_physical_chirpcount = 0
fig, ax = plt.subplots(
1, 2, figsize=(14 * ps.cm, 7 * ps.cm), sharey=True, sharex=True
)
# Iterate over all recordings and save chirp- and event-timestamps
good_recs = np.asarray([0, 15])
for i, folder in tqdm(enumerate(foldernames[good_recs])):
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
# winner_count += len(winner)
# loser_count += len(loser)
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)
)
# winner_onset_chirpcount += len(winner_onsets[-1])
# winner_offset_chirpcount += len(winner_offsets[-1])
# winner_physical_chirpcount += len(winner_physicals[-1])
# loser_onset_chirpcount += len(loser_onsets[-1])
# loser_offset_chirpcount += len(loser_offsets[-1])
# loser_physical_chirpcount += len(loser_physicals[-1])
# 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))
# loser_offsets_jackknife = jackknife(
# loser,
# nresamples=nbootstraps,
# subsetsize=0.9,
# kde_time=kde_time,
# kernel_width=kernel_width,
# event_times=offsets,
# 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)
ax[i].plot(
kde_time,
loser_offsets_conv / len(offsets),
lw=2,
zorder=100,
c=ps.gblue1,
)
ax[i].fill_between(
kde_time,
np.percentile(loser_offsets_boot[-1], 1, axis=0),
np.percentile(loser_offsets_boot[-1], 99, axis=0),
color="gray",
alpha=0.8,
)
ax[i].plot(
kde_time,
np.median(loser_offsets_boot[-1], axis=0),
color=ps.black,
linewidth=2,
)
ax[i].axvline(0, color=ps.gray, linestyle="--")
# ax[i].fill_between(
# kde_time,
# np.percentile(loser_offsets_jackknife, 5, axis=0),
# np.percentile(loser_offsets_jackknife, 95, axis=0),
# color=ps.blue,
# alpha=0.5)
# ax[i].plot(kde_time, np.median(loser_offsets_jackknife, axis=0),
# color=ps.white, linewidth=2)
ax[i].set_xlim(-60, 60)
fig.supylabel("Chirp rate (a.u.)", fontsize=14)
fig.supxlabel("Time (s)", fontsize=14)
# 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,
# color='gray')
# for kde in winner_offsets_boot[-1]:
# ax[0, 1].plot(kde_time, kde,
# color='gray')
# for kde in winner_physicals_boot[-1]:
# ax[0, 2].plot(kde_time, kde,
# color='gray')
# for kde in loser_onsets_boot[-1]:
# ax[1, 0].plot(kde_time, kde,
# color='gray')
# for kde in loser_offsets_boot[-1]:
# ax[1, 1].plot(kde_time, kde,
# color='gray')
# for kde in loser_physicals_boot[-1]:
# ax[1, 2].plot(kde_time, kde,
# 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)
# 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.subplots_adjust(bottom=0.21, top=0.93)
plt.savefig("../poster/figs/kde.pdf")
plt.show()
if __name__ == "__main__":
main("../data/mount_data/")