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event vs. time pir charts done

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Till Raab 2023-06-02 11:04:08 +02:00
parent 14c7538cb9
commit 11b58dda36
1 changed files with 59 additions and 8 deletions
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event_time_analysis.py
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@ -3,6 +3,7 @@ import sys
import numpy as np import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec import matplotlib.gridspec as gridspec
from matplotlib.patches import Patch
import pandas as pd import pandas as pd
from IPython import embed from IPython import embed
from event_time_correlations import load_and_converete_boris_events, kde, gauss from event_time_correlations import load_and_converete_boris_events, kde, gauss
@ -179,6 +180,9 @@ def main(base_path):
win_sex.append(trial['sex_win']) win_sex.append(trial['sex_win'])
lose_sex.append(trial['sex_lose']) lose_sex.append(trial['sex_lose'])
embed()
quit()
iei_analysis(all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win, win_sex, lose_sex) iei_analysis(all_chirp_times_lose, all_chirp_times_win, all_rise_times_lose, all_rise_times_win, win_sex, lose_sex)
relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$') relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
@ -191,34 +195,42 @@ def main(base_path):
all_chase_chirp_mask = [] all_chase_chirp_mask = []
all_chase_off_chirp_mask = []
all_contact_chirp_mask = []
all_chasing_t = [] all_chasing_t = []
all_chase_off_chirp_mask = []
all_chase_off_t = [] all_chase_off_t = []
all_contact_chirp_mask = []
all_physical_t = [] all_physical_t = []
time_tol = 2
for contact_t, ag_on_t, ag_off_t, chirp_times_lose in zip(all_contact_t, all_ag_on_t, all_ag_off_t, all_chirp_times_lose): for contact_t, ag_on_t, ag_off_t, chirp_times_lose in zip(all_contact_t, all_ag_on_t, all_ag_off_t, all_chirp_times_lose):
if len(contact_t) == 0: if len(contact_t) == 0:
continue continue
# ToDo: the 5 seconds are a little dirty... sometimes 5s is longer than chasing dur
chase_chirp_mask = np.zeros_like(chirp_times_lose) chase_chirp_mask = np.zeros_like(chirp_times_lose)
chase_off_chirp_mask = np.zeros_like(chirp_times_lose) chase_off_chirp_mask = np.zeros_like(chirp_times_lose)
for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t): for chase_on_t, chase_off_t in zip(ag_on_t, ag_off_t):
chase_chirp_mask[(chirp_times_lose >= chase_on_t) & (chirp_times_lose < chase_off_t)] = 1 chase_chirp_mask[(chirp_times_lose >= chase_on_t) & (chirp_times_lose < chase_off_t-time_tol)] = 1
chase_off_chirp_mask[(chirp_times_lose >= chase_off_t-5) & (chirp_times_lose < chase_off_t+5)] = 1 chase_off_chirp_mask[(chirp_times_lose >= chase_off_t-time_tol) & (chirp_times_lose < chase_off_t+time_tol)] = 1
all_chase_chirp_mask.append(chase_chirp_mask) all_chase_chirp_mask.append(chase_chirp_mask)
all_chase_off_chirp_mask.append(chase_off_chirp_mask) all_chase_off_chirp_mask.append(chase_off_chirp_mask)
chasing_t = np.sum(ag_off_t - ag_on_t)
chasing_dur = (ag_off_t - ag_on_t) - time_tol
chasing_dur[chasing_dur < 0] = 0
chasing_t = np.sum(chasing_dur)
all_chasing_t.append(chasing_t) all_chasing_t.append(chasing_t)
all_chase_off_t.append(len(ag_off_t) * 10) all_chase_off_t.append(len(ag_off_t) * time_tol*2)
contact_chirp_mask = np.zeros_like(chirp_times_lose) contact_chirp_mask = np.zeros_like(chirp_times_lose)
for ct in contact_t: for ct in contact_t:
contact_chirp_mask[(chirp_times_lose >= ct-5) & (chirp_times_lose < ct+5)] = 1 contact_chirp_mask[(chirp_times_lose >= ct-time_tol) & (chirp_times_lose < ct+time_tol)] = 1
all_contact_chirp_mask.append(contact_chirp_mask) all_contact_chirp_mask.append(contact_chirp_mask)
all_physical_t.append(len(contact_t) * 10) all_physical_t.append(len(contact_t) * time_tol*2)
all_physical_t = np.array(all_physical_t) all_physical_t = np.array(all_physical_t)
all_chasing_t = np.array(all_chasing_t) all_chasing_t = np.array(all_chasing_t)
@ -249,6 +261,45 @@ def main(base_path):
ax.tick_params(labelsize=10) ax.tick_params(labelsize=10)
plt.show() plt.show()
flat_contact_chirp_mask = np.hstack(all_contact_chirp_mask)
flat_chase_chirp_mask = np.hstack(all_chase_chirp_mask)
flat_chase_off_chirp_mask = np.hstack(all_chase_off_chirp_mask)
flat_chase_chirp_mask[flat_contact_chirp_mask == 1] = 0
flat_chase_off_chirp_mask[flat_contact_chirp_mask == 1] = 0
flat_chase_chirp_mask[flat_chase_off_chirp_mask == 1] = 0
chirps_context_values = [np.sum(flat_contact_chirp_mask) / len(flat_contact_chirp_mask),
np.sum(flat_chase_chirp_mask) / len(flat_chase_chirp_mask),
np.sum(flat_chase_off_chirp_mask) / len(flat_chase_off_chirp_mask)]
chirps_context_values.append(1 - np.sum(chirps_context_values))
time_context_values = [np.sum(all_physical_t), np.sum(all_chasing_t), np.sum(all_chase_off_t)]
time_context_values.append(len(all_chasing_t) * 3*60*60 - np.sum(time_context_values))
time_context_values /= np.sum(time_context_values)
fig, ax = plt.subplots(figsize=(12/2.54,12/2.54))
size = 0.3
outer_colors = ['tab:red', 'tab:orange', 'tab:green', 'tab:grey']
ax.pie(chirps_context_values, radius=1, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w'), startangle=90, center=(0, .5))
ax.pie(time_context_values, radius=1-size, colors=outer_colors,
wedgeprops=dict(width=size, edgecolor='w', alpha=.6), startangle=90, center=(0, .5))
ax.set_title(r'chirp$_{lose}$ context')
legend_elements = [Patch(facecolor='tab:red', edgecolor='w', label='%.1f' % (chirps_context_values[0] * 100) + '%'),
Patch(facecolor='tab:orange', edgecolor='w', label='%.1f' % (chirps_context_values[1] * 100) + '%'),
Patch(facecolor='tab:green', edgecolor='w', label='%.1f' % (chirps_context_values[2] * 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='tab:green', alpha=0.6, edgecolor='w', label='%.1f' % (time_context_values[2] * 100) + '%')]
# ax.text(-0.65, -1.4, 'chirps', fontsize=10, va='center', ha='center')
# ax.text(0.75, -1.4, 'time', fontsize=10, va='center', ha='center')
ax.legend(handles=legend_elements, loc='lower right', ncol=2, bbox_to_anchor=(1.1, -0.15), frameon=False, fontsize=9)
plt.show()
embed() embed()