isb_mean_min = np.rad2deg(np.mean(all_traj_isb_min, axis=0))
isb_norm_mean_min = np.rad2deg(np.mean(all_traj_isb_norm_min, axis=0))
phadke_mean_min = np.rad2deg(np.mean(all_traj_phadke_min, axis=0))
isb_sd_min = np.rad2deg(np.std(all_traj_isb_min, ddof=1, axis=0))
isb_norm_sd_min = np.rad2deg(
np.std(all_traj_isb_norm_min, ddof=1, axis=0))
phadke_sd_min = np.rad2deg(np.std(all_traj_phadke_min, ddof=1, axis=0))
# plot mean +- sd
shaded = dict(color=color_map.colors[idx], alpha=0.25)
line = dict(color=color_map.colors[idx],
marker=markers[idx],
markevery=20)
if activity == 'SA':
shaded['hatch'] = '...'
phadke_ln = mean_sd_plot(axs[1, 0], x, phadke_mean_act, phadke_sd_act,
shaded, line)
isb_ln = mean_sd_plot(axs[2, 0], x, isb_mean_act, isb_sd_act, shaded,
line)
isb_norm_ln = mean_sd_plot(axs[3, 0], x, isb_mean_norm_act,
isb_sd_norm_act, shaded, line)
# plot endpoints
axs[1, 0].errorbar(max_pos + act_order[activity] * 3,
phadke_mean_max,
yerr=phadke_sd_max,
color=color_map.colors[idx],
marker=markers[2],
capsize=3)
axs[2, 0].errorbar(max_pos + act_order[activity] * 3,
isb_mean_max,
yerr=isb_sd_max,
isb_mean_max = np.rad2deg(np.mean(all_traj_isb_max, axis=0))
true_mean_max = np.rad2deg(np.mean(all_traj_true_max, axis=0))
isb_norm_mean_max = np.rad2deg(np.mean(all_traj_isb_norm_max, axis=0))
isb_sd_max = np.rad2deg(np.std(all_traj_isb_max, ddof=1, axis=0))
true_sd_max = np.rad2deg(np.std(all_traj_true_max, ddof=1, axis=0))
isb_norm_sd_max = np.rad2deg(np.std(all_traj_isb_norm_max, ddof=1, axis=0))
isb_poe_mean = np.rad2deg(np.mean(all_traj_isb_poe, axis=0))
isb_poe_sd = np.rad2deg(np.std(all_traj_isb_poe, ddof=1, axis=0))
isb_poe_mean_max = np.rad2deg(np.mean(all_traj_isb_poe_max, axis=0))
isb_poe_sd_max = np.rad2deg(np.std(all_traj_isb_poe_max, ddof=1, axis=0))
# plot mean +- sd
cur_row = act_row[activity.lower()]
true_ln = mean_sd_plot(axs_diff[cur_row], x, true_mean, true_sd,
dict(color=color_map.colors[2], alpha=0.25),
dict(color=color_map.colors[2], marker=markers[2], markevery=20))
isb_norm_ln = mean_sd_plot(axs_diff[cur_row], x, isb_norm_mean, isb_norm_sd,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1], marker=markers[1], markevery=20))
isb_ln = mean_sd_plot(axs_diff[cur_row], x, isb_mean, isb_sd,
dict(color=color_map.colors[0], alpha=0.3),
dict(color=color_map.colors[0], marker=markers[0], markevery=20))
# poe_ln = mean_sd_plot(axs_diff[cur_row], x, isb_poe_mean, isb_poe_sd,
# dict(color=color_map.colors[3], alpha=0.3),
# dict(color=color_map.colors[3], marker=markers[0], markevery=20))
# plot endpoint
axs_diff[cur_row].errorbar(x[-1] + (x[-1] - x[0]) * 0.1, true_mean_max, yerr=true_sd_max,
color=color_map.colors[2], marker=markers[2], capsize=3)
axs_diff[cur_row].errorbar(x[-1] + (x[-1] - x[0]) * 0.14, isb_norm_mean_max, yerr=isb_norm_sd_max,
# means
true_mean = np.rad2deg(np.mean(all_traj_true, axis=0))
isb_mean = np.rad2deg(np.mean(all_traj_isb, axis=0))
isb_norm_mean = np.rad2deg(np.mean(all_traj_isb_norm, axis=0))
phadke_mean = np.rad2deg(np.mean(all_traj_phadke, axis=0))
# sds
true_sd = np.rad2deg(np.std(all_traj_true, ddof=1, axis=0))
isb_sd = np.rad2deg(np.std(all_traj_isb, ddof=1, axis=0))
isb_norm_sd = np.rad2deg(np.std(all_traj_isb_norm, ddof=1, axis=0))
phadke_sd = np.rad2deg(np.std(all_traj_phadke, ddof=1, axis=0))
# plots mean +- sd
phadke_ln = mean_sd_plot(
axs[idx_act, 0], x, phadke_mean, phadke_sd,
dict(color=color_map.colors[1], alpha=0.2),
dict(color=color_map.colors[1], marker=markers[1], markevery=20))
true_left_ln = mean_sd_plot(
axs[idx_act, 0], x, true_mean, true_sd,
dict(color=color_map.colors[2], alpha=0.2, hatch='...'),
dict(color=color_map.colors[2], marker=markers[2], markevery=20))
isb_ln = mean_sd_plot(
axs[idx_act, 1], x, isb_mean, isb_sd,
dict(color=color_map.colors[0], alpha=0.2),
dict(color=color_map.colors[0], marker=markers[0], markevery=20))
isb_norm_ln = mean_sd_plot(
axs[idx_act, 1], x, isb_norm_mean, isb_norm_sd,
dict(color=color_map.colors[3], alpha=0.2),
dict(color=color_map.colors[3], marker=markers[2], markevery=20))
true_right_ln = mean_sd_plot(
axs[idx_act, 1], x, true_mean, true_sd,
elev_mean = np.rad2deg(np.nanmean(all_traj_elev, axis=0))
poe_mean = np.rad2deg(np.nanmean(all_traj_poe, axis=0))
axial_mean = np.rad2deg(np.nanmean(all_traj_axial, axis=0))
elev_sd = np.rad2deg(np.nanstd(all_traj_elev, ddof=1, axis=0))
poe_sd = np.rad2deg(np.nanstd(all_traj_poe, ddof=1, axis=0))
axial_sd = np.rad2deg(np.nanstd(all_traj_axial, ddof=1, axis=0))
present_counts = np.count_nonzero(~np.isnan(all_traj_elev), axis=0)
print(activity + ': ')
print(present_counts)
cur_row = act_row[activity.lower()]
elev_ln = mean_sd_plot(
axs_std_hum[cur_row, 0], ours_ht, elev_mean, elev_sd,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0], marker=markers[0]))
poe_ln = mean_sd_plot(
axs_std_hum[cur_row, 0], ours_ht, poe_mean, poe_sd,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1], marker=markers[1]))
ax_ln = mean_sd_plot(
axs_std_hum[cur_row, 1], ours_ht, axial_mean, axial_sd,
dict(color=color_map.colors[2], alpha=0.25),
dict(color=color_map.colors[2], marker=markers[2]))
if idx == 0:
leg_patch_ours_std_hum.append(elev_ln[0])
leg_patch_ours_std_hum.append(poe_ln[0])
isb_norm_sd = np.rad2deg(np.std(all_traj_isb_norm, ddof=1, axis=0))
isb_mean_max = np.rad2deg(np.mean(all_traj_isb_max, axis=0))
isb_norm_mean_max = np.rad2deg(np.mean(all_traj_isb_norm_max, axis=0))
phadke_mean_max = np.rad2deg(np.mean(all_traj_phadke_max, axis=0))
true_mean_max = np.rad2deg(np.mean(all_traj_true_max, axis=0))
isb_sd_max = np.rad2deg(np.std(all_traj_isb_max, ddof=1, axis=0))
isb_norm_sd_max = np.rad2deg(
np.std(all_traj_isb_norm_max, ddof=1, axis=0))
phadke_sd_max = np.rad2deg(np.std(all_traj_phadke_max, ddof=1, axis=0))
true_sd_max = np.rad2deg(np.std(all_traj_true_max, ddof=1, axis=0))
# plot mean +- sd
cur_row = act_row[activity.lower()]
true_ln_left = mean_sd_plot(
axs_diff[cur_row, 0], x, true_mean, true_sd,
dict(color=color_map.colors[2], alpha=0.3, hatch='...'),
dict(color=color_map.colors[2], marker=markers[2], markevery=20))
phadke_ln = mean_sd_plot(
axs_diff[cur_row, 0], x, phadke_mean, phadke_sd,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1], marker=markers[1], markevery=20))
true_ln_right = mean_sd_plot(
axs_diff[cur_row, 1], x, true_mean, true_sd,
dict(color=color_map.colors[2], alpha=0.3, hatch='...'),
dict(color=color_map.colors[2], marker=markers[2], markevery=20))
isb_ln = mean_sd_plot(
axs_diff[cur_row, 1], x, isb_mean, isb_sd,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0], marker=markers[0], markevery=20))
isb_norm_ln = mean_sd_plot(
alpha, two_tailed=True)
m_v_f_true = spm1d.stats.ttest2(all_traj_true_m[:, 1:],
all_traj_true_f[:, 1:],
equal_var=False).inference(
alpha, two_tailed=True)
# plot mean +- sd
shaded_m = dict(color=color_map.colors[0], alpha=0.25)
shaded_f = dict(color=color_map.colors[1], alpha=0.25)
line_m = dict(color=color_map.colors[0],
marker=markers[0],
markevery=20)
line_f = dict(color=color_map.colors[1],
marker=markers[1],
markevery=20)
m_true_ln = mean_sd_plot(axs_gender[0, 0], x, true_mean_m, true_sd_m,
shaded_m, line_m)
f_true_ln = mean_sd_plot(axs_gender[0, 0], x, true_mean_f, true_sd_f,
shaded_f, line_f)
m_isb_ln = mean_sd_plot(axs_gender[1, 0], x, isb_mean_m, isb_sd_m,
shaded_m, line_m)
f_isb_ln = mean_sd_plot(axs_gender[1, 0], x, isb_mean_f, isb_sd_f,
shaded_f, line_f)
m_phadke_ln = mean_sd_plot(axs_gender[2, 0], x, phadke_mean_m,
phadke_sd_m, shaded_m, line_m)
f_phadke_ln = mean_sd_plot(axs_gender[2, 0], x, phadke_mean_f,
phadke_sd_f, shaded_f, line_f)
# plot spm
spm_shaded = dict(color=color_map.colors[2], alpha=0.25)
spm_line = dict(color=color_map.colors[2])
true_spm_ln = spm_plot(axs_gender[0, 1], x[1:], m_v_f_true, spm_shaded,
..., np.newaxis]
isb_mean_act = np.rad2deg(np.mean(all_traj_isb_act, axis=0))
phadke_mean_act = np.rad2deg(np.mean(all_traj_phadke_act, axis=0))
true_mean_act = np.rad2deg(np.mean(all_traj_true_act, axis=0))
isb_sd_act = np.rad2deg(np.std(all_traj_isb_act, ddof=1, axis=0))
phadke_sd_act = np.rad2deg(
np.std(all_traj_phadke_act, ddof=1, axis=0))
true_sd_act = np.rad2deg(np.std(all_traj_true_act, ddof=1, axis=0))
# plot mean +- sd
shaded = dict(color=color_map.colors[idx], alpha=0.25)
line = dict(color=color_map.colors[idx],
marker=markers[idx],
markevery=20)
true_ln = mean_sd_plot(axs_plane[0, 0], x, true_mean_act,
true_sd_act, shaded, line)
isb_ln = mean_sd_plot(axs_plane[1, 0], x, isb_mean_act, isb_sd_act,
shaded, line)
phadke_ln = mean_sd_plot(axs_plane[2, 0], x, phadke_mean_act,
phadke_sd_act, shaded, line)
mean_lns.append(true_ln[0])
activities.append(activity)
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig_plane.suptitle(traj + ' Axial Rotation Comparison by Plane',
x=0.5,
y=0.99,
fontweight='bold')
plt.subplots_adjust(top=0.93)
elev_mean_gc = np.rad2deg(np.mean(all_traj_elev_gc, axis=0))
poe_mean_gc = np.rad2deg(np.mean(all_traj_poe_gc, axis=0))
axial_mean_gc = np.rad2deg(np.mean(all_traj_axial_gc, axis=0))
elev_sd_gc = np.rad2deg(np.std(all_traj_elev_gc, ddof=1, axis=0))
poe_sd_gc = np.rad2deg(np.std(all_traj_poe_gc, ddof=1, axis=0))
axial_sd_gc = np.rad2deg(np.std(all_traj_axial_gc, ddof=1, axis=0))
present_counts = np.count_nonzero(~np.isnan(all_traj_elev_ac), axis=0)
print(activity + ': ')
print(present_counts)
cur_row = act_row[activity.lower()]
elev_ln_ac = mean_sd_plot(
axs_std_hum[cur_row, 0], ours_ht, elev_mean_ac, elev_sd_ac,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0],
marker=markers[0],
ls='--',
fillstyle='none'))
poe_ln_ac = mean_sd_plot(
axs_std_hum[cur_row, 0], ours_ht, poe_mean_ac, poe_sd_ac,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1],
marker=markers[1],
ls='--',
fillstyle='none'))
ax_ln_ac = mean_sd_plot(
axs_std_hum[cur_row, 1], ours_ht, axial_mean_ac, axial_sd_ac,
dict(color=color_map.colors[2], alpha=0.25),
dict(color=color_map.colors[2],
marker=markers[2],
alpha, two_tailed=True)
lt35_v_gt45_true = spm1d.stats.ttest2(all_traj_true_lt35[:, 1:],
all_traj_true_gt45[:, 1:],
equal_var=False).inference(
alpha, two_tailed=True)
# plot mean +- sd
shaded_lt35 = dict(color=color_map.colors[0], alpha=0.25)
shaded_gt45 = dict(color=color_map.colors[1], alpha=0.25)
line_lt35 = dict(color=color_map.colors[0],
marker=markers[0],
markevery=20)
line_gt45 = dict(color=color_map.colors[1],
marker=markers[1],
markevery=20)
lt35_true_ln = mean_sd_plot(axs_age[0, 0], x, true_mean_lt35,
true_sd_lt35, shaded_lt35, line_lt35)
gt45_true_ln = mean_sd_plot(axs_age[0, 0], x, true_mean_gt45,
true_sd_gt45, shaded_gt45, line_gt45)
lt35_isb_ln = mean_sd_plot(axs_age[1, 0], x, isb_mean_lt35,
isb_sd_lt35, shaded_lt35, line_lt35)
gt45_isb_ln = mean_sd_plot(axs_age[1, 0], x, isb_mean_gt45,
isb_sd_gt45, shaded_gt45, line_gt45)
lt35_phadke_ln = mean_sd_plot(axs_age[2, 0], x, phadke_mean_lt35,
phadke_sd_lt35, shaded_lt35, line_lt35)
gt45_phadke_ln = mean_sd_plot(axs_age[2, 0], x, phadke_mean_gt45,
phadke_sd_gt45, shaded_gt45, line_gt45)
# plot spm
spm_shaded = dict(color=color_map.colors[2], alpha=0.25)
spm_line = dict(color=color_map.colors[2])
true_spm_ln = spm_plot(axs_age[0, 1], x[1:], lt35_v_gt45_true,
all_traj_true_axial = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot,
args=['gh', 'common_coarse_up', 'true_axial_rot', None]),
axis=0)
all_traj_axial = all_traj_axial - all_traj_axial[:, 0][..., np.newaxis]
all_traj_true_axial = all_traj_true_axial - all_traj_true_axial[:, 0][
..., np.newaxis]
axial_mean = np.rad2deg(np.nanmean(all_traj_axial, axis=0))
true_axial_mean = np.rad2deg(np.nanmean(all_traj_true_axial, axis=0))
axial_sd = np.rad2deg(np.nanstd(all_traj_axial, ddof=1, axis=0))
true_axial_sd = np.rad2deg(
np.nanstd(all_traj_true_axial, ddof=1, axis=0))
ax_ln = mean_sd_plot(
axs_axial[0, 0], ours_ht, axial_mean, axial_sd,
dict(color=color_map.colors[idx], alpha=0.25),
dict(color=color_map.colors[idx], marker=markers[idx]))
true_ax_ln = mean_sd_plot(
axs_axial[0, 1], ours_ht, true_axial_mean, true_axial_sd,
dict(color=color_map.colors[idx], alpha=0.25),
dict(color=color_map.colors[idx],
marker=markers[idx],
ls='--',
fillstyle='none'))
leg_patch_ours_axial.append(ax_ln[0])
leg_patch_ours_axial.append(true_ax_ln[0])
present_counts = np.count_nonzero(~np.isnan(all_traj_axial), axis=0)
print(activity + ': ')
all_traj_phadke_up[:, 1:],
all_traj_phadke_down[:, 1:]).inference(alpha, two_tailed=True)
up_v_down_true = spm1d.stats.ttest_paired(
all_traj_true_up[:, 1:],
all_traj_true_down[:, 1:]).inference(alpha, two_tailed=True)
# plot mean +- sd
shaded_up = dict(color=color_map.colors[0], alpha=0.25)
shaded_down = dict(color=color_map.colors[1], alpha=0.25)
line_up = dict(color=color_map.colors[0],
marker=markers[0],
markevery=20)
line_down = dict(color=color_map.colors[1],
marker=markers[1],
markevery=20)
up_true_ln = mean_sd_plot(axs_updown[0, 0], x, true_mean_up,
true_sd_up, shaded_up, line_up)
down_true_ln = mean_sd_plot(axs_updown[0, 0], x, true_mean_down,
true_sd_down, shaded_down, line_down)
up_isb_ln = mean_sd_plot(axs_updown[1, 0], x, isb_mean_up, isb_sd_up,
shaded_up, line_up)
down_isb_ln = mean_sd_plot(axs_updown[1, 0], x, isb_mean_down,
isb_sd_down, shaded_down, line_down)
up_phadke_ln = mean_sd_plot(axs_updown[2, 0], x, phadke_mean_up,
phadke_sd_up, shaded_up, line_up)
down_phadke_ln = mean_sd_plot(axs_updown[2, 0], x, phadke_mean_down,
phadke_sd_down, shaded_down, line_down)
# plot spm
spm_shaded = dict(color=color_map.colors[2], alpha=0.25)
spm_line = dict(color=color_map.colors[2])
true_spm_ln = spm_plot(axs_updown[0, 1], x[1:], up_v_down_true,
extract_sub_rot_norm, args=['gh', 'common_fine_up', 'true_axial_rot', None, 'up']), axis=0)
all_traj_true_aa = np.stack(act_df_aa['traj_interp'].apply(
extract_sub_rot_norm, args=['gh', 'common_fine_up', 'true_axial_rot', None, 'up']), axis=0)
# means and standard deviations
true_mean_gc = np.rad2deg(np.mean(all_traj_true_gc, axis=0))
true_mean_ac = np.rad2deg(np.mean(all_traj_true_ac, axis=0))
true_mean_aa = np.rad2deg(np.mean(all_traj_true_aa, axis=0))
true_sd_gc = np.rad2deg(np.std(all_traj_true_gc, ddof=1, axis=0))
true_sd_ac = np.rad2deg(np.std(all_traj_true_ac, ddof=1, axis=0))
true_sd_aa = np.rad2deg(np.std(all_traj_true_aa, ddof=1, axis=0))
# plot mean +- sd
cur_row = act_row[act_gc.lower()]
true_ln_gc = mean_sd_plot(axs_diff[cur_row], x, true_mean_gc, true_sd_gc,
dict(color=color_map.colors[0], alpha=0.3),
dict(color=color_map.colors[0], marker=markers[0], markevery=20))
true_ln_ac = mean_sd_plot(axs_diff[cur_row], x, true_mean_ac, true_sd_ac,
dict(color=color_map.colors[1], alpha=0.3),
dict(color=color_map.colors[1], marker=markers[1], markevery=20))
true_ln_aa = mean_sd_plot(axs_diff[cur_row], x, true_mean_aa, true_sd_aa,
dict(color=color_map.colors[2], alpha=0.3),
dict(color=color_map.colors[2], marker=markers[2], markevery=20))
if idx == 0:
leg_mean.append(true_ln_gc[0])
leg_mean.append(true_ln_ac[0])
leg_mean.append(true_ln_aa[0])
# figure title and legend
plt.tight_layout(pad=0.5, h_pad=1.5, w_pad=0.5)
