510 lines
24 KiB
Python
510 lines
24 KiB
Python
import os
|
|
import sys
|
|
import argparse
|
|
import time
|
|
import itertools
|
|
|
|
import numpy as np
|
|
try:
|
|
import cupy as cp
|
|
except ImportError:
|
|
import numpy as cp
|
|
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.gridspec as gridspec
|
|
import pandas as pd
|
|
from IPython import embed
|
|
from tqdm import tqdm
|
|
|
|
female_color, male_color = '#e74c3c', '#3498db'
|
|
|
|
|
|
def load_and_converete_boris_events(trial_path, recording, sr):
|
|
def converte_video_frames_to_grid_idx(event_frames, led_frames, led_idx):
|
|
event_idx_grid = (event_frames - led_frames[0]) / (led_frames[-1] - led_frames[0]) * (led_idx[-1] - led_idx[0]) + led_idx[0]
|
|
return event_idx_grid
|
|
|
|
# idx in grid-recording
|
|
led_idx = pd.read_csv(os.path.join(trial_path, 'led_idxs.csv'), header=None).iloc[:, 0].to_numpy()
|
|
# frames where LED gets switched on
|
|
led_frames = np.load(os.path.join(trial_path, 'LED_frames.npy'))
|
|
|
|
times, behavior, t_ag_on_off, t_contact, video_FPS = load_boris(trial_path, recording)
|
|
|
|
contact_frame = np.array(np.round(t_contact * video_FPS), dtype=int)
|
|
ag_on_off_frame = np.array(np.round(t_ag_on_off * video_FPS), dtype=int)
|
|
|
|
# led_t_GRID = led_idx / sr
|
|
contact_t_GRID = converte_video_frames_to_grid_idx(contact_frame, led_frames, led_idx) / sr
|
|
ag_on_off_t_GRID = converte_video_frames_to_grid_idx(ag_on_off_frame, led_frames, led_idx) / sr
|
|
|
|
return contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames
|
|
|
|
|
|
def load_boris(trial_path, recording):
|
|
boris_file = '-'.join(recording.split('-')[:3]) + '.csv'
|
|
|
|
data = pd.read_csv(os.path.join(trial_path, boris_file))
|
|
times = data['Start (s)']
|
|
behavior = data['Behavior']
|
|
|
|
t_ag_on = times[behavior == 0]
|
|
t_ag_off = times[behavior == 1]
|
|
|
|
t_ag_on_off = []
|
|
for t in t_ag_on:
|
|
t1 = np.array(t_ag_off)[t_ag_off > t]
|
|
if len(t1) >= 1:
|
|
t_ag_on_off.append(np.array([t, t1[0]]))
|
|
|
|
t_contact = times[behavior == 2]
|
|
|
|
return times, behavior, np.array(t_ag_on_off), t_contact.to_numpy(), data['FPS'][0]
|
|
|
|
|
|
def gauss(t, shift, sigma, size, norm = False):
|
|
if not hasattr(shift, '__len__'):
|
|
g = np.exp(-((t - shift) / sigma) ** 2 / 2) * size
|
|
if norm:
|
|
g /= np.sum(g)
|
|
return g
|
|
else:
|
|
t = np.array([t, ] * len(shift))
|
|
res = np.exp(-((t.transpose() - shift).transpose() / sigma) ** 2 / 2) * size
|
|
return res
|
|
|
|
|
|
def event_centered_times(centered_event_times, surrounding_event_times, max_dt = np.inf):
|
|
|
|
event_dt = []
|
|
for Cevent_t in centered_event_times:
|
|
Cdt = np.array(surrounding_event_times - Cevent_t)
|
|
event_dt.extend(Cdt[np.abs(Cdt) <= max_dt])
|
|
|
|
return np.array(event_dt)
|
|
|
|
|
|
def kde(event_dt, conv_t, kernal_w = 1, kernal_h = 0.2):
|
|
conv_array = np.zeros(len(conv_t))
|
|
for e in event_dt:
|
|
conv_array += gauss(conv_t, e, kernal_w, kernal_h)
|
|
return conv_array
|
|
|
|
|
|
def permutation_kde(event_dt, conv_t, repetitions = 2000, max_mem_use_GB = 4, kernal_w = 1, kernal_h = 0.2):
|
|
def chunk_permutation(select_event_dt, conv_tt, n_chuck, max_jitter, kernal_w, kernal_h):
|
|
# array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
|
|
# event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
|
|
event_dt_perm = cp.tile(select_event_dt, (len(conv_tt), n_chuck, 1))
|
|
jitter = cp.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
|
|
jitter = cp.expand_dims(jitter, axis=0)
|
|
|
|
event_dt_perm += jitter
|
|
# conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
|
|
|
|
gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
|
|
|
|
kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
|
|
|
|
try:
|
|
kde_3d_numpy = cp.asnumpy(kde_3d)
|
|
del event_dt_perm, gauss_3d, kde_3d
|
|
return kde_3d_numpy
|
|
|
|
except AttributeError:
|
|
del event_dt_perm, gauss_3d
|
|
return kde_3d
|
|
|
|
t0 = time.time()
|
|
|
|
max_jitter = float(2*cp.max(conv_t))
|
|
select_event_dt = event_dt[np.abs(event_dt) <= float(cp.max(conv_t)) + max_jitter*2]
|
|
|
|
# conv_t = cp.arange(-max_dt, max_dt, 1)
|
|
conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
|
|
|
|
chunk_size = int(np.floor(max_mem_use_GB / (select_event_dt.nbytes * conv_t.size / 1e9)))
|
|
chunk_collector =[]
|
|
|
|
for _ in range(repetitions // chunk_size):
|
|
chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, chunk_size, max_jitter, kernal_w, kernal_h)
|
|
chunk_collector.extend(chunk_boot_KDE)
|
|
# # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
|
|
# # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
|
|
# event_dt_perm = cp.tile(event_dt, (len(conv_t), chunk_size, 1))
|
|
# jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
|
|
# jitter = np.expand_dims(jitter, axis=0)
|
|
#
|
|
# event_dt_perm += jitter
|
|
# # conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
|
|
#
|
|
# gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
|
|
# kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
|
|
# try:
|
|
# kde_3d_numpy = cp.asnumpy(kde_3d)
|
|
# chunk_collector.extend(kde_3d_numpy)
|
|
# except AttributeError:
|
|
# chunk_collector.extend(kde_3d)
|
|
# del event_dt_perm, gauss_3d, kde_3d
|
|
chunk_boot_KDE = chunk_permutation(select_event_dt, conv_tt, repetitions % chunk_size, max_jitter, kernal_w, kernal_h)
|
|
chunk_collector.extend(chunk_boot_KDE)
|
|
chunk_collector = np.array(chunk_collector)
|
|
|
|
# ToDo: this works but is incorrect i think
|
|
# chunk_collector /= np.sum(chunk_collector, axis=1).reshape(chunk_collector.shape[0], 1)
|
|
print(f'bootstrap with {repetitions:.0f} repetitions took {time.time() - t0:.2f}s.')
|
|
|
|
# fig, ax = plt.subplots()
|
|
# for i in range(len(chunk_collector)):
|
|
# ax.plot(cp.asnumpy(conv_t), chunk_collector[i])
|
|
|
|
return chunk_collector
|
|
|
|
|
|
def jackknife_kde(event_dt, conv_t, repetitions = 2000, max_mem_use_GB = 2, jack_pct = 0.9, kernal_w = 1, kernal_h = 0.2):
|
|
def chunk_jackknife(select_event_dt, conv_tt, n_chuck, jack_pct, kernal_w, kernal_h):
|
|
event_dt_rep = cp.tile(select_event_dt, (n_chuck, 1))
|
|
idx = cp.random.rand(*event_dt_rep.shape).argsort(1)[:, :int(event_dt_rep.shape[-1]*jack_pct)]
|
|
event_dt_jk = event_dt_rep[cp.arange(event_dt_rep.shape[0])[:, None], idx]
|
|
event_dt_jk_full = cp.tile(event_dt_jk, (len(conv_tt), 1, 1))
|
|
|
|
gauss_3d = cp.exp(-((conv_tt - event_dt_jk_full) / kernal_w) ** 2 / 2) * kernal_h
|
|
|
|
kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
|
|
|
|
try:
|
|
kde_3d_numpy = cp.asnumpy(kde_3d)
|
|
del event_dt_rep, idx, event_dt_jk, event_dt_jk_full, gauss_3d, kde_3d
|
|
return kde_3d_numpy
|
|
|
|
except AttributeError:
|
|
del event_dt_rep, idx, event_dt_jk, event_dt_jk_full, gauss_3d
|
|
return kde_3d
|
|
|
|
t0 = time.time()
|
|
|
|
# max_jitter = 2*max_dt
|
|
select_event_dt = event_dt[np.abs(event_dt) <= float(cp.max(conv_t)) * 2]
|
|
|
|
if len(select_event_dt) == 0:
|
|
return np.zeros((repetitions, len(conv_t)))
|
|
|
|
# conv_t = cp.arange(-max_dt, max_dt, 1)
|
|
conv_tt = cp.reshape(conv_t, (len(conv_t), 1, 1))
|
|
chunk_size = int(np.floor(max_mem_use_GB / (select_event_dt.nbytes * jack_pct * conv_t.size / 1e9)))
|
|
|
|
chunk_collector =[]
|
|
|
|
for _ in range(repetitions // chunk_size):
|
|
chunk_jackknife_KDE = chunk_jackknife(select_event_dt, conv_tt, chunk_size, jack_pct, kernal_w, kernal_h)
|
|
chunk_collector.extend(chunk_jackknife_KDE)
|
|
del chunk_jackknife_KDE
|
|
# # array.shape = (120, 100, 15486) = (len(conv_t), repetitions, len(event_dt))
|
|
# # event_dt_perm = cp.tile(event_dt, (len(conv_t), repetitions, 1))
|
|
# event_dt_perm = cp.tile(event_dt, (len(conv_t), chunk_size, 1))
|
|
# jitter = np.random.uniform(-max_jitter, max_jitter, size=(event_dt_perm.shape[1], event_dt_perm.shape[2]))
|
|
# jitter = np.expand_dims(jitter, axis=0)
|
|
#
|
|
# event_dt_perm += jitter
|
|
# # conv_t_perm = cp.tile(conv_tt, (1, repetitions, len(event_dt)))
|
|
#
|
|
# gauss_3d = cp.exp(-((conv_tt - event_dt_perm) / kernal_w) ** 2 / 2) * kernal_h
|
|
# kde_3d = cp.sum(gauss_3d, axis = 2).transpose()
|
|
# try:
|
|
# kde_3d_numpy = cp.asnumpy(kde_3d)
|
|
# chunk_collector.extend(kde_3d_numpy)
|
|
# except AttributeError:
|
|
# chunk_collector.extend(kde_3d)
|
|
# del event_dt_perm, gauss_3d, kde_3d
|
|
chunk_jackknife_KDE = chunk_jackknife(select_event_dt, conv_tt, repetitions % chunk_size, jack_pct, kernal_w, kernal_h)
|
|
chunk_collector.extend(chunk_jackknife_KDE)
|
|
del chunk_jackknife_KDE
|
|
chunk_collector = np.array(chunk_collector)
|
|
|
|
print(f'jackknife with {repetitions:.0f} repetitions took {time.time() - t0:.2f}s.')
|
|
|
|
return chunk_collector
|
|
|
|
|
|
def single_kde(event_dt, conv_t, kernal_w = 1, kernal_h = 0.2):
|
|
|
|
single_kdes = cp.zeros((len(event_dt), len(conv_t)))
|
|
for enu, e_dt in enumerate(event_dt):
|
|
Ce_dt = e_dt[np.abs(e_dt) <= float(cp.max(conv_t)) * 2]
|
|
conv_tt = cp.reshape(conv_t, (len(conv_t), 1))
|
|
Ce_dt_tile = cp.tile(Ce_dt, (len(conv_tt), 1))
|
|
|
|
gauss_3d = cp.exp(-((conv_tt - Ce_dt_tile) / kernal_w) ** 2 / 2) * kernal_h
|
|
single_kdes[enu] = cp.sum(gauss_3d, axis=1)
|
|
|
|
|
|
return cp.asnumpy(single_kdes)
|
|
|
|
|
|
def main(base_path):
|
|
if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr')):
|
|
os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr'))
|
|
|
|
trial_summary = pd.read_csv(os.path.join(base_path, 'trial_summary.csv'), index_col=0)
|
|
chirp_notes = pd.read_csv(os.path.join(base_path, 'chirp_notes.csv'), index_col=0)
|
|
# trial_summary = trial_summary[chirp_notes['good'] == 1]
|
|
trial_mask = chirp_notes['good'] == 1
|
|
|
|
# ToDo: do chirp on chirp and rise on rise
|
|
lose_chrips_centered_on_ag_off_t = []
|
|
lose_chrips_centered_on_ag_on_t = []
|
|
lose_chrips_centered_on_contact_t = []
|
|
lose_chrips_centered_on_win_rises = []
|
|
lose_chrips_centered_on_win_chirp = []
|
|
lose_chirp_count = []
|
|
|
|
win_chrips_centered_on_ag_off_t = []
|
|
win_chrips_centered_on_ag_on_t = []
|
|
win_chrips_centered_on_contact_t = []
|
|
win_chrips_centered_on_lose_rises = []
|
|
win_chrips_centered_on_lose_chirp = []
|
|
win_chirp_count = []
|
|
|
|
lose_rises_centered_on_ag_off_t = []
|
|
lose_rises_centered_on_ag_on_t = []
|
|
lose_rises_centered_on_contact_t = []
|
|
lose_rises_centered_on_win_chirps = []
|
|
lose_rises_count = []
|
|
|
|
win_rises_centered_on_ag_off_t = []
|
|
win_rises_centered_on_ag_on_t = []
|
|
win_rises_centered_on_contact_t = []
|
|
win_rises_centered_on_lose_chirps = []
|
|
win_rises_count = []
|
|
|
|
sex_win = []
|
|
sex_lose = []
|
|
|
|
for index, trial in tqdm(trial_summary.iterrows()):
|
|
trial_path = os.path.join(base_path, trial['recording'])
|
|
|
|
if trial['group'] < 5:
|
|
continue
|
|
if not os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
|
|
continue
|
|
if not os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
|
|
continue
|
|
if trial['draw'] == 1:
|
|
continue
|
|
|
|
ids = np.load(os.path.join(trial_path, 'analysis', 'ids.npy'))
|
|
times = np.load(os.path.join(trial_path, 'times.npy'))
|
|
sorter = -1 if trial['win_ID'] != ids[0] else 1
|
|
|
|
### event times --> BORIS behavior
|
|
contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
|
|
load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
|
|
|
|
### communication
|
|
if not os.path.exists(os.path.join(trial_path, 'chirp_times_cnn.npy')):
|
|
continue
|
|
|
|
chirp_t = np.load(os.path.join(trial_path, 'chirp_times_cnn.npy'))
|
|
chirp_ids = np.load(os.path.join(trial_path, 'chirp_ids_cnn.npy'))
|
|
chirp_times = [chirp_t[chirp_ids == trial['win_ID']], chirp_t[chirp_ids == trial['lose_ID']]]
|
|
|
|
rise_idx = np.load(os.path.join(trial_path, 'analysis', 'rise_idx.npy'))[::sorter]
|
|
rise_idx_int = [np.array(rise_idx[i][~np.isnan(rise_idx[i])], dtype=int) for i in range(len(rise_idx))]
|
|
rise_times = [times[rise_idx_int[0]], times[rise_idx_int[1]]]
|
|
|
|
### collect for correlations ####
|
|
# chirps
|
|
if trial_mask[index]:
|
|
lose_chrips_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], chirp_times[1]))
|
|
lose_chrips_centered_on_ag_on_t.append(event_centered_times(ag_on_off_t_GRID[:, 0], chirp_times[1]))
|
|
lose_chrips_centered_on_contact_t.append(event_centered_times(contact_t_GRID, chirp_times[1]))
|
|
lose_chrips_centered_on_win_rises.append(event_centered_times(rise_times[0], chirp_times[1]))
|
|
lose_chrips_centered_on_win_chirp.append(event_centered_times(chirp_times[0], chirp_times[1]))
|
|
lose_chirp_count.append(len(chirp_times[1]))
|
|
|
|
win_chrips_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], chirp_times[0]))
|
|
win_chrips_centered_on_ag_on_t.append(event_centered_times(ag_on_off_t_GRID[:, 0], chirp_times[0]))
|
|
win_chrips_centered_on_contact_t.append(event_centered_times(contact_t_GRID, chirp_times[0]))
|
|
win_chrips_centered_on_lose_rises.append(event_centered_times(rise_times[1], chirp_times[0]))
|
|
win_chrips_centered_on_lose_chirp.append(event_centered_times(chirp_times[1], chirp_times[0]))
|
|
|
|
win_chirp_count.append(len(chirp_times[0]))
|
|
else:
|
|
lose_chrips_centered_on_ag_off_t.append(np.array([]))
|
|
lose_chrips_centered_on_ag_on_t.append(np.array([]))
|
|
lose_chrips_centered_on_contact_t.append(np.array([]))
|
|
lose_chrips_centered_on_win_rises.append(np.array([]))
|
|
lose_chrips_centered_on_win_chirp.append(np.array([]))
|
|
lose_chirp_count.append(np.nan)
|
|
|
|
win_chrips_centered_on_ag_off_t.append(np.array([]))
|
|
win_chrips_centered_on_ag_on_t.append(np.array([]))
|
|
win_chrips_centered_on_contact_t.append(np.array([]))
|
|
win_chrips_centered_on_lose_rises.append(np.array([]))
|
|
win_chrips_centered_on_lose_chirp.append(np.array([]))
|
|
win_chirp_count.append(np.nan)
|
|
|
|
# rises
|
|
lose_rises_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], rise_times[1]))
|
|
lose_rises_centered_on_ag_on_t.append(event_centered_times(ag_on_off_t_GRID[:, 0], rise_times[1]))
|
|
lose_rises_centered_on_contact_t.append(event_centered_times(contact_t_GRID, rise_times[1]))
|
|
lose_rises_centered_on_win_chirps.append(event_centered_times(chirp_times[0], rise_times[1]))
|
|
lose_rises_count.append(len(rise_times[1]))
|
|
|
|
win_rises_centered_on_ag_off_t.append(event_centered_times(ag_on_off_t_GRID[:, 1], rise_times[0]))
|
|
win_rises_centered_on_ag_on_t.append(event_centered_times(ag_on_off_t_GRID[:, 0], rise_times[0]))
|
|
win_rises_centered_on_contact_t.append(event_centered_times(contact_t_GRID, rise_times[0]))
|
|
win_rises_centered_on_lose_chirps.append(event_centered_times(chirp_times[1], rise_times[0]))
|
|
win_rises_count.append(len(rise_times[0]))
|
|
|
|
sex_win.append(trial['sex_win'])
|
|
sex_lose.append(trial['sex_lose'])
|
|
|
|
sex_win = np.array(sex_win)
|
|
sex_lose = np.array(sex_lose)
|
|
# embed()
|
|
# quit()
|
|
max_dt = 30
|
|
conv_t_dt = 0.5
|
|
jack_pct = 0.9
|
|
|
|
conv_t = cp.arange(-max_dt, max_dt+conv_t_dt, conv_t_dt)
|
|
conv_t_numpy = cp.asnumpy(conv_t)
|
|
# kde_array = kde(np.hstack(lose_chrips_centered_on_ag_off_t), conv_t, kernal_w = 1, kernal_h = 1)
|
|
for centered_times, event_counts, title in \
|
|
[[lose_chrips_centered_on_ag_off_t, lose_chirp_count, r'chirp$_{lose}$ on chase$_{off}$'],
|
|
[lose_chrips_centered_on_ag_on_t, lose_chirp_count, r'chirp$_{lose}$ on chase$_{on}$'],
|
|
[lose_chrips_centered_on_contact_t, lose_chirp_count, r'chirp$_{lose}$ on contact'],
|
|
[lose_chrips_centered_on_win_rises, lose_chirp_count, r'chirp$_{lose}$ on rise$_{win}$'],
|
|
[lose_chrips_centered_on_win_chirp, lose_chirp_count, r'chirp$_{lose}$ on chirp$_{win}$'],
|
|
|
|
[win_chrips_centered_on_ag_off_t, win_chirp_count, r'chirp$_{win}$ on chase$_{off}$'],
|
|
[win_chrips_centered_on_ag_on_t, win_chirp_count, r'chirp$_{win}$ on chase$_{on}$'],
|
|
[win_chrips_centered_on_contact_t, win_chirp_count, r'chirp$_{win}$ on contact'],
|
|
[win_chrips_centered_on_lose_rises, win_chirp_count, r'chirp$_{win}$ on rise$_{lose}$'],
|
|
[win_chrips_centered_on_lose_chirp, win_chirp_count, r'chirp$_{win}$ on chirp$_{lose}$'],
|
|
|
|
[lose_rises_centered_on_ag_off_t, lose_rises_count, r'rise$_{lose}$ on chase$_{off}$'],
|
|
[lose_rises_centered_on_ag_on_t, lose_rises_count, r'rise$_{lose}$ on chase$_{on}$'],
|
|
[lose_rises_centered_on_contact_t, lose_rises_count, r'rise$_{lose}$ on contact'],
|
|
[lose_rises_centered_on_win_chirps, lose_rises_count, r'rise$_{lose}$ on chirp$_{win}$'],
|
|
|
|
[win_rises_centered_on_ag_off_t, win_rises_count, r'rise$_{win}$ on chase$_{off}$'],
|
|
[win_rises_centered_on_ag_on_t, win_rises_count, r'rise$_{win}$ on chase$_{on}$'],
|
|
[win_rises_centered_on_contact_t, win_rises_count, r'rise$_{win}$ on contact'],
|
|
[win_rises_centered_on_lose_chirps, win_rises_count, r'rise$_{win}$ on chirp$_{lose}$']]:
|
|
|
|
save_str = title.replace('$', '').replace('{', '').replace('}', '').replace(' ', '_')
|
|
|
|
###########################################################################################################
|
|
### by pairing ###
|
|
centered_times_pairing = []
|
|
for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2):
|
|
centered_times_pairing.append([])
|
|
for i in range(len(centered_times)):
|
|
if sex_w == sex_win[i] and sex_l == sex_lose[i]:
|
|
centered_times_pairing[-1].append(centered_times[i])
|
|
|
|
event_counts_pairings = [np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'm')]),
|
|
np.nansum(np.array(event_counts)[(sex_win == 'm') & (sex_lose == 'f')]),
|
|
np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'm')]),
|
|
np.nansum(np.array(event_counts)[(sex_win == 'f') & (sex_lose == 'f')])]
|
|
color = [male_color, female_color, male_color, female_color]
|
|
linestyle = ['-', '--', '--', '-']
|
|
|
|
perm_p_pairings = []
|
|
jk_p_pairings = []
|
|
fig = plt.figure(figsize=(20/2.54, 12/2.54))
|
|
gs = gridspec.GridSpec(2, 2, left=0.1, bottom=0.1, right=0.95, top=0.9)
|
|
ax = []
|
|
ax.append(fig.add_subplot(gs[0, 0]))
|
|
ax.append(fig.add_subplot(gs[0, 1], sharey=ax[0]))
|
|
ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
|
|
ax.append(fig.add_subplot(gs[1, 1], sharey=ax[2], sharex=ax[1]))
|
|
|
|
for enu, (centered_times_p, event_count_p) in enumerate(zip(centered_times_pairing, event_counts_pairings)):
|
|
boot_kde = permutation_kde(np.hstack(centered_times_p), conv_t, kernal_w=1, kernal_h=1)
|
|
jk_kde = jackknife_kde(np.hstack(centered_times_p), conv_t, jack_pct=jack_pct, kernal_w=1, kernal_h=1)
|
|
|
|
perm_p1, perm_p50, perm_p99 = np.percentile(boot_kde, (1, 50, 99), axis=0)
|
|
perm_p_pairings.append([perm_p1, perm_p50, perm_p99])
|
|
|
|
jk_p1, jk_p50, jk_p99 = np.percentile(jk_kde, (1, 50, 99), axis=0)
|
|
jk_p_pairings.append([jk_p1, jk_p50, jk_p99])
|
|
|
|
ax[enu].fill_between(conv_t_numpy, perm_p1 / event_count_p, perm_p99 / event_count_p, color='cornflowerblue', alpha=.8)
|
|
ax[enu].plot(conv_t_numpy, perm_p50 / event_count_p, color='dodgerblue', alpha=1, lw=3)
|
|
|
|
ax[enu].fill_between(conv_t_numpy, jk_p1 / event_count_p / jack_pct, jk_p99 / event_count_p / jack_pct, color=color[enu], alpha=.8)
|
|
ax[enu].plot(conv_t_numpy, jk_p50 / event_count_p / jack_pct, color=color[enu], alpha=1, lw=3, linestyle=linestyle[enu])
|
|
|
|
ax_m = ax[enu].twinx()
|
|
counter = 0
|
|
for enu2, centered_events in enumerate(centered_times_p):
|
|
Cevents = centered_events[np.abs(centered_events) <= max_dt]
|
|
if len(Cevents) != 0:
|
|
ax_m.plot(Cevents, np.ones(len(Cevents)) * counter, '|', markersize=8, color='k', alpha=.1)
|
|
counter += 1
|
|
|
|
ax_m.set_yticks([])
|
|
ax[enu].set_xlim(-max_dt, max_dt)
|
|
ax[enu].tick_params(labelsize=10)
|
|
|
|
plt.setp(ax[1].get_yticklabels(), visible=False)
|
|
plt.setp(ax[3].get_yticklabels(), visible=False)
|
|
|
|
plt.setp(ax[0].get_xticklabels(), visible=False)
|
|
plt.setp(ax[1].get_xticklabels(), visible=False)
|
|
|
|
ax[2].set_xlabel('time [s]', fontsize=12)
|
|
ax[3].set_xlabel('time [s]', fontsize=12)
|
|
ax[0].set_ylabel('event rate [Hz]', fontsize=12)
|
|
ax[2].set_ylabel('event rate [Hz]', fontsize=12)
|
|
fig.suptitle(title)
|
|
|
|
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}_by_sexes.png'), dpi=300)
|
|
plt.close()
|
|
|
|
###########################################################################################################
|
|
### all pairings ###
|
|
boot_kde = permutation_kde(np.hstack(centered_times), conv_t, kernal_w=1, kernal_h=1)
|
|
jk_kde = jackknife_kde(np.hstack(centered_times), conv_t, jack_pct=jack_pct, kernal_w=1, kernal_h=1)
|
|
|
|
perm_p1, perm_p50, perm_p99 = np.percentile(boot_kde, (1, 50, 99), axis=0)
|
|
jk_p1, jk_p50, jk_p99 = np.percentile(jk_kde, (1, 50, 99), axis=0)
|
|
|
|
fig = plt.figure(figsize=(20/2.54, 12/2.54))
|
|
gs = gridspec.GridSpec(1, 1, left=0.1, bottom=0.1, right=0.95, top=0.95)
|
|
ax = fig.add_subplot(gs[0, 0])
|
|
# ax.fill_between(conv_t_numpy, perm_p1/np.sum(event_counts), perm_p99/np.sum(event_counts), color='cornflowerblue', alpha=.8)
|
|
# ax.plot(conv_t_numpy, perm_p50/np.sum(event_counts), color='dodgerblue', alpha=1, lw=3)
|
|
ax.fill_between(conv_t_numpy, perm_p1/len(np.hstack(centered_times)), perm_p99/len(np.hstack(centered_times)), color='cornflowerblue', alpha=.8)
|
|
ax.plot(conv_t_numpy, perm_p50/len(np.hstack(centered_times)), color='dodgerblue', alpha=1, lw=3)
|
|
|
|
# ax.fill_between(conv_t_numpy, jk_p1/np.sum(event_counts)/jack_pct, jk_p99/np.sum(event_counts)/jack_pct, color='tab:red', alpha=.8)
|
|
# ax.plot(conv_t_numpy, jk_p50/np.sum(event_counts)/jack_pct, color='firebrick', alpha=1, lw=3)
|
|
ax.fill_between(conv_t_numpy, jk_p1/len(np.hstack(centered_times))/jack_pct, jk_p99/len(np.hstack(centered_times))/jack_pct, color='tab:red', alpha=.8)
|
|
ax.plot(conv_t_numpy, jk_p50/len(np.hstack(centered_times))/jack_pct, color='firebrick', alpha=1, lw=3)
|
|
|
|
ax_m = ax.twinx()
|
|
for enu, centered_events in enumerate(centered_times):
|
|
Cevents = centered_events[np.abs(centered_events) <= max_dt]
|
|
if len(Cevents) != 0:
|
|
ax_m.plot(Cevents, np.ones(len(Cevents)) * counter, '|', markersize=8, color='k', alpha=.1)
|
|
counter += 1
|
|
# ax_m.plot(Cevents, np.ones(len(Cevents)) * enu, '|', markersize=8, color='k', alpha=.1)
|
|
|
|
ax_m.set_yticks([])
|
|
ax.set_xlabel('time [s]', fontsize=12)
|
|
ax.set_ylabel('event rate [Hz]', fontsize=12)
|
|
ax.set_title(title)
|
|
ax.set_xlim(-max_dt, max_dt)
|
|
ax.tick_params(labelsize=10)
|
|
|
|
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{save_str}.png'), dpi=300)
|
|
plt.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main(sys.argv[1])
|