print(extract_sig(induced_zero, x)) print('Max: {:.2f}'.format(np.max(np.absolute(induced_mean)))) print('LatMed') print(extract_sig(induced_x_zero, x)) print('Max: {:.2f}'.format(np.max(np.absolute(induced_mean_x)))) print('RePro') print(extract_sig(induced_y_zero, x)) print('Max: {:.2f}'.format(np.max(np.absolute(induced_mean_y)))) print('Tilt') print(extract_sig(induced_z_zero, x)) print('Max: {:.2f}'.format(np.max(np.absolute(induced_mean_z)))) # plot mean +- sd cur_row = act_row[activity.lower()] induced_ln = mean_sd_plot( axs_axial[cur_row, 0], x, induced_mean, induced_sd, dict(color=color_map.colors[0], alpha=0.2, hatch='ooo'), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) induced_x_ln = mean_sd_plot( axs_axial[cur_row, 0], x, induced_mean_x, induced_sd_x, dict(color=color_map.colors[1], alpha=0.3), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) induced_y_ln = mean_sd_plot( axs_axial[cur_row, 0], x, induced_mean_y, induced_sd_y, dict(color=color_map.colors[7], alpha=0.3), dict(color=color_map.colors[7], marker=markers[2], markevery=20)) induced_z_ln = mean_sd_plot( axs_axial[cur_row, 0], x, induced_mean_z, induced_sd_z, dict(color=color_map.colors[3], alpha=0.2, hatch='xxx'), dict(color=color_map.colors[3], marker=markers[3], markevery=20)) # plot spm
gh_mean = np.rad2deg(np.mean(traj_gh, axis=0)) ht_sd = np.rad2deg(np.std(traj_ht, axis=0, ddof=1)) st_sd = np.rad2deg(np.std(traj_st, axis=0, ddof=1)) gh_sd = np.rad2deg(np.std(traj_gh, axis=0, ddof=1)) ht_max_mean = np.rad2deg(np.mean(traj_ht_max, axis=0)) st_max_mean = np.rad2deg(np.mean(traj_st_max, axis=0)) gh_max_mean = np.rad2deg(np.mean(traj_gh_max, axis=0)) ht_max_sd = np.rad2deg(np.std(traj_ht_max, axis=0, ddof=1)) st_max_sd = np.rad2deg(np.std(traj_st_max, axis=0, ddof=1)) gh_max_sd = np.rad2deg(np.std(traj_gh_max, axis=0, ddof=1)) # plot mean +- sd cur_row = act_row[activity.lower()] gh_ln = mean_sd_plot(axs_elev[cur_row], x_elev, gh_mean, gh_sd, dict(color=color_map.colors[0], alpha=0.25, hatch='...'), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_ln = mean_sd_plot(axs_elev[cur_row], x_elev, st_mean, st_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) ht_ln = mean_sd_plot(axs_elev[cur_row], x_elev, ht_mean, ht_sd, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2], marker=markers[2], markevery=20)) axs_elev[cur_row].errorbar(max_pos, gh_max_mean, yerr=gh_max_sd, color=color_map.colors[0], marker=markers[0], capsize=3) axs_elev[cur_row].errorbar(max_pos + 3, st_max_mean, yerr=st_max_sd, color=color_map.colors[1], marker=markers[1], capsize=3) axs_elev[cur_row].errorbar(max_pos - 3, ht_max_mean, yerr=ht_max_sd, color=color_map.colors[2], marker=markers[2], capsize=3)
st_lt35_mean = np.rad2deg(np.mean(traj_st_lt35, axis=0)) st_gt45_mean = np.rad2deg(np.mean(traj_st_gt45, axis=0)) gh_lt35_mean = np.rad2deg(np.mean(traj_gh_lt35, axis=0)) gh_gt45_mean = np.rad2deg(np.mean(traj_gh_gt45, axis=0)) # sds ht_lt35_sd = np.rad2deg(np.std(traj_ht_lt35, axis=0, ddof=1)) ht_gt45_sd = np.rad2deg(np.std(traj_ht_gt45, axis=0, ddof=1)) st_lt35_sd = np.rad2deg(np.std(traj_st_lt35, axis=0, ddof=1)) st_gt45_sd = np.rad2deg(np.std(traj_st_gt45, axis=0, ddof=1)) gh_lt35_sd = np.rad2deg(np.std(traj_gh_lt35, axis=0, ddof=1)) gh_gt45_sd = np.rad2deg(np.std(traj_gh_gt45, axis=0, ddof=1)) # plots mean +- sd ht_lt35_ln = mean_sd_plot( axs[idx_act, 0], x, ht_lt35_mean, ht_lt35_sd, dict(color=color_map.colors[0], alpha=0.2), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) ht_gt45_ln = mean_sd_plot( axs[idx_act, 0], x, ht_gt45_mean, ht_gt45_sd, dict(color=color_map.colors[1], alpha=0.2), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) st_lt35_ln = mean_sd_plot( axs[idx_act, 1], x, st_lt35_mean, st_lt35_sd, dict(color=color_map.colors[0], alpha=0.2), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_gt45_ln = mean_sd_plot( axs[idx_act, 1], x, st_gt45_mean, st_gt45_sd, dict(color=color_map.colors[1], alpha=0.2), dict(color=color_map.colors[1], marker=markers[1], markevery=20))
**infer_params) print('Activity: {}'.format(activity)) print('HT') print(extract_sig(ht_zero, x)) print('GH') print(extract_sig(gh_zero, x)) print('Min axial rotation: {:.2f} max axial rotation: {:.2f}'.format( np.min(true_mean_gh), np.max(true_mean_gh))) print('ST') print(extract_sig(st_zero, x)) # plot mean +- sd cur_row = act_row[activity.lower()] true_gh_ln = mean_sd_plot( axs_axial[cur_row, 0], x, true_mean_gh, true_sd_gh, dict(color=color_map.colors[0], alpha=0.25, hatch='...'), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) true_st_ln = mean_sd_plot( axs_axial[cur_row, 0], x, true_mean_st, true_sd_st, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) true_ht_ln = mean_sd_plot( axs_axial[cur_row, 0], x, true_mean_ht, true_sd_ht, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2], marker=markers[2], markevery=20)) # plot spm ht_t_ln, ht_alpha = spm_plot_alpha( axs_axial[cur_row, 1], x, ht_zero, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2]))
np.std(act_all_traj_induced_z, ddof=1, axis=0)) # plot mean +- sd shaded = dict(color=color_map.colors[idx], alpha=0.2) if activity == 'CA': shaded['hatch'] = 'oo' if activity == 'SA': shaded['alpha'] = 0.28 if activity == 'FE': shaded['alpha'] = 0.3 line = dict(color=color_map.colors[idx], marker=markers[idx], markevery=20) induced_ln = mean_sd_plot(axs_plane[0, 0], x, induced_mean, induced_sd, shaded, line) induced_x_ln = mean_sd_plot(axs_plane[1, 0], x, induced_mean_x, induced_sd_x, shaded, line) induced_y_ln = mean_sd_plot(axs_plane[2, 0], x, induced_mean_y, induced_sd_y, shaded, line) induced_z_ln = mean_sd_plot(axs_plane[3, 0], x, induced_mean_z, induced_sd_z, shaded, line) mean_lns_start.append(induced_ln[0]) activities_start.append(activity) # figure title and legend plt.tight_layout(pad=0.5, h_pad=1.5, w_pad=0.5) fig_plane.suptitle('ST-induced HT Axial Rotation Comparison by Plane', x=0.5, y=0.99,
st_contrib_max_sd = np.rad2deg(np.std(st_contrib_max, ddof=1, axis=0)) gh_euler_max_sd = np.rad2deg(np.std(gh_euler_max, ddof=1, axis=0)) gh_contrib_max_sd = np.rad2deg(np.std(gh_contrib_max, ddof=1, axis=0)) # spm st_spm = spm_test(st_euler, st_contrib).inference(alpha, two_tailed=True, **infer_params) gh_spm = spm_test(gh_euler, gh_contrib).inference(alpha, two_tailed=True, **infer_params) # plot mean +- sd cur_row = act_row[activity.lower()] st_euler_ln = mean_sd_plot( axs[cur_row, 0], x, st_euler_mean, st_euler_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_contrib_ln = mean_sd_plot( axs[cur_row, 0], x, st_contrib_mean, st_contrib_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) gh_euler_ln = mean_sd_plot( axs[cur_row, 1], x, gh_euler_mean, gh_euler_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[2], markevery=20)) gh_contrib_ln = mean_sd_plot( axs[cur_row, 1], x, gh_contrib_mean, gh_contrib_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[3], markevery=20)) axs[cur_row, 0].errorbar(max_pos - 3,
st_poe_mean = np.nanmean(st_poe_per, axis=0) gh_elev_mean = np.nanmean(gh_elev_per, axis=0) gh_axial_mean = np.nanmean(gh_axial_per, axis=0) gh_poe_mean = np.nanmean(gh_poe_per, axis=0) st_elev_sd = np.nanstd(st_elev_per, axis=0, ddof=1) st_axial_sd = np.nanstd(st_axial_per, axis=0, ddof=1) st_poe_sd = np.nanstd(st_poe_per, axis=0, ddof=1) gh_elev_sd = np.nanstd(gh_elev_per, axis=0, ddof=1) gh_axial_sd = np.nanstd(gh_axial_per, axis=0, ddof=1) gh_poe_sd = np.nanstd(gh_poe_per, axis=0, ddof=1) # plot mean +- sd cur_row = act_row[activity.lower()] st_elev_ln = mean_sd_plot( axs[cur_row, 0], x, st_elev_mean, st_elev_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_axial_ln = mean_sd_plot( axs[cur_row, 0], x, st_axial_mean, st_axial_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[0], markevery=20)) st_poe_ln = mean_sd_plot( axs[cur_row, 0], x, st_poe_mean, st_poe_sd, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2], marker=markers[0], markevery=20)) gh_elev_ln = mean_sd_plot( axs[cur_row, 1], x, gh_elev_mean, gh_elev_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[1], markevery=20)) gh_axial_ln = mean_sd_plot(
all_traj_ht_gt45).inference(alpha, two_tailed=True, **infer_params) st_lt35_vs_gt35 = spm_test(all_traj_st_lt35, all_traj_st_gt45).inference(alpha, two_tailed=True, **infer_params) gh_lt35_vs_gt35 = spm_test(all_traj_gh_lt35, all_traj_gh_gt45).inference(alpha, two_tailed=True, **infer_params) # plot mean and SD cur_row = act_row[activity.lower()] ht_ln_lt35 = mean_sd_plot( axs[cur_row, 0], x, ht_mean_lt35, ht_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_ln_lt35 = mean_sd_plot( axs[cur_row, 1], x, st_mean_lt35, st_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[1], markevery=20)) gh_ln_lt35 = mean_sd_plot( axs[cur_row, 2], x, gh_mean_lt35, gh_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[2], markevery=20)) ht_ln_gt45 = mean_sd_plot( axs[cur_row, 0], x, ht_mean_gt45, ht_sd_gt45, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[0], markevery=20)) st_ln_gt45 = mean_sd_plot(
np.std(ht_euler_ur_elev_diff_max, axis=0, ddof=1)) ht_contrib_max_sd = np.rad2deg( np.std(ht_euler_contrib_diff_max, axis=0, ddof=1)) # spm ht_euler_diff_vs_euler_add = spm_test(ht_euler_ur_elev_diff, 0).inference(alpha, two_tailed=True, **infer_params) # ht_euler_diff_vs_contribs = spm_test(ht_euler_contrib_diff, 0).inference(alpha, two_tailed=True, # **infer_params) # plot mean +- sd cur_row = act_row[activity.lower()] ht_euler_add_ln = mean_sd_plot( axs[cur_row], x, ht_euler_add_mean, ht_euler_add_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) ht_contribs_ln = mean_sd_plot( axs[cur_row], x, ht_contrib_mean, ht_contrib_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) # plot at max axs[cur_row].errorbar(max_pos, ht_euler_add_max_mean, yerr=ht_euler_add_max_sd, color=color_map.colors[0], marker=markers[0], capsize=3) axs[cur_row].errorbar(max_pos - 3, ht_contrib_max_mean,
extract_sub_rot_norm, args=['st', 'common_fine_up', 'contribs', 2, 'up']), axis=0) # means and standard deviations poe_mean = np.rad2deg(np.mean(traj_poe, axis=0)) st_axial_mean = np.rad2deg(np.mean(traj_st_axial, axis=0)) poe_sd = np.rad2deg(np.std(traj_poe, axis=0, ddof=1)) st_axial_sd = np.rad2deg(np.std(traj_st_axial, axis=0, ddof=1)) color = color_map.colors[colors[activity.lower()]] marker = markers[activity.lower()] # plot mean and SD poe_ln = mean_sd_plot( axs_elev[0], x_elev, poe_mean, poe_sd, dict(color=color, alpha=0.25, hatch='oo' if activity == 'CA' else None), dict(color=color, marker=marker, markevery=20, ms=4)) st_axial_ln = mean_sd_plot( axs_elev[1], x_elev, st_axial_mean, st_axial_sd, dict(color=color, alpha=0.25, hatch='oo' if activity == 'CA' else None), dict(color=color, marker=marker, markevery=20, ms=4)) # at maximum traj_poe_max = np.stack(activity_df['traj_interp'].apply( sub_rot_at_max_elev, args=['gh', 'euler.gh_phadke', 1, None]), axis=0) traj_st_axial_max = np.stack(activity_df['traj_interp'].apply( sub_rot_at_max_elev, args=['st', 'contribs', 2, 'up']),
# means latmed_mean = np.rad2deg(np.mean(all_traj_latmed, axis=0)) repro_mean = np.rad2deg(np.mean(all_traj_repro, axis=0)) tilt_mean = np.rad2deg(np.mean(all_traj_tilt, axis=0)) total_mean = np.rad2deg(np.mean(all_traj_total, axis=0)) # sds latmed_sd = np.rad2deg(np.std(all_traj_latmed, ddof=1, axis=0)) repro_sd = np.rad2deg(np.std(all_traj_repro, ddof=1, axis=0)) tilt_sd = np.rad2deg(np.std(all_traj_tilt, ddof=1, axis=0)) total_sd = np.rad2deg(np.std(all_traj_total, ddof=1, axis=0)) # plots mean +- sd repro_ln = mean_sd_plot( axs[idx_act, 0], x, repro_mean, repro_sd, dict(color=color_map.colors[1], alpha=0.2), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) total_ln = mean_sd_plot( axs[idx_act, 0], x, total_mean, total_sd, dict(color=color_map.colors[2], alpha=0.2, hatch='ooo'), dict(color=color_map.colors[2], marker=markers[2], markevery=20)) tilt_ln = mean_sd_plot( axs[idx_act, 0], x, tilt_mean, tilt_sd, dict(color=color_map.colors[7], alpha=0.2), dict(color=color_map.colors[7], marker=markers[3], markevery=20)) latmed_ln = mean_sd_plot( axs[idx_act, 0], x, latmed_mean, latmed_sd, dict(color=color_map.colors[0], alpha=0.2, hatch='xxx'), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) ax_inset = axs[idx_act, 0].inset_axes([0.02, 0.05, 0.1, 0.5])
st_sd_y = np.rad2deg(np.std(traj_st_y, ddof=1, axis=0)) st_sd_z = np.rad2deg(np.std(traj_st_z, ddof=1, axis=0)) st_max_mean = np.rad2deg(np.mean(traj_st_max, axis=0)) st_max_mean_x = np.rad2deg(np.mean(traj_st_max_x, axis=0)) st_max_mean_y = np.rad2deg(np.mean(traj_st_max_y, axis=0)) st_max_mean_z = np.rad2deg(np.mean(traj_st_max_z, axis=0)) st_max_sd = np.rad2deg(np.std(traj_st_max, ddof=1, axis=0)) st_max_sd_x = np.rad2deg(np.std(traj_st_max_x, ddof=1, axis=0)) st_max_sd_y = np.rad2deg(np.std(traj_st_max_y, ddof=1, axis=0)) st_max_sd_z = np.rad2deg(np.std(traj_st_max_z, ddof=1, axis=0)) # plot mean +- sd cur_row = act_row[activity.lower()] st_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_mean, st_sd, dict(color=color_map.colors[0], alpha=0.2, hatch='ooo'), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_x_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_mean_x, st_sd_x, dict(color=color_map.colors[1], alpha=0.3), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) st_y_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_mean_y, st_sd_y, dict(color=color_map.colors[7], alpha=0.3), dict(color=color_map.colors[7], marker=markers[2], markevery=20)) st_z_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_mean_z, st_sd_z, dict(color=color_map.colors[3], alpha=0.2, hatch='xxx'), dict(color=color_map.colors[3], marker=markers[3], markevery=20)) axs_elev[cur_row].errorbar(max_pos,
axis=0) # means and standard deviations poe_mean = np.rad2deg(np.mean(traj_poe, axis=0)) st_axial_mean = np.rad2deg(np.mean(traj_st_axial, axis=0)) poe_sd = np.rad2deg(np.std(traj_poe, axis=0, ddof=1)) st_axial_sd = np.rad2deg(np.std(traj_st_axial, axis=0, ddof=1)) st_axial_med = np.rad2deg(np.median(traj_st_axial, axis=0)) st_axial_25 = np.rad2deg(np.quantile(traj_st_axial, 0.25, axis=0)) st_axial_75 = np.rad2deg(np.quantile(traj_st_axial, 0.75, axis=0)) # plot mean and SD poe_ln = mean_sd_plot( axs[0], x, poe_mean, poe_sd, dict(color=color_map.colors[act_idx], alpha=0.25), dict(color=color_map.colors[act_idx], marker=markers[0], markevery=20)) st_axial_ln = mean_sd_plot( axs[1], x, st_axial_mean, st_axial_sd, dict(color=color_map.colors[act_idx], alpha=0.25), dict(color=color_map.colors[act_idx], marker=markers[1], markevery=20)) # at maximum traj_poe_max = np.stack(activity_df['traj_interp'].apply( sub_rot_at_max_elev, args=['gh', 'euler.gh_phadke', 1, None]), axis=0) traj_st_axial_max = np.stack(activity_df['traj_interp'].apply( sub_rot_at_max_elev, args=['st', 'contribs', 2, 'up']),
# means and standard deviations st_lt35_mean = np.rad2deg(np.mean(traj_st_lt35, axis=0)) st_gt45_mean = np.rad2deg(np.mean(traj_st_gt45, axis=0)) st_lt35_max_mean = np.rad2deg(np.mean(traj_st_lt35_max, axis=0)) st_gt45_max_mean = np.rad2deg(np.mean(traj_st_gt45_max, axis=0)) st_lt35_sd = np.rad2deg(np.std(traj_st_lt35, axis=0, ddof=1)) st_gt45_sd = np.rad2deg(np.std(traj_st_gt45, axis=0, ddof=1)) st_lt35_max_sd = np.rad2deg(np.std(traj_st_lt35_max, axis=0, ddof=1)) st_gt45_max_sd = np.rad2deg(np.std(traj_st_gt45_max, axis=0, ddof=1)) # plot mean +- sd cur_row = act_row[activity.lower()] st_lt35_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_lt35_mean, st_lt35_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_gt45_ln = mean_sd_plot( axs_elev[cur_row], x_elev, st_gt45_mean, st_gt45_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) axs_elev[cur_row].errorbar(max_pos, st_lt35_max_mean, yerr=st_lt35_max_sd, color=color_map.colors[0], marker=markers[0], capsize=3) axs_elev[cur_row].errorbar(max_pos, st_gt45_max_mean, yerr=st_gt45_max_sd,
new_shr_sd_gt45 = np.std(new_shr_gt45, ddof=1, axis=0) # spm spm_old = spm_test(old_shr_lt35, old_shr_gt45, equal_var=False).inference(alpha, two_tailed=True, **infer_params) spm_new = spm_test(new_shr_lt35, new_shr_gt45, equal_var=False).inference(alpha, two_tailed=True, **infer_params) # plot mean +- sd cur_row = act_row[activity.lower()] old_shr_ln_lt35 = mean_sd_plot( axs[cur_row, 0], x, old_shr_mean_lt35, old_shr_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) old_shr_ln_gt45 = mean_sd_plot( axs[cur_row, 0], x, old_shr_mean_gt45, old_shr_sd_gt45, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) new_shr_ln_lt35 = mean_sd_plot( axs[cur_row, 1], x, new_shr_mean_lt35, new_shr_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) new_shr_ln_gt45 = mean_sd_plot( axs[cur_row, 1], x, new_shr_mean_gt45, new_shr_sd_gt45, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[1], markevery=20)) # plot spm
# means and standard deviations old_shr_mean = np.mean(old_shr, axis=0) new_shr_mean = np.mean(new_shr, axis=0) old_shr_sd = np.std(old_shr, ddof=1, axis=0) new_shr_sd = np.std(new_shr, ddof=1, axis=0) # spm spm_res = spm_test(old_shr, new_shr).inference(alpha, two_tailed=True, **infer_params) # plot mean +- sd cur_row = act_row[activity.lower()] old_shr_ln = mean_sd_plot( axs[cur_row], x, old_shr_mean, old_shr_sd, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) new_shr_ln = mean_sd_plot( axs[cur_row], x, new_shr_mean, new_shr_sd, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[0], markevery=20)) # plot spm x_sig = sig_filter(spm_res, x) axs[cur_row].plot(x_sig, np.repeat(12, x_sig.size), color='k', lw=2) if idx == 0: leg_mean.append(old_shr_ln[0]) leg_mean.append(new_shr_ln[0]) # figure title and legend
two_tailed=True, **infer_params) gh_euler_spm = spm_test(gh_euler_lt35, gh_euler_gt45, equal_var=False).inference(alpha, two_tailed=True, **infer_params) gh_contrib_spm = spm_test(gh_contrib_lt35, gh_contrib_gt45, equal_var=False).inference(alpha, two_tailed=True, **infer_params) # plot mean +- sd cur_row = act_row[activity.lower()] st_euler_ln_lt35 = mean_sd_plot( axs_st[cur_row, 0], x, st_euler_mean_lt35, st_euler_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_contrib_ln_lt35 = mean_sd_plot( axs_st[cur_row, 1], x, st_contrib_mean_lt35, st_contrib_sd_lt35, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2], marker=markers[2], markevery=20)) gh_euler_ln_lt35 = mean_sd_plot( axs_gh[cur_row, 0], x, gh_euler_mean_lt35, gh_euler_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) gh_contrib_ln_lt35 = mean_sd_plot( axs_gh[cur_row, 1], x, gh_contrib_mean_lt35, gh_contrib_sd_lt35, dict(color=color_map.colors[2], alpha=0.25), dict(color=color_map.colors[2], marker=markers[2], markevery=20)) st_euler_ln_gt45 = mean_sd_plot(
all_traj_ht_m).inference(alpha, two_tailed=True, **infer_params) st_f_vs_m = spm_test(all_traj_st_f, all_traj_st_m).inference(alpha, two_tailed=True, **infer_params) gh_f_vs_m = spm_test(all_traj_gh_f, all_traj_gh_m).inference(alpha, two_tailed=True, **infer_params) # plot mean and SD cur_row = act_row[activity.lower()] ht_ln_f = mean_sd_plot( axs[cur_row, 0], x, ht_mean_f, ht_sd_f, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) st_ln_f = mean_sd_plot( axs[cur_row, 1], x, st_mean_f, st_sd_f, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[1], markevery=20)) gh_ln_f = mean_sd_plot( axs[cur_row, 2], x, gh_mean_f, gh_sd_f, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[2], markevery=20)) ht_ln_m = mean_sd_plot( axs[cur_row, 0], x, ht_mean_m, ht_sd_m, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[0], markevery=20)) st_ln_m = mean_sd_plot(
repro_mean_gt45 = np.rad2deg(np.mean(all_traj_repro_gt45, axis=0)) poe_mean_gt45 = np.rad2deg(np.mean(all_traj_poe_gt45, axis=0)) repro_sd_gt45 = np.rad2deg(np.std(all_traj_repro_gt45, ddof=1, axis=0)) poe_sd_gt45 = np.rad2deg(np.std(all_traj_poe_gt45, ddof=1, axis=0)) # spm repro_lt35_vs_gt35 = spm_test(all_traj_repro_lt35, all_traj_repro_gt45).inference(alpha, two_tailed=True, **infer_params) poe_lt35_vs_gt35 = spm_test(all_traj_poe_lt35, all_traj_poe_gt45).inference(alpha, two_tailed=True, **infer_params) # plot mean and SD cur_row = act_row[activity.lower()] repro_ln_lt35 = mean_sd_plot(axs[cur_row, 0], x, repro_mean_lt35, repro_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[0], markevery=20)) poe_ln_lt35 = mean_sd_plot(axs[cur_row, 1], x, poe_mean_lt35, poe_sd_lt35, dict(color=color_map.colors[0], alpha=0.25), dict(color=color_map.colors[0], marker=markers[2], markevery=20)) repro_ln_gt45 = mean_sd_plot(axs[cur_row, 0], x, repro_mean_gt45, repro_sd_gt45, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[0], markevery=20)) poe_ln_gt45 = mean_sd_plot(axs[cur_row, 1], x, poe_mean_gt45, poe_sd_gt45, dict(color=color_map.colors[1], alpha=0.25), dict(color=color_map.colors[1], marker=markers[2], markevery=20)) # plot SPM repro_x_sig = sig_filter(repro_lt35_vs_gt35, x) poe_x_sig = sig_filter(poe_lt35_vs_gt35, x)