def grp_plotter(activity_df, col_name, pdf_file_path, grp_c, plot_directives,
section):
with PdfPages(pdf_file_path) as grp_pdf:
for name, plot_dir in plot_directives.items():
fig = plt.figure()
ax = fig.subplots()
ind_lines = []
ind_leg_entries = []
avg_lines = []
avg_entries = []
quat_avg_lines = []
quat_avg_entries = []
for grp, grp_df in activity_df.groupby(col_name):
ind_plotter_fnc = partial(ind_plotter_color_spec,
c=grp_c[grp][0])
ind_traj_plot_lines, agg_lines, quat_mean_lines = summary_plotter(
grp_df['traj_interp'], plot_dir.traj, section[0],
section[1], section[2], section[3], plot_dir.decomp_method,
plot_dir.sub_rot, ax, ind_plotter_fnc, grp_c[grp][1],
grp_c[grp][2])
ind_lines.append(ind_traj_plot_lines.iloc[0][0])
ind_leg_entries.append(grp)
avg_lines.append(agg_lines[0])
avg_entries.append(grp + r' Mean$\pm$SE')
if quat_mean_lines is not None:
quat_avg_lines.append(quat_mean_lines[0])
quat_avg_entries.append(grp + ' Mean(Quat)')
ax.legend(ind_lines,
ind_leg_entries,
handlelength=0.75,
handletextpad=0.25,
columnspacing=0.5,
loc='lower right')
ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
style_axes(ax, 'Humerothoracic Elevation (Deg)', plot_dir.y_label)
fig.tight_layout()
fig.suptitle(plot_dir.title)
if quat_avg_lines:
fig.legend(avg_lines + quat_avg_lines,
avg_entries + quat_avg_entries,
loc='lower left',
handlelength=0.75,
handletextpad=0.25,
ncol=2)
else:
fig.legend(avg_lines,
avg_entries,
loc='lower left',
handlelength=0.75,
handletextpad=0.25)
grp_pdf.savefig(fig)
fig.clf()
plt.close(fig)
#%%
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'erar': 0, 'era90': 1}
x = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
axs = fig.subplots(3, 2)
# style axes, add x and y labels
for i in range(3):
for j in range(2):
style_axes(axs[i, j], 'Motion Completion (%)' if i == 2 else None,
'Rotation (deg)' if j == 0 else None)
axs[i, j].yaxis.set_major_locator(plticker.MultipleLocator(10))
# plot
leg_mean = []
max_pos = 140
for idx, (activity, activity_df) in enumerate(
db_er_endpts.groupby('Activity', observed=True)):
st_contribs = np.stack(activity_df['st_contribs_interp'], axis=0)
gh_contribs = np.stack(activity_df['gh_contribs_interp'], axis=0)
trajs_st_elev = st_contribs[:, :, 1]
trajs_gh_elev = gh_contribs[:, :, 1]
trajs_st_axial = st_contribs[:, :, 2]
trajs_gh_axial = gh_contribs[:, :, 2]
db_elev['traj_interp'].apply(add_st_induced,
args=[st_induced_axial_rot_fha])
#%%
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig_plane = plt.figure(figsize=(190 / 25.4, 230 / 25.4))
axs_plane = fig_plane.subplots(4, 2)
# style axes, add x and y labels
style_axes(axs_plane[0, 0], None, 'Axial Rotation (Deg)')
style_axes(axs_plane[1, 0], None, 'Axial Rotation (Deg)')
style_axes(axs_plane[2, 0], None, 'Axial Rotation (Deg)')
style_axes(axs_plane[3, 0], 'Humerothoracic Elevation (Deg)',
'Axial Rotation (Deg)')
style_axes(axs_plane[0, 1], None, 'SPM{F}')
style_axes(axs_plane[1, 1], None, 'SPM{F}')
style_axes(axs_plane[2, 1], None, 'SPM{F}')
style_axes(axs_plane[3, 1], 'Humerothoracic Elevation (Deg)', 'SPM{F}')
all_traj_induced = np.stack(db_elev_equal['traj_interp'].apply(
extract_sub_rot_norm,
args=['st', 'common_fine_up', 'induced_axial_rot', 3, 'up']),
axis=0)
all_traj_induced_x = np.stack(db_elev_equal['traj_interp'].apply(
extract_sub_rot_norm,
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
gs = fig.add_gridspec(6, 2)
axs_elev = [
fig.add_subplot(gs[:2, 0]),
fig.add_subplot(gs[2:4, 0]),
fig.add_subplot(gs[4:6, 0])
]
axs_er = [fig.add_subplot(gs[:3, 1]), fig.add_subplot(gs[3:6, 1])]
for i in range(3):
style_axes(axs_elev[i], 'GH Axial Rotation (Deg)' if i == 2 else None,
'ST Axial Rotation (Deg)')
for i in range(2):
style_axes(axs_er[i], 'GH Axial Rotation (Deg)' if i == 1 else None,
'ST Axial Rotation (Deg)')
# plot elevation
leg_elev_mean = []
r_loc = [(10, -19), (20, -12), (19, 16)]
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
traj_st = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm,
args=['st', 'common_fine_up', 'contribs', 2, 'up']),
axis=0)
traj_gh = np.stack(activity_df['traj_interp'].apply(
x_er = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
gs = fig.add_gridspec(6, 2)
axs_elev = [
fig.add_subplot(gs[:2, 0]),
fig.add_subplot(gs[2:4, 0]),
fig.add_subplot(gs[4:6, 0])
]
axs_er = [fig.add_subplot(gs[:3, 1]), fig.add_subplot(gs[3:6, 1])]
for i in range(3):
style_axes(axs_elev[i],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'ST Axial Rotation (Deg)')
for i in range(2):
style_axes(axs_er[i], 'Motion Completion (%)' if i == 1 else None,
'ST Axial Rotation (Deg)')
# plot elevation
leg_elev_mean = []
spm_y = {'ca': 2, 'sa': 4, 'fe': 21}
for idx, (activity, activity_df) in enumerate(
db_elev.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['traj_interp'].apply(
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig_axial = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs_axial = fig_axial.subplots(3, 2)
fig_norm = plt.figure(figsize=(120 / 25.4, 190 / 25.4))
ax_norm = fig_norm.subplots(3, 1)
for ax in ax_norm:
ax.axhline(0.05, ls='--', color='grey')
style_axes(ax, 'Humerothoracic Elevation (Deg)', 'p-value')
# style axes, add x and y labels
style_axes(axs_axial[0, 0], None, 'Induced Axial Rotation (Deg)')
style_axes(axs_axial[1, 0], None, 'Induced Axial Rotation (Deg)')
style_axes(axs_axial[2, 0], 'Humerothoracic Elevation (Deg)',
'Induced Axial Rotation (Deg)')
style_axes(axs_axial[0, 1], None, 'SPM{t}')
style_axes(axs_axial[1, 1], None, 'SPM{t}')
style_axes(axs_axial[2, 1], 'Humerothoracic Elevation (Deg)', 'SPM{t}')
# plot
leg_patch_mean = []
leg_patch_t = []
alpha_patch = []
for idx, (activity, activity_df) in enumerate(
plot_utils.init_graphing(params.backend)
plt.close('all')
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
fig_st = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs_st = fig_st.subplots(3, 2)
fig_gh = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs_gh = fig_gh.subplots(3, 2)
st_lims = [(-5, 50), (-5, 50), (-5, 50)]
gh_lims = [(-10, 95), (0, 90), (-10, 95)]
for i in range(3):
for j in range(2):
style_axes(axs_st[i, j],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Elevation (deg)' if j == 0 else None)
style_axes(axs_gh[i, j],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Elevation (deg)' if j == 0 else None)
axs_st[i, j].set_ylim(st_lims[i][0], st_lims[i][1])
axs_gh[i, j].set_ylim(gh_lims[i][0], gh_lims[i][1])
axs_gh[i, j].yaxis.set_major_locator(
plticker.MultipleLocator(base=10.0))
axs_st[i, j].yaxis.set_major_locator(
plticker.MultipleLocator(base=10.0))
leg_mean_st = []
leg_mean_gh = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
params.torso_def +
('_' + params.scap_lateral) + '.pdf')
with PdfPages(pdf_file_path) as activity_pdf:
for trial_name, df_row in activity_df.iterrows():
st_traj = df_row['traj_interp'].st
gh_traj = df_row['traj_interp'].gh
up_down = df_row['up_down_analysis']
start_idx = up_down.max_run_up_start_idx
end_idx = up_down.max_run_up_end_idx
sec = slice(start_idx, end_idx + 1)
fig = plt.figure()
ax = fig.subplots()
ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax, 'HT Elevation Angle',
'Rotation (deg)')
ht_elev = np.rad2deg(-df_row['ht'].euler.ht_isb[sec, 1])
st_poe_plt = ax.plot(ht_elev,
np.rad2deg(st_traj.contribs[sec, 0]),
label='ST PoE')
st_elev_plt = ax.plot(ht_elev,
-np.rad2deg(st_traj.contribs[sec, 1]),
label='ST Elev')
st_axial_plt = ax.plot(ht_elev,
np.rad2deg(st_traj.contribs[sec, 2]),
label='ST Axial')
ax.plot(ht_elev,
np.rad2deg(gh_traj.contribs[sec, 0]),
'--',
plot_dir.decomp_method, plot_dir.sub_rot, ax,
ind_plotter, 'black', 'red')
if quat_mean_lines:
ax.legend([agg_lines.lines[0], quat_mean_lines[0]],
[r'Mean$\pm$SE', 'Mean(Quat)'],
loc='lower right',
handlelength=0.75,
handletextpad=0.25)
else:
ax.legend([agg_lines.lines[0]], [r'Mean$\pm$SE'],
loc='lower right',
handlelength=0.75,
handletextpad=0.25)
ax.xaxis.set_major_locator(
plticker.MultipleLocator(base=10.0))
style_axes(ax, 'Humerothoracic Elevation (Deg)',
plot_dir.y_label)
fig.tight_layout()
fig.subplots_adjust(bottom=0.2)
fig.suptitle(plot_dir.title)
fig.legend(ncol=10,
handlelength=0.75,
handletextpad=0.25,
columnspacing=0.5,
loc='lower left',
fontsize=8)
activity_pdf.savefig(fig)
fig.clf()
plt.close(fig)
# plot by gender
pdf_file_path = output_path / (activity + '_' + params.torso_def +
db_elev = db.loc[db['Trial_Name'].str.contains('_CA_|_SA_|_FE_')].copy()
prepare_db(db_elev, params.torso_def, params.scap_lateral, params.dtheta_fine, params.dtheta_coarse,
[params.min_elev, params.max_elev])
db_elev['traj_interp'].apply(add_st_gh_contrib)
#%%
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
contrib_axes = ['PoE', 'Elevation', 'Axial']
plt.rcParams['axes.prop_cycle'] = plt.cycler('color', plt.cm.tab20c.colors)
plot_utils.init_graphing(params.backend)
plt.close('all')
for activity, activity_df in db_elev.groupby('Activity', observed=True):
fig_gh = plt.figure()
ax_gh = fig_gh.subplots()
ax_gh.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax_gh, 'HT Elevation Angle', 'Alignment (deg)')
fig_st = plt.figure()
ax_st = fig_st.subplots()
ax_st.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax_st, 'HT Elevation Angle', 'Alignment (deg)')
for trial_name, df_row in activity_df.iterrows():
age_grp = df_row['age_group']
ht = df_row['ht']
st = df_row['st']
gh = df_row['gh']
up_down = df_row['up_down_analysis']
start_idx = up_down.max_run_up_start_idx
end_idx = up_down.max_run_up_end_idx
sec = slice(start_idx, end_idx + 1)
args=[st_induced_axial_rot_fha])
#%%
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig_axial = plt.figure(figsize=(90 / 25.4, 210 / 25.4))
axs_axial = fig_axial.subplots(3, 1)
# style axes, add x and y labels
style_axes(axs_axial[0], None, 'Induced Axial Rotation (Deg)')
style_axes(axs_axial[1], None, 'Induced Axial Rotation (Deg)')
style_axes(axs_axial[2], 'Humerothoracic Elevation (Deg)',
'Induced Axial Rotation (Deg)')
# plot
leg_patch_mean = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
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)
all_traj_induced_x = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm,
args=['st', 'common_fine_up', 'induced_axial_rot', 0, 'up']),
infer_params = {'force_iterations': True}
alpha = 0.05
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
plot_utils.init_graphing(params.backend)
plt.close('all')
x = np.arange(0, 100 + 0.25, 0.25)
fig = plt.figure(figsize=(110 / 25.4, 190 / 25.4), dpi=params.dpi)
axs = fig.subplots(3, 1)
for i in range(3):
style_axes(axs[i], 'Motion Completion (%)' if i == 2 else None, 'SHR')
axs[i].set_ylim(0, 12)
leg_mean = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
old_shr = np.stack(activity_df['old_shr_interp'], axis=0)
new_shr = np.stack(activity_df['new_shr_interp'], axis=0)
# 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)
start_idx = db_row.up_down_analysis.max_run_up_start_idx
end_idx = db_row.up_down_analysis.max_run_up_end_idx + 1
sec = slice(start_idx, end_idx)
ht_elev = -np.rad2deg(db_row['ht'].euler.ht_isb[sec, 1])
for seg in ['ht', 'gh']:
euler_int = db_row[seg + '_contribs_euler_rates']
euler = db_row[seg].euler.yxy_intrinsic
fig = plt.figure(figsize=(120 / 25.4, 190 / 25.4))
axs = fig.subplots(3, 1)
y_labels = ['Plane of Elevation', 'Elevation', 'Axial Rotation']
for i in range(3):
style_axes(axs[i],
seg.upper() + ' Elevation (Deg)' if i == 2 else None,
y_labels[i])
elev_lns = []
poe_lns = []
axial_lns = []
poe_ln = axs[0].plot(ht_elev, np.rad2deg(euler[sec, 0]))
elev_ln = axs[1].plot(ht_elev, np.rad2deg(euler[sec, 1]))
axial_ln = axs[2].plot(ht_elev,
np.rad2deg(db_row[seg].true_axial_rot[sec]))
poe_int_ln = axs[0].plot(ht_elev,
np.rad2deg(euler[0, 0] + euler_int[sec, 0]),
'--',
dashes=(5, 10))
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
axs = fig.subplots(3, 2)
# style axes, add x and y labels
for i in range(3):
for j in range(2):
style_axes(axs[i, j],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Motion Allocation (%)' if j == 0 else None)
axs[i, j].yaxis.set_major_locator(plticker.MultipleLocator(10))
axs[i, j].set_ylim(0, 100)
# plot
max_pos = 140
leg_mean = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
trajs_st_elev = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm,
args=['st', 'common_fine_up', 'contribs', 1, 'up']),
axis=0)
trajs_gh_elev = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm,
db_elev['traj_interp'].apply(add_st_gh_contrib)
#%%
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 90 / 25.4))
axs = fig.subplots(1, 3)
for i in range(3):
style_axes(axs[i], 'Humerothoracic Elevation (deg)',
'PoE (deg)' if i == 0 else 'ST Axial Rotation (deg)')
# # set axes limits
# ax_limits = [(20, 70), (-40, 50)]
# for i in range(3):
# for j in range(2):
# axs[i, j].set_ylim(ax_limits[j][0], ax_limits[j][1])
# axs[i, j].yaxis.set_major_locator(ticker.MultipleLocator(10))
# style_axes(axs[i, j], 'Humerothoracic Elevation (Deg)' if i == 2 else None,
# 'ReProtraction (Deg)' if j == 0 else 'PoE (Deg)')
# plot
max_pos = 140
leg_mean = []
for act_idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
gs = fig.add_gridspec(2, 6)
axs_elev = [
fig.add_subplot(gs[0, :2]),
fig.add_subplot(gs[0, 2:4]),
fig.add_subplot(gs[0, 4:6])
]
y_labels = [
'GH PoE (deg)', 'ST-contributed Axial Rotation (deg)',
'ST-contributed Axial Rotation (deg)'
]
for i in range(3):
style_axes(
axs_elev[i], 'Mean GH PoE (deg)'
if i == 2 else 'Humerothoracic Elevation (deg)', y_labels[i])
if i == 0:
axs_elev[i].set_ylim(-40, 38)
else:
axs_elev[i].set_ylim(-35, 30)
# plot
max_pos = 140
leg_mean = []
poes = []
st_axials = []
r_loc = [(-10, -25), (30, 10), (10, -5)]
for act_idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
traj_poe = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot,
gh_poe_per = (np.abs(gh_poe) / gh_total) * 100
gh_elev_per = (np.abs(gh_elev) / gh_total) * 100
gh_axial_per = (np.abs(gh_axial) / gh_total) * 100
st_poe_per = (np.abs(st_poe) / st_total) * 100
st_elev_per = (np.abs(st_elev) / st_total) * 100
st_axial_per = (np.abs(st_axial) / st_total) * 100
gh_array = [np.abs(gh_poe_per), gh_elev_per, gh_axial_per]
st_array = [np.abs(st_poe_per), st_elev_per, st_axial_per]
for axis_idx, contrib_axis in enumerate(contrib_axes):
fig_gh = plt.figure()
ax_gh = fig_gh.subplots()
ax_gh.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax_gh, 'HT Elevation Angle', 'Percentage (%)')
fig_st = plt.figure()
ax_st = fig_st.subplots()
ax_st.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax_st, 'HT Elevation Angle', 'Percentage (%)')
for trial_idx, (trial_name, age_grp) in enumerate(zip(activity_df['Trial_Name'], activity_df['age_group'])):
ax_gh.plot(x, gh_array[axis_idx][trial_idx], label='_'.join(trial_name.split('_')[0:2]))
ax_st.plot(x, st_array[axis_idx][trial_idx], label='_'.join(trial_name.split('_')[0:2]))
ax_gh.plot(x, np.mean(gh_array[axis_idx], axis=0), color='k', label='Mean')
ax_st.plot(x, np.mean(st_array[axis_idx], axis=0), color='k', label='Mean')
fig_gh.tight_layout()
fig_gh.subplots_adjust(bottom=0.2)
params.scap_lateral,
params.dtheta_fine,
params.dtheta_coarse, [params.min_elev, params.max_elev],
should_clean=False)
(db['up_min_ht'], db['up_max_ht'], db['down_min_ht'],
db['down_max_ht']) = zip(
*(db['up_down_analysis'].apply(extract_up_down_min_max)))
plot_utils.init_graphing(params.backend)
plot_dirs = [['ht', 'ht_isb', 'HT'], ['gh', 'gh_isb', 'GH'],
['st', 'st_isb', 'ST']]
for plot_dir in plot_dirs:
traj = db.loc['U35_002_SA_t01', plot_dir[0]]
traj_euler = getattr(traj, 'euler')
fig = plt.figure(figsize=(14, 7), tight_layout=True)
ax = fig.subplots(2, 3)
for i in range(3):
ax[0, i].plot(np.rad2deg(getattr(traj_euler, plot_dir[1])[:, i]))
ax[1, i].plot(traj.pos[:, i])
if i == 0:
plot_utils.style_axes(ax[0, i], None, 'Orientation (deg)')
plot_utils.style_axes(ax[1, i], 'Frame Index (Zero-Based)',
'Position (mm)')
else:
plot_utils.style_axes(ax[0, i], None, None)
plot_utils.style_axes(ax[1, i], 'Frame Index (Zero-Based)',
None)
fig.suptitle(plot_dir[2])
plot_utils.make_interactive()
plt.show()
yticks = [np.arange(0, 13, 2), np.arange(0, 16, 5), np.arange(-1, 3.1, 1)]
ylim = [(0, 12), (0, 18), (-1.5, 3.5)]
for i in range(3):
axs_axial_twin[i] = axs_axial[i, 0].twinx()
axs_axial_twin[i].set_yticks(yticks[i])
style_axes_add_right(axs_axial_twin[i], 'Axial Rotation SHR')
axs_axial_twin[i].axhline(axial_shr_dash[i], ls='--', color='grey')
axs_axial_twin[i].set_ylim(ylim[i][0], ylim[i][1])
axs_axial[i, 0].set_zorder(1)
axs_axial[i, 0].patch.set_visible(False)
axs_axial_twin[i].yaxis.set_tick_params(direction='in', width=2, pad=3)
# style axes, add x and y labels
for i in range(3):
style_axes(axs_axial[i, 0],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Axial Rotation (Deg)')
axs_axial[i, 1].yaxis.set_label_position('right')
axs_axial[i, 1].yaxis.tick_right()
style_axes_right(axs_axial[i, 1],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'SPM{t}')
axs_axial[i, 1].yaxis.set_tick_params(direction='in', width=2, pad=3)
fig_norm = plt.figure(figsize=(120 / 25.4, 190 / 25.4))
ax_norm = fig_norm.subplots(3, 1)
for ax in ax_norm:
ax.axhline(0.05, ls='--', color='grey')
style_axes(ax, 'Humerothoracic Elevation (Deg)', 'p-value')
# plot
x_er = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
gs = fig.add_gridspec(6, 2)
axs_elev = [
fig.add_subplot(gs[:2, 0]),
fig.add_subplot(gs[2:4, 0]),
fig.add_subplot(gs[4:6, 0])
]
axs_er = [fig.add_subplot(gs[:3, 1]), fig.add_subplot(gs[3:6, 1])]
for i in range(3):
style_axes(axs_elev[i],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'PoE Contribution (Deg)')
for i in range(2):
style_axes(axs_er[i], 'Motion Completion (%)' if i == 1 else None,
'PoE Contribution (Deg)')
# plot elevation
leg_elev_mean = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
traj_st = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot_norm,
args=['st', 'common_fine_up', 'contribs', 0, 'up']),
axis=0)
traj_gh = np.stack(activity_df['traj_interp'].apply(
x = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
alpha = 0.05
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
plot_utils.init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs = fig.subplots(2, 3)
for row_idx, row in enumerate(axs):
for col_idx, ax in enumerate(row):
ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
ax.yaxis.set_major_locator(plticker.MultipleLocator(base=5.0))
x_label = 'Percent Complete (%)' if row_idx == 1 else None
y_label = 'Axial Rotation (Deg)' if col_idx == 0 else None
style_axes(ax, x_label, y_label)
mean_lns = []
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_ht_lt35 = np.stack(lt35_df['ht_true_axial'], axis=0)
traj_ht_gt45 = np.stack(gt45_df['ht_true_axial'], axis=0)
traj_st_lt35 = np.stack(lt35_df['st_induced_total'], axis=0)
traj_st_gt45 = np.stack(gt45_df['st_induced_total'], axis=0)
traj_gh_lt35 = np.stack(lt35_df['gh_true_axial'], axis=0)
traj_gh_gt45 = np.stack(gt45_df['gh_true_axial'], axis=0)
#%%
x = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
# we need more colors to encompass all subjects than the default color scale offers
plt.rcParams['axes.prop_cycle'] = plt.cycler('color', plt.cm.tab20c.colors)
plot_utils.init_graphing(params.backend)
plt.close('all')
for activity, activity_df in db_er.groupby('Activity', observed=True):
# overall
pdf_file_path = output_path / (activity + '_' + params.torso_def +
('_' + params.scap_lateral) + '.pdf')
with PdfPages(pdf_file_path) as activity_pdf:
for dir_name, plot_directive in plot_directives.items():
fig = plt.figure()
ax = fig.subplots()
ax.xaxis.set_major_locator(plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax, 'Percent Completion (%)',
plot_directive.y_label)
for trial_name, interp_data in zip(activity_df['Trial_Name'],
activity_df[dir_name]):
ax.plot(x,
np.rad2deg(interp_data),
label='_'.join(trial_name.split('_')[0:2]))
fig.tight_layout()
fig.subplots_adjust(bottom=0.2)
fig.suptitle(activity + ' ' + plot_directive.title)
fig.legend(ncol=10,
handlelength=0.75,
handletextpad=0.25,
columnspacing=0.5,
loc='lower left',
fontsize=8)
activity_pdf.savefig(fig)
plt.rcParams['axes.prop_cycle'] = plt.cycler('color', plt.cm.tab20c.colors)
plot_utils.init_graphing(params.backend)
plt.close('all')
for activity, activity_df in db_er.groupby('Activity', observed=True):
# overall
pdf_file_path = output_path / ('contribs_' + activity + '_' +
params.torso_def +
('_' + params.scap_lateral) + '.pdf')
with PdfPages(pdf_file_path) as activity_pdf:
for joint in joints:
for dim_idx, plot_dir in plot_dirs.items():
fig = plt.figure()
ax = fig.subplots()
ax.xaxis.set_major_locator(
plticker.MultipleLocator(base=10.0))
plot_utils.style_axes(ax, 'Percent Completion (%)',
'Rotation (deg)')
for trial_name, interp_data in zip(
activity_df['Trial_Name'],
activity_df[joint + '_contribs_interp']):
ax.plot(x,
np.rad2deg(interp_data[:, dim_idx]),
label='_'.join(trial_name.split('_')[0:2]))
fig.tight_layout()
fig.subplots_adjust(bottom=0.2)
fig.suptitle(joint.upper() + ' ' + plot_dir)
fig.legend(ncol=10,
handlelength=0.75,
handletextpad=0.25,
columnspacing=0.5,
loc='lower left',
fontsize=8)
colors = {'ca': 1, 'sa': 2, 'fe': 0}
x_elev = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 100 / 25.4), dpi=params.dpi)
axs_elev = fig.subplots(1, 3)
y_labels = [
'GH PoE (deg)', 'ST-contributed Axial Rotation (deg)',
'ST-contributed Axial Rotation (deg)'
]
for i in range(3):
style_axes(
axs_elev[i], 'Mean GH PoE (deg)'
if i == 2 else 'Humerothoracic Elevation (deg)', y_labels[i])
if i == 0:
axs_elev[i].set_ylim(-40, 38)
else:
axs_elev[i].set_ylim(-35, 30)
# plot
max_pos = 140
leg_mean = []
poes = []
st_axials = []
r_loc = [(-10, -25), (30, 10), (10, -5)]
for act_idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
traj_poe = np.stack(activity_df['traj_interp'].apply(
extract_sub_rot,
alpha = 0.05
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
plot_utils.init_graphing(params.backend)
plt.close('all')
x = np.arange(0, 100 + 0.25, 0.25)
fig = plt.figure(figsize=(110 / 25.4, 190 / 25.4))
axs = fig.subplots(3, 1)
for i in range(3):
style_axes(axs[i],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Elevation (deg)')
axs[i].set_ylim(0, 12)
leg_mean = []
for idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
old_shr = np.stack(activity_df['old_shr_interp'], axis=0)
new_shr = np.stack(activity_df['new_shr_interp'], axis=0)
# 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)
alpha = 0.05
color_map = plt.get_cmap('Dark2')
markers = ['^', 'o', 's', '*']
act_row = {'erar': 0, 'era90': 1}
x = np.arange(0, 100 + params.dtheta_fine, params.dtheta_fine)
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4), dpi=params.dpi)
axs = fig.subplots(2, 2)
# style axes, add x and y labels
for i in range(2):
for j in range(2):
style_axes(axs[i, j], 'Motion Completion (%)' if i == 1 else None,
'Motion Allocation (%)' if j == 0 else None)
axs[i, j].yaxis.set_major_locator(plticker.MultipleLocator(10))
axs[i, j].set_ylim(0, 100)
# plot
max_pos = 140
leg_mean = []
# leg_patch_t = []
# alpha_patch = []
for idx, (activity, activity_df) in enumerate(
db_er_endpts.groupby('Activity', observed=True)):
st_contribs = np.stack(activity_df['st_contribs_interp'], axis=0)
gh_contribs = np.stack(activity_df['gh_contribs_interp'], axis=0)
trajs_st_elev = st_contribs[:, 1:, 1]
trajs_gh_elev = gh_contribs[:, 1:, 1]
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs = fig.subplots(3, 3)
# set axes limits
ax_limits = [(-35, 10), (-40, 20), (-20, 40)]
for i in range(3):
for j in range(3):
axs[i, j].set_ylim(ax_limits[i][0], ax_limits[i][1])
axs[i, j].yaxis.set_major_locator(ticker.MultipleLocator(10))
style_axes(axs[i, j],
'Humerothoracic Elevation (Deg)' if i == 2 else None,
'Axial Rotation (Deg)' if j == 0 else None)
# plot
leg_mean = []
for act_idx, (activity, activity_df) in enumerate(
db_elev.groupby('Activity', observed=True)):
f_df = activity_df[activity_df['Gender'] == 'F']
m_df = activity_df[activity_df['Gender'] == 'M']
all_traj_ht_f = np.stack(f_df['traj_interp'].apply(
extract_sub_rot_norm,
args=['ht', 'common_fine_up', 'true_axial_rot', None, 'up']),
axis=0)
all_traj_gh_f = np.stack(f_df['traj_interp'].apply(
extract_sub_rot_norm,
act_row = {'ca': 0, 'sa': 1, 'fe': 2}
x = db_elev.iloc[0]['traj_interp'].common_ht_range_fine
init_graphing(params.backend)
plt.close('all')
fig = plt.figure(figsize=(190 / 25.4, 190 / 25.4))
axs = fig.subplots(3, 2)
# set axes limits
ax_limits = [(20, 70), (-40, 50)]
for i in range(3):
for j in range(2):
axs[i, j].set_ylim(ax_limits[j][0], ax_limits[j][1])
axs[i, j].yaxis.set_major_locator(ticker.MultipleLocator(10))
style_axes(axs[i, j], 'Humerothoracic Elevation (Deg)' if i == 2 else None,
'ReProtraction (Deg)' if j == 0 else 'PoE (Deg)')
# plot
leg_mean = []
for act_idx, (activity, activity_df) in enumerate(db_elev.groupby('Activity', observed=True)):
db = db[~db['Trial_Name'].str.contains('|'.join(params.excluded_trials))]
lt35_df = activity_df[activity_df['age_group'] == '<35']
gt45_df = activity_df[activity_df['age_group'] == '>45']
all_traj_repro_lt35 = np.stack(lt35_df['traj_interp'].apply(
extract_sub_rot, args=['st', 'common_fine_up', 'euler.st_isb', 0]), axis=0)
all_traj_poe_lt35 = np.stack(lt35_df['traj_interp'].apply(
extract_sub_rot, args=['gh', 'common_fine_up', 'euler.gh_isb', 0]), axis=0)
all_traj_repro_gt45 = np.stack(gt45_df['traj_interp'].apply(
extract_sub_rot, args=['st', 'common_fine_up', 'euler.st_isb', 0]), axis=0)
