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plot_spt.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Apr 06 14:57:56 2018
@author: AdminXPS
"""
import os
import matplotlib.patches as patches
# pour demander à l'utilisateur de sélectionner des fichiers
import matplotlib.pyplot as plt
import numpy as np
#
# filenames_case1 = askopenfilenames(title="Choisir les fichiers de la première condition:",filetypes=[("Fichiers C3D","*.c3d")])
#
# Calcul des paramètres spatio temporels
# subject_spt_case1 = param_spt_allfiles(filenames_case1)
#
# name_case_1 = "left"
# name_case_2 = "right"
# color_case_1 = 'tab:orange'
# color_case_2 = 'tab:blue'
#
# subject_spt_case1 = subject_spt_case1["left"]
# subject_spt_case1 = subject_spt_case1["right"]
def plot_spt(subject_spt_case1, color_case_1,
subject_spt_case2, color_case_2,
norm_spt, report_directory,
legend_1="", legend_2="", title="SPT"):
for spt_type in ["normal", "adm"]:
if spt_type == "normal":
list_spt = ["cadence",
"length_cycle",
"walking_speed",
"step_length",
"step_width",
"stance_phase_perc",
"swing_phase_perc",
"simple_stance_perc",
"double_stance_perc"]
name_spt = ["Cadence (Pas/min)",
"Longueur du cycle (m)",
"Vitesse de marche (m/s)",
"Longueur du pas (m)",
"Largeur du pas (m)",
"Phase d\'appui (%)",
"Phase oscillante (%)",
"Simple appui (%)",
"Double appui (%)"]
list_echelle = [[0, 150],
[0, 1.5],
[0, 1.5],
[0, 1],
[0, 0.3],
[0, 80],
[0, 80],
[0, 80],
[0, 40]]
elif spt_type == "adm":
list_spt = ["cadence_adm",
"length_cycle_adm",
"walking_speed_adm",
"step_length_adm",
"step_width_adm",
"stance_phase_perc",
"swing_phase_perc",
"simple_stance_perc",
"double_stance_perc"]
name_spt = ["Cadence (adm)",
"Longueur du cycle (adm)",
"Vitesse de marche (adm)",
"Longueur du pas (adm)",
"Largeur du pas (adm)",
"Phase d\'appui (%)",
"Phase oscillante (%)",
"Simple appui (%)",
"Double appui (%)"]
list_echelle = [[0, 50],
[0, 2],
[0, 0.8],
[0, 1.5],
[0, 0.5],
[0, 80],
[0, 80],
[0, 80],
[0, 40]]
title = title + "_adm"
size_first_graph = 3
indice_list = np.array(range(10)) + size_first_graph
# fig_new,axis_new = plt.subplots(len(list_spt)*2+1,1,figsize=(8.27,11.69),dpi=200)
fig = plt.figure(figsize=(8.27, 11.69), dpi=100)
grid = plt.GridSpec(size_first_graph + len(list_spt), 1, wspace=0.4, hspace=1.5)
ax_temp = fig.add_subplot(grid[0:size_first_graph, 0]) # axis_new[0]
ax_temp.set_ylim([0, 3])
ax_temp.set_xlim([0, 100])
plt.title("% Gait cycle")
Case1_FO = subject_spt_case1["mean"]["stance_phase_perc"]
Case2_FO = subject_spt_case2["mean"]["stance_phase_perc"]
Casenorm_FO = norm_spt["mean"]["stance_phase_perc"]
ax_temp.add_patch(patches.Rectangle((0, 0), 100, 1, facecolor=color_case_1, zorder=-10))
ax_temp.add_patch(patches.Rectangle((0, 1), 100, 1, facecolor=color_case_2, zorder=-10))
ax_temp.add_patch(patches.Rectangle((0, 2), 100, 1, facecolor=(0.5, 0.5, 0.5), zorder=-10))
ax_temp.add_patch(
patches.Rectangle((Case1_FO, 0), 100 - Case1_FO, 1, alpha=0.5, facecolor=[0.8, 0.8, 0.8], zorder=0))
ax_temp.add_patch(
patches.Rectangle((Case2_FO, 1), 100 - Case2_FO, 1, alpha=0.5, facecolor=[0.8, 0.8, 0.8], zorder=0))
ax_temp.add_patch(
patches.Rectangle((Casenorm_FO, 2), 100 - Casenorm_FO, 1, alpha=0.5, facecolor=[0.8, 0.8, 0.8], zorder=0))
# Left
# C_FO
Case1_CFO_up = subject_spt_case1["mean"]["percentage_CTFO"] - \
subject_spt_case1["std"]["percentage_CTFO"]
Case1_CFO_down = subject_spt_case1["mean"]["percentage_CTFO"] + \
subject_spt_case1["std"]["percentage_CTFO"]
ax_temp.plot([Case1_CFO_up, Case1_CFO_up], [0, 1], color='k')
ax_temp.plot([Case1_CFO_down, Case1_CFO_down], [0, 1], color='k')
# C_FS
Case1_CFS_up = subject_spt_case1["mean"]["percentage_CTFS"] - \
subject_spt_case1["std"]["percentage_CTFS"]
Case1_CFS_down = subject_spt_case1["mean"]["percentage_CTFS"] + \
subject_spt_case1["std"]["percentage_CTFS"]
ax_temp.plot([Case1_CFS_up, Case1_CFS_up], [0, 1], color='k')
ax_temp.plot([Case1_CFS_down, Case1_CFS_down], [0, 1], color='k')
# FO
Case1_FO_up = subject_spt_case1["mean"]["stance_phase_perc"] - \
subject_spt_case1["std"]["stance_phase_perc"]
Case1_FO_down = subject_spt_case1["mean"]["stance_phase_perc"] + \
subject_spt_case1["std"]["stance_phase_perc"]
ax_temp.plot([Case1_FO_up, Case1_FO_up], [0, 1], color='k')
ax_temp.plot([Case1_FO_down, Case1_FO_down], [0, 1], color='k')
# Right
# C_FO
Case2_CFO_up = subject_spt_case2["mean"]["percentage_CTFO"] - \
subject_spt_case2["std"]["percentage_CTFO"]
Case2_CFO_down = subject_spt_case2["mean"]["percentage_CTFO"] + \
subject_spt_case2["std"]["percentage_CTFO"]
ax_temp.plot([Case2_CFO_up, Case2_CFO_up], [1, 2], color='k')
ax_temp.plot([Case2_CFO_down, Case2_CFO_down], [1, 2], color='k')
# C_FS
Case2_CFS_up = subject_spt_case2["mean"]["percentage_CTFS"] - \
subject_spt_case2["std"]["percentage_CTFS"]
Case2_CFS_down = subject_spt_case2["mean"]["percentage_CTFS"] + \
subject_spt_case2["std"]["percentage_CTFS"]
ax_temp.plot([Case2_CFS_up, Case2_CFS_up], [1, 2], color='k')
ax_temp.plot([Case2_CFS_down, Case2_CFS_down], [1, 2], color='k')
# FO
Case2_FO_up = subject_spt_case2["mean"]["stance_phase_perc"] - \
subject_spt_case2["std"]["stance_phase_perc"]
Case2_FO_down = subject_spt_case2["mean"]["stance_phase_perc"] + \
subject_spt_case2["std"]["stance_phase_perc"]
ax_temp.plot([Case2_FO_up, Case2_FO_up], [1, 2], color='k')
ax_temp.plot([Case2_FO_down, Case2_FO_down], [1, 2], color='k')
# Norm
# C_FO
Casenorm_CFO_up = norm_spt["mean"]["percentage_CTFO"] - \
norm_spt["std"]["percentage_CTFO"]
Casenorm_CFO_down = norm_spt["mean"]["percentage_CTFO"] + \
norm_spt["std"]["percentage_CTFO"]
ax_temp.plot([Casenorm_CFO_up, Casenorm_CFO_up], [2, 3], color='k')
ax_temp.plot([Casenorm_CFO_down, Casenorm_CFO_down], [2, 3], color='k')
# C_FS
Casenorm_CFS_up = norm_spt["mean"]["percentage_CTFS"] - \
norm_spt["std"]["percentage_CTFS"]
Casenorm_CFS_down = norm_spt["mean"]["percentage_CTFS"] + \
norm_spt["std"]["percentage_CTFS"]
ax_temp.plot([Casenorm_CFS_up, Casenorm_CFS_up], [2, 3], color='k')
ax_temp.plot([Casenorm_CFS_down, Casenorm_CFS_down], [2, 3], color='k')
# FO
Casenorm_FO_up = norm_spt["mean"]["stance_phase_perc"] - \
norm_spt["std"]["stance_phase_perc"]
Casenorm_FO_down = norm_spt["mean"]["stance_phase_perc"] + \
norm_spt["std"]["stance_phase_perc"]
ax_temp.plot([Casenorm_FO_up, Casenorm_FO_up], [2, 3], color='k')
ax_temp.plot([Casenorm_FO_down, Casenorm_FO_down], [2, 3], color='k')
ax_temp.spines['top'].set_visible(False)
ax_temp.spines['right'].set_visible(False)
ax_temp.spines['left'].set_visible(False)
ax_temp.get_yaxis().set_ticks([])
ax_temp.plot([101, 101], [3, 4], color=color_case_1, label=legend_1.replace('\n', ' '))
ax_temp.plot([101, 101], [3, 4], color=color_case_2, label=legend_2.replace('\n', ' '))
lgd = ax_temp.legend(loc='upper center', bbox_to_anchor=(
0.5, 1.35), ncol=2, prop={'size': 13})
# Tracer des paramètres
# cadence
for key, name, echelle, indice in zip(list_spt, name_spt, list_echelle, indice_list):
ax_temp = fig.add_subplot(grid[indice:indice + 1, 0])
ax_temp.set_ylim([0, 3])
ax_temp.set_xlim(echelle)
Case1_down = subject_spt_case1["mean"][key] - subject_spt_case1["std"][key]
ax_temp.add_patch(patches.Rectangle(
(Case1_down, 0.25), subject_spt_case1["std"][key] * 2, 0.5,
facecolor=color_case_1, zorder=0))
Case2_down = subject_spt_case2["mean"][key] + subject_spt_case2["std"][key]
ax_temp.add_patch(patches.Rectangle(
(Case2_down, 2.25), subject_spt_case2["std"][key] * 2, 0.5,
facecolor=color_case_2, zorder=0))
if key in norm_spt["mean"].keys():
Casenorm_down = norm_spt["mean"][key] - norm_spt["std"][key]
ax_temp.add_patch(patches.Rectangle(
(Casenorm_down, 1.25), norm_spt["std"][key] * 2, 0.5,
facecolor=(0.5, 0.5, 0.5), zorder=0))
ax_temp.spines['top'].set_visible(False)
ax_temp.spines['right'].set_visible(False)
ax_temp.spines['left'].set_visible(False)
ax_temp.get_yaxis().set_ticks([])
plt.title(name)
plt.tight_layout()
report_directory_final = os.path.join(report_directory, 'SPT')
if not os.path.isdir(report_directory_final):
os.makedirs(report_directory_final)
file_name_visuel = os.path.join(report_directory_final, title + '_Visuel.png')
print('Sauvegarde du fichier ' + title + '_Visuel.png')
fig.savefig(file_name_visuel, bbox_extra_artists=(lgd,), bbox_inches='tight')
plt.close(fig)
list_temp = []
# ["",name_case_1, name_case_2,"Norme"]
for key, name in zip(list_spt, name_spt):
value_1 = '%.2f' % subject_spt_case1["mean"][key] + \
u"\u00B1" + '%.2f' % subject_spt_case1["std"][key]
value_2 = '%.2f' % subject_spt_case2["mean"][key] + \
u"\u00B1" + '%.2f' % subject_spt_case2["std"][key]
value_norm = '%.2f' % norm_spt["mean"][key] + \
u"\u00B1" + '%.2f' % norm_spt["std"][key]
list_temp.append([name, value_1, value_2, value_norm])
fig, axis = plt.subplots(1, 1, dpi=100)
# fig,axis = plt.subplots(1,1,figsize=(8.27,11.69),dpi=100)
collabel = ("", legend_1, legend_2, "norme")
the_table = axis.table(cellText=list_temp, colLabels=collabel, loc='center', edges='open')
table_props = the_table.properties()
table_cells = table_props['child_artists']
for cell in table_cells:
cell._text.set_fontsize(15)
the_table._cells[(0, 1)]._text.set_color(color_case_1)
# the_table._cells[(0,1)].set_fontsize(40)
the_table._cells[(0, 2)]._text.set_color(color_case_2)
axis.axis('tight')
axis.axis('off')
for ind_row in range(len(list_spt) + 1):
the_table._cells[(ind_row, 3)]._text.set_color('grey')
the_table.auto_set_column_width([-1, 0, 1, 2, 3])
the_table.scale(1.0, 2.0)
plt.tight_layout()
file_name_chiffre = os.path.join(report_directory_final, title + '.png')
print('Sauvegarde du fichier ' + title)
fig.savefig(file_name_chiffre, bbox_inches='tight')
plt.close(fig)
if spt_type == "normal":
file_name_visuel_final = file_name_visuel
file_name_chiffre_final = file_name_chiffre
print(file_name_visuel_final)
return file_name_visuel_final, file_name_chiffre_final