raab/GP2023_chirp_detection
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implemented plots for all recordings incl bootstrapping, std is over 9000

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sprause 2023-01-24 15:22:40 +01:00
parent dc2074222c
commit d3e77d20cc
1 changed files with 153 additions and 97 deletions
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code/eventchirpsplots.py
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@ -4,12 +4,15 @@ 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.plotstyle import PlotStyle
from modules.datahandling import causal_kde1d, acausal_kde1d
logger = makeLogger(__name__)
ps = PlotStyle()
class Behavior:
"""Load behavior data from csv file as class attributes
@ -31,7 +34,7 @@ class Behavior:
"""
def __init__(self, folder_path: str) -> None:
print(f'{folder_path}')
LED_on_time_BORIS = np.load(os.path.join(folder_path, 'LED_on_time.npy'), allow_pickle=True)
self.time = np.load(os.path.join(folder_path, "times.npy"), allow_pickle=True)
csv_filename = [f for f in os.listdir(folder_path) if f.endswith('.csv')][0] # check if there are more than one csv file
@ -137,11 +140,16 @@ def event_triggered_chirps(
continue
else:
centered_chirps.append(chirps_around_event - event_timestamp)
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
# Kernel density estimation
time = np.arange(-time_before_event, time_after_event, dt)
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
# Kernel density estimation with some if's
if len(centered_chirps) == 0:
centered_chirps = np.array([])
centered_chirps_convolved = np.zeros(len(time))
else:
centered_chirps = np.concatenate(centered_chirps, axis=0) # convert list of arrays to one array for plotting
centered_chirps_convolved = (acausal_kde1d(centered_chirps, time, width)) / len(event)
return event_chirps, centered_chirps, centered_chirps_convolved
@ -150,12 +158,13 @@ def main(datapath: str):
foldernames = [datapath + x + '/' for x in os.listdir(datapath) if os.path.isdir(datapath + x)]
all_chirps = []
all_chirps_fish_ids = []
all_chasing_onsets = []
all_chasing_offsets = []
all_physicals = []
nrecording_chirps = []
nrecording_chirps_fish_ids = []
nrecording_chasing_onsets = []
nrecording_chasing_offsets = []
nrecording_physicals = []
# Iterate over all recordings and save chirp- and event-timestamps
for folder in foldernames:
# exclude folder with empty LED_on_time.npy
if folder == '../data/mount_data/2020-05-12-10_00/':
@ -167,9 +176,9 @@ def main(datapath: str):
category = bh.behavior
timestamps = bh.start_s
chirps = bh.chirps
all_chirps.append(chirps)
nrecording_chirps.append(chirps)
chirps_fish_ids = bh.chirps_ids
all_chirps_fish_ids.append(chirps_fish_ids)
nrecording_chirps_fish_ids.append(chirps_fish_ids)
fish_ids = np.unique(chirps_fish_ids)
# Correct for doubles in chasing on- and offsets to get the right on-/offset pairs
@ -178,120 +187,172 @@ def main(datapath: str):
# Split categories
chasing_onsets = timestamps[category == 0]
all_chasing_onsets.append(chasing_onsets)
nrecording_chasing_onsets.append(chasing_onsets)
chasing_offsets = timestamps[category == 1]
all_chasing_offsets.append(chasing_offsets)
nrecording_chasing_offsets.append(chasing_offsets)
physical_contacts = timestamps[category == 2]
all_physicals.append(physical_contacts)
nrecording_physicals.append(physical_contacts)
embed()
# chasing_durations = []
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
# for onset, offset in zip(chasing_onsets, chasing_offsets):
# duration = offset - onset
# chasing_durations.append(duration)
# fig, ax = plt.subplots()
# ax.boxplot(chasing_durations)
# plt.show()
# plt.close()
# # Associate chirps to individual fish
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# fish = [len(fish1), len(fish2)]
# Concolution over all recordings
# Rasterplot for each recording
# Define time window for chirps around event analysis
time_before_event = 30
time_after_event = 60
dt = 0.01
width = 1
width = 1.5 # width of kernel, currently gaussian kernel
time = np.arange(-time_before_event, time_after_event, dt)
##### Chirps around events, all fish, one recording #####
# Chirps around chasing onsets
_, centered_chasing_onset_chirps, cc_chasing_onset_chirps = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around chasing offsets
_, centered_chasing_offset_chirps, cc_chasing_offset_chirps = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around physical contacts
_, centered_physical_chirps, cc_physical_chirps = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
## Shuffled chirps ##
nbootstrapping = 1000
nshuffled_chirps_onset = []
nshuffled_chirps_offset = []
nshuffled_chirps_physical = []
for i in range(nbootstrapping):
# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
np.random.shuffle(interchirp_intervals)
shuffled_chirps = np.cumsum(interchirp_intervals)
# Shuffled chasing onset chirps
_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_onset.append(cc_shuffled_onset_chirps)
# Shuffled chasing offset chirps
_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_offset.append(cc_shuffled_offset_chirps)
# Shuffled physical contact chirps
_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_chirps_physical.append(cc_shuffled_physical_chirps)
##### Chirps around events, all fish, all recordings #####
# Centered chirps per event type
nrecording_centered_onset_chirps = []
nrecording_centered_offset_chirps = []
nrecording_centered_physical_chirps = []
# Bootstrapped chirps per recording and per event: 27[1000[n]] 27 recs, 1000 shuffles, n chirps
nrecording_shuffled_convolved_onset_chirps = []
nrecording_shuffled_convolved_offset_chirps = []
nrecording_shuffled_convolved_physical_chirps = []
nbootstrapping = 10
for i in range(len(nrecording_chirps)):
chirps = nrecording_chirps[i]
chasing_onsets = nrecording_chasing_onsets[i]
chasing_offsets = nrecording_chasing_offsets[i]
physical_contacts = nrecording_physicals[i]
# Chirps around chasing onsets
_, centered_chasing_onset_chirps, _ = event_triggered_chirps(chasing_onsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around chasing offsets
_, centered_chasing_offset_chirps, _ = event_triggered_chirps(chasing_offsets, chirps, time_before_event, time_after_event, dt, width)
# Chirps around physical contacts
_, centered_physical_chirps, _ = event_triggered_chirps(physical_contacts, chirps, time_before_event, time_after_event, dt, width)
nrecording_centered_onset_chirps.append(centered_chasing_onset_chirps)
nrecording_centered_offset_chirps.append(centered_chasing_offset_chirps)
nrecording_centered_physical_chirps.append(centered_physical_chirps)
## Shuffled chirps ##
nshuffled_onset_chirps = []
nshuffled_offset_chirps = []
nshuffled_physical_chirps = []
for i in tqdm(range(nbootstrapping)):
# Calculate interchirp intervals; add first chirp timestamp in beginning to get equal lengths
interchirp_intervals = np.append(np.array([chirps[0]]), np.diff(chirps))
np.random.shuffle(interchirp_intervals)
shuffled_chirps = np.cumsum(interchirp_intervals)
# Shuffled chasing onset chirps
_, _, cc_shuffled_onset_chirps = event_triggered_chirps(chasing_onsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_onset_chirps.append(cc_shuffled_onset_chirps)
# Shuffled chasing offset chirps
_, _, cc_shuffled_offset_chirps = event_triggered_chirps(chasing_offsets, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_offset_chirps.append(cc_shuffled_offset_chirps)
# Shuffled physical contact chirps
_, _, cc_shuffled_physical_chirps = event_triggered_chirps(physical_contacts, shuffled_chirps, time_before_event, time_after_event, dt, width)
nshuffled_physical_chirps.append(cc_shuffled_physical_chirps)
nrecording_shuffled_convolved_onset_chirps.append(nshuffled_onset_chirps)
nrecording_shuffled_convolved_offset_chirps.append(nshuffled_offset_chirps)
nrecording_shuffled_convolved_physical_chirps.append(nshuffled_physical_chirps)
# vstack um 1. Dim zu cutten
nrecording_shuffled_convolved_onset_chirps = np.vstack(nrecording_shuffled_convolved_onset_chirps)
nrecording_shuffled_convolved_offset_chirps = np.vstack(nrecording_shuffled_convolved_offset_chirps)
nrecording_shuffled_convolved_physical_chirps = np.vstack(nrecording_shuffled_convolved_physical_chirps)
shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(nshuffled_chirps_onset, (5, 50, 95), axis=0)
shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(nshuffled_chirps_offset, (5, 50, 95), axis=0)
shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(nshuffled_chirps_physical, (5, 50, 95), axis=0)
shuffled_q5_onset, shuffled_median_onset, shuffled_q95_onset = np.percentile(
nrecording_shuffled_convolved_onset_chirps, (5, 50, 95), axis=0)
shuffled_q5_offset, shuffled_median_offset, shuffled_q95_offset = np.percentile(
nrecording_shuffled_convolved_offset_chirps, (5, 50, 95), axis=0)
shuffled_q5_physical, shuffled_median_physical, shuffled_q95_physical = np.percentile(
nrecording_shuffled_convolved_physical_chirps, (5, 50, 95), axis=0)
# Flatten all chirps
all_chirps = np.concatenate(nrecording_chirps).ravel() # not centered
# Flatten event timestamps
all_onsets = np.concatenate(nrecording_chasing_onsets).ravel() # not centered
all_offsets = np.concatenate(nrecording_chasing_offsets).ravel() # not centered
all_physicals = np.concatenate(nrecording_physicals).ravel() # not centered
# Flatten all chirps around events
all_onset_chirps = np.concatenate(nrecording_centered_onset_chirps).ravel() # centered
all_offset_chirps = np.concatenate(nrecording_centered_offset_chirps).ravel() # centered
all_physical_chirps = np.concatenate(nrecording_centered_physical_chirps).ravel() # centered
# Convolute all chirps
# Divide by total number of each event over all recordings
all_onset_chirps_convolved = (acausal_kde1d(all_onset_chirps, time, width)) / len(all_onsets)
all_offset_chirps_convolved = (acausal_kde1d(all_offset_chirps, time, width)) / len(all_offsets)
all_physical_chirps_convolved = (acausal_kde1d(all_physical_chirps, time, width)) / len(all_physicals)
# Plot all events with all shuffled
fig, ax = plt.subplots(1, 3, figsize=(50 / 2.54, 15 / 2.54), constrained_layout=True, sharey='all')
offset = [1.35]
fig, ax = plt.subplots(1, 3, figsize=(28*ps.cm, 16*ps.cm, ), constrained_layout=True, sharey='all')
# offsets = np.arange(1,28,1)
ax[0].set_xlabel('Time[s]')
# Plot chasing onsets
ax[0].set_ylabel('Chirp rate [Hz]')
ax[0].plot(time, cc_chasing_onset_chirps, color='tab:blue', zorder=2)
ax[0].plot(time, all_onset_chirps_convolved, color=ps.yellow, zorder=2)
ax0 = ax[0].twinx()
ax0.eventplot(np.array([centered_chasing_onset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:green'], alpha=0.25, zorder=1)
ax0.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
nrecording_centered_onset_chirps = np.asarray(nrecording_centered_onset_chirps, dtype=object)
ax0.eventplot(np.array(nrecording_centered_onset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax0.vlines(0, 0, 1.5, ps.black, 'dashed')
ax[0].set_zorder(ax0.get_zorder()+1)
ax[0].patch.set_visible(False)
ax0.set_yticklabels([])
ax0.set_yticks([])
ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color='tab:gray', alpha=0.5)
ax[0].plot(time, shuffled_median_onset, color='k')
ax[0].fill_between(time, shuffled_q5_onset, shuffled_q95_onset, color=ps.gray, alpha=0.5)
ax[0].plot(time, shuffled_median_onset, color=ps.black)
# Plot chasing offets
ax[1].set_xlabel('Time[s]')
ax[1].plot(time, cc_chasing_offset_chirps, color='tab:blue', zorder=2)
ax[1].plot(time, all_offset_chirps_convolved, color=ps.orange, zorder=2)
ax1 = ax[1].twinx()
ax1.eventplot(np.array([centered_chasing_offset_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:purple'], alpha=0.25, zorder=1)
ax1.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
nrecording_centered_offset_chirps = np.asarray(nrecording_centered_offset_chirps, dtype=object)
ax1.eventplot(np.array(nrecording_centered_offset_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax1.vlines(0, 0, 1.5, ps.black, 'dashed')
ax[1].set_zorder(ax1.get_zorder()+1)
ax[1].patch.set_visible(False)
ax1.set_yticklabels([])
ax1.set_yticks([])
ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color='tab:gray', alpha=0.5)
ax[1].plot(time, shuffled_median_offset, color='k')
ax[1].fill_between(time, shuffled_q5_offset, shuffled_q95_offset, color=ps.gray, alpha=0.5)
ax[1].plot(time, shuffled_median_offset, color=ps.black)
# Plot physical contacts
ax[2].set_xlabel('Time[s]')
ax[2].plot(time, cc_physical_chirps, color='tab:blue', zorder=2)
ax[2].plot(time, all_physical_chirps_convolved, color=ps.maroon, zorder=2)
ax2 = ax[2].twinx()
ax2.eventplot(np.array([centered_physical_chirps]), lineoffsets=offset, linelengths=0.1, colors=['tab:red'], alpha=0.25, zorder=1)
ax2.vlines(0, 0, 1.5, 'tab:grey', 'dashed')
nrecording_centered_physical_chirps = np.asarray(nrecording_centered_physical_chirps, dtype=object)
ax2.eventplot(np.array(nrecording_centered_physical_chirps), linelengths=0.5, colors=ps.gray, alpha=0.25, zorder=1)
ax2.vlines(0, 0, 1.5, ps.black, 'dashed')
ax[2].set_zorder(ax2.get_zorder()+1)
ax[2].patch.set_visible(False)
ax2.set_yticklabels([])
ax2.set_yticks([])
ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color='tab:gray', alpha=0.5)
ax[2].plot(time, shuffled_median_physical, color='k')
ax[2].fill_between(time, shuffled_q5_physical, shuffled_q95_physical, color=ps.gray, alpha=0.5)
ax[2].plot(time, shuffled_median_physical, ps.black)
plt.show()
# plt.close()
embed()
# chasing_durations = []
# # Calculate chasing duration to evaluate a nice time window for kernel density estimation
# for onset, offset in zip(chasing_onsets, chasing_offsets):
# duration = offset - onset
# chasing_durations.append(duration)
# fig, ax = plt.subplots()
# ax.boxplot(chasing_durations)
# plt.show()
# plt.close()
# # Associate chirps to individual fish
# fish1 = chirps[chirps_fish_ids == fish_ids[0]]
# fish2 = chirps[chirps_fish_ids == fish_ids[1]]
# fish = [len(fish1), len(fish2)]
# Concolution over all recordings
# Rasterplot for each recording
# #### Chirps around events, winner VS loser, one recording ####
@ -386,17 +447,12 @@ def main(datapath: str):
# ax5.set_yticks([])
# plt.show()
# plt.close()
embed()
exit()
for i in range(len(fish_ids)):
fish = fish_ids[i]
chirps_temp = chirps[chirps_fish_ids == fish]
print(fish)
# for i in range(len(fish_ids)):
# fish = fish_ids[i]
# chirps_temp = chirps[chirps_fish_ids == fish]
# print(fish)
#### Chirps around events, only losers, one recording ####