raab/competition_experiments
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Till Raab 2023-06-29 12:32:53 +02:00
parent 8e3eb7722b
commit 159c1060bc
1 changed files with 73 additions and 33 deletions
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104
event_time_analysis.py
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@ -82,6 +82,32 @@ def iei_analysis(event_times, win_sex, lose_sex, kernal_w, title=''):
plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_iei.png'), dpi=300) plt.savefig(os.path.join(os.path.split(__file__)[0], 'figures', 'event_meta', f'{title}_iei.png'), dpi=300)
plt.close() plt.close()
embed()
quit()
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() # plt.show()
return iei return iei
@ -527,11 +553,9 @@ def main(base_path):
_ = iei_analysis(all_rise_times_lose, win_sex, lose_sex, kernal_w=5, title=r'rises$_{lose}$') _ = 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$_{win}$') _ = iei_analysis(all_rise_times_win, win_sex, lose_sex, kernal_w=50, title=r'rises$_{win}$')
embed()
quit()
fig, ax = plt.subplots() fig, ax = plt.subplots()
n, bin_edges = np.histogram(np.hstack(inter_chirp_interval_lose), bins = np.arange(0, 20, 0.05)) n, bin_edges = np.histogram(np.hstack(inter_chirp_interval_lose), bins = np.arange(0, 20, 0.05))
# ax.hist(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])) 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() ylim = ax.get_ylim()
med_ici = np.nanmedian(np.hstack(inter_chirp_interval_lose)) med_ici = np.nanmedian(np.hstack(inter_chirp_interval_lose))
@ -556,9 +580,17 @@ def main(base_path):
else: else:
burst_chirp_mask.append(np.array([])) 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)): for i in range(len(burst_chirp_mask)):
# for i in range(5): ax[0].plot(all_chirp_times_lose[i], np.ones_like(all_chirp_times_lose[i]) * i, '|', markersize=12, color='grey')
# fig, ax = plt.subplots()
if len(burst_chirp_mask[i]) == 0: if len(burst_chirp_mask[i]) == 0:
continue continue
@ -582,21 +614,22 @@ def main(base_path):
else: else:
chirps_in_burst_distro[j] = len(chirps_in_burst[chirps_in_burst == j + 1]) chirps_in_burst_distro[j] = len(chirps_in_burst[chirps_in_burst == j + 1])
fig = plt.figure(figsize=(21/2.54, 19/2.54))
gs = gridspec.GridSpec(2, 1, 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[1, 0]))
ax[0].plot(all_chirp_times_lose[i], np.ones_like(all_chirp_times_lose[i]), '|', markersize=20, color='grey')
for cbs, cbe in zip(all_chirp_times_lose[i][chirp_idx_burst_start], all_chirp_times_lose[i][chirp_idx_burst_end]): 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], [1, 1], '-k', lw=2) ax[0].plot([cbs, cbe], [i, i], '-k', lw=2)
ax[0].set_ylim(.5, 1.5)
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)
ax[1].bar(np.arange(len(chirps_in_burst_distro))+1, chirps_in_burst_distro)
ax[1].plot(np.arange(len(chirps_in_burst_distro))+1,
chirps_in_burst_distro*(np.arange(len(chirps_in_burst_distro))+1), color='firebrick', lw=2)
plt.show() plt.show()
### event progressions ### ### event progressions ###
print('') print('')
relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$') relative_rate_progression(all_chirp_times_lose, title=r'chirp$_{lose}$')
@ -718,10 +751,10 @@ def main(base_path):
ax[4].plot(chase_dur_all_chirp[all_chirp_mask == 0], dt_start_all_chirp[all_chirp_mask == 0], '.', color='cornflowerblue') 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') 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') 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') 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[4].plot([0, 60], [0, 60], '-k', lw=1)
ax[5].plot([0, 60], [0, 60], '-k', lw=1) ax[5].plot([0, 60], [0, 60], '-k', lw=1)
@ -729,13 +762,27 @@ def main(base_path):
n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0]) n = n / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99 n = n / np.max(n) * chase_dur_pct99
ax[4].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[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, _ = 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 / np.sum(n) / (chase_dur_bins[1] - chase_dur_bins[0])
n = n / chase_dur_count_above_th n = n / chase_dur_count_above_th
n = n / np.max(n) * chase_dur_pct99 n = n / np.max(n) * chase_dur_pct99
ax[5].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[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[5].invert_yaxis()
ax[4].set_xlim(right=chase_dur_pct99+2) ax[4].set_xlim(right=chase_dur_pct99+2)
@ -755,17 +802,10 @@ def main(base_path):
ax[4].set_ylabel(r'$\Delta$t chase$_{on}$ - chirps', 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) ax[5].set_ylabel(r'$\Delta$t chirps - chase$_{off}$', fontsize=12)
# embed()
# quit()
plt.show() plt.show()
# fig, ax = plt.subplots() embed()
# n, bin_edges = np.histogram(dt_start_first_chirp[~np.isnan(dt_start_first_chirp)] / chase_dur[~np.isnan(dt_start_first_chirp)], bins = np.arange(0, 1.05, .05)) quit()
# ax.bar(bin_edges[:-1] + (bin_edges[1]-bin_edges[0])/2, n/np.sum(n)/(bin_edges[1] - bin_edges[0]), width=0.8*(bin_edges[1]-bin_edges[0]))
#
# n, bin_edges = np.histogram(dt_end_first_chirp[~np.isnan(dt_end_first_chirp)] / chase_dur[~np.isnan(dt_end_first_chirp)], bins = np.arange(0, 1.05, .05))
# ax.bar(bin_edges[:-1] + (bin_edges[1]-bin_edges[0])/2, n/np.sum(n)/(bin_edges[1] - bin_edges[0]), width=0.8*(bin_edges[1]-bin_edges[0]))
if __name__ == '__main__': if __name__ == '__main__':
main(sys.argv[1]) main(sys.argv[1])