Enhanced results, animation and graph for CMBBE2024

README.md

@@ -69,7 +69,7 @@ conda install -cconda-forge conda-libmamba-solver
 
 After, install the dependencies
 ```bash
-conda install numpy matplotlib pytest casadi biorbd -cconda-forge --solver=libmamba
+conda install numpy matplotlib pytest casadi biorbd pyorerun -cconda-forge --solver=libmamba
 ```
 
 Finally, install the bioptim setup.py file located in your cocofest/external/bioptim folder

cocofest/__init__.py

@@ -26,3 +26,4 @@
 )
 from .result.plot import PlotCyclingResult
 from .result.pickle import SolutionToPickle
+from .result.animate import PickleAnimate

cocofest/custom_objectives.py

@@ -88,7 +88,7 @@ def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX:
         muscle_fatigue = horzcat(
             *[controller.states["A_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))]
         )
-        return 1 / sum1(muscle_fatigue)
+        return sum1(muscle_fatigue)
 
     @staticmethod
     def minimize_overall_muscle_force_production(controller: PenaltyController) -> MX:
@@ -106,6 +106,6 @@ def minimize_overall_muscle_force_production(controller: PenaltyController) -> M
         """
         muscle_name_list = controller.model.bio_model.muscle_names
         muscle_force = horzcat(
-            *[controller.states["F_" + muscle_name_list[x]].cx ** 3 for x in range(len(muscle_name_list))]
+            *[controller.states["F_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))]
         )
         return sum1(muscle_force)

cocofest/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py

@@ -5,19 +5,17 @@
 The files will contain the time, states, controls and parameters of the ocp.
 """
 
-import pickle
-
 from bioptim import (
     Axis,
     ConstraintFcn,
     ConstraintList,
     Node,
     Solver,
-    SolutionMerge,
 )
 
-from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk
+from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk, SolutionToPickle
 
+# Scaling alpha_a and a_scale parameters for each muscle proportionally to the muscle PCSA and fiber type 2 proportion
 # Fiber type proportion from [1]
 biceps_fiber_type_2_proportion = 0.607
 triceps_fiber_type_2_proportion = 0.465
@@ -32,13 +30,12 @@
 ]
 
 # PCSA (cm²) from [2]
-biceps_pcsa = 12.7
 triceps_pcsa = 28.3
+biceps_pcsa = 12.7
 brachioradialis_pcsa = 11.6
-
-biceps_a_scale_proportion = 12.7 / 28.3
 triceps_a_scale_proportion = 1
-brachioradialis_a_scale_proportion = 11.6 / 28.3
+biceps_a_scale_proportion = biceps_pcsa / triceps_pcsa
+brachioradialis_a_scale_proportion = brachioradialis_pcsa / triceps_pcsa
 a_scale_proportion_list = [
     biceps_a_scale_proportion,
     biceps_a_scale_proportion,
@@ -48,6 +45,8 @@
     brachioradialis_a_scale_proportion,
 ]
 
+# Build the functional electrical stimulation models according
+# to number and name of muscle in the musculoskeletal model used
 fes_muscle_models = [
     DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"),
     DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"),
@@ -57,14 +56,16 @@
     DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA"),
 ]
 
+# Applying the scaling
 for i in range(len(fes_muscle_models)):
     fes_muscle_models[i].alpha_a = fes_muscle_models[i].alpha_a * alpha_a_proportion_list[i]
     fes_muscle_models[i].a_scale = fes_muscle_models[i].a_scale * a_scale_proportion_list[i]
 
 minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0
 pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"]
 n_stim = 60
-n_shooting = 2
+n_shooting = 25
+# Step time of 1ms -> 1sec / (40Hz * 25) = 0.001s
 
 constraint = ConstraintList()
 constraint.add(
@@ -100,22 +101,7 @@
     )
 
     sol = ocp.solve(Solver.IPOPT(_max_iter=10000))
-
-    time = sol.decision_time(to_merge=[SolutionMerge.PHASES, SolutionMerge.NODES])
-    states = sol.decision_states(to_merge=[SolutionMerge.PHASES, SolutionMerge.NODES])
-    controls = sol.decision_controls(to_merge=[SolutionMerge.PHASES, SolutionMerge.NODES])
-    parameters = sol.decision_parameters()
-
-    dictionary = {
-        "time": time,
-        "states": states,
-        "controls": controls,
-        "parameters": parameters,
-    }
-
-    with open("result_file/pulse_duration_" + pickle_file_list[i], "wb") as file:
-        pickle.dump(dictionary, file)
-
+    SolutionToPickle(sol, "pulse_duration_" + pickle_file_list[i], "result_file/").pickle()
 
 # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005).
 # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation.

cocofest/examples/dynamics/reaching_task/visualization/animation.py

@@ -0,0 +1,8 @@
+from cocofest import PickleAnimate
+
+PickleAnimate("../result_file/pulse_duration_minimize_muscle_fatigue.pkl").animate(
+    model_path="../../../msk_models/arm26.bioMod"
+)
+PickleAnimate("../result_file/pulse_duration_minimize_muscle_fatigue.pkl").multiple_animations(
+    ["../result_file/pulse_duration_minimize_muscle_force.pkl"], model_path="../../../msk_models/arm26.bioMod"
+)

cocofest/examples/dynamics/reaching_task/visualization/force_and_fatigue_subplot.py

@@ -0,0 +1,224 @@
+"""
+This script is used to make the graph of the muscle force and fatigue for the reaching task.
+The data used to make the graph is from the result file of the optimization.
+"""
+
+import pickle
+import matplotlib.pyplot as plt
+import numpy as np
+
+pickle_path = [
+    r"../result_file/pulse_duration_minimize_muscle_force.pkl",
+    r"../result_file/pulse_duration_minimize_muscle_fatigue.pkl",
+]
+
+with open(pickle_path[0], "rb") as f:
+    data_minimize_force = pickle.load(f)
+
+with open(pickle_path[1], "rb") as f:
+    data_minimize_fatigue = pickle.load(f)
+
+force_muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"]
+fatigue_muscle_keys = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"]
+muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"]
+
+# Force graph
+fig, axs = plt.subplots(3, 2, figsize=(6, 7))
+fig.suptitle("Muscle force", fontsize=20, fontweight="bold", fontname="Times New Roman")
+index = 0
+
+for j in range(2):
+    for i in range(3):
+        axs[i][j].set_xlim(left=0, right=1.5)
+        axs[i][j].set_ylim(bottom=0, top=250)
+        if j == 0:
+            plt.setp(
+                axs[i][j],
+                xticks=[0, 0.5, 1, 1.5],
+                xticklabels=[],
+                yticks=[0, 75, 150, 225],
+                yticklabels=[0, 75, 150, 225],
+            )
+
+        else:
+            plt.setp(
+                axs[i][j],
+                xticks=[0, 0.5, 1, 1.5],
+                xticklabels=[],
+                yticks=[0, 75, 150, 225],
+                yticklabels=[],
+            )
+
+        if i == 2:
+            plt.setp(
+                axs[i][j],
+                xticks=[0, 0.5, 1, 1.5],
+                xticklabels=[0, 0.5, 1, 1.5],
+            )
+
+        axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][force_muscle_keys[index]], lw=5)
+        axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][force_muscle_keys[index]], lw=5)
+        axs[i][j].text(
+            0.5,
+            0.9,
+            f"{muscle_names[index]}",
+            transform=axs[i][j].transAxes,
+            ha="center",
+            va="center",
+            fontsize=14,
+            weight="bold",
+            font="Times New Roman",
+        )
+
+        labels = axs[i][j].get_xticklabels() + axs[i][j].get_yticklabels()
+        [label.set_fontname("Times New Roman") for label in labels]
+        [label.set_fontsize(14) for label in labels]
+
+        index += 1
+
+fig.text(0.5, 0.02, "Time (s)", ha="center", va="center", fontsize=18, weight="bold", font="Times New Roman")
+fig.text(
+    0.025,
+    0.5,
+    "Force (N)",
+    ha="center",
+    va="center",
+    rotation="vertical",
+    fontsize=18,
+    weight="bold",
+    font="Times New Roman",
+)
+fig.legend(
+    ["Force", "Fatigue"], loc="upper right", ncol=1, prop={"family": "Times New Roman", "size": 14, "weight": "bold"}
+)
+plt.show()
+
+# Joint angle graph
+joint_keys = ["Shoulder", "Elbow"]
+fig, axs = plt.subplots(2, 1, figsize=(3, (2 / 3) * 7))
+fig.suptitle("Joint angle", fontsize=20, fontweight="bold", fontname="Times New Roman")
+
+for i in range(2):
+    axs[i].set_xlim(left=0, right=1.5)
+
+    if i == 1:
+        plt.setp(
+            axs[i],
+            xticks=[0, 0.5, 1, 1.5],
+            xticklabels=[0, 0.5, 1, 1.5],
+        )
+    else:
+        plt.setp(
+            axs[i],
+            xticks=[0, 0.5, 1, 1.5],
+            xticklabels=[],
+        )
+
+    force_angles = data_minimize_force["states"]["q"][i] * 180 / 3.14
+    fatigue_angles = data_minimize_fatigue["states"]["q"][i] * 180 / 3.14
+
+    axs[i].plot(data_minimize_force["time"], force_angles, lw=5)
+    axs[i].plot(data_minimize_fatigue["time"], fatigue_angles, lw=5)
+    axs[i].text(
+        0.05,
+        0.9,
+        f"{joint_keys[i]}",
+        transform=axs[i].transAxes,
+        ha="left",
+        va="center",
+        fontsize=14,
+        weight="bold",
+        font="Times New Roman",
+    )
+    labels = axs[i].get_xticklabels() + axs[i].get_yticklabels()
+    [label.set_fontname("Times New Roman") for label in labels]
+    [label.set_fontsize(14) for label in labels]
+
+fig.text(
+    0.05,
+    0.5,
+    "Joint angle (°)",
+    ha="center",
+    va="center",
+    rotation="vertical",
+    fontsize=18,
+    weight="bold",
+    font="Times New Roman",
+)
+axs[1].text(0.75, -25, "Time (s)", ha="center", va="center", fontsize=18, weight="bold", font="Times New Roman")
+fig.legend(
+    ["Force", "Fatigue"], loc="upper right", ncol=1, prop={"family": "Times New Roman", "size": 14, "weight": "bold"}
+)
+plt.show()
+
+# Fatigue graph
+a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"]
+a_sum_base_line = 0
+a_force_sum_list = []
+a_fatigue_sum_list = []
+for key_a in a_list:
+    a_sum_base_line += data_minimize_force["states"][key_a][0]
+for i in range(len(data_minimize_force["time"])):
+    a_force_sum = 0
+    a_fatigue_sum = 0
+    for key_a in a_list:
+        a_force_sum += data_minimize_force["states"][key_a][i]
+        a_fatigue_sum += data_minimize_fatigue["states"][key_a][i]
+
+    a_force_sum_list.append(a_force_sum)
+    a_fatigue_sum_list.append(a_fatigue_sum)
+
+a_force_diff_list = []
+a_fatigue_diff_list = []
+fatigue_minimization_percentage_gain_list = []
+for i in range(len(data_minimize_force["time"])):
+    a_force_diff_list.append((a_force_sum_list[i] - a_force_sum_list[0]) * 1000)
+    a_fatigue_diff_list.append((a_fatigue_sum_list[i] - a_fatigue_sum_list[0]) * 1000)
+
+    fatigue_minimization_percentage_gain_list.append(
+        (a_fatigue_sum_list[i] - a_force_sum_list[i]) / (a_force_sum_list[0] - a_force_sum_list[-1]) * 100
+    )
+
+fig, axs = plt.subplots(1, 1, figsize=(3, (1 / 3) * 7))
+fig.suptitle("Muscle fatigue", fontsize=20, fontweight="bold", fontname="Times New Roman")
+
+axs.set_xlim(left=0, right=1.5)
+plt.setp(
+    axs,
+    xticks=[0, 0.5, 1, 1.5],
+    xticklabels=[0, 0.5, 1, 1.5],
+)
+
+a_force_sum_percentage = (np.array(a_force_sum_list) / a_sum_base_line) * 100
+a_fatigue_sum_percentage = (np.array(a_fatigue_sum_list) / a_sum_base_line) * 100
+
+axs.plot(data_minimize_force["time"], a_force_sum_percentage, lw=5)
+axs.plot(data_minimize_force["time"], a_fatigue_sum_percentage, lw=5)
+
+axs.set_xlim(left=0, right=1.5)
+
+plt.setp(
+    axs,
+    xticks=[0, 0.5, 1, 1.5],
+    xticklabels=[0, 0.5, 1, 1.5],
+)
+
+labels = axs.get_xticklabels() + axs.get_yticklabels()
+[label.set_fontname("Times New Roman") for label in labels]
+[label.set_fontsize(14) for label in labels]
+fig.text(
+    0.05,
+    0.5,
+    "Fatigue percentage (%)",
+    ha="center",
+    va="center",
+    rotation="vertical",
+    fontsize=18,
+    weight="bold",
+    font="Times New Roman",
+)
+axs.text(0.75, 96.3, "Time (s)", ha="center", va="center", fontsize=18, weight="bold", font="Times New Roman")
+plt.legend(
+    ["Force", "Fatigue"], loc="upper right", ncol=1, prop={"family": "Times New Roman", "size": 14, "weight": "bold"}
+)
+plt.show()