Structure refactor

.github/workflows/run_tests_win.yml

@@ -38,7 +38,6 @@ jobs:
 
       - name: Print mamba info
         run: |
-          mamba config --show
           mamba info
           mamba list
 
@@ -90,7 +89,6 @@ jobs:
 
       - name: Print mamba info
         run: |
-          mamba config --show
           mamba info
 
       - name: Install bioptim on Windows

README.md

@@ -148,7 +148,14 @@ You can also compute the models form initial value problem.
 For that, use the IvpFes class to build the computed problem.
 
 ```python
-ocp = IvpFes(model=DingModelFrequency(), ...)
+from cocofest import IvpFes, DingModelFrequencyWithFatigue
+
+fes_parameters = {"model": DingModelFrequencyWithFatigue(), "n_stim": 10}
+ivp_parameters = {"n_shooting": 20, "final_time": 1}
+
+ivp = IvpFes(fes_parameters, ivp_parameters)
+
+result, time = ivp.integrate()
 ```
 
 ## Summation truncation

cocofest/custom_constraints.py

@@ -1,5 +1,5 @@
 """
-This custom constraint are for the functional electrical stimulation frequency and intensity.
+This class regroups all the custom constraints that are used in the optimization problem.
 """
 
 from casadi import MX, SX
@@ -13,7 +13,7 @@ def pulse_time_apparition_as_phase(controller: PenaltyController) -> MX | SX:
         return controller.time.cx - controller.parameters["pulse_apparition_time"].cx[controller.phase_idx]
 
     @staticmethod
-    def pulse_time_apparition_bimapping(controller: PenaltyController) -> MX | SX:
+    def equal_to_first_pulse_interval_time(controller: PenaltyController) -> MX | SX:
         if controller.ocp.n_phases <= 1:
             RuntimeError("There is only one phase, the bimapping constraint is not possible")
 
@@ -25,7 +25,7 @@ def pulse_time_apparition_bimapping(controller: PenaltyController) -> MX | SX:
         return first_phase_tf - current_phase_tf
 
     @staticmethod
-    def pulse_duration_bimapping(controller: PenaltyController) -> MX | SX:
+    def equal_to_first_pulse_duration(controller: PenaltyController) -> MX | SX:
         if controller.ocp.n_phases <= 1:
             RuntimeError("There is only one phase, the bimapping constraint is not possible")
         return (
@@ -34,7 +34,7 @@ def pulse_duration_bimapping(controller: PenaltyController) -> MX | SX:
         )
 
     @staticmethod
-    def pulse_intensity_bimapping(controller: PenaltyController) -> MX | SX:
+    def equal_to_first_pulse_intensity(controller: PenaltyController) -> MX | SX:
         if controller.ocp.n_phases <= 1:
             RuntimeError("There is only one phase, the bimapping constraint is not possible")
         return (

examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py → cocofest/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py

@@ -27,10 +27,8 @@
     n_stim=n_stim,
     n_shooting=10,
     final_time=1,
-    time_min=0.01,
-    time_max=0.1,
-    time_bimapping=True,
-    custom_objective=objective_functions,
+    pulse_event={"min": 0.01, "max": 0.1, "bimapping": True},
+    objective={"custom": objective_functions},
     with_residual_torque=True,
 )
 

examples/dynamics/intensity_optimization_cycling_multi_muscle.py → cocofest/examples/dynamics/intensity_optimization_cycling_multi_muscle.py

@@ -1,5 +1,5 @@
 """
-This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work.
+This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Hmed's 2018 intensity work.
 This ocp was build to maintain an elbow angle of 90 degrees.
 The stimulation frequency will be optimized between 1 and 10 Hz as well as the pulse intensity between minimal
 sensitivity threshold and 130mA to satisfy the maintained elbow. No residual torque is allowed.
@@ -48,15 +48,14 @@
     n_stim=n_stim,
     n_shooting=5,
     final_time=1,
-    time_min=0.05,
-    time_max=1,
-    time_bimapping=True,
-    pulse_intensity_min=minimum_pulse_intensity,
-    pulse_intensity_max=130,
-    pulse_intensity_bimapping=False,
+    pulse_event={"min": 0.05, "max": 1, "bimapping": True},
+    pulse_intensity={
+        "min": minimum_pulse_intensity,
+        "max": 130,
+        "bimapping": False,
+    },
     with_residual_torque=True,
-    custom_objective=objective_functions,
-    q_tracking=track_q,
+    objective={"custom": objective_functions, "q_tracking": track_q},
     use_sx=True,
 )
 

examples/dynamics/intensity_optimization_hold_position.py → cocofest/examples/dynamics/intensity_optimization_hold_position.py

@@ -1,5 +1,5 @@
 """
-This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work.
+This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Hmed's 2018 intensity work.
 This ocp was build to maintain an elbow angle of 90 degrees.
 The stimulation frequency will be optimized between 1 and 10 Hz as well as the pulse intensity between minimal
 sensitivity threshold and 130mA to satisfy the maintained elbow. No residual torque is allowed.
@@ -55,14 +55,14 @@
     n_stim=n_stim,
     n_shooting=10,
     final_time=1,
-    time_min=0.1,
-    time_max=1,
-    time_bimapping=True,
-    pulse_intensity_min=minimum_pulse_intensity,
-    pulse_intensity_max=130,
-    pulse_intensity_bimapping=False,
+    pulse_event={"min": 0.1, "max": 1, "bimapping": True},
+    pulse_intensity={
+        "min": minimum_pulse_intensity,
+        "max": 130,
+        "bimapping": False,
+    },
     with_residual_torque=False,
-    custom_objective=objective_functions,
+    objective={"custom": objective_functions},
 )
 
 sol = ocp.solve(Solver.IPOPT(_max_iter=1000))

examples/dynamics/intensity_optimization_hold_position_multi_muscle.py → cocofest/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py

@@ -1,5 +1,5 @@
 """
-This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work.
+This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Hmed's 2018 intensity work.
 This ocp was build to maintain an elbow angle of 90 degrees.
 The stimulation frequency will be optimized between 1 and 10 Hz as well as the pulse intensity between minimal
 sensitivity threshold and 130mA to satisfy the maintained elbow. No residual torque is allowed.
@@ -61,14 +61,14 @@
     n_stim=n_stim,
     n_shooting=10,
     final_time=1,
-    time_min=0.1,
-    time_max=1,
-    time_bimapping=True,
-    pulse_intensity_min=minimum_pulse_intensity,
-    pulse_intensity_max=130,
-    pulse_intensity_bimapping=False,
+    pulse_event={"min": 0.1, "max": 1, "bimapping": True},
+    pulse_intensity={
+        "min": minimum_pulse_intensity,
+        "max": 130,
+        "bimapping": False,
+    },
     with_residual_torque=False,
-    custom_objective=objective_functions,
+    objective={"custom": objective_functions},
 )
 
 sol = ocp.solve(Solver.IPOPT(_max_iter=1000))

examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py → cocofest/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py

@@ -47,13 +47,11 @@
     n_stim=n_stim,
     n_shooting=n_shooting,
     final_time=1,
-    time_min=0.01,
-    time_max=1,
-    time_bimapping=False,
+    pulse_event={"min": 0.01, "max": 1, "bimapping": False},
     with_residual_torque=True,
-    custom_objective=objective_functions,
-    muscle_force_length_relationship=True,
-    muscle_force_velocity_relationship=False,
+    objective={"custom": objective_functions},
+    activate_force_length_relationship=True,
+    activate_force_velocity_relationship=False,
     minimize_muscle_fatigue=True,
 )
 

examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py → cocofest/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py

@@ -43,13 +43,15 @@
     n_stim=n_stim,
     n_shooting=10,
     final_time=1,
-    pulse_duration_min=minimum_pulse_duration,
-    pulse_duration_max=0.0006,
-    pulse_duration_bimapping=False,
+    pulse_duration={
+        "min": minimum_pulse_duration,
+        "max": 0.0006,
+        "bimapping": False,
+    },
     with_residual_torque=False,
-    custom_objective=objective_functions,
-    muscle_force_length_relationship=True,
-    muscle_force_velocity_relationship=False,
+    objective={"custom": objective_functions},
+    activate_force_length_relationship=True,
+    activate_force_velocity_relationship=False,
     minimize_muscle_fatigue=True,
 )
 

examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py → cocofest/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py

@@ -1,5 +1,5 @@
 """
-This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work.
+This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Hmed's 2018 intensity work.
 Those ocp were build to move the elbow from 0 to 90 degrees angle.
 The stimulation frequency will be set to 10Hz and pulse intensity will be optimized to satisfy the motion and to minimize the overall muscle fatigue.
 Intensity can be optimized from sensitivity threshold to 130mA. No residual torque is allowed.
@@ -45,13 +45,15 @@
     n_stim=n_stim,
     n_shooting=10,
     final_time=1,
-    pulse_intensity_min=minimum_pulse_intensity,
-    pulse_intensity_max=130,
-    pulse_intensity_bimapping=False,
+    pulse_intensity={
+        "min": minimum_pulse_intensity,
+        "max": 130,
+        "bimapping": False,
+    },
     with_residual_torque=False,
-    custom_objective=objective_functions,
-    muscle_force_length_relationship=True,
-    muscle_force_velocity_relationship=False,
+    objective={"custom": objective_functions},
+    activate_force_length_relationship=True,
+    activate_force_velocity_relationship=False,
     minimize_muscle_fatigue=True,
 )
 

examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py → cocofest/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py

@@ -17,7 +17,7 @@
 
 sol_list = []
 sol_time = []
-muscle_force_length_relationship = [False, True]
+activate_force_length_relationship = [False, True]
 
 for i in range(2):
     ocp = OcpFesMsk.prepare_ocp(
@@ -28,10 +28,10 @@
         n_stim=10,
         n_shooting=10,
         final_time=1,
-        pulse_duration=0.00025,
+        pulse_duration={"fixed": 0.00025},
         with_residual_torque=False,
-        muscle_force_length_relationship=muscle_force_length_relationship[i],
-        muscle_force_velocity_relationship=muscle_force_length_relationship[i],
+        activate_force_length_relationship=activate_force_length_relationship[i],
+        activate_force_velocity_relationship=activate_force_length_relationship[i],
         use_sx=False,
     )
     sol = ocp.solve(Solver.IPOPT(_max_iter=1000))

examples/dynamics/reaching_task/reaching_task_frequency_optimization.py → cocofest/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py

@@ -13,6 +13,7 @@
     ConstraintList,
     Node,
     Solver,
+    SolutionMerge,
 )
 
 from cocofest import DingModelFrequencyWithFatigue, OcpFesMsk
@@ -88,23 +89,22 @@
         n_stim=n_stim,
         n_shooting=n_shooting,
         final_time=1,
-        time_min=0.01,
-        time_max=0.1,
-        time_bimapping=False,
+        pulse_event={"min": 0.01, "max": 0.1, "bimapping": False},
         with_residual_torque=False,
         custom_constraint=constraint,
-        muscle_force_length_relationship=True,
-        muscle_force_velocity_relationship=True,
+        activate_force_length_relationship=True,
+        activate_force_velocity_relationship=True,
         minimize_muscle_fatigue=True if pickle_file_list[i] == "minimize_muscle_fatigue.pkl" else False,
         minimize_muscle_force=True if pickle_file_list[i] == "minimize_muscle_force.pkl" else False,
         use_sx=False,
     )
 
-    sol = ocp.solve(Solver.IPOPT(_max_iter=10000)).merge_phases()
-    time = sol.time
-    states = sol.states
-    controls = sol.controls
-    parameters = sol.parameters
+    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,

examples/dynamics/reaching_task/reaching_task_intensity_optimization.py → cocofest/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py

@@ -13,6 +13,7 @@
     ConstraintList,
     Node,
     Solver,
+    SolutionMerge,
 )
 
 from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk
@@ -91,23 +92,26 @@
         n_stim=n_stim,
         n_shooting=n_shooting,
         final_time=1,
-        pulse_intensity_min=minimum_pulse_intensity,
-        pulse_intensity_max=80,
-        pulse_intensity_bimapping=False,
+        pulse_intensity={
+            "min": minimum_pulse_intensity,
+            "max": 80,
+            "bimapping": False,
+        },
         with_residual_torque=False,
         custom_constraint=constraint,
-        muscle_force_length_relationship=True,
-        muscle_force_velocity_relationship=True,
+        activate_force_length_relationship=True,
+        activate_force_velocity_relationship=True,
         minimize_muscle_fatigue=True if pickle_file_list[i] == "minimize_muscle_fatigue.pkl" else False,
         minimize_muscle_force=True if pickle_file_list[i] == "minimize_muscle_force.pkl" else False,
         use_sx=False,
     )
 
-    sol = ocp.solve(Solver.IPOPT(_max_iter=10000)).merge_phases()
-    time = sol.time
-    states = sol.states
-    controls = sol.controls
-    parameters = sol.parameters
+    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,

examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py → cocofest/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py

@@ -13,6 +13,7 @@
     ConstraintList,
     Node,
     Solver,
+    SolutionMerge,
 )
 
 from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk
@@ -76,7 +77,6 @@
 )
 
 for i in range(len(pickle_file_list)):
-
     ocp = OcpFesMsk.prepare_ocp(
         biorbd_model_path="../../msk_models/arm26.bioMod",
         bound_type="start_end",
@@ -85,23 +85,26 @@
         n_stim=n_stim,
         n_shooting=n_shooting,
         final_time=1.5,
-        pulse_duration_min=minimum_pulse_duration,
-        pulse_duration_max=0.0006,
-        pulse_duration_bimapping=False,
+        pulse_duration={
+            "min": minimum_pulse_duration,
+            "max": 0.0006,
+            "bimapping": False,
+        },
         with_residual_torque=False,
         custom_constraint=constraint,
-        muscle_force_length_relationship=True,
-        muscle_force_velocity_relationship=True,
+        activate_force_length_relationship=True,
+        activate_force_velocity_relationship=True,
         minimize_muscle_fatigue=True if pickle_file_list[i] == "minimize_muscle_fatigue.pkl" else False,
         minimize_muscle_force=True if pickle_file_list[i] == "minimize_muscle_force.pkl" else False,
         use_sx=False,
     )
 
-    sol = ocp.solve(Solver.IPOPT(_max_iter=10000)).merge_phases()
-    time = sol.time
-    states = sol.states
-    controls = sol.controls
-    parameters = sol.parameters
+    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,