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)
