-
Notifications
You must be signed in to change notification settings - Fork 0
/
plot_kinematic.py
224 lines (200 loc) · 11.7 KB
/
plot_kinematic.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
# -*- coding: utf-8 -*-
"""
Created on Fri Apr 06 11:41:00 2018
@author: AdminXPS
"""
import os
import matplotlib.pyplot as plt
import numpy as np
# Chaque cote contiendra paramètres spatio temporel, kinematic, kinetics
# extraction des event en list
def plot_kinematic(subject_kin_case1, subject_spt_case1, color1,
subject_kin_case2, subject_spt_case2, color2,
norm_spt, norm_kin, report_directory,
legend_1="test1", legend_2="test2", title="Kinematic"):
# Analysis of what can be plotted
# Is there a left side and how much file
nbr_file_case1 = subject_kin_case1['all']['Knee_Fle'].shape[1]
# Is there a right side and how much file
nbr_file_case2 = subject_kin_case2['all']['Knee_Fle'].shape[1]
legend_1 = legend_1 + '(' + str(nbr_file_case1) + ')'
legend_2 = legend_2 + '(' + str(nbr_file_case2) + ')'
for trace in ['mean', 'control']:
x = np.linspace(0, 101, 101)
list_kin = ["Pelvis_Fle", "Pelvis_Abd", "Pelvis_Ier",
"Hip_Fle", "Hip_Abd", "Hip_Ier",
"Knee_Fle", "Knee_Abd", "Knee_Ier",
"Ankle_Fle", "Foot_Progression", "Foot_tilt"]
list_name = ["Pelvis tilt", "Pelvis obliquity", "Pelvis rotation",
"Hip flexion", "Hip abduction", "Hip rotation",
"Knee flexion", "Knee abduction", "Knee rotation",
"Ankle dorsiflexion", "Foot progression", "Foot tilt"]
list_ylim = [(-20, 40), (-30, 30), (-30, 30),
(-30, 60), (-30, 30), (-30, 30),
(-10, 70), (-30, 30), (-30, 30),
(-30, 30), (-90, 30), (-100, 25)]
list_ytick = [[-20, -5, 10, 25, 40], [-30, -15, 0, 15, 30], [-30, -15, 0, 15, 30],
[-30, -5, 20, 45, 60], [-30, -15, 0, 15, 30], [-30, -15, 0, 15, 30],
[-10, 10, 30, 50, 70], [-30, -15, 0, 15, 30], [-30, -15, 0, 15, 30],
[-30, -15, 0, 15, 30], [-90, -60, -30, 0, 30], [-100, -70, -40, -10, 20]]
list_yticklabel = [["-20", "Post", "deg", "Ant", "40"],
["-30", "Down", "deg", "Up", "30"],
["-30", "Ext", "deg", "Int", "30"],
["-30", "Ext", "deg", "Flex", "60"],
["-30", "Abd", "deg", "Add", "30"],
["-30", "Ext", "deg", "Int", "30"],
["-10", "Ext", "deg", "Flex", "70"],
["-30", "Val", "deg", "Var", "30"],
["-30", "Ext", "deg", "Int", "30"],
["-30", "Plant", "deg", "Dorsi", "30"],
["-90", "Ext", "deg", "Int", "30"],
["-100", "Neg", "deg", "Pos", "20"]]
fig, axis = plt.subplots(4, 3, figsize=(8.27, 11.69), dpi=100)
for ind_kin, name_kin in enumerate(list_kin):
i_row = ind_kin / 3
i_collumn = ind_kin % 3
# Definition du subplot à utiliser
ax_temp = axis[i_row, i_collumn]
ylim_inf = list_ylim[ind_kin][0]
ylim_sup = list_ylim[ind_kin][1]
ylim_huitieme = ylim_sup - (ylim_sup - ylim_inf) / 8.0
# tracer des lignes
ax_temp.plot([0, 100], [0, 0], 'k')
for value in np.arange(list_ylim[ind_kin][0] + 10, list_ylim[ind_kin][1] + 10, step=10):
ax_temp.plot([0, 100], [value, value], color=(0.5, 0.5, 0.5), linewidth=1.0)
# tracer des évenements spatio temporel
# foot off
fo_mean_case1 = subject_spt_case1["mean"]["stance_phase_perc"]
fo_mean_case2 = subject_spt_case2["mean"]["stance_phase_perc"]
fo_mean_norm = norm_spt["mean"]["stance_phase_perc"]
fo_std_case1 = subject_spt_case1["std"]["stance_phase_perc"]
fo_std_case2 = subject_spt_case2["std"]["stance_phase_perc"]
fo_std_norm = norm_spt["std"]["stance_phase_perc"]
ax_temp.fill_between([fo_mean_norm - fo_std_norm,
fo_mean_norm + fo_std_norm],
[ylim_inf, ylim_inf], [ylim_sup, ylim_sup],
facecolor='0.5', alpha=0.5)
if nbr_file_case1 > 0:
ax_temp.fill_between([fo_mean_case1 - fo_std_case1,
fo_mean_case1 + fo_std_case1],
[ylim_inf, ylim_inf], [ylim_sup, ylim_sup],
facecolor=color1, alpha=0.5)
if nbr_file_case2 > 0:
ax_temp.fill_between([fo_mean_case2 - fo_std_case2,
fo_mean_case2 + fo_std_case2],
[ylim_inf, ylim_inf], [ylim_sup, ylim_sup],
facecolor=color2, alpha=0.5)
# controlateral foot strike
ctfs_mean_case1 = subject_spt_case1["mean"]["percentage_CTFS"]
ctfs_mean_case2 = subject_spt_case2["mean"]["percentage_CTFS"]
ctfs_mean_norm = norm_spt["mean"]["percentage_CTFS"]
ctfs_std_case1 = subject_spt_case1["std"]["percentage_CTFS"]
ctfs_std_case2 = subject_spt_case2["std"]["percentage_CTFS"]
ctfs_std_norm = norm_spt["std"]["percentage_CTFS"]
ax_temp.fill_between([ctfs_mean_norm - ctfs_std_norm,
ctfs_mean_norm + ctfs_std_norm],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor='0.5', alpha=0.5)
if nbr_file_case1 > 0:
ax_temp.fill_between([ctfs_mean_case1 - ctfs_std_case1,
ctfs_mean_case1 + ctfs_std_case1],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor=color1, alpha=0.5)
if nbr_file_case2 > 0:
ax_temp.fill_between([ctfs_mean_case2 - ctfs_std_case2,
ctfs_mean_case2 + ctfs_std_case2],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor=color2, alpha=0.5)
# controlateral foot off
ctfo_mean_case1 = subject_spt_case1["mean"]["percentage_CTFO"]
ctfo_mean_case2 = subject_spt_case2["mean"]["percentage_CTFO"]
ctfo_mean_norm = norm_spt["mean"]["percentage_CTFO"]
ctfo_std_case1 = subject_spt_case1["std"]["percentage_CTFO"]
ctfo_std_case2 = subject_spt_case2["std"]["percentage_CTFO"]
ctfo_std_norm = norm_spt["std"]["percentage_CTFO"]
ax_temp.fill_between([ctfo_mean_norm - ctfo_std_norm,
ctfo_mean_norm + ctfo_std_norm],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor='0.5', alpha=0.5)
if nbr_file_case1 > 0:
ax_temp.fill_between([ctfo_mean_case1 - ctfo_std_case1,
ctfo_mean_case1 + ctfo_std_case1],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor=color1, alpha=0.5)
if nbr_file_case2 > 0:
ax_temp.fill_between([ctfo_mean_case2 - ctfo_std_case2,
ctfo_mean_case2 + ctfo_std_case2],
[ylim_huitieme, ylim_huitieme], [ylim_sup, ylim_sup],
facecolor=color2, alpha=0.5)
# controlateral foot off
# tracer des courbes
if not (i_row == 3 and i_collumn == 2):
norm_mean = norm_kin["mean"][list_kin[ind_kin]]
norm_std = norm_kin["std"][list_kin[ind_kin]]
norm_X = norm_kin["mean"]['X_value']
ax_temp.fill_between(norm_X, norm_mean - norm_std, norm_mean +
norm_std, facecolor='0.5', alpha=0.5)
# Tracer des courbes
if trace == 'mean':
mean_1 = subject_kin_case1["mean"][list_kin[ind_kin]]
mean_2 = subject_kin_case2["mean"][list_kin[ind_kin]]
std_1 = subject_kin_case1["std"][list_kin[ind_kin]]
std_2 = subject_kin_case2["std"][list_kin[ind_kin]]
if nbr_file_case1 > 0:
ax_temp.fill_between(x, mean_1 - std_1, mean_1 + std_1,
facecolor=color1, alpha=0.5)
if nbr_file_case2 > 0:
ax_temp.fill_between(x, mean_2 - std_2, mean_2 + std_2,
facecolor=color2, alpha=0.5)
if nbr_file_case1 > 0:
ax_temp.plot(x, mean_1, color1, label=legend_1)
if nbr_file_case2 > 0:
ax_temp.plot(x, mean_2, color2, label=legend_2)
title_final = title
report_directory_final = os.path.join(report_directory, 'Cinematique')
if not os.path.isdir(report_directory_final):
os.makedirs(report_directory_final)
elif trace == 'control':
all_1 = subject_kin_case1["all"][list_kin[ind_kin]]
all_2 = subject_kin_case2["all"][list_kin[ind_kin]]
if nbr_file_case1 > 0:
ax_temp.plot(x, all_1[:, 0], color1, label=legend_1)
if nbr_file_case2 > 0:
ax_temp.plot(x, all_2[:, 0], color2, label=legend_2)
if nbr_file_case1 > 1:
ax_temp.plot(x, all_1[:, 1:], color1)
if nbr_file_case2 > 2:
ax_temp.plot(x, all_2[:, 1:], color2)
title_final = title + '_control'
report_directory_final = os.path.join(report_directory, 'Controle des donnees')
if not os.path.isdir(report_directory_final):
os.makedirs(report_directory_final)
# Reglages des élement du graphique
ax_temp.set_title(list_name[ind_kin], fontsize=15)
ax_temp.set_ylim(list_ylim[ind_kin])
ax_temp.set_xlim((0, 100))
ax_temp.set_xticks([0, 20, 40, 60, 80, 100])
ax_temp.set_xticklabels(["0", "20", "40", "60", "80", "100"])
ax_temp.set_yticks(list_ytick[ind_kin])
ax_temp.set_yticklabels(list_yticklabel[ind_kin])
ax_temp.spines['right'].set_visible(False)
ax_temp.spines['top'].set_visible(False)
if i_row == 3:
ax_temp.set_xlabel("% Gait cycle")
if (i_row == 0 and i_collumn == 1):
# ax_temp.legend()
lgd = ax_temp.legend(loc='upper center', bbox_to_anchor=(
0.5, 1.35), ncol=2, prop={'size': 13})
# fig.suptitle(title, fontsize=16)
# plt.tight_layout(pad=7.0, w_pad=0.3, h_pad=1.0)
plt.tight_layout()
# gs1.tight_layout(fig, rect=[0, 0.03, 1, 0.95])
# plt.show(block=False)
file_name = os.path.join(report_directory_final, title_final + '.png')
print("Sauvegarde du fichier " + title_final)
fig.savefig(file_name, bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.close(fig)
if trace == 'mean':
file_name_final = file_name
# fig.savefig('samplefigure.png')
return file_name_final