import os
import sys
import itertools
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from matplotlib.patches import Patch
import pandas as pd
import scipy.stats as scp
from IPython import embed
from event_time_correlations import load_and_converete_boris_events, kde, gauss
female_color, male_color = '#e74c3c', '#3498db'
def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
iei = []
weighted_mean_iei = []
median_iei = []
for i in range(len(event_times)):
night_iei = np.diff(event_times[i][event_times[i] <= 3600*3])
iei.append(np.diff(event_times[i]))
if len(night_iei) == 0:
weighted_mean_iei.append(np.nan)
median_iei.append(np.nan)
else:
weighted_mean_iei.append(np.sum((night_iei) * night_iei) / np.sum(night_iei))
median_iei.append(np.median(night_iei))
weighted_mean_iei = np.array(weighted_mean_iei)
median_iei = np.array(median_iei)
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.2, right=0.95, top=0.9, width_ratios=[5, 1], wspace=.3)
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[0, 1]))
for i in range(len(iei)):
if win_sex[i] == 'm':
if lose_sex[i] == 'm':
color, linestyle = male_color, '-'
sp = 0
else:
color, linestyle = male_color, '--'
sp = 1
else:
if lose_sex[i] == 'm':
color, linestyle = female_color, '--'
sp = 2
else:
color, linestyle = female_color, '-'
sp = 3
conv_y = np.arange(0, np.percentile(np.hstack(iei), 80), .5)
kde_array = kde(iei[i], conv_y, kernal_w=kernal_w, kernal_h=1)
# kde_array /= np.sum(kde_array)
ax[0].plot(conv_y, kde_array, zorder=2, color=color, linestyle=linestyle, lw=2)
ax[1].boxplot([weighted_mean_iei[~np.isnan(weighted_mean_iei)],
median_iei[~np.isnan(median_iei)]], positions=[0, 1], sym='', widths=0.5)
ax[0].set_xlim(conv_y[0], conv_y[-1])
ax[0].set_ylabel('KDE', fontsize=12)
ax[0].set_xlabel('inter event interval [s]', fontsize=12)
fig.suptitle(title, fontsize=12)
for a in ax:
a.tick_params(labelsize=10)
ax[1].set_xticks(np.arange(2))
ax[1].set_xticklabels([r'weighted$_{time}$', 'median'], rotation=45)
ax[1].set_ylabel('inter event interval [s]', fontsize=12)
# ax[0]
# 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)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_iei.png'), dpi=300)
plt.close()
# all_r = []
# all_p = []
# for lag in np.arange(1, 6):
# fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
# gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95)
# ax = fig.add_subplot(gs[0, 0])
# plot_x = []
# plot_y = []
# for trial_iei in iei:
# plot_x.extend(trial_iei[:-lag])
# plot_y.extend(trial_iei[lag:])
# ax.plot(plot_x, plot_y, '.', color='k')
# r, p = scp.pearsonr(plot_x, plot_y)
# all_r.append(r)
# all_p.append(p)
#
# ax.set_xlim(-1, 120)
# ax.set_ylim(-1, 120)
# plt.show()
# plt.show()
return iei
def relative_rate_progression(all_event_t, title=''):
stop_t = 3*60*60
snippet_len = 15*60
snippet_starts = np.arange(0, stop_t, snippet_len)
all_snippet_ratio = []
for event_t in all_event_t:
if len(event_t) == 0:
continue
expected_snippet_count = len(event_t[event_t <= stop_t]) / (stop_t / snippet_len)
snippet_ratio = []
for s0 in snippet_starts:
snippet_count = len(event_t[(event_t >= s0) & (event_t < s0 + snippet_len)])
snippet_ratio.append(snippet_count/expected_snippet_count)
all_snippet_ratio.append(snippet_ratio)
all_snippet_ratio = np.array(all_snippet_ratio)
fig = plt.figure(figsize=(20/2.54, 12/2.54))
gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95)
ax = fig.add_subplot(gs[0, 0])
plot_t = np.repeat(snippet_starts, 2)
plot_t[1::2] += snippet_len
for event_ratios in all_snippet_ratio:
plot_ratios = np.repeat(event_ratios, 2)
ax.plot(plot_t / 3600, plot_ratios, color='grey', lw=1, alpha=0.5)
# ax.plot(snippet_starts + snippet_len/2, event_ratios)
mean_ratio = np.median(all_snippet_ratio, axis=0)
plot_mean_ratio = np.repeat(mean_ratio, 2)
ax.plot(plot_t / 3600, plot_mean_ratio, color='k', lw=3)
ax.plot(plot_t / 3600, np.ones_like(plot_t), linestyle='dotted', lw=2, color='k')
ax.set_xlabel('time [h]', fontsize=12)
ax.set_ylabel('norm. event rate', fontsize=12)
ax.set_title(title)
ax.tick_params(labelsize=10)
ax.set_xlim(0, 3)
ax.set_ylim(0, 5)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_progression.png'), dpi=300)
plt.close()
# plt.show()
x = np.hstack(all_snippet_ratio)
y = np.hstack(np.tile(snippet_starts, (all_snippet_ratio.shape[0], 1)))
r, p = scp.pearsonr(x, y)
print(f'Progression {title}: pearson-r={r:.2f} p={p:.3f}')
def chase_time_progression(all_ag_on_t, all_ag_off_t):
stop_t = 3*60*60
snippet_len = 15*60
snippet_starts = np.arange(0, stop_t, snippet_len)
all_snippet_chase_dur = []
for a_on, a_off in zip(all_ag_on_t, all_ag_off_t):
if len(a_on) == 0:
continue
mean_chase_dur = np.mean(a_off - a_on)
snippet_chase_dur = []
for s0 in snippet_starts:
snippet_mask = (a_on > s0) & (a_on <= s0+snippet_len)
if np.any(snippet_mask):
snippet_chase_dur.append(np.mean(a_off[snippet_mask] - a_on[snippet_mask]))
else:
snippet_chase_dur.append(np.nan)
all_snippet_chase_dur.append(np.array(snippet_chase_dur) / mean_chase_dur)
all_snippet_chase_dur = np.array(all_snippet_chase_dur)
fig = plt.figure(figsize=(20/2.54, 12/2.54))
gs = gridspec.GridSpec(1, 1, left=.1, bottom=.1, right=0.95, top=0.95)
ax = fig.add_subplot(gs[0, 0])
plot_t = np.repeat(snippet_starts, 2)
plot_t[1::2] += snippet_len
for trial_snippet_chase_dur in all_snippet_chase_dur:
plot_ratios = np.repeat(trial_snippet_chase_dur, 2)
ax.plot(plot_t / 3600, plot_ratios, color='grey', lw=1, alpha=0.5)
# ax.plot(snippet_starts + snippet_len/2, event_ratios)
mean_ratio = np.nanmean(all_snippet_chase_dur, axis=0)
plot_mean_ratio = np.repeat(mean_ratio, 2)
ax.plot(plot_t / 3600, plot_mean_ratio, color='k', lw=3)
ax.plot(plot_t / 3600, np.ones_like(plot_t), linestyle='dotted', lw=2, color='k')
ax.set_xlabel('time [h]', fontsize=12)
ax.set_ylabel('chase duration / mean(chase duration)', fontsize=12)
ax.set_title('progression chase duration ')
ax.tick_params(labelsize=10)
ax.set_xlim(0, 3)
ax.set_ylim(0, 5)
x = np.hstack(all_snippet_chase_dur)
y = np.hstack(np.tile(snippet_starts, (all_snippet_chase_dur.shape[0], 1)))
r, p = scp.pearsonr(x[~np.isnan(x)], y[~np.isnan(x)])
print(f'Progression chase duration: pearson-r={r:.2f} p={p:.3f}')
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', 'chase_duration_progression.png'), dpi=300)
plt.close()
def event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name):
print('')
all_pre_chase_event_mask = []
all_chase_event_mask = []
all_end_chase_event_mask = []
all_after_chase_event_mask = []
all_before_contact_event_mask = []
all_after_contact_event_mask = []
all_pre_chase_time = []
all_chase_time = []
all_end_chase_time = []
all_after_chase_time = []
all_before_contact_time = []
all_after_contact_time = []
video_trial_win_sex = []
video_trial_lose_sex = []
time_tol = 5
for enu, contact_t, ag_on_t, ag_off_t, event_times in zip(
np.arange(len(all_contact_t)), all_contact_t, all_ag_on_t, all_ag_off_t, all_event_t):
if len(ag_on_t) == 0:
continue
if len(event_times) == 0:
continue
pre_chase_event_mask = np.zeros_like(event_times)
chase_event_mask = np.zeros_like(event_times)
end_chase_event_mask = np.zeros_like(event_times)
after_chase_event_mask = np.zeros_like(event_times)
video_trial_win_sex.append(win_sex[enu])
video_trial_lose_sex.append(lose_sex[enu])
for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
pre_chase_event_mask[(event_times >= chase_on_t - time_tol) & (event_times < chase_on_t)] = 1
chase_event_mask[(event_times >= chase_on_t) & (event_times < chase_off_t - time_tol)] = 1
end_chase_event_mask[(event_times >= chase_off_t - time_tol) & (event_times < chase_off_t)] = 1
after_chase_event_mask[(event_times >= chase_off_t) & (event_times < chase_off_t + time_tol)] = 1
all_pre_chase_event_mask.append(pre_chase_event_mask)
all_chase_event_mask.append(chase_event_mask)
all_end_chase_event_mask.append(end_chase_event_mask)
all_after_chase_event_mask.append(after_chase_event_mask)
all_pre_chase_time.append(len(ag_on_t) * time_tol)
chasing_dur = (ag_off_t - ag_on_t) - time_tol
chasing_dur[chasing_dur < 0] = 0
all_chase_time.append(np.sum(chasing_dur))
all_end_chase_time.append(len(ag_on_t) * time_tol)
all_after_chase_time.append(len(ag_on_t) * time_tol)
before_countact_event_mask = np.zeros_like(event_times)
after_countact_event_mask = np.zeros_like(event_times)
for ct in contact_t:
before_countact_event_mask[(event_times >= ct - time_tol) & (event_times < ct)] = 1
after_countact_event_mask[(event_times >= ct) & (event_times < ct + time_tol)] = 1
all_before_contact_event_mask.append(before_countact_event_mask)
all_after_contact_event_mask.append(after_countact_event_mask)
all_before_contact_time.append(len(contact_t) * time_tol)
all_after_contact_time.append(len(contact_t) * time_tol)
all_pre_chase_time = np.array(all_pre_chase_time)
all_chase_time = np.array(all_chase_time)
all_end_chase_time = np.array(all_end_chase_time)
all_after_chase_time = np.array(all_after_chase_time)
all_before_contact_time = np.array(all_before_contact_time)
all_after_contact_time = np.array(all_after_contact_time)
video_trial_win_sex = np.array(video_trial_win_sex)
video_trial_lose_sex = np.array(video_trial_lose_sex)
all_pre_chase_time_ratio = all_pre_chase_time / (3 * 60 * 60)
all_chase_time_ratio = all_chase_time / (3 * 60 * 60)
all_end_chase_time_ratio = all_end_chase_time / (3 * 60 * 60)
all_after_chase_time_ratio = all_after_chase_time / (3 * 60 * 60)
all_before_countact_time_ratio = all_before_contact_time / (3 * 60 * 60)
all_after_countact_time_ratio = all_after_contact_time / (3 * 60 * 60)
all_pre_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_pre_chase_event_mask)))
all_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_chase_event_mask)))
all_end_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_end_chase_event_mask)))
all_after_chase_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_after_chase_event_mask)))
all_before_countact_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_before_contact_event_mask)))
all_after_countact_event_ratio = np.array(list(map(lambda x: np.sum(x) / len(x), all_after_contact_event_mask)))
for x, y, name in [[all_pre_chase_event_ratio, all_pre_chase_time_ratio, 'pre chase'],
[all_chase_event_ratio, all_chase_time_ratio, 'while chase'],
[all_end_chase_event_ratio, all_end_chase_time_ratio, 'end chase'],
[all_after_chase_event_ratio, all_after_chase_time_ratio, 'after chase'],
[all_before_countact_event_ratio, all_before_countact_time_ratio, 'pre contact'],
[all_after_countact_event_ratio, all_after_countact_time_ratio, 'post contact']]:
t, p = scp.ttest_rel(x, y)
print(f'{event_name} {name}: t={t:.2f} p={p:.3f}')
fig = plt.figure(figsize=(20 / 2.54, 12 / 2.54))
gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.15, right=0.95, top=0.9)
ax = fig.add_subplot(gs[0, 0])
ax_pie = fig.add_subplot(gs[0, 1])
ax.boxplot([all_pre_chase_event_ratio / all_pre_chase_time_ratio,
all_chase_event_ratio / all_chase_time_ratio,
all_end_chase_event_ratio / all_end_chase_time_ratio,
all_after_chase_event_ratio / all_after_chase_time_ratio,
all_before_countact_event_ratio / all_before_countact_time_ratio,
all_after_countact_event_ratio / all_after_countact_time_ratio], positions=np.arange(6), sym='',
zorder=2)
ylim = list(ax.get_ylim())
ylim[0] = -.1 if ylim[0] < -.1 else ylim[0]
ylim[1] = 1.1 if ylim[1] < 1.1 else ylim[1]
##############################################################################
for sex_w, sex_l in itertools.product(['m', 'f'], repeat=2):
mec = 'k' if sex_w == sex_l else 'None'
if 'lose' in event_name:
marker = 'o'
c = male_color if sex_l == 'm' else female_color
elif "win" in event_name:
marker = 'p'
c = male_color if sex_w == 'm' else female_color
else:
print('error')
embed()
quit()
values = np.array(all_pre_chase_event_ratio / all_pre_chase_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 0, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
values = np.array(all_chase_event_ratio / all_chase_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 1, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
values = np.array(all_end_chase_event_ratio / all_end_chase_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 2, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
values = np.array(all_after_chase_event_ratio / all_after_chase_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 3, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
values = np.array(all_before_countact_event_ratio / all_before_countact_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 4, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
values = np.array(all_after_countact_event_ratio / all_after_countact_time_ratio)[
(video_trial_win_sex == sex_w) & (video_trial_lose_sex == sex_l)]
ax.plot(np.ones_like(values) * 5, values, marker=marker, linestyle='None', color=c, mec=mec, markersize=8,
zorder=1)
##############################################################################
ax.plot(np.arange(7) - 1, np.ones(7), linestyle='dotted', lw=2, color='k')
ax.set_xlim(-0.5, 5.5)
ax.set_ylim(ylim[0], ylim[1])
ax.set_ylabel(r'rel. count$_{event}$ / rel. time$_{event}$', fontsize=12)
ax.set_xticks(np.arange(6))
ax.set_xticklabels([r'chase$_{before}$', r'chasing', r'chase$_{end}$', r'chase$_{after}$', 'contact$_{before}$',
'contact$_{after}$'], rotation=45)
ax.tick_params(labelsize=10)
fig.suptitle(f'{event_name}: n={len(np.hstack(all_event_t))}')
###############################################
flat_pre_chase_event_mask = np.hstack(all_pre_chase_event_mask)
flat_chase_event_mask = np.hstack(all_chase_event_mask)
flat_end_chase_event_mask = np.hstack(all_end_chase_event_mask)
flat_after_chase_event_mask = np.hstack(all_after_chase_event_mask)
flat_before_countact_event_mask = np.hstack(all_before_contact_event_mask)
flat_after_countact_event_mask = np.hstack(all_after_contact_event_mask)
flat_pre_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0
flat_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0
flat_end_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0
flat_after_chase_event_mask[(flat_before_countact_event_mask == 1) | (flat_after_countact_event_mask == 1)] = 0
event_context_values = [np.sum(flat_pre_chase_event_mask) / len(flat_pre_chase_event_mask),
np.sum(flat_chase_event_mask) / len(flat_chase_event_mask),
np.sum(flat_end_chase_event_mask) / len(flat_end_chase_event_mask),
np.sum(flat_after_chase_event_mask) / len(flat_after_chase_event_mask),
np.sum(flat_before_countact_event_mask) / len(flat_before_countact_event_mask),
np.sum(flat_after_countact_event_mask) / len(flat_after_countact_event_mask)]
event_context_values.append(1 - np.sum(event_context_values))
time_context_values = [np.sum(all_pre_chase_time), np.sum(all_chase_time), np.sum(all_end_chase_time),
np.sum(all_after_chase_time), np.sum(all_before_contact_time),
np.sum(all_after_contact_time)]
time_context_values.append(len(all_pre_chase_time) * 3 * 60 * 60 - np.sum(time_context_values))
time_context_values /= np.sum(time_context_values)
event_context_values2 = []
time_context_values2 = []
for event_value, time_value in zip(event_context_values[:-1], time_context_values[:-1]):
event_context_values2.append(event_value)
time_context_values2.append(time_value)
if event_value >= time_value:
time_context_values2.append(event_value - time_value)
event_context_values2.append(0)
else:
event_context_values2.append(time_value - event_value)
time_context_values2.append(0)
event_context_values2.append(1-np.sum(event_context_values2))
time_context_values2.append(1-np.sum(time_context_values2))
# fig, ax_pie = plt.subplots(figsize=(12/2.54,12/2.54))
size = 0.3
# embed()
# quit()
outer_colors = ['tab:red', 'tab:grey',
'tab:orange', 'tab:grey',
'yellow', 'tab:grey',
'tab:green', 'tab:grey',
'k', 'tab:grey', 'tab:brown',
'tab:grey', 'tab:grey']
ax_pie.pie(event_context_values2, radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, 1))
ax_pie.pie(time_context_values2, radius=1 - size, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, 1))
ax_pie.set_title(r'event context')
legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (event_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', edgecolor='w',
label='%.1f' % (event_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', edgecolor='w', label='%.1f' % (event_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', edgecolor='w',
label='%.1f' % (event_context_values[3] * 100) + '%'),
Patch(facecolor='k', edgecolor='w', label='%.1f' % (event_context_values[4] * 100) + '%'),
Patch(facecolor='tab:brown', edgecolor='w',
label='%.1f' % (event_context_values[5] * 100) + '%'),
Patch(facecolor='tab:red', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[1] * 100) + '%'),
Patch(facecolor='yellow', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[2] * 100) + '%'),
Patch(facecolor='tab:green', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[3] * 100) + '%'),
Patch(facecolor='k', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[4] * 100) + '%'),
Patch(facecolor='tab:brown', alpha=0.6, edgecolor='w',
label='%.1f' % (time_context_values[5] * 100) + '%')]
ax_pie.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.15, -0.3), frameon=False,
fontsize=9)
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_time_corr', f'{event_name}_categories.png'),
dpi=300)
plt.close()
# plt.show()
def main(base_path):
if not os.path.exists(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta')):
os.makedirs(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta'))
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_mask = chirp_notes['good'] == 1
### data processing #######################
all_rise_times_lose = []
all_rise_times_win = []
all_chirp_times_lose = []
all_chirp_times_win = []
win_sex = []
lose_sex = []
all_contact_t = []
all_ag_on_t = []
all_ag_off_t = []
for index, trial in trial_summary.iterrows():
print(index, len(trial_summary))
got_boris = False
trial_path = os.path.join(base_path, trial['recording'])
if trial['group'] < 3:
continue
if trial['draw'] == 1:
continue
if os.path.exists(os.path.join(trial_path, 'led_idxs.csv')):
got_boris = True
if os.path.exists(os.path.join(trial_path, 'LED_frames.npy')):
got_boris = True
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
if got_boris:
contact_t_GRID, ag_on_off_t_GRID, led_idx, led_frames = \
load_and_converete_boris_events(trial_path, trial['recording'], sr=20_000)
all_contact_t.append(contact_t_GRID)
all_ag_on_t.append(ag_on_off_t_GRID[:, 0])
all_ag_off_t.append(ag_on_off_t_GRID[:, 1])
else:
all_contact_t.append(np.array([]))
all_ag_on_t.append(np.array([]))
all_ag_off_t.append(np.array([]))
### 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]]]
all_rise_times_lose.append(rise_times[1])
all_rise_times_win.append(rise_times[0])
if trial_mask[index]:
all_chirp_times_lose.append(chirp_times[1])
all_chirp_times_win.append(chirp_times[0])
else:
all_chirp_times_lose.append(np.array([]))
all_chirp_times_win.append(np.array([]))
win_sex.append(trial['sex_win'])
lose_sex.append(trial['sex_lose'])
win_sex = np.array(win_sex)
lose_sex = np.array(lose_sex)
### inter event intervalls ###
inter_chirp_interval_lose = iei_analysis(all_chirp_times_lose, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{lose}$')
_ = iei_analysis(all_chirp_times_win, win_sex, lose_sex, kernal_w=1, title=r'chirps$_{win}$')
_ = iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$')
_ = iei_analysis(all_rise_times_win, win_sex, lose_sex, kernal_w=50, title=r'rises$_{win}$')
fig, ax = plt.subplots()
n, bin_edges = np.histogram(np.hstack(inter_chirp_interval_lose), bins = np.arange(0, 20, 0.05))
ax.bar(bin_edges[:-1] + (bin_edges[1] - bin_edges[0])/2, n/np.sum(n)/(bin_edges[1] - bin_edges[0]), width=(bin_edges[1] - bin_edges[0]))
ylim = ax.get_ylim()
med_ici = np.nanmedian(np.hstack(inter_chirp_interval_lose))
ax.plot([med_ici, med_ici], [ylim[0], ylim[1]], '-k', lw=2)
plt.show()
chirp_dt_burst_th = med_ici
print(np.nanpercentile(np.hstack(inter_chirp_interval_lose), (50, 75, 95)))
# chirp_dt_burst_th = bin_edges[np.argmax(n)] - (bin_edges[1] - bin_edges[0]) / 2
burst_chirp_mask = []
for enu, ici in enumerate(inter_chirp_interval_lose):
if len(ici) >= 1:
trial_burst_chirp_mask = np.zeros_like(ici)
trial_burst_chirp_mask[ici < chirp_dt_burst_th] = 1
trial_burst_chirp_mask[1:][(ici[:-1] < chirp_dt_burst_th) & (ici[1:] >= chirp_dt_burst_th)] = 2
last = 2 if trial_burst_chirp_mask[-1] == 1 else 0
trial_burst_chirp_mask = np.append(trial_burst_chirp_mask, np.array(last))
burst_chirp_mask.append(trial_burst_chirp_mask)
else:
burst_chirp_mask.append(np.array([]))
fig = plt.figure(figsize=(21/2.54, 19/2.54))
gs = gridspec.GridSpec(1, 2, left=0.1, bottom=0.1, right=0.95, top=0.95)
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[0, 1]))
all_chirps_in_burst_distro = []
for i in range(len(burst_chirp_mask)):
ax[0].plot(all_chirp_times_lose[i], np.ones_like(all_chirp_times_lose[i]) * i, '|', markersize=12, color='grey')
if len(burst_chirp_mask[i]) == 0:
continue
chirp_idx_burst_start = np.arange(len(all_chirp_times_lose[i])-1)[(burst_chirp_mask[i][:-1] != 1) & (burst_chirp_mask[i][1:] == 1)] + 1
if burst_chirp_mask[i][0] == 1:
chirp_idx_burst_start = np.append(0, chirp_idx_burst_start)
chirp_idx_burst_end = np.arange(len(all_chirp_times_lose[i]))[(burst_chirp_mask[i] == 2)]
chirp_idx_burst_start = np.array(chirp_idx_burst_start, dtype=int)
chirp_idx_burst_end = np.array(chirp_idx_burst_end, dtype=int)
chirps_in_burst = chirp_idx_burst_end - chirp_idx_burst_start + 1
if len(chirps_in_burst) == 0:
continue
chirps_in_burst_distro = np.zeros(np.max(chirps_in_burst))
for j in range(np.max(chirps_in_burst)):
if j == 0:
chirps_in_burst_distro[j] = len(burst_chirp_mask[i][burst_chirp_mask[i] == 0])
else:
chirps_in_burst_distro[j] = len(chirps_in_burst[chirps_in_burst == j + 1])
for cbs, cbe in zip(all_chirp_times_lose[i][chirp_idx_burst_start], all_chirp_times_lose[i][chirp_idx_burst_end]):
ax[0].plot([cbs, cbe], [i, i], '-k', lw=2)
all_chirps_in_burst_distro.append(chirps_in_burst_distro)
max_chirps_in_burst = np.max(list(map(lambda x: len(x), all_chirps_in_burst_distro)))
collective_chirps_in_burst = np.zeros((len(all_chirps_in_burst_distro), max_chirps_in_burst))
for trial in range(len(all_chirps_in_burst_distro)):
collective_chirps_in_burst[trial, :len(all_chirps_in_burst_distro[trial])] = all_chirps_in_burst_distro[trial]
ax[1].bar(np.arange(collective_chirps_in_burst.shape[1])+1, collective_chirps_in_burst.sum(0))
ax[1].plot(np.arange(collective_chirps_in_burst.shape[1])+1,
collective_chirps_in_burst.sum(0) * (np.arange(collective_chirps_in_burst.shape[1])+1), color='firebrick', lw=2)
plt.show()
### event progressions ###
print('')
relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
relative_rate_progression(all_chirp_times_win, title=r'chirp$_{win}$')
relative_rate_progression(all_rise_times_lose, title=r'rises$_{lose}$')
relative_rate_progression(all_rise_times_win, title=r'rises$_{win}$')
relative_rate_progression(all_contact_t, title=r'contact')
relative_rate_progression(all_ag_on_t, title=r'chasing')
chase_time_progression(all_ag_on_t, all_ag_off_t)
### event category signals ###
for all_event_t, event_name in zip([all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win],
[r'chirps$_{lose}$', r'chirps$_{win}$', r'rises$_{lose}$', r'rises$_{win}$']):
event_category_signal(all_event_t, all_contact_t, all_ag_on_t, all_ag_off_t, win_sex, lose_sex, event_name)
#################################
chase_dur = []
chase_chirp_count = []
dt_start_first_chirp = []
dt_end_first_chirp = []
dt_start_all_chirp = []
dt_end_all_chirp = []
all_chirp_mask = []
chase_dur_all_chirp = []
for ag_on_t, ag_off_t, chirp_times_lose, trial_chirp_burst_mask in \
zip(all_ag_on_t, all_ag_off_t, all_chirp_times_lose, burst_chirp_mask):
if len(chirp_times_lose) == 0:
continue
for a_on, a_off in zip(ag_on_t, ag_off_t):
chase_dur.append(a_off - a_on)
chirp_t_oi = chirp_times_lose[(chirp_times_lose > a_on) & (chirp_times_lose <= a_off)]
chirp_t_oi_mask = trial_chirp_burst_mask[(chirp_times_lose > a_on) & (chirp_times_lose <= a_off)]
chase_chirp_count.append(len(chirp_t_oi))
if len(chirp_t_oi) >= 1:
dt_start_first_chirp.append(chirp_t_oi[0] - a_on)
dt_end_first_chirp.append(a_off - chirp_t_oi[0])
dt_start_all_chirp.extend(chirp_t_oi - a_on)
dt_end_all_chirp.extend(a_off - chirp_t_oi)
all_chirp_mask.extend(chirp_t_oi_mask)
chase_dur_all_chirp.extend(np.ones_like(chirp_t_oi) * (a_off - a_on))
else:
dt_start_first_chirp.append(np.nan)
dt_end_first_chirp.append(np.nan)
dt_start_first_chirp = np.array(dt_start_first_chirp)
dt_end_first_chirp = np.array(dt_end_first_chirp)
dt_start_all_chirp = np.array(dt_start_all_chirp)
dt_end_all_chirp = np.array(dt_end_all_chirp)
all_chirp_mask = np.array(all_chirp_mask)
chase_dur_all_chirp = np.array(chase_dur_all_chirp)
chase_chirp_count = np.array(chase_chirp_count)
chase_dur = np.array(chase_dur)
chirp_rate = chase_chirp_count / chase_dur
chase_dur_per_chirp_count = []
positions = np.arange(np.max(chase_chirp_count)+1)
for cc in positions:
chase_dur_per_chirp_count.append([])
chase_dur_per_chirp_count[-1].extend(chase_dur[chase_chirp_count == cc])
################################################
chase_dur_pct99 = np.percentile(chase_dur, 99)
chase_dur_bins = np.arange(0, chase_dur_pct99+1, 2.5)
chase_dur_count_above_th = np.zeros_like(chase_dur_bins[:-1])
for enu, chase_dur_th in enumerate(chase_dur_bins[:-1]):
chase_dur_count_above_th[enu] = len(chase_dur[chase_dur >= chase_dur_th])
################################################
fig = plt.figure(figsize=(21/2.54, 28/2.54))
gs = gridspec.GridSpec(3, 2, left=.15, bottom=0.1, right=0.95, top=0.95)
ax = []
ax.append(fig.add_subplot(gs[0, 0]))
ax.append(fig.add_subplot(gs[0, 1], sharex=ax[0]))
ax.append(fig.add_subplot(gs[1, 0], sharex=ax[0]))
ax.append(fig.add_subplot(gs[1, 1], sharex=ax[0]))
ax.append(fig.add_subplot(gs[2, 0], sharex=ax[0]))
ax.append(fig.add_subplot(gs[2, 1], sharex=ax[0]))
ax[0].plot(chase_dur, chase_chirp_count, '.')
ax[0].boxplot(chase_dur_per_chirp_count, positions=positions, vert=False, sym='')
ax[1].plot(chase_dur, np.array(chase_chirp_count) / np.array(chase_dur), '.')
ax[2].plot(chase_dur, dt_start_first_chirp, '.')
ax[3].plot(chase_dur, dt_end_first_chirp, '.')
ax[2].plot([0, 60], [0, 60], '-k', lw=1)
ax[3].plot([0, 60], [0, 60], '-k', lw=1)
n, _ = np.histogram(dt_start_first_chirp, bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[2].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0]) / 2, n,
height=(chase_dur_bins[1] - chase_dur_bins[0]) * 0.8, color='firebrick', alpha=0.5, zorder=2)
n, _ = np.histogram(dt_end_first_chirp, bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[3].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0]) / 2, n,
height=(chase_dur_bins[1] - chase_dur_bins[0]) * 0.8, color='firebrick', alpha=0.5, zorder=2)
ax[3].invert_yaxis()
ax[2].set_xlim(right=chase_dur_pct99 + 2)
ax[2].set_ylim(top=chase_dur_pct99 + 2)
ax[3].set_xlim(right=chase_dur_pct99 + 2)
ax[3].set_ylim(bottom=chase_dur_pct99 + 2)
ax[4].plot(chase_dur_all_chirp[all_chirp_mask == 0], dt_start_all_chirp[all_chirp_mask == 0], '.', color='cornflowerblue', alpha = 0.5)
ax[4].plot(chase_dur_all_chirp[all_chirp_mask != 0], dt_start_all_chirp[all_chirp_mask != 0], '.', color='k', alpha = 0.5)
ax[5].plot(chase_dur_all_chirp[all_chirp_mask == 0], dt_end_all_chirp[all_chirp_mask == 0], '.', color='cornflowerblue', alpha = 0.5)
ax[5].plot(chase_dur_all_chirp[all_chirp_mask != 0], dt_end_all_chirp[all_chirp_mask != 0], '.', color='k', alpha = 0.5)
ax[4].plot([0, 60], [0, 60], '-k', lw=1)
ax[5].plot([0, 60], [0, 60], '-k', lw=1)
n, _ = np.histogram(dt_start_all_chirp, bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[4].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='firebrick', alpha=0.52, zorder=2)
n, _ = np.histogram(dt_start_all_chirp[all_chirp_mask != 0], bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[4].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4*3, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='k', alpha=0.52, zorder=2)
n, _ = np.histogram(dt_end_all_chirp, bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[5].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='firebrick', alpha=0.5, zorder=2)
n, _ = np.histogram(dt_end_all_chirp[all_chirp_mask != 0], bins=chase_dur_bins)
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99
ax[5].barh(chase_dur_bins[:-1] + (chase_dur_bins[1] - chase_dur_bins[0])/4*3, n, height=(chase_dur_bins[1] - chase_dur_bins[0])*0.4, color='k', alpha=0.5, zorder=2)
ax[5].invert_yaxis()
ax[4].set_xlim(right=chase_dur_pct99+2)
ax[4].set_ylim(top=chase_dur_pct99+2)
ax[5].set_xlim(right=chase_dur_pct99+2)
ax[5].set_ylim(bottom=chase_dur_pct99+2)
ax[4].set_xlabel(r'chase$_{duration}$ [s]', fontsize=12)
ax[5].set_xlabel(r'chase$_{duration}$ [s]', fontsize=12)
ax[0].set_ylabel('chirps [n]', fontsize=12)
ax[1].set_ylabel('chirp rate [Hz]', fontsize=12)
ax[2].set_ylabel(r'$\Delta$t chase$_{on}$ - chirp$_{0}$', fontsize=12)
ax[3].set_ylabel(r'$\Delta$t chirp$_{0}$ - chase$_{off}$', fontsize=12)
ax[4].set_ylabel(r'$\Delta$t chase$_{on}$ - chirps', fontsize=12)
ax[5].set_ylabel(r'$\Delta$t chirps - chase$_{off}$', fontsize=12)
plt.show()
embed()
quit()
if __name__ == '__main__':
main(sys.argv[1])