axs_diff[cur_row, 0].errorbar(x[-1] + (x[-1] - x[0]) * 0.14,
phadke_max_mean,
yerr=phadke_max_sd,
color=color_map.colors[1],
marker=markers[1],
capsize=3)
axs_diff[cur_row, 0].errorbar(x[-1] + (x[-1] - x[0]) * 0.12,
true_max_mean,
yerr=true_max_sd,
color=color_map.colors[2],
marker=markers[2],
capsize=3)
# plot spm
isb_t_ln = spm_plot(axs_diff[cur_row, 1], x[1:], isb_vs_true,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0]))
phadke_t_ln = spm_plot(axs_diff[cur_row, 1], x[1:], phadke_vs_true,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1]))
axs_diff_dist[cur_row].axhline(0.05, ls='--')
isb_vs_true_dist_ln = axs_diff_dist[cur_row].plot(
x[1:], isb_vs_true_dist[1], color=color_map.colors[0])
phadke_vs_true_dist_ln = axs_diff_dist[cur_row].plot(
x[1:], phadke_vs_true_dist[1], color=color_map.colors[1])
leg_patch_dist.append(isb_vs_true_dist_ln[0])
leg_patch_dist.append(phadke_vs_true_dist_ln[0])
if idx == 0:
leg_patch_mean.append(isb_ln[0])
true_ln = mean_sd_plot(
axs_diff0[cur_row, 0], 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))
phadke_ln = mean_sd_plot(
axs_diff0[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))
isb_ln = mean_sd_plot(
axs_diff0[cur_row, 0], 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))
# plot spm
isb_t_ln = spm_plot(axs_diff0[cur_row, 1], x[1:], isb_zero,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0]))
phadke_t_ln = spm_plot(axs_diff0[cur_row, 1], x[1:], phadke_zero,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1]))
true_t_ln = spm_plot(axs_diff0[cur_row, 1], x[1:], true_zero,
dict(color=color_map.colors[2], alpha=0.25),
dict(color=color_map.colors[2]))
axs_diff0_dist[cur_row].axhline(0.05, ls='--')
isb_dist_ln = axs_diff0_dist[cur_row].plot(x[1:],
isb_zero_norm[1],
color=color_map.colors[0])
phadke_dist_ln = axs_diff0_dist[cur_row].plot(
x[1:], phadke_zero_norm[1], color=color_map.colors[1])
true_dist_ln = axs_diff0_dist[cur_row].plot(x[1:],
'FE': 3
})).to_numpy(dtype=np.int)
subj_rm = (db_elev_equal['Subject_Short'].map(subj_name_to_number)
).to_numpy()
spm_one_way_rm_true = spm1d.stats.anova1rm(
all_traj_true_rm[:, 1:], group_rm, subj_rm).inference(alpha=0.05)
spm_one_way_rm_isb = spm1d.stats.anova1rm(
all_traj_isb_rm[:, 1:], group_rm, subj_rm).inference(alpha=0.05)
spm_one_way_rm_phadke = spm1d.stats.anova1rm(
all_traj_phadke_rm[:, 1:], group_rm, subj_rm).inference(alpha=0.05)
shaded_spm = dict(color=color_map.colors[4], alpha=0.25)
line_spm = dict(color=color_map.colors[5])
shaded_spm_rm = dict(color=color_map.colors[6], alpha=0.25)
line_spm_rm = dict(color=color_map.colors[7])
one_way_ln_true = spm_plot(axs_plane[0, 1], x[1:], spm_one_way_true,
shaded_spm, line_spm)
one_way_ln_isb = spm_plot(axs_plane[1, 1], x[1:], spm_one_way_isb,
shaded_spm, line_spm)
one_way_ln_phadke = spm_plot(axs_plane[2, 1], x[1:],
spm_one_way_phadke, shaded_spm, line_spm)
one_way_rm_ln_true = spm_plot(axs_plane[0,
1], x[1:], spm_one_way_rm_true,
shaded_spm_rm, line_spm_rm)
one_way_rm_ln_isb = spm_plot(axs_plane[1,
1], x[1:], spm_one_way_rm_isb,
shaded_spm_rm, line_spm_rm)
one_way_rm_ln_phadke = spm_plot(axs_plane[2, 1], x[1:],
spm_one_way_rm_phadke, shaded_spm_rm,
line_spm_rm)
mean_lns = []
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,
spm_line)
isb_spm_ln = spm_plot(axs_gender[1, 1], x[1:], m_v_f_isb, spm_shaded,
spm_line)
phadke_spm_ln = spm_plot(axs_gender[2, 1], x[1:], m_v_f_phadke,
spm_shaded, spm_line)
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig_gender.suptitle(activity +
' Glenohumeral Axial Rotation Comparison',
x=0.5,
y=0.99,
fontweight='bold')
plt.subplots_adjust(top=0.93)
axs_gender[0, 0].legend([m_true_ln[0], f_true_ln[0]],
['Male', 'Female'],
..., np.newaxis]
gc_vs_ac_orient = spm1d.stats.ttest_paired(
all_traj_axial_ac, all_traj_axial_gc).inference(alpha,
two_tailed=True)
gc_vs_ac_rot = spm1d.stats.ttest_paired(
all_traj_axial_ac_norm[:, 1:],
all_traj_axial_gc_norm[:, 1:]).inference(alpha, two_tailed=True)
gc_vs_ac_rot_norm_start = spm1d.stats.ttest_paired(
all_traj_axial_ac_norm_start,
all_traj_axial_gc_norm_start).inference(alpha, two_tailed=True)
# plot spm
cur_row = act_row[activity.lower()]
orient_ln = spm_plot(axs_axial_spm[cur_row, 0], x, gc_vs_ac_orient,
dict(color=color_map.colors[0], alpha=0.25),
dict(color=color_map.colors[0]))
rotation_ln = spm_plot(axs_axial_spm[cur_row, 1], x[1:], gc_vs_ac_rot,
dict(color=color_map.colors[1], alpha=0.25),
dict(color=color_map.colors[1]))
rotation_start_ln = spm_plot(
axs_axial_spm[cur_row, 2], x, gc_vs_ac_rot_norm_start,
dict(color=color_map.colors[2], alpha=0.25),
dict(color=color_map.colors[2]))
leg_patch_axial_spm.append(orient_ln[0])
leg_patch_axial_spm.append(rotation_start_ln[0])
leg_patch_axial_spm.append(rotation_ln[0])
# figure title and axes legends
plt.tight_layout(pad=0.25, h_pad=2.0, w_pad=0.5)
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,
spm_shaded, spm_line)
isb_spm_ln = spm_plot(axs_age[1, 1], x[1:], lt35_v_gt45_isb,
spm_shaded, spm_line)
phadke_spm_ln = spm_plot(axs_age[2, 1], x[1:], lt35_v_gt45_phadke,
spm_shaded, spm_line)
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig_age.suptitle(activity + ' Glenohumeral Axial Rotation Comparison',
x=0.5,
y=0.99,
fontweight='bold')
plt.subplots_adjust(top=0.93)
axs_age[0, 0].legend([lt35_true_ln[0], gt45_true_ln[0]],
['Less Than 35', 'Greater Than 45'],
loc='upper left',
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,
spm_shaded, spm_line)
isb_spm_ln = spm_plot(axs_updown[1, 1], x[1:], up_v_down_isb,
spm_shaded, spm_line)
phadke_spm_ln = spm_plot(axs_updown[2, 1], x[1:], up_v_down_phadke,
spm_shaded, spm_line)
# figure title and legend
plt.tight_layout(pad=0.25, h_pad=1.5, w_pad=0.5)
fig_updown.suptitle(
activity + ' Glenohumeral Up vs Down Axial Rotation Comparison',
x=0.5,
y=0.99,
fontweight='bold')
plt.subplots_adjust(top=0.93)
axs_updown[0, 0].legend([up_true_ln[0], down_true_ln[0]],
['Up', 'Down'],