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)
# spm
if activity.lower() == 'fe':
st_vs_gh = spm_test(traj_st - traj_gh, 0).inference(alpha, two_tailed=True, **infer_params)
else:
st_vs_gh = spm_test(-(traj_st - traj_gh), 0).inference(alpha, two_tailed=True, **infer_params)
x_sig = sig_filter(st_vs_gh, x_elev)
axs_elev[cur_row].plot(x_sig, np.repeat(spm_y[activity.lower()], x_sig.size), color='k')
print('Activity: {}'.format(activity))
print(extract_sig(st_vs_gh, x_elev))
print('P-values: ')
print(output_spm_p(st_vs_gh))
print('HT Max: {:.2f}'.format(np.abs(ht_max_mean)))
print('ST Max: {:.2f}'.format(np.abs(st_max_mean)))
print('GH Max: {:.2f}'.format(np.abs(gh_max_mean)))
print('Percentage: {:.2f}'.format(st_max_mean / ht_max_mean * 100))
if idx == 0:
leg_elev_mean.append(ht_ln[0])
leg_elev_mean.append(st_ln[0])
leg_elev_mean.append(gh_ln[0])
# plot external rotation
print('\nEXTERNAL ROTATION')
for idx_act, (activity, activity_df) in enumerate(db_er_endpts.groupby('Activity', observed=True)):
yerr=st_gt45_max_sd,
color=color_map.colors[1],
marker=markers[1],
capsize=3)
# spm
lt35_vs_gt45 = spm_test(traj_st_lt35,
traj_st_gt45).inference(alpha,
two_tailed=True,
**infer_params)
x_sig = sig_filter(lt35_vs_gt45, x_elev)
axs_elev[cur_row].plot(x_sig,
np.repeat(spm_y[activity.lower()], x_sig.size),
color='k')
print(activity)
print(extract_sig(lt35_vs_gt45, x_elev))
if idx == 0:
leg_elev_mean.append(st_lt35_ln[0])
leg_elev_mean.append(st_gt45_ln[0])
for idx_act, (activity, activity_df) in enumerate(
db_er_endpts.groupby('Activity', observed=True)):
lt35_df = activity_df[activity_df['age_group'] == '<35']
gt45_df = activity_df[activity_df['age_group'] == '>45']
traj_st_lt35 = np.stack(lt35_df['st_contribs_interp'], axis=0)[:, :, 2]
traj_st_gt45 = np.stack(gt45_df['st_contribs_interp'], axis=0)[:, :, 2]
# means and standard deviations
st_lt35_mean = np.rad2deg(np.mean(traj_st_lt35, axis=0))
true_sd_st = np.rad2deg(np.std(all_traj_true_st, ddof=1, axis=0))
# spm
ht_zero = spm_test(all_traj_true_ht, 0).inference(alpha,
two_tailed=True,
**infer_params)
gh_zero = spm_test(all_traj_true_gh, 0).inference(alpha,
two_tailed=True,
**infer_params)
st_zero = spm_test(all_traj_true_st, 0).inference(alpha,
two_tailed=True,
**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,
induced_x_zero = spm_test(all_traj_induced_x,
0).inference(alpha,
two_tailed=True,
**infer_params)
induced_y_zero = spm_test(all_traj_induced_y,
0).inference(alpha,
two_tailed=True,
**infer_params)
induced_z_zero = spm_test(all_traj_induced_z,
0).inference(alpha,
two_tailed=True,
**infer_params)
print('Activity: {}'.format(activity))
print('Total')
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,
# plot spm
x_sig_euler_add = sig_filter(ht_euler_diff_vs_euler_add, x)
# x_sig_contribs = sig_filter(ht_euler_diff_vs_contribs, x)
axs[cur_row].plot(x_sig_euler_add,
np.repeat(spm_y[cur_row], x_sig_euler_add.size),
color=color_map.colors[0],
lw=2)
# axs[cur_row].plot(x_sig_contribs, np.repeat(spm_y[cur_row] - 1, x_sig_contribs.size),
# color=color_map.colors[1], lw=2)
# print info
print(activity)
print('Euler Diff at Max: {:.2f}'.format(ht_euler_add_max_mean))
print('Contrib Diff at Max: {:.2f}'.format(ht_contrib_max_mean))
print('HT Elevation significance:')
print(extract_sig(ht_euler_diff_vs_euler_add, x))
print(output_spm_p(ht_euler_diff_vs_euler_add))
if idx == 0:
leg_mean.append(ht_euler_add_ln[0])
leg_mean.append(ht_contribs_ln[0])
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig.suptitle('Difference from HT elevation',
x=0.5,
y=0.99,
fontweight='bold')
plt.subplots_adjust(top=0.91)
leg_left = fig.legend(
leg_mean, ['ST UR + GH Elevation', 'ST + GH Contrib to Elevation'],
gh_x_sig = sig_filter(gh_spm, x)
axs[cur_row, 0].plot(st_x_sig,
np.repeat(2, st_x_sig.size),
color='k',
lw=2)
axs[cur_row, 1].plot(gh_x_sig,
np.repeat(0, gh_x_sig.size),
color='k',
lw=2)
print(activity)
print('Mean Diff at Max ST: {:.2f}'.format(st_contrib_max_mean -
st_euler_max_mean))
print('Mean Diff at Max GH: {:.2f}'.format(gh_contrib_max_mean -
gh_euler_max_mean))
print('ST HT Elevation angles')
print(extract_sig(st_spm, x))
print(output_spm_p(st_spm))
print('GH HT Elevation angles')
print(extract_sig(gh_spm, x))
print(output_spm_p(gh_spm))
if idx == 0:
leg_mean.append(st_euler_ln[0])
leg_mean.append(st_contrib_ln[0])
leg_mean.append(gh_euler_ln[0])
leg_mean.append(gh_contrib_ln[0])
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig.suptitle(
'Comparison between Euler Angles and\nST/GH Contribution for Measuring Elevation',
dict(color=color_map.colors[0], marker=markers[0], markevery=20))
st_f_ln = mean_sd_plot(axs_elev[cur_row], x_elev, st_f_mean, st_f_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_m_max_mean, yerr=st_m_max_sd, color=color_map.colors[0],
marker=markers[0], capsize=3)
axs_elev[cur_row].errorbar(max_pos, st_f_max_mean, yerr=st_f_max_sd, color=color_map.colors[1],
marker=markers[1], capsize=3)
# spm
m_vs_f = spm_test(traj_st_m, traj_st_f).inference(alpha, two_tailed=True, **infer_params)
x_sig = sig_filter(m_vs_f, x_elev)
axs_elev[cur_row].plot(x_sig, np.repeat(spm_y[activity.lower()], x_sig.size), color='k')
print(activity)
print(extract_sig(m_vs_f, x_elev))
if idx == 0:
leg_elev_mean.append(st_m_ln[0])
leg_elev_mean.append(st_f_ln[0])
for idx_act, (activity, activity_df) in enumerate(db_er_endpts.groupby('Activity', observed=True)):
m_df = activity_df[activity_df['Gender'] == 'M']
f_df = activity_df[activity_df['Gender'] == 'F']
traj_st_m = np.stack(m_df['st_contribs_interp'], axis=0)[:, :, 2]
traj_st_f = np.stack(f_df['st_contribs_interp'], axis=0)[:, :, 2]
# means and standard deviations
st_m_mean = np.rad2deg(np.mean(traj_st_m, axis=0))
st_f_mean = np.rad2deg(np.mean(traj_st_f, axis=0))
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)
# print out
print(activity)
print('New SHR at 0: {:.2f}'.format(new_shr_mean[0]))
print('New SHR at 100: {:.2f}'.format(new_shr_mean[-1]))
print('Old SHR at 0: {:.2f}'.format(old_shr_mean[0]))
print('Old SHR at 100: {:.2f}'.format(old_shr_mean[-1]))
print('Motion % significant')
print(extract_sig(spm_res, x))
print(output_spm_p(spm_res))
if idx == 0:
leg_mean.append(old_shr_ln[0])
leg_mean.append(new_shr_ln[0])
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig.suptitle('SHR Comparison', x=0.47, y=0.99, fontweight='bold')
plt.subplots_adjust(top=0.93)
leg_left = fig.legend(leg_mean[:2], ['Euler SHR', 'Coordinated SHR'],
loc='upper right',
bbox_to_anchor=(1, 0.92),
ncol=1,
handlelength=1.5,
dict(color=color_map.colors[0]))
repro_t_ln, repro_alpha = spm_plot_alpha(
axs[idx_act, 1], x[1:], repro_vs_zero,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1]))
tilt_t_ln, tilt_alpha = spm_plot_alpha(
axs[idx_act, 1], x[1:], tilt_vs_zero,
dict(color=color_map.colors[7], alpha=0.25),
dict(color=color_map.colors[7]))
empty_ln = axs[idx_act, 1].plot(np.nan, color='none')
# print significance
print('Activity: {}'.format(activity))
print('Total')
print('Max Mean: {:.2f}'.format(np.max(np.abs(total_mean))))
print(extract_sig(total_vs_zero, x))
print('LatMed')
print('Max Mean: {:.2f}'.format(np.max(np.abs(latmed_mean))))
print(extract_sig(latmed_vs_zero, x))
print('RePro')
print('Max Mean: {:.2f}'.format(np.max(np.abs(repro_mean))))
print(extract_sig(repro_vs_zero, x))
print('Tilt')
print('Max Mean: {:.2f}'.format(np.max(np.abs(tilt_mean))))
print(extract_sig(tilt_vs_zero, x))
if idx_act == 0:
# legend lines
mean_lns.append(repro_ln[0])
mean_lns.append(total_ln[0])
mean_lns.append(tilt_ln[0])
x_sig_repro = sig_filter(latmed_vs_repro, x_elev)
x_sig_tilt = sig_filter(latmed_vs_tilt, x_elev)
axs_elev[cur_row].plot(x_sig_repro,
np.repeat(spm_y[activity.lower()],
x_sig_repro.size),
'-',
color='k')
axs_elev[cur_row].plot(x_sig_tilt,
np.repeat(spm_y[activity.lower()] - 3,
x_sig_tilt.size),
'-',
color='k')
print('Activity: {}'.format(activity))
print('Repro Sig')
print(extract_sig(latmed_vs_repro, x_elev))
print('Tilt Sig')
print(extract_sig(latmed_vs_tilt, x_elev))
print('P-values: ')
print(output_spm_p(latmed_vs_repro))
print(output_spm_p(latmed_vs_tilt))
print('LatMed Max Deg: {:.2f}'.format(st_max_mean_x))
print('Repro Max Deg: {:.2f}'.format(st_max_mean_y))
print('Tilt Max Deg: {:.2f}'.format(st_max_mean_z))
print('Total Max Deg: {:.2f}'.format(st_max_mean))
print('LatMed Max Percentage: {:.2f}'.format(st_max_mean_x /
st_max_mean * 100))
print('Repro Max Percentage: {:.2f}'.format(st_max_mean_y /
st_max_mean * 100))
print('Tilt Max Percentage: {:.2f}'.format(st_max_mean_z /
st_max_mean * 100))
spm_one_way_rm_induced_z = spm1d.stats.anova1rm(all_traj_induced_z, group_rm, subj_rm).inference(alpha=0.05)
shaded_spm_rm = dict(color=color_map.colors[6], alpha=0.25, hatch='ooo')
line_spm_rm = dict(color=color_map.colors[7])
one_way_rm_ln_induced, alpha_ln_induced = spm_plot_alpha(axs_plane[0, 1], x, spm_one_way_rm_induced,
shaded_spm_rm, line_spm_rm)
one_way_rm_ln_induced_x, alpha_ln_induced_x = spm_plot_alpha(axs_plane[1, 1], x, spm_one_way_rm_induced_x,
shaded_spm_rm, line_spm_rm)
one_way_rm_ln_induced_y, alpha_ln_induced_y = spm_plot_alpha(axs_plane[2, 1], x, spm_one_way_rm_induced_y,
shaded_spm_rm, line_spm_rm)
one_way_rm_ln_induced_z, alpha_ln_induced_z = spm_plot_alpha(axs_plane[3, 1], x, spm_one_way_rm_induced_z,
shaded_spm_rm, line_spm_rm)
print('Different between planes')
print('Total')
print(extract_sig(spm_one_way_rm_induced, x))
print('MedLat')
print(extract_sig(spm_one_way_rm_induced_x, x))
print('Repro')
print(extract_sig(spm_one_way_rm_induced_y, x))
print('Tilt')
print(extract_sig(spm_one_way_rm_induced_z, x))
mean_lns = []
activities = []
t_lns = []
alpha_lns = []
for idx, (activity, activity_df) in enumerate(db_elev.groupby('Activity', observed=True)):
act_all_traj_induced = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm, args=['st', 'common_fine_up', 'induced_axial_rot', 3, 'up']), axis=0)
act_all_traj_induced_x = np.stack(activity_df['traj_interp'].apply(