], 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)