],
bins=15,
color=[RIGHTCOLOR, LEFTCOLOR],
normed=True)
dstr_axs[0].set_xlabel('change in number of wingbeats')
dstr_axs[0].set_ylabel('probability')
dstr_axs[1].set_xlabel('change in |L-R|')
dstr_axs[1].set_ylabel('probability')
# perform t-test between right motion and left motion triggered wbs
_, p_lr_wbf = ttest_ind(triggered_wbs[right_motion_idxs],
triggered_wbs[left_motion_idxs])
_, p_lr_lmr = ttest_ind(triggered_mean_lmrs[right_motion_idxs],
triggered_mean_lmrs[left_motion_idxs])
print 'P-value between WBF response to left and right motion: {}'.format(
p_lr_wbf)
print 'P-value between LMR response to left and right motion: {}'.format(
p_lr_lmr)
for ax in trig_avg_axs.flatten():
ax.set_xlim(-1, 3)
set_colors(ax, AXCOLOR)
set_fontsize(ax, FONTSIZE)
for ax in dstr_axs:
set_colors(ax, AXCOLOR)
set_fontsize(ax, FONTSIZE)
plt.show()
heading_ensemble.fetch_data(session)
if heading_ensemble._data is None:
continue
time_vector = np.arange(len(heading_ensemble.mean))
ax = axs[s_ctr, o_ctr]
ax.errorbar(time_vector, heading_ensemble.mean, lw=3,
yerr=heading_ensemble.sem, color=COLORS_EXPT[expt])
ax.set_ylim(Y_LIM)
ax.set_xlim(X_LIM)
if s_ctr == 1:
ax.set_xlabel('timesteps')
if o_ctr == 0:
ax.set_ylabel('heading (degree)')
if s_ctr == 0 and o_ctr == 1:
ax.set_title('empirical')
if s_ctr == 1 and o_ctr == 1:
ax.set_title('infotaxis')
for ax in axs.flatten():
set_fontsize(ax, FONT_SIZE)
axs[0, 0].legend(['0.3 m/s', '0.4 m/s', '0.6 m/s'], fontsize=24)
plt.show(block=True)
break
heading_ensemble.fetch_data(session)
if heading_ensemble._data is None:
continue
time_vector = np.arange(len(heading_ensemble.mean))
axs = axss[e_ctr]
ax = axs[s_ctr, o_ctr]
ax.errorbar(time_vector, heading_ensemble.mean, lw=3,
yerr=heading_ensemble.sem, color=COLORS[c])
ax.set_xlim(X_LIM)
ax.set_ylim(Y_LIM)
if s_ctr == o_ctr == 0:
ax.legend(['early', 'late'], fontsize=FONT_SIZE)
if s_ctr == 0 and o_ctr == 1:
ax.set_title('wind {} \n empirical \n'.format(WIND_SPEEDS[e_ctr]))
elif s_ctr == 1 and o_ctr == 1:
ax.set_title('infotaxis \n'.format(WIND_SPEEDS[e_ctr]))
if s_ctr == 1:
ax.set_xlabel('timesteps')
if o_ctr == 0:
ax.set_ylabel('heading (deg)')
for axs in axss:
[set_fontsize(ax, FONT_SIZE) for ax in axs.flatten()]
plt.show(block=True)
if heading_ensemble._data is None:
continue
time_vector = np.arange(len(heading_ensemble.mean))
axs = axss[e_ctr]
ax = axs[s_ctr, o_ctr]
ax.errorbar(time_vector,
heading_ensemble.mean,
lw=3,
yerr=heading_ensemble.sem,
color=COLORS[c])
ax.set_xlim(X_LIM)
ax.set_ylim(Y_LIM)
if s_ctr == o_ctr == 0:
ax.legend(['early', 'late'], fontsize=FONT_SIZE)
if s_ctr == 0 and o_ctr == 1:
ax.set_title('wind {} \n empirical \n'.format(
WIND_SPEEDS[e_ctr]))
elif s_ctr == 1 and o_ctr == 1:
ax.set_title('infotaxis \n'.format(WIND_SPEEDS[e_ctr]))
if s_ctr == 1:
ax.set_xlabel('timesteps')
if o_ctr == 0:
ax.set_ylabel('heading (deg)')
for axs in axss:
[set_fontsize(ax, FONT_SIZE) for ax in axs.flatten()]
plt.show(block=True)
