From 3d87d28c24b216fec0d4530d0db61e25caa27eec Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 12 Jan 2024 20:15:45 -0500 Subject: [PATCH 01/63] implementing musculoskeletal dynamics to model --- cocofest/models/ding2003.py | 6 + cocofest/models/dynamical_model.py | 317 + examples/getting_started/arm26_biceps.bioMod | 256 + .../getting_started/mesh/arm_r_humerus.vtp | 1228 + examples/getting_started/mesh/arm_r_ulna.vtp | 842 + examples/getting_started/mesh/ground_ribs.vtp | 10998 +++++++ .../musculoskeletal_dynamic.py | 310 + examples/sensitivity/truncation/Figure.svg | 23959 ++++++++++++++++ 8 files changed, 37916 insertions(+) create mode 100644 cocofest/models/dynamical_model.py create mode 100644 examples/getting_started/arm26_biceps.bioMod create mode 100644 examples/getting_started/mesh/arm_r_humerus.vtp create mode 100644 examples/getting_started/mesh/arm_r_ulna.vtp create mode 100644 examples/getting_started/mesh/ground_ribs.vtp create mode 100644 examples/getting_started/musculoskeletal_dynamic.py create mode 100644 examples/sensitivity/truncation/Figure.svg diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index 2648532f..5292b311 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -236,6 +236,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition=None, + optional_nlp=None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -267,6 +268,11 @@ def dynamics( f=states[1], t=time, t_stim_prev=stim_apparition, + ) if optional_nlp is None else optional_nlp.system_dynamics( + cn=states[0], + f=states[1], + t=time, + t_stim_prev=stim_apparition, ), defects=None, ) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py new file mode 100644 index 00000000..4fff32fc --- /dev/null +++ b/cocofest/models/dynamical_model.py @@ -0,0 +1,317 @@ +from typing import Callable +from copy import deepcopy + +from casadi import vertcat, MX, SX, Function +from bioptim import ( + BiorbdModel, + OptimalControlProgram, + NonLinearProgram, + ConfigureProblem, + DynamicsFunctions, + DynamicsEvaluation, + FatigueList, +) + +from cocofest import DingModelFrequency, DingModelIntensityFrequency, DingModelPulseDurationFrequency + + +class FESActuatedBiorbdModel(BiorbdModel): + def __init__(self, name: str = None, + biorbd_path: str = None, + muscles_model: DingModelFrequency() = None, + # muscles_name: list = None # TODO : for loop to create different muscles + ): + super().__init__(biorbd_path) + self._name = name + self.bio_model = BiorbdModel(biorbd_path) + self.bounds_from_ranges_q = self.bio_model.bounds_from_ranges("q") + self.bounds_from_ranges_qdot = self.bio_model.bounds_from_ranges("qdot") + # self.muscles_dynamics_model = muscles_model_list # * self.nb_muscles # TODO : for loop to create different muscles + # self.muscles_name_list = muscles_name_list + + self.muscles_dynamics_model = muscles_model + self.bio_stim_model = [self.bio_model] + [self.muscles_dynamics_model] + + # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value + # TODO : find a way to compare names and remove the muscle that is not in the model + + # ---- Absolutely needed methods ---- # + def serialize(self) -> tuple[Callable, dict]: + # TODO : make different serialize for biceps and triceps... different parameters value + # This is where you can serialize your model + # This is useful if you want to save your model and load it later + # return ( + # FESActuatedBiorbdModel, + # { + # "tauc": self.muscles_dynamics_model[0].tauc, + # "a_rest": self.muscles_dynamics_model[0].a_rest, + # "tau1_rest": self.muscles_dynamics_model[0].tau1_rest, + # "km_rest": self.muscles_dynamics_model[0].km_rest, + # "tau2": self.muscles_dynamics_model[0].tau2, + # "alpha_a": self.muscles_dynamics_model[0].alpha_a, + # "alpha_tau1": self.muscles_dynamics_model[0].alpha_tau1, + # "alpha_km": self.muscles_dynamics_model[0].alpha_km, + # "tau_fat": self.muscles_dynamics_model[0].tau_fat, + # }, + # ) + return self.muscles_dynamics_model.serialize() + + # ---- Needed for the example ---- # + # TODO update for 3 models + @property + def name_dof(self) -> list[str]: + return self.bio_stim_model[0].name_dof + # self.bio_model[0].name_dof + # ["Cn", "F", "A", "Tau1", "Km", "q", "qdot", "tau"] + + def muscle_name_dof(self) -> list[str]: + return self.muscles_dynamics_model.name_dof + + @property + def nb_state(self) -> int: + nb_state = 0 + # for muscle_model in self.muscles_dynamics_model: # TODO : for loop to create different muscles + # nb_state += muscle_model.nb_state + + nb_state += self.muscles_dynamics_model.nb_state + nb_state += self.bio_model[0].name_dof + return nb_state + + @property + def name(self) -> None | str: + return self._name + + @staticmethod + def muscle_dynamic( + time: MX | SX, + states: MX | SX, + controls: MX | SX, + parameters: MX | SX, + stochastic_variables: MX | SX, + nlp: NonLinearProgram, + muscle_model: DingModelFrequency | DingModelIntensityFrequency | DingModelPulseDurationFrequency, + stim_apparition=None, + ) -> DynamicsEvaluation: + """ + The custom dynamics function that provides the derivative of the states: dxdt = f(t, x, u, p, s) + + Parameters + ---------- + time: MX | SX + The time of the system + states: MX | SX + The state of the system + controls: MX | SX + The controls of the system + parameters: MX | SX + The parameters acting on the system + stochastic_variables: MX | SX + The stochastic variables of the system + nlp: NonLinearProgram + A reference to the phase + + Returns + ------- + The derivative of the states in the tuple[MX | SX] format + """ + + q = DynamicsFunctions.get(nlp.states["q"], states) + qdot = DynamicsFunctions.get(nlp.states["qdot"], states) + tau = DynamicsFunctions.get(nlp.controls["tau"], controls) + + muscles_tau = 0 + dxdt_muscle_list = vertcat() + + # for muscle_model in muscle_model_list: #TODO : for different muscles + # muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, nb_phases).dxdt + # muscle_forces = muscle_dxdt[1] + # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() + # dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) + + # muscle_nlp = nlp + # muscle_nlp.model = nlp.model.muscles_dynamics_model + + muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, optional_nlp=nlp.model.muscles_dynamics_model).dxdt + muscle_forces = muscle_dxdt[1] + muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() #TODO find a way to make it work in sx + dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) + + # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq) + dq = DynamicsFunctions.compute_qdot(nlp, q, qdot) + ddq = nlp.model.forward_dynamics(q, qdot, muscles_tau + tau) + # ddq = nlp.model.forward_dynamics(q, qdot, muscles_tau) + + dxdt = vertcat(dxdt_muscle_list, dq, ddq) + + return DynamicsEvaluation(dxdt=dxdt, defects=None) + + #TODO : disociate the force and fatigue model in ding model with a flag + + #TODO : Remove FES_driven and all other similar fun in dingmodel + + # MAYBE LATER FOR N MUSCLE + # def fes_driven( + # self, + # ocp, + # nlp, + # **extra_params, + # ): + # """ + # Configure the dynamics for a muscle driven program. + # If with_excitations is set to True, then the muscle activations are computed from the muscle dynamics. + # The tau from muscle is computed using the muscle activations. + # If with_residual_torque is set to True, then tau are used as supplementary force in the + # case muscles are too weak. + # + # Parameters + # ---------- + # ocp: OptimalControlProgram + # A reference to the ocp + # nlp: NonLinearProgram + # A reference to the phase + # + # """ + # nlp.parameters = ocp.parameters + # DynamicsFunctions.apply_parameters(nlp.parameters.cx_start, nlp) + # extra_params["nb_phases"] = ocp.n_phases + # + # if not isinstance(FESActuatedBiorbdModel.muscle_dynamic, (tuple, list)): + # FESActuatedBiorbdModel.muscle_dynamic = (FESActuatedBiorbdModel.muscle_dynamic,) + # + # + # for func in FESActuatedBiorbdModel.muscle_dynamic: + # muscle_dynamics_eval = func( + # nlp.time_cx, + # nlp.states.scaled.cx_start, + # nlp.controls.scaled.cx_start, + # nlp.parameters.cx, + # nlp.stochastic_variables.scaled.cx, + # nlp, + # self.muscles_dynamics_model, + # extra_params["nb_phases"], + # ) + # # + # # dynamics_dxdt = vertcat(dynamics_dxdt, muscle_dynamics_eval.dxdt) + # + # nlp.dynamics_func.append( + # Function( + # "ForwardDyn", + # [ + # nlp.time_cx, + # nlp.states.scaled.cx_start, + # nlp.controls.scaled.cx_start, + # nlp.parameters.cx, + # nlp.stochastic_variables.scaled.cx, + # ], + # [muscle_dynamics_eval.dxdt], + # ["t", "x", "u", "p", "s"], + # ["xdot"], + # ), + # ) + + def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgram): + """ + Tell the program which variables are states and controls. + The user is expected to use the ConfigureProblem.configure_xxx functions. + Parameters + ---------- + ocp: OptimalControlProgram + A reference to the ocp + nlp: NonLinearProgram + A reference to the phase + """ + # for i in range(len(self.muscles_dynamics_model)): + + # self.muscles_dynamics_model[i].configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscles_name[i]) + # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value + + self.muscles_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.muscles_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + + # TODO : for fatigue model + # self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + # self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( + # ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + # self.muscles_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + + ConfigureProblem.configure_q(ocp, nlp, as_states=True, as_controls=False) + ConfigureProblem.configure_qdot(ocp, nlp, as_states=True, as_controls=False) + ConfigureProblem.configure_tau(ocp, nlp, as_states=False, as_controls=True) + # stim_apparition = self.muscles_dynamics_model.get_stim_prev(ocp, nlp) + + time_type = "mx" if "time" in ocp.parameters.keys() else None + stim_apparition = [ocp.node_time(phase_idx=i, node_idx=0, type=time_type) for i in range(nlp.phase_idx + 1)] + ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.muscle_dynamic, muscle_model=self.muscles_dynamics_model, stim_apparition=stim_apparition) + + + @staticmethod + def configure_q(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: bool = False): + """ + Configure the generalized coordinates + + Parameters + ---------- + nlp: NonLinearProgram + A reference to the phase + as_states: bool + If the generalized coordinates should be a state + as_controls: bool + If the generalized coordinates should be a control + as_states_dot: bool + If the generalized velocities should be a state_dot + """ + name = "q" + name_q = [name] + ConfigureProblem.configure_new_variable( + name, name_q, ocp, nlp, as_states, as_controls, as_states_dot + ) + + @staticmethod + def configure_qdot(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: bool = False): + """ + Configure the generalized velocities + + Parameters + ---------- + nlp: NonLinearProgram + A reference to the phase + as_states: bool + If the generalized velocities should be a state + as_controls: bool + If the generalized velocities should be a control + as_states_dot: bool + If the generalized velocities should be a state_dot + """ + + name = "qdot" + name_qdot = [name] + ConfigureProblem.configure_new_variable( + name, name_qdot, ocp, nlp, as_states, as_controls, as_states_dot + ) + + @staticmethod + def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: FatigueList = None): + """ + Configure the generalized forces + + Parameters + ---------- + nlp: NonLinearProgram + A reference to the phase + as_states: bool + If the generalized forces should be a state + as_controls: bool + If the generalized forces should be a control + fatigue: FatigueList + If the dynamics with fatigue should be declared + """ + + name = "tau" + name_tau = ["tau"] + ConfigureProblem.configure_new_variable( + name, name_tau, ocp, nlp, as_states, as_controls, fatigue=fatigue + ) + +if __name__ == "__main__": + FESActuatedBiorbdModel(biorbd_path="msk_model/arm26_unmesh.bioMod", + muscles_model=DingModelFrequency()) \ No newline at end of file diff --git a/examples/getting_started/arm26_biceps.bioMod b/examples/getting_started/arm26_biceps.bioMod new file mode 100644 index 00000000..8050c204 --- /dev/null +++ b/examples/getting_started/arm26_biceps.bioMod @@ -0,0 +1,256 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + meshfile mesh/ground_ribs.vtp + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + marker r_acromion + parent base + position -0.01256 0.04 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ -1 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + // Markers + marker r_humerus_epicondyle + parent r_humerus + position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 + endmarker + marker COM_arm + parent r_humerus + position 0 -0.18049599999999999 0 + endmarker + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + // Markers + marker r_radius_styloid + parent r_ulna_radius_hand + position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 + endmarker + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +// base > r_ulna_radius_hand +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle BIClong + Type degroote + statetype degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint diff --git a/examples/getting_started/mesh/arm_r_humerus.vtp b/examples/getting_started/mesh/arm_r_humerus.vtp new file mode 100644 index 00000000..19fcf553 --- /dev/null +++ b/examples/getting_started/mesh/arm_r_humerus.vtp @@ -0,0 +1,1228 @@ + + + + + + + -0.241965 0.928387 -0.282046 + -0.421615 0.891296 -0.166834 + -0.670330 0.714611 -0.199971 + -0.776204 0.488631 -0.398431 + -0.649453 0.374399 -0.661844 + -0.390814 0.431251 -0.813195 + 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16326 + + + + + diff --git a/examples/getting_started/musculoskeletal_dynamic.py b/examples/getting_started/musculoskeletal_dynamic.py new file mode 100644 index 00000000..e38f3fb7 --- /dev/null +++ b/examples/getting_started/musculoskeletal_dynamic.py @@ -0,0 +1,310 @@ +import numpy as np + +from bioptim import ( + BoundsList, + ConstraintList, + ConstraintFcn, + OdeSolverBase, + BiMapping, + Node, + ControlType, + DynamicsList, + InitialGuessList, + InterpolationType, + ObjectiveFcn, + ObjectiveList, + OdeSolver, + OptimalControlProgram, + Solver, +) + +from cocofest import DingModelFrequency, FESActuatedBiorbdModel + +class FESActuatedBiorbdModelOCP(OptimalControlProgram): + def __init__(self, biorbd_model_path: str, + muscles_model: DingModelFrequency(), + # muscles_name_list: list[str], + n_stim: int, + final_time: float, + n_shooting: int, + time_min: float = None, + time_max: float = None, + time_bimapping: bool = False, + ode_solver: OdeSolverBase = OdeSolver.RK4(), + control_type: ControlType = ControlType.CONSTANT,): + + """ + Prepare the ocp + + Parameters + ---------- + biorbd_model_path: str + The path to the bioMod + final_time: float + The time at the final node + n_shooting: int + The number of shooting points + ode_solver: OdeSolverBase + The ode solver to use + + Returns + ------- + The OptimalControlProgram ready to be solved + """ + bio_models = [FESActuatedBiorbdModel(name="arm26_only_biceps", + biorbd_path=biorbd_model_path, + muscles_model=muscles_model) + for i in range(n_stim)] + + nq = bio_models[0].bio_model.nb_q + nqdot = bio_models[0].bio_model.nb_qdot + target = np.zeros((nq + nqdot, 1)) + target[1, 0] = 3.14 + + n_shooting = [n_shooting] * n_stim + + constraints = ConstraintList() + final_time_phase = None + # parameter_bimapping = BiMappingList() + phase_time_bimapping = None + + if time_min is None and time_max is None: + step = final_time / n_stim + final_time_phase = (step,) + for i in range(n_stim - 1): + final_time_phase = final_time_phase + (step,) + + else: + for i in range(n_stim): + constraints.add( + ConstraintFcn.TIME_CONSTRAINT, + node=Node.END, + min_bound=time_min, + max_bound=time_max, + phase=i, + ) + + if time_bimapping is True: + phase_time_bimapping = BiMapping(to_second=[0 for _ in range(n_stim)], to_first=[0]) + + final_time_phase = [time_min] * n_stim + + # step = final_time / n_stim + # final_time_phase = (step,) + # for i in range(n_stim - 1): + # final_time_phase = final_time_phase + (step,) + + # Add objective functions + objective_functions = ObjectiveList() + for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10000, quadratic=True, phase=i) + # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=target[:nq, :], weight=10000, phase=n_stim-1) + + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[0, 1.6]]).T, weight=10000, + phase=n_stim - 1) + + # constraints.add(ConstraintFcn.TRACK_STATE, node=Node.END, index=1, target=1.6, phase=n_stim - 1) + # constraints.add(ConstraintFcn.TRACK_STATE, key="q", index=1, node=Node.END, phase=n_stim - 1, target=1.6) + + + # Dynamics + dynamics = DynamicsList() + for i in range(n_stim): + dynamics.add( + bio_models[i].declare_model_variables, + dynamic_function=bio_models[i].muscle_dynamic, + phase=i, + ) + + # TODO : for n muscles + # # States bounds + # x_bounds = BoundsList() + # for i in range(len(muscles_model_list)): + # muscle_state_list = muscles_model_list[i].name_dof + # starting_bounds, min_bounds, max_bounds = ( + # muscles_model_list[i].standard_rest_values(), + # muscles_model_list[i].standard_rest_values(), + # muscles_model_list[i].standard_rest_values(), + # ) + # + # for j in range(len(muscle_state_list)): + # if muscle_state_list[j] == "Cn" or muscle_state_list[j] == "F": + # max_bounds[j] = 1000 + # elif muscle_state_list[j] == "Tau1" or muscle_state_list[j] == "Km": + # max_bounds[j] = 1 + # elif muscle_state_list[j] == "A": + # min_bounds[j] = 0 + # + # starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) + # starting_bounds_max = np.concatenate((starting_bounds, max_bounds, max_bounds), axis=1) + # middle_bound_min = np.concatenate((min_bounds, min_bounds, min_bounds), axis=1) + # middle_bound_max = np.concatenate((max_bounds, max_bounds, max_bounds), axis=1) + # + # for k in range(n_stim): + # for l in range(len(muscle_state_list)): + # if k == 0: + # x_bounds.add( + # key=muscle_state_list[l] + "_" + muscles_name_list[i], + # min_bound=np.array([starting_bounds_min[l]]), + # max_bound=np.array([starting_bounds_max[l]]), + # phase=k, + # interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + # ) + # else: + # x_bounds.add( + # key=muscle_state_list[l] + "_" + muscles_name_list[i], + # min_bound=np.array([middle_bound_min[l]]), + # max_bound=np.array([middle_bound_max[l]]), + # phase=k, + # interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + # ) + + # States bounds + x_bounds = BoundsList() + # for i in range(len(muscles_model_list)): + + muscle_state_list = muscles_model.name_dof + starting_bounds, min_bounds, max_bounds = ( + muscles_model.standard_rest_values(), + muscles_model.standard_rest_values(), + muscles_model.standard_rest_values(), + ) + + for j in range(len(muscle_state_list)): + if muscle_state_list[j] == "Cn" or muscle_state_list[j] == "F": + max_bounds[j] = 1000 + elif muscle_state_list[j] == "Tau1" or muscle_state_list[j] == "Km": + max_bounds[j] = 1 + elif muscle_state_list[j] == "A": + min_bounds[j] = 0 + + starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) + starting_bounds_max = np.concatenate((starting_bounds, max_bounds, max_bounds), axis=1) + middle_bound_min = np.concatenate((min_bounds, min_bounds, min_bounds), axis=1) + middle_bound_max = np.concatenate((max_bounds, max_bounds, max_bounds), axis=1) + + for k in range(n_stim): + for l in range(len(muscle_state_list)): + if k == 0: + x_bounds.add( + key=muscle_state_list[l], # + "_" + muscles_name_list[i], + min_bound=np.array([starting_bounds_min[l]]), + max_bound=np.array([starting_bounds_max[l]]), + phase=k, + interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + ) + else: + x_bounds.add( + key=muscle_state_list[l], # + "_" + muscles_name_list[i], + min_bound=np.array([middle_bound_min[l]]), + max_bound=np.array([middle_bound_max[l]]), + phase=k, + interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + ) + + starting_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) + starting_max_q_bounds = np.array([[0, 0, 0], [0, 3.14159265, 3.14159265]]) + middle_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) + middle_max_q_bounds = np.array([[0, 0, 0], [3.14159265, 3.14159265, 3.14159265]]) + + starting_min_qdot_bounds = np.array([[0, 0, 0], [0, -31.41592654, -31.41592654]]) + starting_max_qdot_bounds = np.array([[0, 0, 0], [0, 31.41592654, 31.41592654]]) + middle_min_qdot_bounds = np.array([[0, 0, 0], [-31.41592654, -31.41592654, -31.41592654]]) + middle_max_qdot_bounds = np.array([[0, 0, 0], [31.41592654, 31.41592654, 31.41592654]]) + + for i in range(n_stim): + # q_bounds = bio_models[i].bounds_from_ranges_q + # qdot_bounds = bio_models[i].bounds_from_ranges_qdot + # q_bounds[0, :] = (0, 0, 0) + # qdot_bounds[0, :] = (0, 0, 0) + # q_bounds[1, :] = (0, 0, 2.5) + + min_bounds = middle_min_q_bounds + max_bounds = middle_max_q_bounds + + qdot_min_bounds = middle_min_qdot_bounds + qdot_max_bounds = middle_max_qdot_bounds + + if i == 0: + # q_bounds[1, :] = (0, 0, 2.5) + # qdot_bounds[:, 0] = 0 + + min_bounds = starting_min_q_bounds + max_bounds = starting_max_q_bounds + + qdot_min_bounds = starting_min_qdot_bounds + qdot_max_bounds = starting_max_qdot_bounds + + # x_bounds.add(key="q", bounds=q_bounds, phase=i) + + x_bounds.add(key="q", min_bound=min_bounds, max_bound=max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) + + # x_bounds.add(key="qdot", bounds=qdot_bounds, phase=i) + + x_bounds.add(key="qdot", min_bound=qdot_min_bounds, max_bound=qdot_max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) + + # TODO : for n muscles + # States initial guess + # x_init = InitialGuessList() + # for i in range(n_stim): + # for j in range(len(muscles_model_list)): + # muscle_state_list = muscles_model_list[j].name_dof + # for k in range(len(muscle_state_list)): + # x_init.add(key=muscle_state_list[k] + "_" + muscles_name_list[j], initial_guess=muscles_model_list[j].standard_rest_values()[k], phase=i) + # x_init.add(key="q", initial_guess=[1.57] * bio_models[i].nb_q, phase=i) + + x_init = InitialGuessList() + for i in range(n_stim): + muscle_state_list = muscles_model.name_dof + for k in range(len(muscle_state_list)): + x_init.add(key=muscle_state_list[k], # + "_" + muscles_name_list[j], + initial_guess=muscles_model.standard_rest_values()[k], phase=i) + x_init.add(key="q", initial_guess=[0] * bio_models[i].nb_q, phase=i) + + # Controls bounds + tau_min, tau_max, tau_init = [0, -1], [0, 1], [0, 0] + # tau_min, tau_max, tau_init = [0, 0], [0, 0], [0, 0] + + u_bounds = BoundsList() + for i in range(n_stim): + u_bounds.add(key="tau", min_bound=tau_min, max_bound=tau_max, phase=i) + + # Controls initial guess + u_init = InitialGuessList() + for i in range(n_stim): + u_init.add(key="tau", initial_guess=tau_init, phase=i) + + super().__init__( + bio_model=bio_models, + dynamics=dynamics, + n_shooting=n_shooting, + phase_time=final_time_phase, + x_bounds=x_bounds, + u_bounds=u_bounds, + x_init=x_init, + u_init=u_init, + objective_functions=objective_functions, + constraints=constraints, + time_phase_mapping=phase_time_bimapping, + ode_solver=ode_solver, + control_type=control_type, + use_sx=True, + ) + +if __name__ == "__main__": + ocp = FESActuatedBiorbdModelOCP("/arm26_biceps.bioMod", + muscles_model=DingModelFrequency(), + # muscles_name_list=["biceps"], + final_time=1, + n_shooting=5, + n_stim=10, + time_min=0.01, + time_max=0.1, + time_bimapping=True, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) + # Solver.IPOPT(show_online_optim=True) + # sol.graphs(show_bounds=True) + sol.animate() + sol.graphs(show_bounds=True) diff --git a/examples/sensitivity/truncation/Figure.svg b/examples/sensitivity/truncation/Figure.svg new file mode 100644 index 00000000..4a4093e5 --- /dev/null +++ b/examples/sensitivity/truncation/Figure.svg @@ -0,0 +1,23959 @@ + + + + + + + + 2023-12-22T10:38:58.413270 + image/svg+xml + + + Matplotlib v3.7.2, https://matplotlib.org/ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + From 9f65e1167bbd54b8d34a2e51ad64629456f06d8d Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 15 Jan 2024 15:59:57 -0500 Subject: [PATCH 02/63] continuing the work on dynamical behaviour (bound issue) --- cocofest/models/dynamical_model.py | 8 +- .../getting_started/arm26_biceps_1ddl.bioMod | 254 ++++++++++++++++++ .../musculoskeletal_dynamic.py | 99 ++++--- 3 files changed, 320 insertions(+), 41 deletions(-) create mode 100644 examples/getting_started/arm26_biceps_1ddl.bioMod diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 4fff32fc..6ae52e01 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -1,7 +1,6 @@ from typing import Callable -from copy import deepcopy -from casadi import vertcat, MX, SX, Function +from casadi import vertcat, MX, SX from bioptim import ( BiorbdModel, OptimalControlProgram, @@ -131,9 +130,10 @@ def muscle_dynamic( # muscle_nlp = nlp # muscle_nlp.model = nlp.model.muscles_dynamics_model - muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, optional_nlp=nlp.model.muscles_dynamics_model).dxdt + muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, + optional_nlp=nlp.model.muscles_dynamics_model).dxdt muscle_forces = muscle_dxdt[1] - muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() #TODO find a way to make it work in sx + muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq) diff --git a/examples/getting_started/arm26_biceps_1ddl.bioMod b/examples/getting_started/arm26_biceps_1ddl.bioMod new file mode 100644 index 00000000..21f518c5 --- /dev/null +++ b/examples/getting_started/arm26_biceps_1ddl.bioMod @@ -0,0 +1,254 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + meshfile mesh/ground_ribs.vtp + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + marker r_acromion + parent base + position -0.01256 0.04 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + // Markers + marker r_humerus_epicondyle + parent r_humerus + position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 + endmarker + marker COM_arm + parent r_humerus + position 0 -0.18049599999999999 0 + endmarker + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + // Markers + marker r_radius_styloid + parent r_ulna_radius_hand + position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 + endmarker + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +// base > r_ulna_radius_hand +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle BIClong + Type degroote + statetype degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint diff --git a/examples/getting_started/musculoskeletal_dynamic.py b/examples/getting_started/musculoskeletal_dynamic.py index e38f3fb7..0e2a3a20 100644 --- a/examples/getting_started/musculoskeletal_dynamic.py +++ b/examples/getting_started/musculoskeletal_dynamic.py @@ -12,6 +12,7 @@ InitialGuessList, InterpolationType, ObjectiveFcn, + PhaseDynamics, ObjectiveList, OdeSolver, OptimalControlProgram, @@ -30,7 +31,7 @@ def __init__(self, biorbd_model_path: str, time_min: float = None, time_max: float = None, time_bimapping: bool = False, - ode_solver: OdeSolverBase = OdeSolver.RK4(), + ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), control_type: ControlType = ControlType.CONSTANT,): """ @@ -97,11 +98,19 @@ def __init__(self, biorbd_model_path: str, # Add objective functions objective_functions = ObjectiveList() for i in range(n_stim): - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10000, quadratic=True, phase=i) - # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=target[:nq, :], weight=10000, phase=n_stim-1) + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) + # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[0, 1.6]]).T, weight=10000, + # phase=n_stim - 1) + + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=0, node=Node.END, weight=10000, phase=n_stim - 1, quadratic=True, target=1.6 + ) + + # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[1.6]*5]).T, + # weight=10000, + # phase=n_stim - 1, + # node=Node.ALL,) - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[0, 1.6]]).T, weight=10000, - phase=n_stim - 1) # constraints.add(ConstraintFcn.TRACK_STATE, node=Node.END, index=1, target=1.6, phase=n_stim - 1) # constraints.add(ConstraintFcn.TRACK_STATE, key="q", index=1, node=Node.END, phase=n_stim - 1, target=1.6) @@ -113,7 +122,10 @@ def __init__(self, biorbd_model_path: str, dynamics.add( bio_models[i].declare_model_variables, dynamic_function=bio_models[i].muscle_dynamic, + expand_dynamics=True, + expand_continuity=False, phase=i, + phase_dynamics=PhaseDynamics.ONE_PER_NODE, ) # TODO : for n muscles @@ -171,7 +183,9 @@ def __init__(self, biorbd_model_path: str, ) for j in range(len(muscle_state_list)): - if muscle_state_list[j] == "Cn" or muscle_state_list[j] == "F": + if muscle_state_list[j] == "Cn": + max_bounds[j] = 10 + elif muscle_state_list[j] == "F": max_bounds[j] = 1000 elif muscle_state_list[j] == "Tau1" or muscle_state_list[j] == "Km": max_bounds[j] = 1 @@ -202,46 +216,57 @@ def __init__(self, biorbd_model_path: str, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, ) - starting_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) - starting_max_q_bounds = np.array([[0, 0, 0], [0, 3.14159265, 3.14159265]]) - middle_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) - middle_max_q_bounds = np.array([[0, 0, 0], [3.14159265, 3.14159265, 3.14159265]]) + # starting_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) + # starting_max_q_bounds = np.array([[0, 0, 0], [0, 3.14159265, 3.14159265]]) + # middle_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) + # middle_max_q_bounds = np.array([[0, 0, 0], [3.14159265, 3.14159265, 3.14159265]]) + # + # starting_min_qdot_bounds = np.array([[0, 0, 0], [0, -31.41592654, -31.41592654]]) + # starting_max_qdot_bounds = np.array([[0, 0, 0], [0, 31.41592654, 31.41592654]]) + # middle_min_qdot_bounds = np.array([[0, 0, 0], [-31.41592654, -31.41592654, -31.41592654]]) + # middle_max_qdot_bounds = np.array([[0, 0, 0], [31.41592654, 31.41592654, 31.41592654]]) - starting_min_qdot_bounds = np.array([[0, 0, 0], [0, -31.41592654, -31.41592654]]) - starting_max_qdot_bounds = np.array([[0, 0, 0], [0, 31.41592654, 31.41592654]]) - middle_min_qdot_bounds = np.array([[0, 0, 0], [-31.41592654, -31.41592654, -31.41592654]]) - middle_max_qdot_bounds = np.array([[0, 0, 0], [31.41592654, 31.41592654, 31.41592654]]) - for i in range(n_stim): - # q_bounds = bio_models[i].bounds_from_ranges_q - # qdot_bounds = bio_models[i].bounds_from_ranges_qdot - # q_bounds[0, :] = (0, 0, 0) - # qdot_bounds[0, :] = (0, 0, 0) - # q_bounds[1, :] = (0, 0, 2.5) + # starting_min_q_bounds = np.array([[0, 0, 0]]) + # starting_max_q_bounds = np.array([[0, 3.14159265, 3.14159265]]) + # middle_min_q_bounds = np.array([[0, 0, 0]]) + # middle_max_q_bounds = np.array([[3.14159265, 3.14159265, 3.14159265]]) + # + # starting_min_qdot_bounds = np.array([[0, -31.41592654, -31.41592654]]) + # starting_max_qdot_bounds = np.array([[0, 31.41592654, 31.41592654]]) + # middle_min_qdot_bounds = np.array([[-31.41592654, -31.41592654, -31.41592654]]) + # middle_max_qdot_bounds = np.array([[31.41592654, 31.41592654, 31.41592654]]) - min_bounds = middle_min_q_bounds - max_bounds = middle_max_q_bounds + for i in range(n_stim): + q_x_bounds = bio_models[i].bounds_from_ranges("q") + qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") - qdot_min_bounds = middle_min_qdot_bounds - qdot_max_bounds = middle_max_qdot_bounds + # min_bounds = middle_min_q_bounds + # max_bounds = middle_max_q_bounds + # # + # qdot_min_bounds = middle_min_qdot_bounds + # qdot_max_bounds = middle_max_qdot_bounds if i == 0: - # q_bounds[1, :] = (0, 0, 2.5) + # q_bounds[0, :] = (0, 0, 2.5) # qdot_bounds[:, 0] = 0 - min_bounds = starting_min_q_bounds - max_bounds = starting_max_q_bounds + # min_bounds = starting_min_q_bounds + # max_bounds = starting_max_q_bounds + # + # qdot_min_bounds = starting_min_qdot_bounds + # qdot_max_bounds = starting_max_qdot_bounds - qdot_min_bounds = starting_min_qdot_bounds - qdot_max_bounds = starting_max_qdot_bounds + q_x_bounds[:, [0, -1]] = 0 # Start at 0... + qdot_x_bounds[:, [0, -1]] = 0 # Start and end without any velocity - # x_bounds.add(key="q", bounds=q_bounds, phase=i) + x_bounds.add(key="q", bounds=q_x_bounds, phase=i) - x_bounds.add(key="q", min_bound=min_bounds, max_bound=max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) + # x_bounds.add(key="q", min_bound=min_bounds, max_bound=max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) - # x_bounds.add(key="qdot", bounds=qdot_bounds, phase=i) + x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) - x_bounds.add(key="qdot", min_bound=qdot_min_bounds, max_bound=qdot_max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) + # x_bounds.add(key="qdot", min_bound=qdot_min_bounds, max_bound=qdot_max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) # TODO : for n muscles # States initial guess @@ -262,8 +287,8 @@ def __init__(self, biorbd_model_path: str, x_init.add(key="q", initial_guess=[0] * bio_models[i].nb_q, phase=i) # Controls bounds - tau_min, tau_max, tau_init = [0, -1], [0, 1], [0, 0] - # tau_min, tau_max, tau_init = [0, 0], [0, 0], [0, 0] + # tau_min, tau_max, tau_init = [0, -1], [0, 1], [0, 0] + tau_min, tau_max, tau_init = [-1], [1], [0] u_bounds = BoundsList() for i in range(n_stim): @@ -292,11 +317,11 @@ def __init__(self, biorbd_model_path: str, ) if __name__ == "__main__": - ocp = FESActuatedBiorbdModelOCP("/arm26_biceps.bioMod", + ocp = FESActuatedBiorbdModelOCP("/arm26_biceps_1ddl.bioMod", muscles_model=DingModelFrequency(), # muscles_name_list=["biceps"], final_time=1, - n_shooting=5, + n_shooting=10, n_stim=10, time_min=0.01, time_max=0.1, From 83c97dc32d8d6f6e6102485b863effc6039ff1a1 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 16 Jan 2024 18:14:24 -0500 Subject: [PATCH 03/63] bound and major error in forward dynamics input correction --- cocofest/__init__.py | 1 + cocofest/models/dynamical_model.py | 76 +--------- ....py => musculoskeletal_dynamic_1muscle.py} | 137 ++---------------- 3 files changed, 20 insertions(+), 194 deletions(-) rename examples/getting_started/{musculoskeletal_dynamic.py => musculoskeletal_dynamic_1muscle.py} (52%) diff --git a/cocofest/__init__.py b/cocofest/__init__.py index 75585e9d..5b73153f 100644 --- a/cocofest/__init__.py +++ b/cocofest/__init__.py @@ -5,6 +5,7 @@ from .models.ding2007_with_fatigue import DingModelPulseDurationFrequencyWithFatigue from .models.hmed2018 import DingModelIntensityFrequency from .models.hmed2018_with_fatigue import DingModelIntensityFrequencyWithFatigue +from .models.dynamical_model import FESActuatedBiorbdModel from .optimization.fes_multi_start import FunctionalElectricStimulationMultiStart from .optimization.fes_ocp import OcpFes from .optimization.fes_identification_ocp import OcpFesId diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 6ae52e01..bbff5089 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -123,92 +123,26 @@ def muscle_dynamic( # for muscle_model in muscle_model_list: #TODO : for different muscles # muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, nb_phases).dxdt - # muscle_forces = muscle_dxdt[1] + # muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() # dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) - # muscle_nlp = nlp - # muscle_nlp.model = nlp.model.muscles_dynamics_model - muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, optional_nlp=nlp.model.muscles_dynamics_model).dxdt - muscle_forces = muscle_dxdt[1] - muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() + muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) + + muscles_tau += - nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T @ muscle_forces + # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq) dq = DynamicsFunctions.compute_qdot(nlp, q, qdot) ddq = nlp.model.forward_dynamics(q, qdot, muscles_tau + tau) - # ddq = nlp.model.forward_dynamics(q, qdot, muscles_tau) dxdt = vertcat(dxdt_muscle_list, dq, ddq) return DynamicsEvaluation(dxdt=dxdt, defects=None) - #TODO : disociate the force and fatigue model in ding model with a flag - - #TODO : Remove FES_driven and all other similar fun in dingmodel - - # MAYBE LATER FOR N MUSCLE - # def fes_driven( - # self, - # ocp, - # nlp, - # **extra_params, - # ): - # """ - # Configure the dynamics for a muscle driven program. - # If with_excitations is set to True, then the muscle activations are computed from the muscle dynamics. - # The tau from muscle is computed using the muscle activations. - # If with_residual_torque is set to True, then tau are used as supplementary force in the - # case muscles are too weak. - # - # Parameters - # ---------- - # ocp: OptimalControlProgram - # A reference to the ocp - # nlp: NonLinearProgram - # A reference to the phase - # - # """ - # nlp.parameters = ocp.parameters - # DynamicsFunctions.apply_parameters(nlp.parameters.cx_start, nlp) - # extra_params["nb_phases"] = ocp.n_phases - # - # if not isinstance(FESActuatedBiorbdModel.muscle_dynamic, (tuple, list)): - # FESActuatedBiorbdModel.muscle_dynamic = (FESActuatedBiorbdModel.muscle_dynamic,) - # - # - # for func in FESActuatedBiorbdModel.muscle_dynamic: - # muscle_dynamics_eval = func( - # nlp.time_cx, - # nlp.states.scaled.cx_start, - # nlp.controls.scaled.cx_start, - # nlp.parameters.cx, - # nlp.stochastic_variables.scaled.cx, - # nlp, - # self.muscles_dynamics_model, - # extra_params["nb_phases"], - # ) - # # - # # dynamics_dxdt = vertcat(dynamics_dxdt, muscle_dynamics_eval.dxdt) - # - # nlp.dynamics_func.append( - # Function( - # "ForwardDyn", - # [ - # nlp.time_cx, - # nlp.states.scaled.cx_start, - # nlp.controls.scaled.cx_start, - # nlp.parameters.cx, - # nlp.stochastic_variables.scaled.cx, - # ], - # [muscle_dynamics_eval.dxdt], - # ["t", "x", "u", "p", "s"], - # ["xdot"], - # ), - # ) - def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgram): """ Tell the program which variables are states and controls. diff --git a/examples/getting_started/musculoskeletal_dynamic.py b/examples/getting_started/musculoskeletal_dynamic_1muscle.py similarity index 52% rename from examples/getting_started/musculoskeletal_dynamic.py rename to examples/getting_started/musculoskeletal_dynamic_1muscle.py index 0e2a3a20..a4cb7936 100644 --- a/examples/getting_started/musculoskeletal_dynamic.py +++ b/examples/getting_started/musculoskeletal_dynamic_1muscle.py @@ -24,7 +24,6 @@ class FESActuatedBiorbdModelOCP(OptimalControlProgram): def __init__(self, biorbd_model_path: str, muscles_model: DingModelFrequency(), - # muscles_name_list: list[str], n_stim: int, final_time: float, n_shooting: int, @@ -90,31 +89,12 @@ def __init__(self, biorbd_model_path: str, final_time_phase = [time_min] * n_stim - # step = final_time / n_stim - # final_time_phase = (step,) - # for i in range(n_stim - 1): - # final_time_phase = final_time_phase + (step,) - # Add objective functions objective_functions = ObjectiveList() for i in range(n_stim): - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) - # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[0, 1.6]]).T, weight=10000, - # phase=n_stim - 1) - - objective_functions.add( - ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=0, node=Node.END, weight=10000, phase=n_stim - 1, quadratic=True, target=1.6 - ) - - # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", target=np.array([[1.6]*5]).T, - # weight=10000, - # phase=n_stim - 1, - # node=Node.ALL,) - - - # constraints.add(ConstraintFcn.TRACK_STATE, node=Node.END, index=1, target=1.6, phase=n_stim - 1) - # constraints.add(ConstraintFcn.TRACK_STATE, key="q", index=1, node=Node.END, phase=n_stim - 1, target=1.6) + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="qdot", weight=1, quadratic=True, phase=n_stim - 1) # Dynamics dynamics = DynamicsList() @@ -128,52 +108,8 @@ def __init__(self, biorbd_model_path: str, phase_dynamics=PhaseDynamics.ONE_PER_NODE, ) - # TODO : for n muscles - # # States bounds - # x_bounds = BoundsList() - # for i in range(len(muscles_model_list)): - # muscle_state_list = muscles_model_list[i].name_dof - # starting_bounds, min_bounds, max_bounds = ( - # muscles_model_list[i].standard_rest_values(), - # muscles_model_list[i].standard_rest_values(), - # muscles_model_list[i].standard_rest_values(), - # ) - # - # for j in range(len(muscle_state_list)): - # if muscle_state_list[j] == "Cn" or muscle_state_list[j] == "F": - # max_bounds[j] = 1000 - # elif muscle_state_list[j] == "Tau1" or muscle_state_list[j] == "Km": - # max_bounds[j] = 1 - # elif muscle_state_list[j] == "A": - # min_bounds[j] = 0 - # - # starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) - # starting_bounds_max = np.concatenate((starting_bounds, max_bounds, max_bounds), axis=1) - # middle_bound_min = np.concatenate((min_bounds, min_bounds, min_bounds), axis=1) - # middle_bound_max = np.concatenate((max_bounds, max_bounds, max_bounds), axis=1) - # - # for k in range(n_stim): - # for l in range(len(muscle_state_list)): - # if k == 0: - # x_bounds.add( - # key=muscle_state_list[l] + "_" + muscles_name_list[i], - # min_bound=np.array([starting_bounds_min[l]]), - # max_bound=np.array([starting_bounds_max[l]]), - # phase=k, - # interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, - # ) - # else: - # x_bounds.add( - # key=muscle_state_list[l] + "_" + muscles_name_list[i], - # min_bound=np.array([middle_bound_min[l]]), - # max_bound=np.array([middle_bound_max[l]]), - # phase=k, - # interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, - # ) - # States bounds x_bounds = BoundsList() - # for i in range(len(muscles_model_list)): muscle_state_list = muscles_model.name_dof starting_bounds, min_bounds, max_bounds = ( @@ -201,7 +137,7 @@ def __init__(self, biorbd_model_path: str, for l in range(len(muscle_state_list)): if k == 0: x_bounds.add( - key=muscle_state_list[l], # + "_" + muscles_name_list[i], + key=muscle_state_list[l], min_bound=np.array([starting_bounds_min[l]]), max_bound=np.array([starting_bounds_max[l]]), phase=k, @@ -209,75 +145,31 @@ def __init__(self, biorbd_model_path: str, ) else: x_bounds.add( - key=muscle_state_list[l], # + "_" + muscles_name_list[i], + key=muscle_state_list[l], min_bound=np.array([middle_bound_min[l]]), max_bound=np.array([middle_bound_max[l]]), phase=k, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, ) - # starting_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) - # starting_max_q_bounds = np.array([[0, 0, 0], [0, 3.14159265, 3.14159265]]) - # middle_min_q_bounds = np.array([[0, 0, 0], [0, 0, 0]]) - # middle_max_q_bounds = np.array([[0, 0, 0], [3.14159265, 3.14159265, 3.14159265]]) - # - # starting_min_qdot_bounds = np.array([[0, 0, 0], [0, -31.41592654, -31.41592654]]) - # starting_max_qdot_bounds = np.array([[0, 0, 0], [0, 31.41592654, 31.41592654]]) - # middle_min_qdot_bounds = np.array([[0, 0, 0], [-31.41592654, -31.41592654, -31.41592654]]) - # middle_max_qdot_bounds = np.array([[0, 0, 0], [31.41592654, 31.41592654, 31.41592654]]) - - - # starting_min_q_bounds = np.array([[0, 0, 0]]) - # starting_max_q_bounds = np.array([[0, 3.14159265, 3.14159265]]) - # middle_min_q_bounds = np.array([[0, 0, 0]]) - # middle_max_q_bounds = np.array([[3.14159265, 3.14159265, 3.14159265]]) - # - # starting_min_qdot_bounds = np.array([[0, -31.41592654, -31.41592654]]) - # starting_max_qdot_bounds = np.array([[0, 31.41592654, 31.41592654]]) - # middle_min_qdot_bounds = np.array([[-31.41592654, -31.41592654, -31.41592654]]) - # middle_max_qdot_bounds = np.array([[31.41592654, 31.41592654, 31.41592654]]) - for i in range(n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") - # min_bounds = middle_min_q_bounds - # max_bounds = middle_max_q_bounds - # # - # qdot_min_bounds = middle_min_qdot_bounds - # qdot_max_bounds = middle_max_qdot_bounds - if i == 0: - # q_bounds[0, :] = (0, 0, 2.5) - # qdot_bounds[:, 0] = 0 + q_x_bounds[:, [0]] = 3.14/(180/5) # Start at 5° + qdot_x_bounds[:, [0]] = 0 # Start without any velocity - # min_bounds = starting_min_q_bounds - # max_bounds = starting_max_q_bounds - # - # qdot_min_bounds = starting_min_qdot_bounds - # qdot_max_bounds = starting_max_qdot_bounds + if i == n_stim-1: + q_x_bounds[:, [-1]] = 3.14/2 # End at 90° - q_x_bounds[:, [0, -1]] = 0 # Start at 0... - qdot_x_bounds[:, [0, -1]] = 0 # Start and end without any velocity + # if i > n_stim-5: + # q_x_bounds.min[0, [0, 1, 2]] = 3.14 / 2 - 0.2 + # q_x_bounds.max[0, [0, 1, 2]] = 3.14 / 2 + 0.2 x_bounds.add(key="q", bounds=q_x_bounds, phase=i) - - # x_bounds.add(key="q", min_bound=min_bounds, max_bound=max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) - x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) - # x_bounds.add(key="qdot", min_bound=qdot_min_bounds, max_bound=qdot_max_bounds, phase=i, interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT) - - # TODO : for n muscles - # States initial guess - # x_init = InitialGuessList() - # for i in range(n_stim): - # for j in range(len(muscles_model_list)): - # muscle_state_list = muscles_model_list[j].name_dof - # for k in range(len(muscle_state_list)): - # x_init.add(key=muscle_state_list[k] + "_" + muscles_name_list[j], initial_guess=muscles_model_list[j].standard_rest_values()[k], phase=i) - # x_init.add(key="q", initial_guess=[1.57] * bio_models[i].nb_q, phase=i) - x_init = InitialGuessList() for i in range(n_stim): muscle_state_list = muscles_model.name_dof @@ -287,8 +179,7 @@ def __init__(self, biorbd_model_path: str, x_init.add(key="q", initial_guess=[0] * bio_models[i].nb_q, phase=i) # Controls bounds - # tau_min, tau_max, tau_init = [0, -1], [0, 1], [0, 0] - tau_min, tau_max, tau_init = [-1], [1], [0] + tau_min, tau_max, tau_init = [-20], [20], [0] u_bounds = BoundsList() for i in range(n_stim): @@ -316,10 +207,10 @@ def __init__(self, biorbd_model_path: str, use_sx=True, ) + if __name__ == "__main__": ocp = FESActuatedBiorbdModelOCP("/arm26_biceps_1ddl.bioMod", muscles_model=DingModelFrequency(), - # muscles_name_list=["biceps"], final_time=1, n_shooting=10, n_stim=10, @@ -332,4 +223,4 @@ def __init__(self, biorbd_model_path: str, # Solver.IPOPT(show_online_optim=True) # sol.graphs(show_bounds=True) sol.animate() - sol.graphs(show_bounds=True) + sol.graphs(show_bounds=False) From f1dcaca748beb3bffe15b82922384adc3ee36098 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 22 Jan 2024 11:10:39 -0500 Subject: [PATCH 04/63] feat: enable the use of all fes model for dynamics ocp + examples --- cocofest/__init__.py | 1 + cocofest/models/ding2003.py | 12 +- cocofest/models/ding2003_with_fatigue.py | 5 +- cocofest/models/ding2007.py | 7 +- cocofest/models/ding2007_with_fatigue.py | 8 +- cocofest/models/dynamical_model.py | 10 +- cocofest/models/hmed2018.py | 8 +- cocofest/models/hmed2018_with_fatigue.py | 8 +- cocofest/optimization/fes_ocp.py | 2 +- cocofest/optimization/fes_ocp_dynamics.py | 267 ++++++++++++++++++ ...cy_optimization_musculoskeletal_dynamic.py | 37 +++ .../musculoskeletal_dynamic_1muscle.py | 226 --------------- ...on_optimization_musculoskeletal_dynamic.py | 41 +++ ...ty_optimization_musculoskeletal_dynamic.py | 42 +++ 14 files changed, 429 insertions(+), 245 deletions(-) create mode 100644 cocofest/optimization/fes_ocp_dynamics.py create mode 100644 examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py delete mode 100644 examples/getting_started/musculoskeletal_dynamic_1muscle.py create mode 100644 examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py create mode 100644 examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py diff --git a/cocofest/__init__.py b/cocofest/__init__.py index 5b73153f..66234015 100644 --- a/cocofest/__init__.py +++ b/cocofest/__init__.py @@ -9,6 +9,7 @@ from .optimization.fes_multi_start import FunctionalElectricStimulationMultiStart from .optimization.fes_ocp import OcpFes from .optimization.fes_identification_ocp import OcpFesId +from .optimization.fes_ocp_dynamics import FESActuatedBiorbdModelOCP from .integration.ivp_fes import IvpFes from .fourier_approx import FourierSeries from .read_data import ExtractData diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index 5292b311..948e7ecc 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -236,7 +236,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition=None, - optional_nlp=None, + nlp_dynamics=None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -262,19 +262,15 @@ def dynamics( The derivative of the states in the tuple[MX] format """ + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( - cn=states[0], - f=states[1], - t=time, - t_stim_prev=stim_apparition, - ) if optional_nlp is None else optional_nlp.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition, ), - defects=None, ) def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgram): diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index ca175200..31df7521 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -188,6 +188,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition=None, + nlp_dynamics=None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -213,8 +214,10 @@ def dynamics( The derivative of the states in the tuple[MX] format """ + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], a=states[2], diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 22e8783e..2fc4b49c 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -153,6 +153,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, + nlp_dynamics=None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -177,15 +178,17 @@ def dynamics( ------- The derivative of the states in the tuple[MX] format """ - pulse_duration_parameters = nlp.model.get_pulse_duration_parameters(nlp.parameters) + pulse_duration_parameters = nlp.model.get_pulse_duration_parameters(nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped impulse_time = pulse_duration_parameters[0] else: impulse_time = pulse_duration_parameters[nlp.phase_idx] + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], t=time, diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 4ab402f6..623b17b2 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -157,6 +157,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, + nlp_dynamics: NonLinearProgram = None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -181,15 +182,18 @@ def dynamics( ------- The derivative of the states in the tuple[MX] format """ - pulse_duration_parameters = nlp.model.get_pulse_duration_parameters(nlp.parameters) + pulse_duration_parameters = nlp.model.get_pulse_duration_parameters( + nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped impulse_time = pulse_duration_parameters[0] else: impulse_time = pulse_duration_parameters[nlp.phase_idx] + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], tau1=states[2], diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index bbff5089..8b1501bb 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -128,7 +128,7 @@ def muscle_dynamic( # dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, - optional_nlp=nlp.model.muscles_dynamics_model).dxdt + nlp_dynamics=nlp.model.muscles_dynamics_model).dxdt muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) muscles_tau += - nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T @ muscle_forces @@ -162,6 +162,14 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg self.muscles_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) self.muscles_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + if 'A' in self.muscles_dynamics_model.name_dof: + self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + if 'Tau1' in self.muscles_dynamics_model.name_dof: + self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + if 'Km' in self.muscles_dynamics_model.name_dof: + self.muscles_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + # TODO : for fatigue model # self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) # self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 4c6735e4..69284b4e 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -216,6 +216,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, + nlp_dynamics: NonLinearProgram = None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -243,7 +244,8 @@ def dynamics( intensity_stim_prev = ( [] ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase - intensity_parameters = nlp.model.get_intensity_parameters(nlp.parameters) + intensity_parameters = nlp.model.get_intensity_parameters( + nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_intensity_parameters(nlp.parameters) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped for i in range(nlp.phase_idx + 1): @@ -252,8 +254,10 @@ def dynamics( for i in range(nlp.phase_idx + 1): intensity_stim_prev.append(intensity_parameters[i]) + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], t=time, diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 4cbde9a4..7f4c3759 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -173,6 +173,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, + nlp_dynamics: NonLinearProgram = None, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -200,7 +201,8 @@ def dynamics( intensity_stim_prev = ( [] ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase - intensity_parameters = nlp.model.get_intensity_parameters(nlp.parameters) + intensity_parameters = nlp.model.get_intensity_parameters( + nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_intensity_parameters(nlp.parameters) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped for i in range(nlp.phase_idx + 1): @@ -209,8 +211,10 @@ def dynamics( for i in range(nlp.phase_idx + 1): intensity_stim_prev.append(intensity_parameters[i]) + dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + return DynamicsEvaluation( - dxdt=nlp.model.system_dynamics( + dxdt=dxdt_fun( cn=states[0], f=states[1], a=states[2], diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index c1b9b61e..e6b53320 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -673,7 +673,7 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu objective_functions = ObjectiveList() if custom_objective: for i in range(len(custom_objective)): - objective_functions.add(custom_objective[0][i]) + objective_functions.add(custom_objective[i][0]) if force_fourier_coef is not None: for phase in range(n_stim): diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py new file mode 100644 index 00000000..a0a309bd --- /dev/null +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -0,0 +1,267 @@ +import numpy as np + +from bioptim import ( + BoundsList, + ControlType, + DynamicsList, + InitialGuessList, + Objective, + ObjectiveList, + OdeSolver, + OptimalControlProgram, + PhaseDynamics, + OdeSolverBase, +) + +from cocofest import ( + DingModelFrequency, + DingModelFrequencyWithFatigue, + DingModelPulseDurationFrequency, + DingModelPulseDurationFrequencyWithFatigue, + DingModelIntensityFrequency, + DingModelIntensityFrequencyWithFatigue, + OcpFes, + FESActuatedBiorbdModel, +) + + +class FESActuatedBiorbdModelOCP: + + @staticmethod + def prepare_ocp(biorbd_model_path: str, + motion_type: str, + motion_data: list, + fes_muscle_model: DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue = None, + n_stim: int = None, + n_shooting: int = None, + final_time: int | float = None, + pulse_mode: str = "Single", + frequency: int | float = None, + round_down: bool = False, + time_min: float = None, + time_max: float = None, + time_bimapping: bool = False, + pulse_duration: int | float = None, + pulse_duration_min: int | float = None, + pulse_duration_max: int | float = None, + pulse_duration_bimapping: bool = False, + pulse_intensity: int | float = None, + pulse_intensity_min: int | float = None, + pulse_intensity_max: int | float = None, + pulse_intensity_bimapping: bool = False, + force_tracking: list = None, + end_node_tracking: int | float = None, + custom_objective: ObjectiveList = None, + use_sx: bool = True, + ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), + control_type: ControlType = ControlType.CONSTANT, + n_threads: int = 1): + + """ + This definition prepares the ocp to be solved + . + Attributes + ---------- + fes_muscle_model: DingModelFrequency | DingModelFrequencyWithFatigue | DingModelPulseDurationFrequency | DingModelPulseDurationFrequencyWithFatigue | DingModelIntensityFrequency | DingModelIntensityFrequencyWithFatigue + The fes model type used for the ocp + n_stim: int + Number of stimulation that will occur during the ocp, it is as well refer as phases + n_shooting: int + Number of shooting point for each individual phases + final_time: float + Refers to the final time of the ocp + force_tracking: list[np.ndarray, np.ndarray] + List of time and associated force to track during ocp optimisation + end_node_tracking: int | float + Force objective value to reach at the last node + time_min: int | float + Minimum time for a phase + time_max: int | float + Maximum time for a phase + time_bimapping: bool + Set phase time constant + pulse_duration: int | float + Setting a chosen pulse time among phases + pulse_duration_min: int | float + Minimum pulse time for a phase + pulse_duration_max: int | float + Maximum pulse time for a phase + pulse_duration_bimapping: bool + Set pulse time constant among phases + pulse_intensity: int | float + Setting a chosen pulse intensity among phases + pulse_intensity_min: int | float + Minimum pulse intensity for a phase + pulse_intensity_max: int | float + Maximum pulse intensity for a phase + pulse_intensity_bimapping: bool + Set pulse intensity constant among phases + custom_objective: list[Objective] + Additional objective for the system + ode_solver: OdeSolver + The ode solver to use + use_sx: bool + The nature of the casadi variables. MX are used if False. + n_threads: int + The number of thread to use while solving (multi-threading if > 1) + """ + + OcpFes._sanity_check( + model=fes_muscle_model, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=final_time, + pulse_mode=pulse_mode, + frequency=frequency, + time_min=time_min, + time_max=time_max, + time_bimapping=time_bimapping, + pulse_duration=pulse_duration, + pulse_duration_min=pulse_duration_min, + pulse_duration_max=pulse_duration_max, + pulse_duration_bimapping=pulse_duration_bimapping, + pulse_intensity=pulse_intensity, + pulse_intensity_min=pulse_intensity_min, + pulse_intensity_max=pulse_intensity_max, + pulse_intensity_bimapping=pulse_intensity_bimapping, + force_tracking=force_tracking, + end_node_tracking=end_node_tracking, + custom_objective=custom_objective, + use_sx=use_sx, + ode_solver=ode_solver, + n_threads=n_threads, + ) + + OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) + + n_stim, final_time = OcpFes._build_phase_parameter( + n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down + ) + + force_fourier_coef = None if force_tracking is None else OcpFes._build_fourrier_coeff(force_tracking) + end_node_tracking = end_node_tracking + + n_shooting = [n_shooting] * n_stim + final_time_phase, constraints, phase_time_bimapping = OcpFes._build_phase_time( + final_time=final_time, + n_stim=n_stim, + pulse_mode=pulse_mode, + time_min=time_min, + time_max=time_max, + time_bimapping=time_bimapping, + ) + parameters, parameters_bounds, parameters_init, parameter_objectives = OcpFes._build_parameters( + model=fes_muscle_model, + n_stim=n_stim, + pulse_duration=pulse_duration, + pulse_duration_min=pulse_duration_min, + pulse_duration_max=pulse_duration_max, + pulse_duration_bimapping=pulse_duration_bimapping, + pulse_intensity=pulse_intensity, + pulse_intensity_min=pulse_intensity_min, + pulse_intensity_max=pulse_intensity_max, + pulse_intensity_bimapping=pulse_intensity_bimapping, + ) + + if len(constraints) == 0 and len(parameters) == 0: + raise ValueError( + "This is not an optimal control problem," + " add parameter to optimize or use the IvpFes method to build your problem" + ) + + bio_models = [FESActuatedBiorbdModel(name=None, + biorbd_path=biorbd_model_path, + muscles_model=fes_muscle_model) + for i in range(n_stim)] + + dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) + x_bounds, x_init = OcpFes._set_bounds(fes_muscle_model, n_stim) + x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init) + u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(n_stim) + objective_functions = OcpFes._set_objective( + n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective + ) + + return OptimalControlProgram( + bio_model=bio_models, + dynamics=dynamics, + n_shooting=n_shooting, + phase_time=final_time_phase, + objective_functions=objective_functions, + time_phase_mapping=phase_time_bimapping, + x_init=x_init, + x_bounds=x_bounds, + u_init=u_init, + u_bounds=u_bounds, + constraints=constraints, + parameters=parameters, + parameter_bounds=parameters_bounds, + parameter_init=parameters_init, + parameter_objectives=parameter_objectives, + control_type=control_type, + use_sx=use_sx, + ode_solver=ode_solver, + n_threads=n_threads, + ) + + @staticmethod + def _declare_dynamics(bio_models, n_stim): + dynamics = DynamicsList() + for i in range(n_stim): + dynamics.add( + bio_models[i].declare_model_variables, + dynamic_function=bio_models[i].muscle_dynamic, + expand_dynamics=True, + expand_continuity=False, + phase=i, + phase_dynamics=PhaseDynamics.ONE_PER_NODE, + ) + return dynamics + + @staticmethod + def _set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init): + start_bounds = 0 + end_bounds = 0 + if motion_type == "start_end": + start_bounds = 3.14 / (180 / motion_data[0]) + end_bounds = 3.14 / (180 / motion_data[1]) + + for i in range(n_stim): + q_x_bounds = bio_models[i].bounds_from_ranges("q") + qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") + + if i == 0: + q_x_bounds[:, [0]] = start_bounds if motion_type == "start_end" else q_x_bounds[:, [0]] + qdot_x_bounds[:, [0]] = 0 # Start without any velocity + + if i == n_stim - 1: + q_x_bounds[:, [-1]] = end_bounds if motion_type == "start_end" else q_x_bounds[:, [-1]] + + x_bounds.add(key="q", bounds=q_x_bounds, phase=i) + x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) + + for i in range(n_stim): + x_init.add(key="q", initial_guess=[0] * bio_models[i].nb_q, phase=i) + + return x_bounds, x_init + + @staticmethod + def _set_controls(n_stim): + # Controls bounds + tau_min, tau_max, tau_init = [-20], [20], [0] + + u_bounds = BoundsList() + for i in range(n_stim): + u_bounds.add(key="tau", min_bound=tau_min, max_bound=tau_max, phase=i) + + # Controls initial guess + u_init = InitialGuessList() + for i in range(n_stim): + u_init.add(key="tau", initial_guess=tau_init, phase=i) + + return u_bounds, u_init diff --git a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py new file mode 100644 index 00000000..afb545da --- /dev/null +++ b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -0,0 +1,37 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model. +This ocp was build to produce a elbow motion from 5 to 120 degrees. +The stimulation frequency will be optimized between 10 and 100 Hz to satisfy the flexion and minimizing required +elbow torque control. +""" + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[5, 120], + fes_muscle_model=DingModelFrequencyWithFatigue(), + 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, + ) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/getting_started/musculoskeletal_dynamic_1muscle.py b/examples/getting_started/musculoskeletal_dynamic_1muscle.py deleted file mode 100644 index a4cb7936..00000000 --- a/examples/getting_started/musculoskeletal_dynamic_1muscle.py +++ /dev/null @@ -1,226 +0,0 @@ -import numpy as np - -from bioptim import ( - BoundsList, - ConstraintList, - ConstraintFcn, - OdeSolverBase, - BiMapping, - Node, - ControlType, - DynamicsList, - InitialGuessList, - InterpolationType, - ObjectiveFcn, - PhaseDynamics, - ObjectiveList, - OdeSolver, - OptimalControlProgram, - Solver, -) - -from cocofest import DingModelFrequency, FESActuatedBiorbdModel - -class FESActuatedBiorbdModelOCP(OptimalControlProgram): - def __init__(self, biorbd_model_path: str, - muscles_model: DingModelFrequency(), - n_stim: int, - final_time: float, - n_shooting: int, - time_min: float = None, - time_max: float = None, - time_bimapping: bool = False, - ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), - control_type: ControlType = ControlType.CONSTANT,): - - """ - Prepare the ocp - - Parameters - ---------- - biorbd_model_path: str - The path to the bioMod - final_time: float - The time at the final node - n_shooting: int - The number of shooting points - ode_solver: OdeSolverBase - The ode solver to use - - Returns - ------- - The OptimalControlProgram ready to be solved - """ - bio_models = [FESActuatedBiorbdModel(name="arm26_only_biceps", - biorbd_path=biorbd_model_path, - muscles_model=muscles_model) - for i in range(n_stim)] - - nq = bio_models[0].bio_model.nb_q - nqdot = bio_models[0].bio_model.nb_qdot - target = np.zeros((nq + nqdot, 1)) - target[1, 0] = 3.14 - - n_shooting = [n_shooting] * n_stim - - constraints = ConstraintList() - final_time_phase = None - # parameter_bimapping = BiMappingList() - phase_time_bimapping = None - - if time_min is None and time_max is None: - step = final_time / n_stim - final_time_phase = (step,) - for i in range(n_stim - 1): - final_time_phase = final_time_phase + (step,) - - else: - for i in range(n_stim): - constraints.add( - ConstraintFcn.TIME_CONSTRAINT, - node=Node.END, - min_bound=time_min, - max_bound=time_max, - phase=i, - ) - - if time_bimapping is True: - phase_time_bimapping = BiMapping(to_second=[0 for _ in range(n_stim)], to_first=[0]) - - final_time_phase = [time_min] * n_stim - - # Add objective functions - objective_functions = ObjectiveList() - for i in range(n_stim): - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) - - # objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="qdot", weight=1, quadratic=True, phase=n_stim - 1) - - # Dynamics - dynamics = DynamicsList() - for i in range(n_stim): - dynamics.add( - bio_models[i].declare_model_variables, - dynamic_function=bio_models[i].muscle_dynamic, - expand_dynamics=True, - expand_continuity=False, - phase=i, - phase_dynamics=PhaseDynamics.ONE_PER_NODE, - ) - - # States bounds - x_bounds = BoundsList() - - muscle_state_list = muscles_model.name_dof - starting_bounds, min_bounds, max_bounds = ( - muscles_model.standard_rest_values(), - muscles_model.standard_rest_values(), - muscles_model.standard_rest_values(), - ) - - for j in range(len(muscle_state_list)): - if muscle_state_list[j] == "Cn": - max_bounds[j] = 10 - elif muscle_state_list[j] == "F": - max_bounds[j] = 1000 - elif muscle_state_list[j] == "Tau1" or muscle_state_list[j] == "Km": - max_bounds[j] = 1 - elif muscle_state_list[j] == "A": - min_bounds[j] = 0 - - starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) - starting_bounds_max = np.concatenate((starting_bounds, max_bounds, max_bounds), axis=1) - middle_bound_min = np.concatenate((min_bounds, min_bounds, min_bounds), axis=1) - middle_bound_max = np.concatenate((max_bounds, max_bounds, max_bounds), axis=1) - - for k in range(n_stim): - for l in range(len(muscle_state_list)): - if k == 0: - x_bounds.add( - key=muscle_state_list[l], - min_bound=np.array([starting_bounds_min[l]]), - max_bound=np.array([starting_bounds_max[l]]), - phase=k, - interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, - ) - else: - x_bounds.add( - key=muscle_state_list[l], - min_bound=np.array([middle_bound_min[l]]), - max_bound=np.array([middle_bound_max[l]]), - phase=k, - interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, - ) - - for i in range(n_stim): - q_x_bounds = bio_models[i].bounds_from_ranges("q") - qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") - - if i == 0: - q_x_bounds[:, [0]] = 3.14/(180/5) # Start at 5° - qdot_x_bounds[:, [0]] = 0 # Start without any velocity - - if i == n_stim-1: - q_x_bounds[:, [-1]] = 3.14/2 # End at 90° - - # if i > n_stim-5: - # q_x_bounds.min[0, [0, 1, 2]] = 3.14 / 2 - 0.2 - # q_x_bounds.max[0, [0, 1, 2]] = 3.14 / 2 + 0.2 - - x_bounds.add(key="q", bounds=q_x_bounds, phase=i) - x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) - - x_init = InitialGuessList() - for i in range(n_stim): - muscle_state_list = muscles_model.name_dof - for k in range(len(muscle_state_list)): - x_init.add(key=muscle_state_list[k], # + "_" + muscles_name_list[j], - initial_guess=muscles_model.standard_rest_values()[k], phase=i) - x_init.add(key="q", initial_guess=[0] * bio_models[i].nb_q, phase=i) - - # Controls bounds - tau_min, tau_max, tau_init = [-20], [20], [0] - - u_bounds = BoundsList() - for i in range(n_stim): - u_bounds.add(key="tau", min_bound=tau_min, max_bound=tau_max, phase=i) - - # Controls initial guess - u_init = InitialGuessList() - for i in range(n_stim): - u_init.add(key="tau", initial_guess=tau_init, phase=i) - - super().__init__( - bio_model=bio_models, - dynamics=dynamics, - n_shooting=n_shooting, - phase_time=final_time_phase, - x_bounds=x_bounds, - u_bounds=u_bounds, - x_init=x_init, - u_init=u_init, - objective_functions=objective_functions, - constraints=constraints, - time_phase_mapping=phase_time_bimapping, - ode_solver=ode_solver, - control_type=control_type, - use_sx=True, - ) - - -if __name__ == "__main__": - ocp = FESActuatedBiorbdModelOCP("/arm26_biceps_1ddl.bioMod", - muscles_model=DingModelFrequency(), - final_time=1, - n_shooting=10, - n_stim=10, - time_min=0.01, - time_max=0.1, - time_bimapping=True, - ) - - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) - # Solver.IPOPT(show_online_optim=True) - # sol.graphs(show_bounds=True) - sol.animate() - sol.graphs(show_bounds=False) diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py new file mode 100644 index 00000000..76abe08e --- /dev/null +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -0,0 +1,41 @@ +""" +This example will do a 10 stimulation example with Ding's 2007 frequency model. +This ocp was build to produce a elbow motion from 5 to 120 degrees. +The stimulation frequency will be optimized between 10 and 100 Hz and pulse duration between minimal sensitivity +threshold and 600us to satisfy the flexion and minimizing required elbow torque control. +""" + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + +minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 +ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[5, 120], + fes_muscle_model=DingModelPulseDurationFrequencyWithFatigue(), + n_stim=n_stim, + n_shooting=10, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=True, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + custom_objective=objective_functions, + ) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py new file mode 100644 index 00000000..06eca5d5 --- /dev/null +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -0,0 +1,42 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 work. +This ocp was build to produce a elbow motion from 5 to 120 degrees. +The stimulation frequency will be optimized between 10 and 100 Hz and pulse intensity between minimal sensitivity +threshold and 130mA to satisfy the flexion and minimizing required elbow torque control. +""" + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity(DingModelIntensityFrequencyWithFatigue()) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[5, 120], + fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), + n_stim=n_stim, + n_shooting=10, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=True, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=130, + pulse_intensity_bimapping=False, + custom_objective=objective_functions, + ) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) From 2de8a5673efab7d3dfe0d7f3d47043f8c9b5c515 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 22 Jan 2024 17:42:13 -0500 Subject: [PATCH 05/63] black --- cocofest/models/ding2003.py | 37 ++-------- cocofest/models/ding2003_with_fatigue.py | 28 ++------ cocofest/models/ding2007.py | 21 ++---- cocofest/models/ding2007_with_fatigue.py | 23 ++----- cocofest/models/dynamical_model.py | 68 +++++++++++-------- cocofest/models/hmed2018.py | 13 ++-- cocofest/models/hmed2018_with_fatigue.py | 31 +++------ cocofest/optimization/fes_multi_start.py | 7 +- cocofest/optimization/fes_ocp.py | 6 +- examples/getting_started/model_integration.py | 8 +-- .../muscle_model_identification.py | 7 +- .../getting_started/pulse_mode_example.py | 14 +--- .../truncation/summation_truncation_graph.py | 10 +-- tests/shard1/test_ivp.py | 30 ++------ tests/shard1/test_ocp_build.py | 51 ++------------ tests/shard1/test_ocp_id.py | 34 ++-------- 16 files changed, 99 insertions(+), 289 deletions(-) diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index 948e7ecc..c0cc8c98 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -27,9 +27,7 @@ class DingModelFrequency: """ def __init__( - self, - name: str = "ding2003", - sum_stim_truncation: int = None, + self, name: str = "ding2003", sum_stim_truncation: int = None, ): self._name = name self._sum_stim_truncation = sum_stim_truncation @@ -94,13 +92,7 @@ def name(self) -> None | str: return self._name # ---- Model's dynamics ---- # - def system_dynamics( - self, - cn: MX, - f: MX, - t: MX = None, - t_stim_prev: list[MX] | list[float] = None, - ) -> MX: + def system_dynamics(self, cn: MX, f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None,) -> MX: """ The system dynamics is the function that describes the models. @@ -264,14 +256,7 @@ def dynamics( dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics - return DynamicsEvaluation( - dxdt=dxdt_fun( - cn=states[0], - f=states[1], - t=time, - t_stim_prev=stim_apparition, - ), - ) + return DynamicsEvaluation(dxdt=dxdt_fun(cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition,),) def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgram): """ @@ -316,13 +301,7 @@ def configure_ca_troponin_complex( name = "Cn" name_cn = [name] ConfigureProblem.configure_new_variable( - name, - name_cn, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_cn, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -352,13 +331,7 @@ def configure_force( name = "F" name_f = [name] ConfigureProblem.configure_new_variable( - name, - name_f, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_f, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 31df7521..75604882 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -27,9 +27,7 @@ class DingModelFrequencyWithFatigue(DingModelFrequency): """ def __init__( - self, - name: str = "ding2003_with_fatigue", - sum_stim_truncation: int = None, + self, name: str = "ding2003_with_fatigue", sum_stim_truncation: int = None, ): super().__init__(name=name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = True @@ -275,13 +273,7 @@ def configure_scaling_factor( name = "A" name_a = [name] ConfigureProblem.configure_new_variable( - name, - name_a, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -311,13 +303,7 @@ def configure_time_state_force_no_cross_bridge( name = "Tau1" name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, - name_tau1, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -347,11 +333,5 @@ def configure_cross_bridges( name = "Km" name_km = [name] ConfigureProblem.configure_new_variable( - name, - name_km, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, ) diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 2fc4b49c..e9193731 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -67,12 +67,7 @@ def serialize(self) -> tuple[Callable, dict]: ) def system_dynamics( - self, - cn: MX, - f: MX, - t: MX = None, - t_stim_prev: list[MX] | list[float] = None, - impulse_time: MX = None, + self, cn: MX, f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None, impulse_time: MX = None, ) -> MX: """ The system dynamics is the function that describes the models. @@ -178,7 +173,11 @@ def dynamics( ------- The derivative of the states in the tuple[MX] format """ - pulse_duration_parameters = nlp.model.get_pulse_duration_parameters(nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + pulse_duration_parameters = ( + nlp.model.get_pulse_duration_parameters(nlp.parameters) + if nlp_dynamics is None + else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped impulse_time = pulse_duration_parameters[0] @@ -188,13 +187,7 @@ def dynamics( dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics return DynamicsEvaluation( - dxdt=dxdt_fun( - cn=states[0], - f=states[1], - t=time, - t_stim_prev=stim_apparition, - impulse_time=impulse_time, - ), + dxdt=dxdt_fun(cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition, impulse_time=impulse_time,), defects=None, ) diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 623b17b2..47e9aacb 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -182,8 +182,11 @@ def dynamics( ------- The derivative of the states in the tuple[MX] format """ - pulse_duration_parameters = nlp.model.get_pulse_duration_parameters( - nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + pulse_duration_parameters = ( + nlp.model.get_pulse_duration_parameters(nlp.parameters) + if nlp_dynamics is None + else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped impulse_time = pulse_duration_parameters[0] @@ -250,13 +253,7 @@ def configure_time_state_force_no_cross_bridge( name = "Tau1" name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, - name_tau1, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -286,11 +283,5 @@ def configure_cross_bridges( name = "Km" name_km = [name] ConfigureProblem.configure_new_variable( - name, - name_km, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, ) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 8b1501bb..c15f257a 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -15,11 +15,13 @@ class FESActuatedBiorbdModel(BiorbdModel): - def __init__(self, name: str = None, - biorbd_path: str = None, - muscles_model: DingModelFrequency() = None, - # muscles_name: list = None # TODO : for loop to create different muscles - ): + def __init__( + self, + name: str = None, + biorbd_path: str = None, + muscles_model: DingModelFrequency() = None, + # muscles_name: list = None # TODO : for loop to create different muscles + ): super().__init__(biorbd_path) self._name = name self.bio_model = BiorbdModel(biorbd_path) @@ -60,8 +62,8 @@ def serialize(self) -> tuple[Callable, dict]: @property def name_dof(self) -> list[str]: return self.bio_stim_model[0].name_dof - # self.bio_model[0].name_dof - # ["Cn", "F", "A", "Tau1", "Km", "q", "qdot", "tau"] + # self.bio_model[0].name_dof + # ["Cn", "F", "A", "Tau1", "Km", "q", "qdot", "tau"] def muscle_name_dof(self) -> list[str]: return self.muscles_dynamics_model.name_dof @@ -127,11 +129,19 @@ def muscle_dynamic( # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() # dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) - muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, stim_apparition, - nlp_dynamics=nlp.model.muscles_dynamics_model).dxdt + muscle_dxdt = muscle_model.dynamics( + time, + states, + controls, + parameters, + stochastic_variables, + nlp, + stim_apparition, + nlp_dynamics=nlp.model.muscles_dynamics_model, + ).dxdt muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) - muscles_tau += - nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T @ muscle_forces + muscles_tau += -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T @ muscle_forces # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) @@ -156,18 +166,19 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg """ # for i in range(len(self.muscles_dynamics_model)): - # self.muscles_dynamics_model[i].configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscles_name[i]) - # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value + # self.muscles_dynamics_model[i].configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscles_name[i]) + # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value self.muscles_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) self.muscles_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - if 'A' in self.muscles_dynamics_model.name_dof: + if "A" in self.muscles_dynamics_model.name_dof: self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - if 'Tau1' in self.muscles_dynamics_model.name_dof: + if "Tau1" in self.muscles_dynamics_model.name_dof: self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( - ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - if 'Km' in self.muscles_dynamics_model.name_dof: + ocp=ocp, nlp=nlp, as_states=True, as_controls=False + ) + if "Km" in self.muscles_dynamics_model.name_dof: self.muscles_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) # TODO : for fatigue model @@ -183,8 +194,13 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg time_type = "mx" if "time" in ocp.parameters.keys() else None stim_apparition = [ocp.node_time(phase_idx=i, node_idx=0, type=time_type) for i in range(nlp.phase_idx + 1)] - ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.muscle_dynamic, muscle_model=self.muscles_dynamics_model, stim_apparition=stim_apparition) - + ConfigureProblem.configure_dynamics_function( + ocp, + nlp, + dyn_func=self.muscle_dynamic, + muscle_model=self.muscles_dynamics_model, + stim_apparition=stim_apparition, + ) @staticmethod def configure_q(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: bool = False): @@ -204,9 +220,7 @@ def configure_q(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: boo """ name = "q" name_q = [name] - ConfigureProblem.configure_new_variable( - name, name_q, ocp, nlp, as_states, as_controls, as_states_dot - ) + ConfigureProblem.configure_new_variable(name, name_q, ocp, nlp, as_states, as_controls, as_states_dot) @staticmethod def configure_qdot(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: bool = False): @@ -227,9 +241,7 @@ def configure_qdot(ocp, nlp, as_states: bool, as_controls: bool, as_states_dot: name = "qdot" name_qdot = [name] - ConfigureProblem.configure_new_variable( - name, name_qdot, ocp, nlp, as_states, as_controls, as_states_dot - ) + ConfigureProblem.configure_new_variable(name, name_qdot, ocp, nlp, as_states, as_controls, as_states_dot) @staticmethod def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: FatigueList = None): @@ -250,10 +262,8 @@ def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: Fatigue name = "tau" name_tau = ["tau"] - ConfigureProblem.configure_new_variable( - name, name_tau, ocp, nlp, as_states, as_controls, fatigue=fatigue - ) + ConfigureProblem.configure_new_variable(name, name_tau, ocp, nlp, as_states, as_controls, fatigue=fatigue) + if __name__ == "__main__": - FESActuatedBiorbdModel(biorbd_path="msk_model/arm26_unmesh.bioMod", - muscles_model=DingModelFrequency()) \ No newline at end of file + FESActuatedBiorbdModel(biorbd_path="msk_model/arm26_unmesh.bioMod", muscles_model=DingModelFrequency()) diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 69284b4e..5e184ea1 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -244,8 +244,11 @@ def dynamics( intensity_stim_prev = ( [] ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase - intensity_parameters = nlp.model.get_intensity_parameters( - nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_intensity_parameters(nlp.parameters) + intensity_parameters = ( + nlp.model.get_intensity_parameters(nlp.parameters) + if nlp_dynamics is None + else nlp_dynamics.get_intensity_parameters(nlp.parameters) + ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped for i in range(nlp.phase_idx + 1): @@ -258,11 +261,7 @@ def dynamics( return DynamicsEvaluation( dxdt=dxdt_fun( - cn=states[0], - f=states[1], - t=time, - t_stim_prev=stim_apparition, - intensity_stim=intensity_stim_prev, + cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition, intensity_stim=intensity_stim_prev, ), defects=None, ) diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 7f4c3759..391ad727 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -201,8 +201,11 @@ def dynamics( intensity_stim_prev = ( [] ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase - intensity_parameters = nlp.model.get_intensity_parameters( - nlp.parameters) if nlp_dynamics is None else nlp_dynamics.get_intensity_parameters(nlp.parameters) + intensity_parameters = ( + nlp.model.get_intensity_parameters(nlp.parameters) + if nlp_dynamics is None + else nlp_dynamics.get_intensity_parameters(nlp.parameters) + ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped for i in range(nlp.phase_idx + 1): @@ -273,13 +276,7 @@ def configure_scaling_factor( name = "A" name_a = [name] ConfigureProblem.configure_new_variable( - name, - name_a, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -309,13 +306,7 @@ def configure_time_state_force_no_cross_bridge( name = "Tau1" name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, - name_tau1, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, ) @staticmethod @@ -345,11 +336,5 @@ def configure_cross_bridges( name = "Km" name_km = [name] ConfigureProblem.configure_new_variable( - name, - name_km, - ocp, - nlp, - as_states, - as_controls, - as_states_dot, + name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, ) diff --git a/cocofest/optimization/fes_multi_start.py b/cocofest/optimization/fes_multi_start.py index 2359c122..793d1673 100644 --- a/cocofest/optimization/fes_multi_start.py +++ b/cocofest/optimization/fes_multi_start.py @@ -328,12 +328,7 @@ def construct_filepath(save_path, combinatorial_parameters): " DingModelIntensityFrequency", ) - def save_results( - self, - sol: Solution, - *combinatorial_parameters, - **extra_parameters, - ) -> None: + def save_results(self, sol: Solution, *combinatorial_parameters, **extra_parameters,) -> None: """ Callback of the post_optimization_callback, this can be used to save the results diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index e6b53320..16d73d9f 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -454,11 +454,7 @@ def _build_phase_time(final_time, n_stim, pulse_mode, time_min, time_max, time_b else: for i in range(n_stim): constraints.add( - ConstraintFcn.TIME_CONSTRAINT, - node=Node.END, - min_bound=time_min, - max_bound=time_max, - phase=i, + ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min, max_bound=time_max, phase=i, ) if time_bimapping is True: diff --git a/examples/getting_started/model_integration.py b/examples/getting_started/model_integration.py index 703d17cf..5e36616d 100644 --- a/examples/getting_started/model_integration.py +++ b/examples/getting_started/model_integration.py @@ -8,13 +8,7 @@ # --- Build ocp --- # # This problem was build to be integrated and has no objectives nor parameter to optimize. -ivp = IvpFes( - model=DingModelFrequencyWithFatigue(), - n_stim=10, - n_shooting=20, - final_time=1, - use_sx=True, -) +ivp = IvpFes(model=DingModelFrequencyWithFatigue(), n_stim=10, n_shooting=20, final_time=1, use_sx=True,) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) diff --git a/examples/getting_started/muscle_model_identification.py b/examples/getting_started/muscle_model_identification.py index ca7c86b9..07c7e1a1 100644 --- a/examples/getting_started/muscle_model_identification.py +++ b/examples/getting_started/muscle_model_identification.py @@ -420,12 +420,7 @@ identified_time_list = [] ivp_from_identification = IvpFes( - model=identified_model, - n_stim=10, - n_shooting=10, - final_time=1, - use_sx=True, - pulse_intensity=pulse_intensity_values, + model=identified_model, n_stim=10, n_shooting=10, final_time=1, use_sx=True, pulse_intensity=pulse_intensity_values, ) # Creating the solution from the initial guess diff --git a/examples/getting_started/pulse_mode_example.py b/examples/getting_started/pulse_mode_example.py index 6bc00297..7074c9f3 100644 --- a/examples/getting_started/pulse_mode_example.py +++ b/examples/getting_started/pulse_mode_example.py @@ -12,12 +12,7 @@ # This example shows how to create a problem with doublet pulses. # The stimulation won't be optimized. ivp = IvpFes( - model=DingModelFrequencyWithFatigue(), - n_stim=20, - n_shooting=10, - final_time=1, - pulse_mode="Doublet", - use_sx=True, + model=DingModelFrequencyWithFatigue(), n_stim=20, n_shooting=10, final_time=1, pulse_mode="Doublet", use_sx=True, ) # Creating the solution from the initial guess @@ -44,12 +39,7 @@ # --- Build ocp --- # # This example shows how to create a problem with triplet pulses. ivp = IvpFes( - model=DingModelFrequencyWithFatigue(), - n_stim=30, - n_shooting=10, - final_time=1, - pulse_mode="Triplet", - use_sx=True, + model=DingModelFrequencyWithFatigue(), n_stim=30, n_shooting=10, final_time=1, pulse_mode="Triplet", use_sx=True, ) # Creating the solution from the initial guess diff --git a/examples/sensitivity/truncation/summation_truncation_graph.py b/examples/sensitivity/truncation/summation_truncation_graph.py index 95713a7a..3d2adb0d 100644 --- a/examples/sensitivity/truncation/summation_truncation_graph.py +++ b/examples/sensitivity/truncation/summation_truncation_graph.py @@ -151,15 +151,9 @@ ) elif computation_time_color_bar_scale == "same": - cbar2 = fig.colorbar( - im3, - ax=axs[1], - label="Computation time (s)", - ticks=[3.033, 4, 5, 6, 7, 8, 9, 10.038], - ) + cbar2 = fig.colorbar(im3, ax=axs[1], label="Computation time (s)", ticks=[3.033, 4, 5, 6, 7, 8, 9, 10.038],) cbar2.ax.set_yticklabels( - [3.033, 4, 5, 6, 7, 8, 9, 10.038], - style="italic", + [3.033, 4, 5, 6, 7, 8, 9, 10.038], style="italic", ) axs[0].set_xlabel("Frequency (Hz)") diff --git a/tests/shard1/test_ivp.py b/tests/shard1/test_ivp.py index 4aea6463..f210ebce 100644 --- a/tests/shard1/test_ivp.py +++ b/tests/shard1/test_ivp.py @@ -39,14 +39,7 @@ def test_ding2003_ivp(model): @pytest.mark.parametrize("model", [DingModelPulseDurationFrequency(), DingModelPulseDurationFrequencyWithFatigue()]) @pytest.mark.parametrize("pulse_duration", [0.0003, [0.0003, 0.0004, 0.0005]]) def test_ding2007_ivp(model, pulse_duration): - ivp = IvpFes( - model=model, - n_stim=3, - n_shooting=10, - final_time=0.3, - pulse_duration=pulse_duration, - use_sx=True, - ) + ivp = IvpFes(model=model, n_stim=3, n_shooting=10, final_time=0.3, pulse_duration=pulse_duration, use_sx=True,) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -77,14 +70,7 @@ def test_ding2007_ivp(model, pulse_duration): @pytest.mark.parametrize("model", [DingModelIntensityFrequency(), DingModelIntensityFrequencyWithFatigue()]) @pytest.mark.parametrize("pulse_intensity", [50, [50, 60, 70]]) def test_hmed2018_ivp(model, pulse_intensity): - ivp = IvpFes( - model=model, - n_stim=3, - n_shooting=10, - final_time=0.3, - pulse_intensity=pulse_intensity, - use_sx=True, - ) + ivp = IvpFes(model=model, n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=pulse_intensity, use_sx=True,) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -234,11 +220,7 @@ def test_all_ivp_errors(): with pytest.raises(ValueError, match="pulse_intensity list must have the same length as n_stim"): IvpFes( - model=DingModelIntensityFrequency(), - n_stim=3, - n_shooting=10, - final_time=0.3, - pulse_intensity=[20, 30], + model=DingModelIntensityFrequency(), n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=[20, 30], ) pulse_intensity = [20, 30, 0.1] @@ -260,11 +242,7 @@ def test_all_ivp_errors(): with pytest.raises(TypeError, match="pulse_intensity must be int, float or list type"): IvpFes( - model=DingModelIntensityFrequency(), - n_stim=3, - n_shooting=10, - final_time=0.3, - pulse_intensity=True, + model=DingModelIntensityFrequency(), n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=True, ) with pytest.raises(ValueError, match="ode_solver must be a OdeSolver type"): diff --git a/tests/shard1/test_ocp_build.py b/tests/shard1/test_ocp_build.py index f7142d85..48b6e35f 100644 --- a/tests/shard1/test_ocp_build.py +++ b/tests/shard1/test_ocp_build.py @@ -257,17 +257,7 @@ (DingModelFrequencyWithFatigue(), None, None, None, None, None, None, None, None), (DingModelPulseDurationFrequency(), 0.0002, None, None, None, None, None, None, None), (DingModelPulseDurationFrequencyWithFatigue(), 0.0002, None, None, None, None, None, None, None), - ( - DingModelPulseDurationFrequency(), - None, - minimum_pulse_duration, - 0.0006, - False, - None, - None, - None, - None, - ), + (DingModelPulseDurationFrequency(), None, minimum_pulse_duration, 0.0006, False, None, None, None, None,), ( DingModelPulseDurationFrequencyWithFatigue(), None, @@ -282,38 +272,13 @@ # (DingModelPulseDurationFrequency(), None, minimum_pulse_duration, 0.0006, True, None, None, None, None), parameter mapping not yet implemented (DingModelIntensityFrequency(), None, None, None, None, 20, None, None, None), (DingModelIntensityFrequencyWithFatigue(), None, None, None, None, 20, None, None, None), - ( - DingModelIntensityFrequency(), - None, - None, - None, - None, - None, - minimum_pulse_intensity, - 130, - False, - ), - ( - DingModelIntensityFrequencyWithFatigue(), - None, - None, - None, - None, - None, - minimum_pulse_intensity, - 130, - False, - ), + (DingModelIntensityFrequency(), None, None, None, None, None, minimum_pulse_intensity, 130, False,), + (DingModelIntensityFrequencyWithFatigue(), None, None, None, None, None, minimum_pulse_intensity, 130, False,), # (DingModelIntensityFrequency(), None, None, None, None, None, minimum_pulse_intensity, 130, True), parameter mapping not yet implemented ], ) @pytest.mark.parametrize( - "time_min, time_max, time_bimapping", - [ - (None, None, False), - (0.01, 0.1, False), - (0.01, 0.1, True), - ], + "time_min, time_max, time_bimapping", [(None, None, False), (0.01, 0.1, False), (0.01, 0.1, True),], ) @pytest.mark.parametrize("use_sx", [True]) # Later add False @pytest.mark.parametrize( @@ -421,13 +386,7 @@ def test_ocp_not_for_optimal_error(): match="This is not an optimal control problem," " add parameter to optimize or use the IvpFes method to build your problem", ): - ocp = OcpFes().prepare_ocp( - model=DingModelFrequency(), - n_stim=1, - n_shooting=10, - final_time=1, - use_sx=True, - ) + ocp = OcpFes().prepare_ocp(model=DingModelFrequency(), n_stim=1, n_shooting=10, final_time=1, use_sx=True,) @pytest.mark.parametrize( diff --git a/tests/shard1/test_ocp_id.py b/tests/shard1/test_ocp_id.py index ad1d279c..652ea784 100644 --- a/tests/shard1/test_ocp_id.py +++ b/tests/shard1/test_ocp_id.py @@ -117,13 +117,7 @@ ] additional_key_settings = { - "a_rest": { - "initial_guess": 1000, - "min_bound": 1, - "max_bound": 10000, - "function": model.set_a_rest, - "scaling": 1, - }, + "a_rest": {"initial_guess": 1000, "min_bound": 1, "max_bound": 10000, "function": model.set_a_rest, "scaling": 1,}, "km_rest": { "initial_guess": 0.5, "min_bound": 0.001, @@ -138,27 +132,14 @@ "function": model.set_tau1_rest, "scaling": 1000, }, - "tau2": { - "initial_guess": 0.5, - "min_bound": 0.0001, - "max_bound": 1, - "function": model.set_tau2, - "scaling": 1000, - }, + "tau2": {"initial_guess": 0.5, "min_bound": 0.0001, "max_bound": 1, "function": model.set_tau2, "scaling": 1000,}, } def test_ocp_id_ding2003(): # --- Creating the simulated data to identify on --- # # Building the Initial Value Problem - ivp = IvpFes( - model=DingModelFrequency(), - n_stim=10, - n_shooting=10, - final_time=1, - use_sx=True, - extend_last_phase=1, - ) + ivp = IvpFes(model=DingModelFrequency(), n_stim=10, n_shooting=10, final_time=1, use_sx=True, extend_last_phase=1,) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -421,8 +402,7 @@ def test_all_ocp_id_errors(): def test_all_id_program_errors(): with pytest.raises( - ValueError, - match="The given model is not valid and should not be including the fatigue equation in the model", + ValueError, match="The given model is not valid and should not be including the fatigue equation in the model", ): DingModelFrequencyForceParameterIdentification(model=DingModelFrequencyWithFatigue()) @@ -620,8 +600,7 @@ def test_all_id_program_errors(): ) with pytest.raises( - ValueError, - match=re.escape(f"The given model parameters are not valid, only None, int and float are accepted"), + ValueError, match=re.escape(f"The given model parameters are not valid, only None, int and float are accepted"), ): DingModelFrequencyForceParameterIdentification( model=DingModelFrequency(), @@ -652,8 +631,7 @@ def test_all_id_program_errors(): ) with pytest.raises( - ValueError, - match=re.escape(f"The given {'a_rest'} parameter is not valid, it must be given or identified"), + ValueError, match=re.escape(f"The given {'a_rest'} parameter is not valid, it must be given or identified"), ): DingModelFrequencyForceParameterIdentification( model=DingModelFrequency(), From b4c72bcd8f7b451c3f4b60099b1aa601afaf1937 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 22 Jan 2024 17:43:15 -0500 Subject: [PATCH 06/63] feat: enabling 2dof model use + example --- cocofest/optimization/fes_ocp_dynamics.py | 170 +- examples/dynamics/arm26_biceps.bioMod | 256 + ...timization_musculoskeletal_dynamic_2ddl.py | 38 + examples/dynamics/mesh/arm_r_humerus.vtp | 1228 ++ examples/dynamics/mesh/arm_r_ulna.vtp | 842 ++ examples/dynamics/mesh/ground_ribs.vtp | 10998 ++++++++++++++++ ...cy_optimization_musculoskeletal_dynamic.py | 25 +- ...on_optimization_musculoskeletal_dynamic.py | 31 +- ...ty_optimization_musculoskeletal_dynamic.py | 35 +- 9 files changed, 13509 insertions(+), 114 deletions(-) create mode 100644 examples/dynamics/arm26_biceps.bioMod create mode 100644 examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py create mode 100644 examples/dynamics/mesh/arm_r_humerus.vtp create mode 100644 examples/dynamics/mesh/arm_r_ulna.vtp create mode 100644 examples/dynamics/mesh/ground_ribs.vtp diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index a0a309bd..58c65107 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -26,41 +26,42 @@ class FESActuatedBiorbdModelOCP: - @staticmethod - def prepare_ocp(biorbd_model_path: str, - motion_type: str, - motion_data: list, - fes_muscle_model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue = None, - n_stim: int = None, - n_shooting: int = None, - final_time: int | float = None, - pulse_mode: str = "Single", - frequency: int | float = None, - round_down: bool = False, - time_min: float = None, - time_max: float = None, - time_bimapping: bool = False, - pulse_duration: int | float = None, - pulse_duration_min: int | float = None, - pulse_duration_max: int | float = None, - pulse_duration_bimapping: bool = False, - pulse_intensity: int | float = None, - pulse_intensity_min: int | float = None, - pulse_intensity_max: int | float = None, - pulse_intensity_bimapping: bool = False, - force_tracking: list = None, - end_node_tracking: int | float = None, - custom_objective: ObjectiveList = None, - use_sx: bool = True, - ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), - control_type: ControlType = ControlType.CONSTANT, - n_threads: int = 1): + def prepare_ocp( + biorbd_model_path: str, + motion_type: str = None, + motion_data: list = None, + fes_muscle_model: DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue = None, + n_stim: int = None, + n_shooting: int = None, + final_time: int | float = None, + pulse_mode: str = "Single", + frequency: int | float = None, + round_down: bool = False, + time_min: float = None, + time_max: float = None, + time_bimapping: bool = False, + pulse_duration: int | float = None, + pulse_duration_min: int | float = None, + pulse_duration_max: int | float = None, + pulse_duration_bimapping: bool = False, + pulse_intensity: int | float = None, + pulse_intensity_min: int | float = None, + pulse_intensity_max: int | float = None, + pulse_intensity_bimapping: bool = False, + force_tracking: list = None, + end_node_tracking: int | float = None, + custom_objective: ObjectiveList = None, + use_sx: bool = True, + ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), + control_type: ControlType = ControlType.CONSTANT, + n_threads: int = 1, + ): """ This definition prepares the ocp to be solved @@ -112,29 +113,29 @@ def prepare_ocp(biorbd_model_path: str, """ OcpFes._sanity_check( - model=fes_muscle_model, - n_stim=n_stim, - n_shooting=n_shooting, - final_time=final_time, - pulse_mode=pulse_mode, - frequency=frequency, - time_min=time_min, - time_max=time_max, - time_bimapping=time_bimapping, - pulse_duration=pulse_duration, - pulse_duration_min=pulse_duration_min, - pulse_duration_max=pulse_duration_max, - pulse_duration_bimapping=pulse_duration_bimapping, - pulse_intensity=pulse_intensity, - pulse_intensity_min=pulse_intensity_min, - pulse_intensity_max=pulse_intensity_max, - pulse_intensity_bimapping=pulse_intensity_bimapping, - force_tracking=force_tracking, - end_node_tracking=end_node_tracking, - custom_objective=custom_objective, - use_sx=use_sx, - ode_solver=ode_solver, - n_threads=n_threads, + model=fes_muscle_model, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=final_time, + pulse_mode=pulse_mode, + frequency=frequency, + time_min=time_min, + time_max=time_max, + time_bimapping=time_bimapping, + pulse_duration=pulse_duration, + pulse_duration_min=pulse_duration_min, + pulse_duration_max=pulse_duration_max, + pulse_duration_bimapping=pulse_duration_bimapping, + pulse_intensity=pulse_intensity, + pulse_intensity_min=pulse_intensity_min, + pulse_intensity_max=pulse_intensity_max, + pulse_intensity_bimapping=pulse_intensity_bimapping, + force_tracking=force_tracking, + end_node_tracking=end_node_tracking, + custom_objective=custom_objective, + use_sx=use_sx, + ode_solver=ode_solver, + n_threads=n_threads, ) OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) @@ -174,15 +175,21 @@ def prepare_ocp(biorbd_model_path: str, " add parameter to optimize or use the IvpFes method to build your problem" ) - bio_models = [FESActuatedBiorbdModel(name=None, - biorbd_path=biorbd_model_path, - muscles_model=fes_muscle_model) - for i in range(n_stim)] + bio_models = [ + FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_model) + for i in range(n_stim) + ] + + FESActuatedBiorbdModelOCP._sanity_check_bounds( + bio_models=bio_models, motion_type=motion_type, motion_data=motion_data + ) dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) x_bounds, x_init = OcpFes._set_bounds(fes_muscle_model, n_stim) - x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init) - u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(n_stim) + x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( + bio_models, motion_type, motion_data, n_stim, x_bounds, x_init + ) + u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim) objective_functions = OcpFes._set_objective( n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective ) @@ -225,22 +232,28 @@ def _declare_dynamics(bio_models, n_stim): @staticmethod def _set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init): - start_bounds = 0 - end_bounds = 0 + if motion_type == "start_end": - start_bounds = 3.14 / (180 / motion_data[0]) - end_bounds = 3.14 / (180 / motion_data[1]) + start_bounds = [] + end_bounds = [] + for i in range(bio_models[0].nb_q): + start_bounds.append(3.14 / (180 / motion_data[0][i]) if motion_data[0][i] != 0 else 0) + end_bounds.append(3.14 / (180 / motion_data[1][i]) if motion_data[1][i] != 0 else 0) for i in range(n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") if i == 0: - q_x_bounds[:, [0]] = start_bounds if motion_type == "start_end" else q_x_bounds[:, [0]] + if motion_type == "start_end": + for j in range(bio_models[i].nb_q): + q_x_bounds[j, [0]] = start_bounds[j] qdot_x_bounds[:, [0]] = 0 # Start without any velocity if i == n_stim - 1: - q_x_bounds[:, [-1]] = end_bounds if motion_type == "start_end" else q_x_bounds[:, [-1]] + if motion_type == "start_end": + for j in range(bio_models[i].nb_q): + q_x_bounds[j, [-1]] = end_bounds[j] x_bounds.add(key="q", bounds=q_x_bounds, phase=i) x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) @@ -251,9 +264,11 @@ def _set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init): return x_bounds, x_init @staticmethod - def _set_controls(n_stim): + def _set_controls(bio_models, n_stim): + # Controls bounds - tau_min, tau_max, tau_init = [-20], [20], [0] + nb_tau = bio_models[0].nb_tau + tau_min, tau_max, tau_init = [-20] * nb_tau, [20] * nb_tau, [0] * nb_tau u_bounds = BoundsList() for i in range(n_stim): @@ -265,3 +280,16 @@ def _set_controls(n_stim): u_init.add(key="tau", initial_guess=tau_init, phase=i) return u_bounds, u_init + + @staticmethod + def _sanity_check_bounds(bio_models, motion_type, motion_data): + for i in range(bio_models[0].nb_q): + if motion_type == "start_end": + if not isinstance(motion_data, list): + raise TypeError("The motion data should be a list of two elements") + if len(motion_data) != 2: + raise ValueError("The motion data should be a list of two elements, start and end position") + if not isinstance(motion_data[0], list) or not isinstance(motion_data[1], list): + raise TypeError("The start and end position should be a list") + if len(motion_data[0]) != bio_models[0].nb_q or len(motion_data[1]) != bio_models[0].nb_q: + raise ValueError("The start and end position should be a list of size nb_q") diff --git a/examples/dynamics/arm26_biceps.bioMod b/examples/dynamics/arm26_biceps.bioMod new file mode 100644 index 00000000..8050c204 --- /dev/null +++ b/examples/dynamics/arm26_biceps.bioMod @@ -0,0 +1,256 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + meshfile mesh/ground_ribs.vtp + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + marker r_acromion + parent base + position -0.01256 0.04 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ -1 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + // Markers + marker r_humerus_epicondyle + parent r_humerus + position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 + endmarker + marker COM_arm + parent r_humerus + position 0 -0.18049599999999999 0 + endmarker + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + // Markers + marker r_radius_styloid + parent r_ulna_radius_hand + position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 + endmarker + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +// base > r_ulna_radius_hand +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle BIClong + Type degroote + statetype degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint diff --git a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py new file mode 100644 index 00000000..7557a86e --- /dev/null +++ b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py @@ -0,0 +1,38 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model. +This ocp was build to produce an elbow motion from 5 to 120 degrees starting and ending with the arm at the vertical. +The stimulation frequency will be optimized between 10 and 100 Hz to satisfy the flexion and minimizing required +elbow torque control. +""" + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps.bioMod", + motion_type="start_end", + motion_data=[[0, 5], [0, 120]], + fes_muscle_model=DingModelFrequencyWithFatigue(), + 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, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/dynamics/mesh/arm_r_humerus.vtp b/examples/dynamics/mesh/arm_r_humerus.vtp new file mode 100644 index 00000000..19fcf553 --- /dev/null +++ b/examples/dynamics/mesh/arm_r_humerus.vtp @@ -0,0 +1,1228 @@ + + + + + + + -0.241965 0.928387 -0.282046 + -0.421615 0.891296 -0.166834 + -0.670330 0.714611 -0.199971 + -0.776204 0.488631 -0.398431 + -0.649453 0.374399 -0.661844 + -0.390814 0.431251 -0.813195 + -0.165920 0.625156 -0.762660 + -0.101672 0.835461 -0.540063 + 0.133272 0.890320 -0.435395 + -0.021126 0.997308 0.070220 + -0.330078 0.933471 0.140289 + -0.761722 0.635977 0.123745 + -0.924037 0.314799 -0.216928 + -0.755872 0.122545 -0.643149 + -0.340825 0.210203 -0.916326 + 0.080834 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b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -19,18 +19,19 @@ for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", - motion_type="start_end", - motion_data=[5, 120], - fes_muscle_model=DingModelFrequencyWithFatigue(), - 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, - ) +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[[5], [120]], + fes_muscle_model=DingModelFrequencyWithFatigue(), + 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, +) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol.animate() diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py index 76abe08e..3203bbf8 100644 --- a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -20,21 +20,22 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 -ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", - motion_type="start_end", - motion_data=[5, 120], - fes_muscle_model=DingModelPulseDurationFrequencyWithFatigue(), - n_stim=n_stim, - n_shooting=10, - final_time=1, - time_min=0.01, - time_max=0.1, - time_bimapping=True, - pulse_duration_min=minimum_pulse_duration, - pulse_duration_max=0.0006, - pulse_duration_bimapping=False, - custom_objective=objective_functions, - ) +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[[5], [120]], + fes_muscle_model=DingModelPulseDurationFrequencyWithFatigue(), + n_stim=n_stim, + n_shooting=10, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=True, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + custom_objective=objective_functions, +) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol.animate() diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index 06eca5d5..ce77d16e 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -19,23 +19,26 @@ for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity(DingModelIntensityFrequencyWithFatigue()) +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp(biorbd_model_path="/arm26_biceps_1ddl.bioMod", - motion_type="start_end", - motion_data=[5, 120], - fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), - n_stim=n_stim, - n_shooting=10, - final_time=1, - time_min=0.01, - time_max=0.1, - time_bimapping=True, - pulse_intensity_min=minimum_pulse_intensity, - pulse_intensity_max=130, - pulse_intensity_bimapping=False, - custom_objective=objective_functions, - ) +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps_1ddl.bioMod", + motion_type="start_end", + motion_data=[[5], [120]], + fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), + n_stim=n_stim, + n_shooting=10, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=True, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=130, + pulse_intensity_bimapping=False, + custom_objective=objective_functions, +) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol.animate() From 0d6b7fef737634ae54f2423d404c84d957963051 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 22 Jan 2024 17:45:24 -0500 Subject: [PATCH 07/63] refactor: renaming ddl into dof --- ...y => frequency_optimization_musculoskeletal_dynamic_2dof.py} | 0 .../{arm26_biceps_1ddl.bioMod => arm26_biceps_1dof.bioMod} | 0 .../frequency_optimization_musculoskeletal_dynamic.py | 2 +- .../pulse_duration_optimization_musculoskeletal_dynamic.py | 2 +- .../pulse_intensity_optimization_musculoskeletal_dynamic.py | 2 +- 5 files changed, 3 insertions(+), 3 deletions(-) rename examples/dynamics/{frequency_optimization_musculoskeletal_dynamic_2ddl.py => frequency_optimization_musculoskeletal_dynamic_2dof.py} (100%) rename examples/getting_started/{arm26_biceps_1ddl.bioMod => arm26_biceps_1dof.bioMod} (100%) diff --git a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py similarity index 100% rename from examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2ddl.py rename to examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py diff --git a/examples/getting_started/arm26_biceps_1ddl.bioMod b/examples/getting_started/arm26_biceps_1dof.bioMod similarity index 100% rename from examples/getting_started/arm26_biceps_1ddl.bioMod rename to examples/getting_started/arm26_biceps_1dof.bioMod diff --git a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py index 14226aab..86cbd8d5 100644 --- a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -20,7 +20,7 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1ddl.bioMod", + biorbd_model_path="arm26_biceps_1dof.bioMod", motion_type="start_end", motion_data=[[5], [120]], fes_muscle_model=DingModelFrequencyWithFatigue(), diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py index 3203bbf8..abd54276 100644 --- a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -21,7 +21,7 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1ddl.bioMod", + biorbd_model_path="/arm26_biceps_1dof.bioMod", motion_type="start_end", motion_data=[[5], [120]], fes_muscle_model=DingModelPulseDurationFrequencyWithFatigue(), diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index ce77d16e..c0b91dcb 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -24,7 +24,7 @@ ) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1ddl.bioMod", + biorbd_model_path="/arm26_biceps_1dof.bioMod", motion_type="start_end", motion_data=[[5], [120]], fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), From 383ae8bf1eac1a48b2030218c7bbe95e847655ec Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 23 Jan 2024 18:33:02 -0500 Subject: [PATCH 08/63] starting multi muscle incorporation --- cocofest/models/ding2003.py | 13 ++- cocofest/models/ding2003_with_fatigue.py | 12 ++- cocofest/models/ding2007.py | 4 +- cocofest/models/ding2007_with_fatigue.py | 4 +- cocofest/models/dynamical_model.py | 5 +- cocofest/models/hmed2018.py | 4 +- cocofest/models/hmed2018_with_fatigue.py | 4 +- cocofest/optimization/fes_ocp_dynamics.py | 97 ++++++++++++++++------- tests/shard1/test_ocp_build.py | 2 +- 9 files changed, 97 insertions(+), 48 deletions(-) diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index c0cc8c98..5f8b68b7 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -27,9 +27,10 @@ class DingModelFrequency: """ def __init__( - self, name: str = "ding2003", sum_stim_truncation: int = None, + self, model_name: str = "ding2003", muscle_name: str = None, sum_stim_truncation: int = None, ): - self._name = name + self._model_name = model_name + self._muscle_name = muscle_name self._sum_stim_truncation = sum_stim_truncation self._with_fatigue = False # ---- Custom values for the example ---- # @@ -88,8 +89,12 @@ def nb_state(self) -> int: return 2 @property - def name(self) -> None | str: - return self._name + def model_name(self) -> None | str: + return self._model_name + + @property + def muscle_name(self) -> None | str: + return self._muscle_name # ---- Model's dynamics ---- # def system_dynamics(self, cn: MX, f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None,) -> MX: diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 75604882..bf9d0466 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -27,9 +27,9 @@ class DingModelFrequencyWithFatigue(DingModelFrequency): """ def __init__( - self, name: str = "ding2003_with_fatigue", sum_stim_truncation: int = None, + self, model_name: str = "ding2003_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None, ): - super().__init__(name=name, sum_stim_truncation=sum_stim_truncation) + super().__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = True # ---- Fatigue models ---- # self.alpha_a = -4.0 * 10e-7 # Value from Ding's experimentation [1] (s^-2) @@ -86,8 +86,12 @@ def nb_state(self) -> int: return 5 @property - def name(self) -> None | str: - return self._name + def model_name(self) -> None | str: + return self._model_name + + @property + def muscle_name(self) -> None | str: + return self._muscle_name # ---- Model's dynamics ---- # def system_dynamics( diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index e9193731..4b028d41 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -25,8 +25,8 @@ class DingModelPulseDurationFrequency(DingModelFrequency): Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, 36(2), 214-222. """ - def __init__(self, name: str = "ding_2007", sum_stim_truncation: int = None): - super(DingModelPulseDurationFrequency, self).__init__(name=name, sum_stim_truncation=sum_stim_truncation) + def __init__(self, model_name: str = "ding_2007", muscle_name: str = None, sum_stim_truncation: int = None): + super(DingModelPulseDurationFrequency, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = False self.impulse_time = None # ---- Custom values for the example ---- # diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 47e9aacb..3a74f2e0 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -26,9 +26,9 @@ class DingModelPulseDurationFrequencyWithFatigue(DingModelPulseDurationFrequency Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, 36(2), 214-222. """ - def __init__(self, name: str = "ding_2007_with_fatigue", sum_stim_truncation: int = None): + def __init__(self, model_name: str = "ding_2007_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None): super(DingModelPulseDurationFrequencyWithFatigue, self).__init__( - name=name, sum_stim_truncation=sum_stim_truncation + model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation ) self._with_fatigue = True diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index c15f257a..24d208d6 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -141,8 +141,9 @@ def muscle_dynamic( ).dxdt muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) - muscles_tau += -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T @ muscle_forces - # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() + moment_arm_matrix = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T + + muscles_tau += moment_arm_matrix @ muscle_forces dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq) diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 5e184ea1..cd803674 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -26,8 +26,8 @@ class DingModelIntensityFrequency(DingModelFrequency): Computers in Biology and Medicine, 101, 218-228. """ - def __init__(self, name: str = "hmed2018", sum_stim_truncation: int = None): - super(DingModelIntensityFrequency, self).__init__(name=name, sum_stim_truncation=sum_stim_truncation) + def __init__(self, model_name: str = "hmed2018", muscle_name: str = None, sum_stim_truncation: int = None): + super(DingModelIntensityFrequency, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = False # ---- Custom values for the example ---- # # ---- Force models ---- # diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 391ad727..3bd169f7 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -26,8 +26,8 @@ class DingModelIntensityFrequencyWithFatigue(DingModelIntensityFrequency): Computers in Biology and Medicine, 101, 218-228. """ - def __init__(self, name: str = "hmed2018_with_fatigue", sum_stim_truncation: int = None): - super(DingModelIntensityFrequencyWithFatigue, self).__init__(name=name, sum_stim_truncation=sum_stim_truncation) + def __init__(self, model_name: str = "hmed2018_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None): + super(DingModelIntensityFrequencyWithFatigue, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = True # ---- Fatigue models ---- # self.alpha_a = -4.0 * 10e-7 # Value from Ding's experimentation [1] (s^-2) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 58c65107..3d6e65af 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -29,14 +29,14 @@ class FESActuatedBiorbdModelOCP: @staticmethod def prepare_ocp( biorbd_model_path: str, - motion_type: str = None, - motion_data: list = None, - fes_muscle_model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue = None, + bound_type: str = None, + bound_data: list = None, + fes_muscle_model: list[DingModelFrequency] + | list[DingModelFrequencyWithFatigue] + | list[DingModelPulseDurationFrequency] + | list[DingModelPulseDurationFrequencyWithFatigue] + | list[DingModelIntensityFrequency] + | list[DingModelIntensityFrequencyWithFatigue] = None, n_stim: int = None, n_shooting: int = None, final_time: int | float = None, @@ -57,6 +57,7 @@ def prepare_ocp( force_tracking: list = None, end_node_tracking: int | float = None, custom_objective: ObjectiveList = None, + with_residual_torque: bool = False, use_sx: bool = True, ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), control_type: ControlType = ControlType.CONSTANT, @@ -113,7 +114,7 @@ def prepare_ocp( """ OcpFes._sanity_check( - model=fes_muscle_model, + model=fes_muscle_model[0], n_stim=n_stim, n_shooting=n_shooting, final_time=final_time, @@ -138,8 +139,12 @@ def prepare_ocp( n_threads=n_threads, ) + FESActuatedBiorbdModelOCP._sanity_check_fes_models(fes_muscle_model) + OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) + FESActuatedBiorbdModelOCP._sanity_check_muscle_model(biorbd_model_path=biorbd_model_path, fes_muscle_model=fes_muscle_model) + n_stim, final_time = OcpFes._build_phase_parameter( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down ) @@ -181,15 +186,15 @@ def prepare_ocp( ] FESActuatedBiorbdModelOCP._sanity_check_bounds( - bio_models=bio_models, motion_type=motion_type, motion_data=motion_data + bio_models=bio_models, bound_type=bound_type, bound_data=bound_data ) dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) x_bounds, x_init = OcpFes._set_bounds(fes_muscle_model, n_stim) x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( - bio_models, motion_type, motion_data, n_stim, x_bounds, x_init + bio_models, bound_type, bound_data, n_stim, x_bounds, x_init ) - u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim) + u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) objective_functions = OcpFes._set_objective( n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective ) @@ -231,27 +236,35 @@ def _declare_dynamics(bio_models, n_stim): return dynamics @staticmethod - def _set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init): + def _set_bounds(bio_models, bound_type, bound_data, n_stim, x_bounds, x_init): - if motion_type == "start_end": + if bound_type == "start_end": start_bounds = [] end_bounds = [] for i in range(bio_models[0].nb_q): - start_bounds.append(3.14 / (180 / motion_data[0][i]) if motion_data[0][i] != 0 else 0) - end_bounds.append(3.14 / (180 / motion_data[1][i]) if motion_data[1][i] != 0 else 0) + start_bounds.append(3.14 / (180 / bound_data[0][i]) if bound_data[0][i] != 0 else 0) + end_bounds.append(3.14 / (180 / bound_data[1][i]) if bound_data[1][i] != 0 else 0) + + elif bound_type == "start": + start_bounds = [] + for i in range(bio_models[0].nb_q): + start_bounds.append(3.14 / (180 / bound_data[i]) if bound_data[i] != 0 else 0) for i in range(n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") if i == 0: - if motion_type == "start_end": + if bound_type == "start_end": + for j in range(bio_models[i].nb_q): + q_x_bounds[j, [0]] = start_bounds[j] + elif bound_type == "start": for j in range(bio_models[i].nb_q): q_x_bounds[j, [0]] = start_bounds[j] qdot_x_bounds[:, [0]] = 0 # Start without any velocity if i == n_stim - 1: - if motion_type == "start_end": + if bound_type == "start_end": for j in range(bio_models[i].nb_q): q_x_bounds[j, [-1]] = end_bounds[j] @@ -264,11 +277,13 @@ def _set_bounds(bio_models, motion_type, motion_data, n_stim, x_bounds, x_init): return x_bounds, x_init @staticmethod - def _set_controls(bio_models, n_stim): - + def _set_controls(bio_models, n_stim, with_residual_torque): # Controls bounds nb_tau = bio_models[0].nb_tau - tau_min, tau_max, tau_init = [-20] * nb_tau, [20] * nb_tau, [0] * nb_tau + if with_residual_torque: + tau_min, tau_max, tau_init = [20] * nb_tau, [20] * nb_tau, [0] * nb_tau + else: + tau_min, tau_max, tau_init = [0] * nb_tau, [0] * nb_tau, [0] * nb_tau u_bounds = BoundsList() for i in range(n_stim): @@ -282,14 +297,38 @@ def _set_controls(bio_models, n_stim): return u_bounds, u_init @staticmethod - def _sanity_check_bounds(bio_models, motion_type, motion_data): + def _sanity_check_bounds(bio_models, bound_type, bound_data): for i in range(bio_models[0].nb_q): - if motion_type == "start_end": - if not isinstance(motion_data, list): - raise TypeError("The motion data should be a list of two elements") - if len(motion_data) != 2: - raise ValueError("The motion data should be a list of two elements, start and end position") - if not isinstance(motion_data[0], list) or not isinstance(motion_data[1], list): + if bound_type == "start_end": + if not isinstance(bound_data, list): + raise TypeError("The bound data should be a list of two elements") + if len(bound_data) != 2: + raise ValueError("The bound data should be a list of two elements, start and end position") + if not isinstance(bound_data[0], list) or not isinstance(bound_data[1], list): raise TypeError("The start and end position should be a list") - if len(motion_data[0]) != bio_models[0].nb_q or len(motion_data[1]) != bio_models[0].nb_q: + if len(bound_data[0]) != bio_models[0].nb_q or len(bound_data[1]) != bio_models[0].nb_q: raise ValueError("The start and end position should be a list of size nb_q") + + @staticmethod + def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_model): + tested_bio_model = FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_model) + fes_muscle_model_name_list = [fes_muscle_model[x].muscle_name for x in range(len(fes_muscle_model))] + for biorbd_muscle in tested_bio_model.muscle_names: + if biorbd_muscle not in fes_muscle_model_name_list: + raise ValueError(f"The muscle {biorbd_muscle} is not in the fes muscle model") + + @staticmethod + def _sanity_check_fes_models(fes_muscle_model): + for i in range(len(fes_muscle_model)): + if not isinstance( + fes_muscle_model[i], + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue, + ): + raise TypeError( + "model must be a DingModelFrequency, DingModelFrequencyWithFatigue, DingModelPulseDurationFrequency, DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequency, DingModelIntensityFrequencyWithFatigue type" + ) diff --git a/tests/shard1/test_ocp_build.py b/tests/shard1/test_ocp_build.py index 48b6e35f..68abf550 100644 --- a/tests/shard1/test_ocp_build.py +++ b/tests/shard1/test_ocp_build.py @@ -310,7 +310,7 @@ def test_ocp_building( use_sx, sum_stim_truncation, ): - if (model.name == "ding2003" or model.name == "ding2003_with_fatigue") and time_min is None and time_max is None: + if (model.model_name == "ding2003" or model.model_name == "ding2003_with_fatigue") and time_min is None and time_max is None: return model._sum_stim_truncation = sum_stim_truncation From 81c2b9f80de20d1dd2e34a290201627a40c2acc2 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 23 Jan 2024 18:33:14 -0500 Subject: [PATCH 09/63] for mayer --- cocofest/optimization/fes_ocp.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index 16d73d9f..12e1ce42 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -669,7 +669,9 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu objective_functions = ObjectiveList() if custom_objective: for i in range(len(custom_objective)): - objective_functions.add(custom_objective[i][0]) + if custom_objective[i]: + for j in range(len(custom_objective[i])): + objective_functions.add(custom_objective[i][j]) if force_fourier_coef is not None: for phase in range(n_stim): From ce0ad2c2d418976ccc47ab6779ef2dc53604b055 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 23 Jan 2024 18:33:57 -0500 Subject: [PATCH 10/63] changed the way of defining bounds --- .../pulse_intensity_optimization_musculoskeletal_dynamic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index c0b91dcb..fdbd4924 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -25,8 +25,8 @@ ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26_biceps_1dof.bioMod", - motion_type="start_end", - motion_data=[[5], [120]], + bound_type="start_end", + bound_data=[[5], [120]], fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), n_stim=n_stim, n_shooting=10, From b409945846603e951664b45127e6aeb82ed5bb76 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Wed, 24 Jan 2024 18:54:56 -0500 Subject: [PATCH 11/63] feat: Enabling several FES muscle in one bio model optimization --- cocofest/models/ding2003.py | 15 +- cocofest/models/ding2003_with_fatigue.py | 25 +- cocofest/models/ding2007.py | 14 +- cocofest/models/ding2007_with_fatigue.py | 21 +- cocofest/models/dynamical_model.py | 149 +++----- cocofest/models/hmed2018.py | 13 +- cocofest/models/hmed2018_with_fatigue.py | 27 +- cocofest/optimization/fes_ocp.py | 3 +- cocofest/optimization/fes_ocp_dynamics.py | 247 ++++++++++-- examples/dynamics/arm26.bioMod | 355 ++++++++++++++++++ examples/dynamics/arm26_biceps_1dof.bioMod | 254 +++++++++++++ examples/dynamics/arm26_cycling.bioMod | 276 ++++++++++++++ ...timization_musculoskeletal_dynamic_2dof.py | 7 +- ...nsity_optimization_cycling_multi_muscle.py | 64 ++++ .../intensity_optimization_track_q.py | 51 +++ ...nsity_optimization_track_q_multi_muscle.py | 56 +++ ...cy_optimization_musculoskeletal_dynamic.py | 7 +- ...on_optimization_musculoskeletal_dynamic.py | 7 +- ...ty_optimization_musculoskeletal_dynamic.py | 3 +- 19 files changed, 1430 insertions(+), 164 deletions(-) create mode 100644 examples/dynamics/arm26.bioMod create mode 100644 examples/dynamics/arm26_biceps_1dof.bioMod create mode 100644 examples/dynamics/arm26_cycling.bioMod create mode 100644 examples/dynamics/intensity_optimization_cycling_multi_muscle.py create mode 100644 examples/dynamics/intensity_optimization_track_q.py create mode 100644 examples/dynamics/intensity_optimization_track_q_multi_muscle.py diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index 5f8b68b7..b4301cf4 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -82,7 +82,8 @@ def serialize(self) -> tuple[Callable, dict]: # ---- Needed for the example ---- # @property def name_dof(self) -> list[str]: - return ["Cn", "F"] + muscle_name = "_" + self.muscle_name if self.muscle_name else "" + return ["Cn"+muscle_name, "F"+muscle_name] @property def nb_state(self) -> int: @@ -274,8 +275,8 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -286,6 +287,7 @@ def configure_ca_troponin_complex( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable of the Ca+ troponin complex (unitless) @@ -303,7 +305,8 @@ def configure_ca_troponin_complex( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Cn" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Cn"+muscle_name name_cn = [name] ConfigureProblem.configure_new_variable( name, name_cn, ocp, nlp, as_states, as_controls, as_states_dot, @@ -316,6 +319,7 @@ def configure_force( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable of the force (N) @@ -333,7 +337,8 @@ def configure_force( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "F" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "F"+muscle_name name_f = [name] ConfigureProblem.configure_new_variable( name, name_f, ocp, nlp, as_states, as_controls, as_states_dot, diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index bf9d0466..9cab1034 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -79,7 +79,8 @@ def serialize(self) -> tuple[Callable, dict]: # ---- Needed for the example ---- # @property def name_dof(self) -> list[str]: - return ["Cn", "F", "A", "Tau1", "Km"] + muscle_name = "_" + self.muscle_name if self.muscle_name else "" + return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] @property def nb_state(self) -> int: @@ -242,11 +243,11 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -257,6 +258,7 @@ def configure_scaling_factor( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable of the scaling factor (N/ms) @@ -274,7 +276,8 @@ def configure_scaling_factor( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "A" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "A"+muscle_name name_a = [name] ConfigureProblem.configure_new_variable( name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, @@ -287,6 +290,7 @@ def configure_time_state_force_no_cross_bridge( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for time constant of force decline at the absence of strongly bound cross-bridges (ms) @@ -304,7 +308,8 @@ def configure_time_state_force_no_cross_bridge( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Tau1" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Tau1"+muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, @@ -317,6 +322,7 @@ def configure_cross_bridges( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for sensitivity of strongly bound cross-bridges to Cn (unitless) @@ -334,7 +340,8 @@ def configure_cross_bridges( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Km" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Km"+muscle_name name_km = [name] ConfigureProblem.configure_new_variable( name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 4b028d41..5d65db28 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -120,7 +120,7 @@ def set_impulse_duration(self, value: list[MX]): self.impulse_time = value @staticmethod - def get_pulse_duration_parameters(nlp_parameters: ParameterList) -> MX: + def get_pulse_duration_parameters(nlp_parameters: ParameterList, muscle_name: str = None) -> MX: """ Get the nlp list of pulse_duration parameters @@ -135,8 +135,12 @@ def get_pulse_duration_parameters(nlp_parameters: ParameterList) -> MX: """ pulse_duration_parameters = vertcat() for j in range(nlp_parameters.mx.shape[0]): - if "pulse_duration" in str(nlp_parameters.mx[j]): + if muscle_name: + if "pulse_duration_"+ muscle_name in str(nlp_parameters.mx[j]): + pulse_duration_parameters = vertcat(pulse_duration_parameters, nlp_parameters.mx[j]) + elif "pulse_duration" in str(nlp_parameters.mx[j]): pulse_duration_parameters = vertcat(pulse_duration_parameters, nlp_parameters.mx[j]) + return pulse_duration_parameters @staticmethod @@ -176,7 +180,7 @@ def dynamics( pulse_duration_parameters = ( nlp.model.get_pulse_duration_parameters(nlp.parameters) if nlp_dynamics is None - else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -202,7 +206,7 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 3a74f2e0..8c160168 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -41,7 +41,8 @@ def __init__(self, model_name: str = "ding_2007_with_fatigue", muscle_name: str # ---- Absolutely needed methods ---- # @property def name_dof(self) -> list[str]: - return ["Cn", "F", "Tau1", "Km"] + muscle_name = "_" + self.muscle_name if self.muscle_name else "" + return ["Cn"+muscle_name, "F"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] @property def nb_state(self) -> int: @@ -185,7 +186,7 @@ def dynamics( pulse_duration_parameters = ( nlp.model.get_pulse_duration_parameters(nlp.parameters) if nlp_dynamics is None - else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters) + else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -219,10 +220,10 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -233,6 +234,7 @@ def configure_time_state_force_no_cross_bridge( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for time constant of force decline at the absence of strongly bound cross-bridges (ms) @@ -250,7 +252,8 @@ def configure_time_state_force_no_cross_bridge( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Tau1" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Tau1"+muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, @@ -263,6 +266,7 @@ def configure_cross_bridges( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for sensitivity of strongly bound cross-bridges to Cn (unitless) @@ -280,7 +284,8 @@ def configure_cross_bridges( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Km" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Km"+muscle_name name_km = [name] ConfigureProblem.configure_new_variable( name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 24d208d6..0b33993e 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -20,62 +20,34 @@ def __init__( name: str = None, biorbd_path: str = None, muscles_model: DingModelFrequency() = None, - # muscles_name: list = None # TODO : for loop to create different muscles ): super().__init__(biorbd_path) self._name = name self.bio_model = BiorbdModel(biorbd_path) self.bounds_from_ranges_q = self.bio_model.bounds_from_ranges("q") self.bounds_from_ranges_qdot = self.bio_model.bounds_from_ranges("qdot") - # self.muscles_dynamics_model = muscles_model_list # * self.nb_muscles # TODO : for loop to create different muscles - # self.muscles_name_list = muscles_name_list self.muscles_dynamics_model = muscles_model - self.bio_stim_model = [self.bio_model] + [self.muscles_dynamics_model] - - # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value - # TODO : find a way to compare names and remove the muscle that is not in the model + self.bio_stim_model = [self.bio_model] + self.muscles_dynamics_model # ---- Absolutely needed methods ---- # - def serialize(self) -> tuple[Callable, dict]: - # TODO : make different serialize for biceps and triceps... different parameters value - # This is where you can serialize your model - # This is useful if you want to save your model and load it later - # return ( - # FESActuatedBiorbdModel, - # { - # "tauc": self.muscles_dynamics_model[0].tauc, - # "a_rest": self.muscles_dynamics_model[0].a_rest, - # "tau1_rest": self.muscles_dynamics_model[0].tau1_rest, - # "km_rest": self.muscles_dynamics_model[0].km_rest, - # "tau2": self.muscles_dynamics_model[0].tau2, - # "alpha_a": self.muscles_dynamics_model[0].alpha_a, - # "alpha_tau1": self.muscles_dynamics_model[0].alpha_tau1, - # "alpha_km": self.muscles_dynamics_model[0].alpha_km, - # "tau_fat": self.muscles_dynamics_model[0].tau_fat, - # }, - # ) - return self.muscles_dynamics_model.serialize() + def serialize(self, index: int = 0) -> tuple[Callable, dict]: + return self.muscles_dynamics_model[index].serialize() # ---- Needed for the example ---- # - # TODO update for 3 models @property - def name_dof(self) -> list[str]: - return self.bio_stim_model[0].name_dof - # self.bio_model[0].name_dof - # ["Cn", "F", "A", "Tau1", "Km", "q", "qdot", "tau"] + def name_dof(self) -> tuple[str]: + return self.bio_model.name_dof - def muscle_name_dof(self) -> list[str]: - return self.muscles_dynamics_model.name_dof + def muscle_name_dof(self, index: int = 0) -> list[str]: + return self.muscles_dynamics_model[index].name_dof @property def nb_state(self) -> int: nb_state = 0 - # for muscle_model in self.muscles_dynamics_model: # TODO : for loop to create different muscles - # nb_state += muscle_model.nb_state - - nb_state += self.muscles_dynamics_model.nb_state - nb_state += self.bio_model[0].name_dof + for muscle_model in self.muscles_dynamics_model: + nb_state += muscle_model.nb_state + nb_state += self.bio_model.nb_q return nb_state @property @@ -90,8 +62,9 @@ def muscle_dynamic( parameters: MX | SX, stochastic_variables: MX | SX, nlp: NonLinearProgram, - muscle_model: DingModelFrequency | DingModelIntensityFrequency | DingModelPulseDurationFrequency, + muscle_models: list[DingModelFrequency] | list[DingModelIntensityFrequency] | list[DingModelPulseDurationFrequency], stim_apparition=None, + state_name_list=None, ) -> DynamicsEvaluation: """ The custom dynamics function that provides the derivative of the states: dxdt = f(t, x, u, p, s) @@ -123,28 +96,33 @@ def muscle_dynamic( muscles_tau = 0 dxdt_muscle_list = vertcat() - # for muscle_model in muscle_model_list: #TODO : for different muscles - # muscle_dxdt = muscle_model.dynamics(time, states, controls, parameters, stochastic_variables, nlp, nb_phases).dxdt - # muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) - # muscles_tau += nlp.model.bio_model.model.muscularJointTorque(muscle_forces, q, qdot).to_mx() - # dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) - - muscle_dxdt = muscle_model.dynamics( - time, - states, - controls, - parameters, - stochastic_variables, - nlp, - stim_apparition, - nlp_dynamics=nlp.model.muscles_dynamics_model, - ).dxdt - muscle_forces = DynamicsFunctions.get(nlp.states["F"], states) - - moment_arm_matrix = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx().T - - muscles_tau += moment_arm_matrix @ muscle_forces - dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) + bio_muscle_names_at_index = [] + for i in range(len(nlp.model.bio_model.model.muscles())): + bio_muscle_names_at_index.append(nlp.model.bio_model.model.muscle(i).name().to_string()) + + for muscle_model in muscle_models: + muscle_states_idx = [i for i in range(len(state_name_list)) if muscle_model.muscle_name in state_name_list[i]] + muscle_states = vertcat() + for i in range(len(muscle_states_idx)): + muscle_states = vertcat(muscle_states, states[muscle_states_idx[i]]) + + muscle_dxdt = muscle_model.dynamics( + time, + muscle_states, + controls, + parameters, + stochastic_variables, + nlp, + stim_apparition, + nlp_dynamics=muscle_model, + ).dxdt + + muscle_forces = DynamicsFunctions.get(nlp.states["F_"+muscle_model.muscle_name], states) + muscle_idx = bio_muscle_names_at_index.index(muscle_model.muscle_name) + moment_arm_matrix_for_the_muscle_and_joint = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T + muscles_tau += moment_arm_matrix_for_the_muscle_and_joint @ muscle_forces + + dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) # You can directly call biorbd function (as for ddq) or call bioptim accessor (as for dq) dq = DynamicsFunctions.compute_qdot(nlp, q, qdot) @@ -165,33 +143,29 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg nlp: NonLinearProgram A reference to the phase """ - # for i in range(len(self.muscles_dynamics_model)): - - # self.muscles_dynamics_model[i].configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscles_name[i]) - # TODO : make different muscle for biceps and triceps... such as DingModelFrequency has different parameters value - - self.muscles_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.muscles_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - - if "A" in self.muscles_dynamics_model.name_dof: - self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - if "Tau1" in self.muscles_dynamics_model.name_dof: - self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( - ocp=ocp, nlp=nlp, as_states=True, as_controls=False - ) - if "Km" in self.muscles_dynamics_model.name_dof: - self.muscles_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - - # TODO : for fatigue model - # self.muscles_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - # self.muscles_dynamics_model.configure_time_state_force_no_cross_bridge( - # ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - # self.muscles_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + state_name_list = [] + for muscle_dynamics_model in self.muscles_dynamics_model: + muscle_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + state_name_list.append("CN_"+muscle_dynamics_model.muscle_name) + muscle_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + state_name_list.append("F_" + muscle_dynamics_model.muscle_name) + if "A_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + muscle_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + state_name_list.append("A_" + muscle_dynamics_model.muscle_name) + if "Tau1_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + muscle_dynamics_model.configure_time_state_force_no_cross_bridge( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name + ) + state_name_list.append("Tau1_" + muscle_dynamics_model.muscle_name) + if "Km_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + muscle_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + state_name_list.append("Km_" + muscle_dynamics_model.muscle_name) ConfigureProblem.configure_q(ocp, nlp, as_states=True, as_controls=False) + state_name_list.append("q") ConfigureProblem.configure_qdot(ocp, nlp, as_states=True, as_controls=False) + state_name_list.append("qdot") ConfigureProblem.configure_tau(ocp, nlp, as_states=False, as_controls=True) - # stim_apparition = self.muscles_dynamics_model.get_stim_prev(ocp, nlp) time_type = "mx" if "time" in ocp.parameters.keys() else None stim_apparition = [ocp.node_time(phase_idx=i, node_idx=0, type=time_type) for i in range(nlp.phase_idx + 1)] @@ -199,8 +173,9 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg ocp, nlp, dyn_func=self.muscle_dynamic, - muscle_model=self.muscles_dynamics_model, + muscle_models=self.muscles_dynamics_model, stim_apparition=stim_apparition, + state_name_list=state_name_list, ) @staticmethod @@ -264,7 +239,3 @@ def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: Fatigue name = "tau" name_tau = ["tau"] ConfigureProblem.configure_new_variable(name, name_tau, ocp, nlp, as_states, as_controls, fatigue=fatigue) - - -if __name__ == "__main__": - FESActuatedBiorbdModel(biorbd_path="msk_model/arm26_unmesh.bioMod", muscles_model=DingModelFrequency()) diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index cd803674..5fd509ff 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -188,7 +188,7 @@ def set_impulse_intensity(self, value: MX): self.impulse_intensity.append(value[i]) @staticmethod - def get_intensity_parameters(nlp_parameters: ParameterList) -> MX: + def get_intensity_parameters(nlp_parameters: ParameterList, muscle_name: str = None) -> MX: """ Get the nlp list of intensity parameters @@ -203,7 +203,10 @@ def get_intensity_parameters(nlp_parameters: ParameterList) -> MX: """ intensity_parameters = vertcat() for j in range(nlp_parameters.mx.shape[0]): - if "pulse_intensity" in str(nlp_parameters.mx[j]): + if muscle_name: + if "pulse_intensity_"+ muscle_name in str(nlp_parameters.mx[j]): + intensity_parameters = vertcat(intensity_parameters, nlp_parameters.mx[j]) + elif "pulse_intensity" in str(nlp_parameters.mx[j]): intensity_parameters = vertcat(intensity_parameters, nlp_parameters.mx[j]) return intensity_parameters @@ -247,7 +250,7 @@ def dynamics( intensity_parameters = ( nlp.model.get_intensity_parameters(nlp.parameters) if nlp_dynamics is None - else nlp_dynamics.get_intensity_parameters(nlp.parameters) + else nlp_dynamics.get_intensity_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -277,8 +280,8 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 3bd169f7..a051ecf5 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -38,7 +38,8 @@ def __init__(self, model_name: str = "hmed2018_with_fatigue", muscle_name: str = # ---- Absolutely needed methods ---- # @property def name_dof(self) -> list[str]: - return ["Cn", "F", "A", "Tau1", "Km"] + muscle_name = "_" + self.muscle_name if self.muscle_name else "" + return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] @property def nb_state(self) -> int: @@ -204,7 +205,7 @@ def dynamics( intensity_parameters = ( nlp.model.get_intensity_parameters(nlp.parameters) if nlp_dynamics is None - else nlp_dynamics.get_intensity_parameters(nlp.parameters) + else nlp_dynamics.get_intensity_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -241,11 +242,11 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) - self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False) + self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -256,6 +257,7 @@ def configure_scaling_factor( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable of the scaling factor (N/ms) @@ -273,7 +275,8 @@ def configure_scaling_factor( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "A" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "A"+muscle_name name_a = [name] ConfigureProblem.configure_new_variable( name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, @@ -286,6 +289,7 @@ def configure_time_state_force_no_cross_bridge( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for time constant of force decline at the absence of strongly bound cross-bridges (ms) @@ -303,7 +307,8 @@ def configure_time_state_force_no_cross_bridge( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Tau1" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Tau1"+muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, @@ -316,6 +321,7 @@ def configure_cross_bridges( as_states: bool, as_controls: bool, as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable for sensitivity of strongly bound cross-bridges to Cn (unitless) @@ -333,7 +339,8 @@ def configure_cross_bridges( as_states_dot: bool If the generalized velocities should be a state_dot """ - name = "Km" + muscle_name = "_" + muscle_name if muscle_name else "" + name = "Km"+muscle_name name_km = [name] ConfigureProblem.configure_new_variable( name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index 12e1ce42..84672bca 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -481,6 +481,7 @@ def _build_parameters( parameters_bounds = BoundsList() parameters_init = InitialGuessList() parameter_objectives = ParameterObjectiveList() + if isinstance(model, DingModelPulseDurationFrequency): if pulse_duration: parameters.add( @@ -673,7 +674,7 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu for j in range(len(custom_objective[i])): objective_functions.add(custom_objective[i][j]) - if force_fourier_coef is not None: + if force_fourier_coef is not None: # TODO : Enable multiple force tracking for multiple muscles for phase in range(n_stim): for i in range(n_shooting[phase]): objective_functions.add( diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 3d6e65af..2524e9e9 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -10,6 +10,10 @@ OdeSolver, OptimalControlProgram, PhaseDynamics, + ParameterList, + ParameterObjectiveList, + InterpolationType, + ObjectiveFcn, OdeSolverBase, ) @@ -31,7 +35,7 @@ def prepare_ocp( biorbd_model_path: str, bound_type: str = None, bound_data: list = None, - fes_muscle_model: list[DingModelFrequency] + fes_muscle_models: list[DingModelFrequency] | list[DingModelFrequencyWithFatigue] | list[DingModelPulseDurationFrequency] | list[DingModelPulseDurationFrequencyWithFatigue] @@ -50,10 +54,12 @@ def prepare_ocp( pulse_duration_min: int | float = None, pulse_duration_max: int | float = None, pulse_duration_bimapping: bool = False, + pulse_duration_similar_for_all_muscles: bool = False, pulse_intensity: int | float = None, pulse_intensity_min: int | float = None, pulse_intensity_max: int | float = None, pulse_intensity_bimapping: bool = False, + pulse_intensity_similar_for_all_muscles: bool = False, force_tracking: list = None, end_node_tracking: int | float = None, custom_objective: ObjectiveList = None, @@ -69,7 +75,7 @@ def prepare_ocp( . Attributes ---------- - fes_muscle_model: DingModelFrequency | DingModelFrequencyWithFatigue | DingModelPulseDurationFrequency | DingModelPulseDurationFrequencyWithFatigue | DingModelIntensityFrequency | DingModelIntensityFrequencyWithFatigue + fes_muscle_models: DingModelFrequency | DingModelFrequencyWithFatigue | DingModelPulseDurationFrequency | DingModelPulseDurationFrequencyWithFatigue | DingModelIntensityFrequency | DingModelIntensityFrequencyWithFatigue The fes model type used for the ocp n_stim: int Number of stimulation that will occur during the ocp, it is as well refer as phases @@ -112,9 +118,9 @@ def prepare_ocp( n_threads: int The number of thread to use while solving (multi-threading if > 1) """ - + # TODO : MAKE A NEW SANITY CHECK FOR THE FES MUSCLE MODEL OcpFes._sanity_check( - model=fes_muscle_model[0], + model=fes_muscle_models[0], n_stim=n_stim, n_shooting=n_shooting, final_time=final_time, @@ -139,16 +145,17 @@ def prepare_ocp( n_threads=n_threads, ) - FESActuatedBiorbdModelOCP._sanity_check_fes_models(fes_muscle_model) + FESActuatedBiorbdModelOCP._sanity_check_fes_models(fes_muscle_models) OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) - FESActuatedBiorbdModelOCP._sanity_check_muscle_model(biorbd_model_path=biorbd_model_path, fes_muscle_model=fes_muscle_model) + FESActuatedBiorbdModelOCP._sanity_check_muscle_model(biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models) n_stim, final_time = OcpFes._build_phase_parameter( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down ) + # NOT AVAILABLE FOR MULTI MUSCLE force_fourier_coef = None if force_tracking is None else OcpFes._build_fourrier_coeff(force_tracking) end_node_tracking = end_node_tracking @@ -161,17 +168,19 @@ def prepare_ocp( time_max=time_max, time_bimapping=time_bimapping, ) - parameters, parameters_bounds, parameters_init, parameter_objectives = OcpFes._build_parameters( - model=fes_muscle_model, + parameters, parameters_bounds, parameters_init, parameter_objectives = FESActuatedBiorbdModelOCP._build_parameters( + model=fes_muscle_models, n_stim=n_stim, pulse_duration=pulse_duration, pulse_duration_min=pulse_duration_min, pulse_duration_max=pulse_duration_max, pulse_duration_bimapping=pulse_duration_bimapping, + pulse_duration_similar_for_all_muscles=pulse_duration_similar_for_all_muscles, pulse_intensity=pulse_intensity, pulse_intensity_min=pulse_intensity_min, pulse_intensity_max=pulse_intensity_max, pulse_intensity_bimapping=pulse_intensity_bimapping, + pulse_intensity_similar_for_all_muscles=pulse_intensity_similar_for_all_muscles, ) if len(constraints) == 0 and len(parameters) == 0: @@ -181,7 +190,7 @@ def prepare_ocp( ) bio_models = [ - FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_model) + FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) for i in range(n_stim) ] @@ -190,9 +199,8 @@ def prepare_ocp( ) dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) - x_bounds, x_init = OcpFes._set_bounds(fes_muscle_model, n_stim) x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( - bio_models, bound_type, bound_data, n_stim, x_bounds, x_init + bio_models, fes_muscle_models, bound_type, bound_data, n_stim, ) u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) objective_functions = OcpFes._set_objective( @@ -236,7 +244,200 @@ def _declare_dynamics(bio_models, n_stim): return dynamics @staticmethod - def _set_bounds(bio_models, bound_type, bound_data, n_stim, x_bounds, x_init): + def _build_parameters( + model, + n_stim, + pulse_duration, + pulse_duration_min, + pulse_duration_max, + pulse_duration_bimapping, + pulse_duration_similar_for_all_muscles, + pulse_intensity, + pulse_intensity_min, + pulse_intensity_max, + pulse_intensity_bimapping, + pulse_intensity_similar_for_all_muscles, + ): + parameters = ParameterList() + parameters_bounds = BoundsList() + parameters_init = InitialGuessList() + parameter_objectives = ParameterObjectiveList() + + for i in range(len(model)): + if isinstance(model[i], DingModelPulseDurationFrequency): + parameter_name = "pulse_duration" if pulse_duration_similar_for_all_muscles else "pulse_duration" + "_" + model[i].muscle_name + if pulse_duration: #TODO : ADD SEVERAL INDIVIDUAL FIXED PULSE DURATION FOR EACH MUSCLE + if (pulse_duration_similar_for_all_muscles and i == 0) or not pulse_duration_similar_for_all_muscles: + parameters.add( + parameter_name=parameter_name, + function=DingModelPulseDurationFrequency.set_impulse_duration, + size=n_stim, + ) + if isinstance(pulse_duration, list): + parameters_bounds.add( + parameter_name, + min_bound=np.array(pulse_duration), + max_bound=np.array(pulse_duration), + interpolation=InterpolationType.CONSTANT, + ) + parameters_init.add(key=parameter_name, initial_guess=np.array(pulse_duration)) + else: + parameters_bounds.add( + parameter_name, + min_bound=np.array([pulse_duration] * n_stim), + max_bound=np.array([pulse_duration] * n_stim), + interpolation=InterpolationType.CONSTANT, + ) + parameters_init[parameter_name] = np.array([pulse_duration] * n_stim) + + elif pulse_duration_min and pulse_duration_max: #TODO : ADD SEVERAL MIN MAX PULSE DURATION FOR EACH MUSCLE + if (pulse_duration_similar_for_all_muscles and i == 0) or not pulse_duration_similar_for_all_muscles: + parameters_bounds.add( + parameter_name, + min_bound=[pulse_duration_min], + max_bound=[pulse_duration_max], + interpolation=InterpolationType.CONSTANT, + ) + parameters_init[parameter_name] = np.array([0] * n_stim) + parameters.add( + parameter_name=parameter_name, + function=DingModelPulseDurationFrequency.set_impulse_duration, + size=n_stim, + ) + + parameter_objectives.add( + ObjectiveFcn.Parameter.MINIMIZE_PARAMETER, + weight=0.0001, + quadratic=True, + target=0, + key=parameter_name, + ) + + if pulse_duration_bimapping: + pass + # parameter_bimapping.add(name="pulse_duration", to_second=[0 for _ in range(n_stim)], to_first=[0]) + # TODO : Fix Bimapping in Bioptim + + if isinstance(model[i], DingModelIntensityFrequency): + parameter_name = "pulse_intensity" if pulse_intensity_similar_for_all_muscles else "pulse_intensity" + "_" + \ + model[i].muscle_name + if pulse_intensity: # TODO : ADD SEVERAL INDIVIDUAL FIXED PULSE INTENSITY FOR EACH MUSCLE + if (pulse_intensity_similar_for_all_muscles and i == 0) or not pulse_intensity_similar_for_all_muscles: + parameters.add( + parameter_name=parameter_name, + function=DingModelIntensityFrequency.set_impulse_intensity, + size=n_stim, + ) + if isinstance(pulse_intensity, list): + parameters_bounds.add( + parameter_name, + min_bound=np.array(pulse_intensity), + max_bound=np.array(pulse_intensity), + interpolation=InterpolationType.CONSTANT, + ) + parameters_init.add(key=parameter_name, initial_guess=np.array(pulse_intensity)) + else: + parameters_bounds.add( + parameter_name, + min_bound=np.array([pulse_intensity] * n_stim), + max_bound=np.array([pulse_intensity] * n_stim), + interpolation=InterpolationType.CONSTANT, + ) + parameters_init[parameter_name] = np.array([pulse_intensity] * n_stim) + + elif pulse_intensity_min and pulse_intensity_max: #TODO : ADD SEVERAL MIN MAX PULSE INTENSITY FOR EACH MUSCLE + if ( + pulse_intensity_similar_for_all_muscles and i == 0) or not pulse_intensity_similar_for_all_muscles: + parameters_bounds.add( + parameter_name, + min_bound=[pulse_intensity_min], + max_bound=[pulse_intensity_max], + interpolation=InterpolationType.CONSTANT, + ) + intensity_avg = (pulse_intensity_min + pulse_intensity_max) / 2 + parameters_init[parameter_name] = np.array([intensity_avg] * n_stim) + parameters.add( + parameter_name=parameter_name, + function=DingModelIntensityFrequency.set_impulse_intensity, + size=n_stim, + ) + + parameter_objectives.add( + ObjectiveFcn.Parameter.MINIMIZE_PARAMETER, + weight=0.0001, + quadratic=True, + target=0, + key=parameter_name, + ) + + if pulse_intensity_bimapping: + pass + # parameter_bimapping.add(name="pulse_intensity", to_second=[0 for _ in range(n_stim)], to_first=[0]) + # TODO : Fix Bimapping in Bioptim + + return parameters, parameters_bounds, parameters_init, parameter_objectives + + @staticmethod + def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): + # ---- STATE BOUNDS REPRESENTATION ---- # + # + # |‾‾‾‾‾‾‾‾‾‾x_max_middle‾‾‾‾‾‾‾‾‾‾‾‾x_max_end‾ + # | max_bounds max_bounds + # x_max_start | + # _starting_bounds_| + # ‾starting_bounds‾| + # x_min_start | + # | min_bounds min_bounds + # ‾‾‾‾‾‾‾‾‾‾x_min_middle‾‾‾‾‾‾‾‾‾‾‾‾x_min_end‾ + + # Sets the bound for all the phases + x_bounds = BoundsList() + x_init = InitialGuessList() + for model in fes_muscle_models: + variable_bound_list = model.name_dof + starting_bounds, min_bounds, max_bounds = ( + model.standard_rest_values(), + model.standard_rest_values(), + model.standard_rest_values(), + ) + muscle_name = model.muscle_name + for i in range(len(variable_bound_list)): + if variable_bound_list[i] == "Cn_"+muscle_name: + max_bounds[i] = 10 + elif variable_bound_list[i] == "F_"+muscle_name: + max_bounds[i] = 1000 + elif variable_bound_list[i] == "Tau1_"+muscle_name or variable_bound_list[i] == "Km_"+muscle_name: + max_bounds[i] = 1 + elif variable_bound_list[i] == "A_"+muscle_name: + min_bounds[i] = 0 + + starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) + starting_bounds_max = np.concatenate((starting_bounds, max_bounds, max_bounds), axis=1) + middle_bound_min = np.concatenate((min_bounds, min_bounds, min_bounds), axis=1) + middle_bound_max = np.concatenate((max_bounds, max_bounds, max_bounds), axis=1) + + for i in range(n_stim): + for j in range(len(variable_bound_list)): + if i == 0: + x_bounds.add( + variable_bound_list[j], + min_bound=np.array([starting_bounds_min[j]]), + max_bound=np.array([starting_bounds_max[j]]), + phase=i, + interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + ) + else: + x_bounds.add( + variable_bound_list[j], + min_bound=np.array([middle_bound_min[j]]), + max_bound=np.array([middle_bound_max[j]]), + phase=i, + interpolation=InterpolationType.CONSTANT_WITH_FIRST_AND_LAST_DIFFERENT, + ) + + for i in range(n_stim): + for j in range(len(variable_bound_list)): + x_init.add(variable_bound_list[j], model.standard_rest_values()[j], phase=i) if bound_type == "start_end": start_bounds = [] @@ -280,8 +481,8 @@ def _set_bounds(bio_models, bound_type, bound_data, n_stim, x_bounds, x_init): def _set_controls(bio_models, n_stim, with_residual_torque): # Controls bounds nb_tau = bio_models[0].nb_tau - if with_residual_torque: - tau_min, tau_max, tau_init = [20] * nb_tau, [20] * nb_tau, [0] * nb_tau + if with_residual_torque: # TODO : ADD SEVERAL INDIVIDUAL FIXED RESIDUAL TORQUE FOR EACH JOINT + tau_min, tau_max, tau_init = [-20] * nb_tau, [20] * nb_tau, [0] * nb_tau else: tau_min, tau_max, tau_init = [0] * nb_tau, [0] * nb_tau, [0] * nb_tau @@ -310,18 +511,20 @@ def _sanity_check_bounds(bio_models, bound_type, bound_data): raise ValueError("The start and end position should be a list of size nb_q") @staticmethod - def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_model): - tested_bio_model = FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_model) - fes_muscle_model_name_list = [fes_muscle_model[x].muscle_name for x in range(len(fes_muscle_model))] + def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): + tested_bio_model = FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) + fes_muscle_models_name_list = [fes_muscle_models[x].muscle_name for x in range(len(fes_muscle_models))] for biorbd_muscle in tested_bio_model.muscle_names: - if biorbd_muscle not in fes_muscle_model_name_list: - raise ValueError(f"The muscle {biorbd_muscle} is not in the fes muscle model") + if biorbd_muscle not in fes_muscle_models_name_list: + raise ValueError(f"The muscle {biorbd_muscle} is not in the fes muscle model " + f"please add it into the fes_muscle_models list by providing the muscle_name =" + f" {biorbd_muscle}") @staticmethod - def _sanity_check_fes_models(fes_muscle_model): - for i in range(len(fes_muscle_model)): + def _sanity_check_fes_models(fes_muscle_models): + for i in range(len(fes_muscle_models)): if not isinstance( - fes_muscle_model[i], + fes_muscle_models[i], DingModelFrequency | DingModelFrequencyWithFatigue | DingModelPulseDurationFrequency diff --git a/examples/dynamics/arm26.bioMod b/examples/dynamics/arm26.bioMod new file mode 100644 index 00000000..ed03d03c --- /dev/null +++ b/examples/dynamics/arm26.bioMod @@ -0,0 +1,355 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + // meshfile mesh/ground_ribs.vtp +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ -2*pi 2*pi +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 +endsegment + +// Segment +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle TRIlong + Type degrootefatigable + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.053650000000000003 -0.013729999999999999 0.14723 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.13400000000000001 + maximalForce 798.51999999999998 + tendonSlackLength 0.14299999999999999 + pennationAngle 0.20943951 + maxVelocity 10 + + fatigueParameters + Type Xia + fatiguerate 0.01 + recoveryrate 0.002 + developfactor 10 + recoveryfactor 10 + endfatigueparameters + endmuscle + + viapoint TRIlong-P2 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.027140000000000001 -0.11441 -0.0066400000000000001 + endviapoint + viapoint TRIlong-P3 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRIlong-P4 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint + + muscle BIClong + Type degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint + + muscle BICshort + Type degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition 0.0046750000000000003 -0.01231 0.13475000000000001 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1321 + maximalForce 435.56 + tendonSlackLength 0.1923 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BICshort-P2 + parent base + muscle BICshort + musclegroup base_to_r_ulna_radius_hand + position -0.0070749999999999997 -0.040039999999999999 0.14507 + endviapoint + viapoint BICshort-P3 + parent r_humerus + muscle BICshort + musclegroup base_to_r_ulna_radius_hand + position 0.011169999999999999 -0.075759999999999994 -0.011010000000000001 + endviapoint + viapoint BICshort-P4 + parent r_humerus + muscle BICshort + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 -0.010789999999999999 + endviapoint + viapoint BICshort-P5 + parent r_humerus + muscle BICshort + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint + +// r_humerus > r_ulna_radius_hand +musclegroup r_humerus_to_r_ulna_radius_hand + OriginParent r_humerus + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle TRIlat + Type degroote + musclegroup r_humerus_to_r_ulna_radius_hand + OriginPosition -0.0059899999999999997 -0.12645999999999999 0.00428 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.1138 + maximalForce 624.29999999999995 + tendonSlackLength 0.098000000000000004 + pennationAngle 0.15707963 + maxVelocity 10 + endmuscle + + viapoint TRIlat-P2 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.023439999999999999 -0.14527999999999999 0.0092800000000000001 + endviapoint + viapoint TRIlat-P3 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRIlat-P4 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint + + muscle TRImed + Type degroote + musclegroup r_humerus_to_r_ulna_radius_hand + OriginPosition -0.0083800000000000003 -0.13694999999999999 -0.0090600000000000003 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.1138 + maximalForce 624.29999999999995 + tendonSlackLength 0.090800000000000006 + pennationAngle 0.15707963 + maxVelocity 10 + endmuscle + + viapoint TRImed-P2 + parent r_humerus + muscle TRImed + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.026009999999999998 -0.15139 -0.010800000000000001 + endviapoint + viapoint TRImed-P3 + parent r_humerus + muscle TRImed + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRImed-P4 + parent r_humerus + muscle TRImed + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint + + muscle BRA + Type degroote + musclegroup r_humerus_to_r_ulna_radius_hand + OriginPosition 0.0067999999999999996 -0.1739 -0.0035999999999999999 + InsertionPosition -0.0032000000000000002 -0.023900000000000001 0.00089999999999999998 + optimalLength 0.085800000000000001 + maximalForce 987.25999999999999 + tendonSlackLength 0.053499999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle diff --git a/examples/dynamics/arm26_biceps_1dof.bioMod b/examples/dynamics/arm26_biceps_1dof.bioMod new file mode 100644 index 00000000..21f518c5 --- /dev/null +++ b/examples/dynamics/arm26_biceps_1dof.bioMod @@ -0,0 +1,254 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + meshfile mesh/ground_ribs.vtp + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + marker r_acromion + parent base + position -0.01256 0.04 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + // Markers + marker r_humerus_epicondyle + parent r_humerus + position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 + endmarker + marker COM_arm + parent r_humerus + position 0 -0.18049599999999999 0 + endmarker + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 + inertia + 0 0 0 + 0 0 0 + 0 0 0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + // Markers + marker r_radius_styloid + parent r_ulna_radius_hand + position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 + endmarker + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +// base > r_ulna_radius_hand +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle BIClong + Type degroote + statetype degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint diff --git a/examples/dynamics/arm26_cycling.bioMod b/examples/dynamics/arm26_cycling.bioMod new file mode 100644 index 00000000..3f66f8f1 --- /dev/null +++ b/examples/dynamics/arm26_cycling.bioMod @@ -0,0 +1,276 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + // meshfile mesh/ground_ribs.vtp +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ -2*pi 2*pi +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 +endsegment + +// Segment +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle TRIlong + Type degrootefatigable + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.053650000000000003 -0.013729999999999999 0.14723 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.13400000000000001 + maximalForce 798.51999999999998 + tendonSlackLength 0.14299999999999999 + pennationAngle 0.20943951 + maxVelocity 10 + + fatigueParameters + Type Xia + fatiguerate 0.01 + recoveryrate 0.002 + developfactor 10 + recoveryfactor 10 + endfatigueparameters + endmuscle + + viapoint TRIlong-P2 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.027140000000000001 -0.11441 -0.0066400000000000001 + endviapoint + viapoint TRIlong-P3 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRIlong-P4 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint + + muscle BIClong + Type degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint + + +// r_humerus > r_ulna_radius_hand +musclegroup r_humerus_to_r_ulna_radius_hand + OriginParent r_humerus + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle TRIlat + Type degroote + musclegroup r_humerus_to_r_ulna_radius_hand + OriginPosition -0.0059899999999999997 -0.12645999999999999 0.00428 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.1138 + maximalForce 624.29999999999995 + tendonSlackLength 0.098000000000000004 + pennationAngle 0.15707963 + maxVelocity 10 + endmuscle + + viapoint TRIlat-P2 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.023439999999999999 -0.14527999999999999 0.0092800000000000001 + endviapoint + viapoint TRIlat-P3 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRIlat-P4 + parent r_humerus + muscle TRIlat + musclegroup r_humerus_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint diff --git a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py index 7557a86e..3bd87a67 100644 --- a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py +++ b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py @@ -21,9 +21,9 @@ ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26_biceps.bioMod", - motion_type="start_end", - motion_data=[[0, 5], [0, 120]], - fes_muscle_model=DingModelFrequencyWithFatigue(), + bound_type="start_end", + bound_data=[[0, 5], [0, 120]], + fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong")], n_stim=n_stim, n_shooting=10, final_time=1, @@ -31,6 +31,7 @@ time_max=0.1, time_bimapping=True, custom_objective=objective_functions, + with_residual_torque=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py new file mode 100644 index 00000000..d17aad3a --- /dev/null +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -0,0 +1,64 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +n_shooting = 10 + +q_target = [np.array([[1.1339, 0.6629]]*(n_shooting+1)).T, + np.array([[0.9943, 0.7676]]*(n_shooting+1)).T, + np.array([[0.7676, 1.0641]]*(n_shooting+1)).T, + np.array([[0.5757, 1.3781]]*(n_shooting+1)).T, + np.array([[0.4536, 1.4653]]*(n_shooting+1)).T, + np.array([[0.6280, 1.3781]]*(n_shooting+1)).T, + np.array([[1.0292, 0.9594]]*(n_shooting+1)).T, + np.array([[1.0990, 0.8373]]*(n_shooting+1)).T, + np.array([[1.1339, 0.6629]]*(n_shooting+1)).T] + +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) + objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0, 1], node=Node.ALL, target=q_target[i], weight=10, quadratic=True, phase=i) + + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_cycling.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat")], + n_stim=n_stim, + n_shooting=10, + final_time=1, + time_min=0.01, + time_max=0.01, + time_bimapping=True, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=130, + pulse_intensity_bimapping=False, + with_residual_torque=False, + custom_objective=objective_functions, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/dynamics/intensity_optimization_track_q.py b/examples/dynamics/intensity_optimization_track_q.py new file mode 100644 index 00000000..05736557 --- /dev/null +++ b/examples/dynamics/intensity_optimization_track_q.py @@ -0,0 +1,51 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +n_shooting = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) + objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0], node=Node.ALL, target=np.array([[1.57]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) +objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0], node=Node.ALL, target=np.array([[0]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps_1dof.bioMod", + bound_type="start", + bound_data=[90], + fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], + 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, + with_residual_torque=True, + custom_objective=objective_functions, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/dynamics/intensity_optimization_track_q_multi_muscle.py b/examples/dynamics/intensity_optimization_track_q_multi_muscle.py new file mode 100644 index 00000000..9b6e5706 --- /dev/null +++ b/examples/dynamics/intensity_optimization_track_q_multi_muscle.py @@ -0,0 +1,56 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +objective_functions = ObjectiveList() +n_stim = 10 +n_shooting = 10 +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) + objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0, 1], node=Node.ALL, target=np.array([[0, 1.57]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) + objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.ALL, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26.bioMod", + bound_type="start", + bound_data=[0, 90], + fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")], + 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, + with_residual_torque=True, + custom_objective=objective_functions, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) diff --git a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py index 86cbd8d5..dd474668 100644 --- a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -21,9 +21,9 @@ ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="arm26_biceps_1dof.bioMod", - motion_type="start_end", - motion_data=[[5], [120]], - fes_muscle_model=DingModelFrequencyWithFatigue(), + bound_type="start_end", + bound_data=[[5], [120]], + fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong")], n_stim=n_stim, n_shooting=10, final_time=1, @@ -31,6 +31,7 @@ time_max=0.1, time_bimapping=True, custom_objective=objective_functions, + with_residual_torque=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py index abd54276..88cd7504 100644 --- a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -22,9 +22,9 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26_biceps_1dof.bioMod", - motion_type="start_end", - motion_data=[[5], [120]], - fes_muscle_model=DingModelPulseDurationFrequencyWithFatigue(), + bound_type="start_end", + bound_data=[[5], [120]], + fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], n_stim=n_stim, n_shooting=10, final_time=1, @@ -35,6 +35,7 @@ pulse_duration_max=0.0006, pulse_duration_bimapping=False, custom_objective=objective_functions, + with_residual_torque=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index fdbd4924..e7331b0a 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -27,7 +27,7 @@ biorbd_model_path="/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], - fes_muscle_model=DingModelIntensityFrequencyWithFatigue(), + fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], n_stim=n_stim, n_shooting=10, final_time=1, @@ -38,6 +38,7 @@ pulse_intensity_max=130, pulse_intensity_bimapping=False, custom_objective=objective_functions, + with_residual_torque=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) From a6da5bf62cf1eed697256db34ee55fa728e1d9e5 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 25 Jan 2024 15:43:19 -0500 Subject: [PATCH 12/63] Enabled fourier track_q --- cocofest/optimization/fes_ocp_dynamics.py | 69 ++++++++++++++++++- ...nsity_optimization_cycling_multi_muscle.py | 68 ++++++++++++------ 2 files changed, 113 insertions(+), 24 deletions(-) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 2524e9e9..54df614d 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -15,6 +15,7 @@ InterpolationType, ObjectiveFcn, OdeSolverBase, + Node ) from cocofest import ( @@ -26,6 +27,7 @@ DingModelIntensityFrequencyWithFatigue, OcpFes, FESActuatedBiorbdModel, + CustomObjective, ) @@ -62,6 +64,7 @@ def prepare_ocp( pulse_intensity_similar_for_all_muscles: bool = False, force_tracking: list = None, end_node_tracking: int | float = None, + q_tracking: list = None, custom_objective: ObjectiveList = None, with_residual_torque: bool = False, use_sx: bool = True, @@ -159,6 +162,12 @@ def prepare_ocp( force_fourier_coef = None if force_tracking is None else OcpFes._build_fourrier_coeff(force_tracking) end_node_tracking = end_node_tracking + q_fourier_coef = [] if q_tracking else None + if q_tracking: + for i in range(len(q_tracking[1])): + q_fourier_coef.append(OcpFes._build_fourrier_coeff([q_tracking[0], q_tracking[1][i]])) + + n_shooting = [n_shooting] * n_stim final_time_phase, constraints, phase_time_bimapping = OcpFes._build_phase_time( final_time=final_time, @@ -203,8 +212,8 @@ def prepare_ocp( bio_models, fes_muscle_models, bound_type, bound_data, n_stim, ) u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) - objective_functions = OcpFes._set_objective( - n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective + objective_functions = FESActuatedBiorbdModelOCP._set_objective( + n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef ) return OptimalControlProgram( @@ -482,7 +491,7 @@ def _set_controls(bio_models, n_stim, with_residual_torque): # Controls bounds nb_tau = bio_models[0].nb_tau if with_residual_torque: # TODO : ADD SEVERAL INDIVIDUAL FIXED RESIDUAL TORQUE FOR EACH JOINT - tau_min, tau_max, tau_init = [-20] * nb_tau, [20] * nb_tau, [0] * nb_tau + tau_min, tau_max, tau_init = [-500] * nb_tau, [500] * nb_tau, [0] * nb_tau else: tau_min, tau_max, tau_init = [0] * nb_tau, [0] * nb_tau, [0] * nb_tau @@ -497,6 +506,60 @@ def _set_controls(bio_models, n_stim, with_residual_torque): return u_bounds, u_init + @staticmethod + def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef): + # Creates the objective for our problem + objective_functions = ObjectiveList() + if custom_objective: + for i in range(len(custom_objective)): + if custom_objective[i]: + for j in range(len(custom_objective[i])): + objective_functions.add(custom_objective[i][j]) + + if force_fourier_coef is not None: # TODO : Enable multiple force tracking for multiple muscles + for phase in range(n_stim): + for i in range(n_shooting[phase]): + objective_functions.add( + CustomObjective.track_state_from_time, + custom_type=ObjectiveFcn.Mayer, + node=i, + fourier_coeff=force_fourier_coef, + key="F", + quadratic=True, + weight=1, + phase=phase, + ) + + if end_node_tracking: + if isinstance(end_node_tracking, int | float): + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + node=Node.END, + key="F", + quadratic=True, + weight=1, + target=end_node_tracking, + phase=n_stim - 1, + ) + + if q_fourier_coef: + for j in range(len(q_fourier_coef)): + for phase in range(n_stim): + for i in range(n_shooting[phase]): + objective_functions.add( + CustomObjective.track_state_from_time, + custom_type=ObjectiveFcn.Mayer, + node=i, + fourier_coeff=q_fourier_coef[j], + key="q", + quadratic=True, + weight=1, + phase=phase, + index=j, + ) + + return objective_functions + @staticmethod def _sanity_check_bounds(bio_models, bound_type, bound_data): for i in range(bio_models[0].nb_q): diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index d17aad3a..387c914a 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -16,49 +16,75 @@ from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP -objective_functions = ObjectiveList() -n_stim = 10 +n_stim = 33 n_shooting = 10 -q_target = [np.array([[1.1339, 0.6629]]*(n_shooting+1)).T, - np.array([[0.9943, 0.7676]]*(n_shooting+1)).T, - np.array([[0.7676, 1.0641]]*(n_shooting+1)).T, - np.array([[0.5757, 1.3781]]*(n_shooting+1)).T, - np.array([[0.4536, 1.4653]]*(n_shooting+1)).T, - np.array([[0.6280, 1.3781]]*(n_shooting+1)).T, - np.array([[1.0292, 0.9594]]*(n_shooting+1)).T, - np.array([[1.0990, 0.8373]]*(n_shooting+1)).T, - np.array([[1.1339, 0.6629]]*(n_shooting+1)).T] +track_q = [np.array([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1]), + [np.array([1.1339, + 0.9943, + 0.7676, + 0.5757, + 0.4536, + 0.6280, + 1.0292, + 1.0990, + 1.1339]), + np.array([0.6629, + 0.7676, + 1.0641, + 1.3781, + 1.4653, + 1.3781, + 0.9594, + 0.8373, + 0.6629])]] +objective_functions = ObjectiveList() for i in range(n_stim): - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) - objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0, 1], node=Node.ALL, target=q_target[i], weight=10, quadratic=True, phase=i) + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() ) +import time +start_time = time.time() ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_cycling.bioMod", - bound_type="start", - bound_data=[0, 5], + biorbd_model_path="/arm26.bioMod", + bound_type="start_end", + bound_data=[[65, 38], [65, 38]], fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat")], + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")], n_stim=n_stim, n_shooting=10, final_time=1, - time_min=0.01, - time_max=0.01, - time_bimapping=True, pulse_intensity_min=minimum_pulse_intensity, pulse_intensity_max=130, pulse_intensity_bimapping=False, - with_residual_torque=False, + with_residual_torque=True, custom_objective=objective_functions, + q_tracking=track_q, + use_sx=True, ) +print("--- %s seconds --- OCP" % (time.time() - start_time)) +start_time = time.time() sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +print("--- %s seconds --- SOL" % (time.time() - start_time)) sol.animate() sol.graphs(show_bounds=False) + +# Fast OCP : +# --- 2.8143112659454346 seconds --- OCP +# --- 55.290322065353394 seconds --- SOL +# 106 1.7460726e+03 + +# Slow OCP : +# --- 84.57249999046326 seconds --- OCP +# --- 56.183839321136475 seconds --- SOL +# 106 1.7460726e+03 From 5b4e7a81266a266e7bb53c0fa9da281cc9452966 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 25 Jan 2024 17:49:44 -0500 Subject: [PATCH 13/63] adding hilldegroot model --- cocofest/models/dynamical_model.py | 49 ++++++++++++++++++++++++++++-- 1 file changed, 47 insertions(+), 2 deletions(-) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 0b33993e..95d1d797 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -1,6 +1,6 @@ from typing import Callable -from casadi import vertcat, MX, SX +from casadi import vertcat, MX, SX, exp, log, sqrt from bioptim import ( BiorbdModel, OptimalControlProgram, @@ -117,8 +117,13 @@ def muscle_dynamic( nlp_dynamics=muscle_model, ).dxdt - muscle_forces = DynamicsFunctions.get(nlp.states["F_"+muscle_model.muscle_name], states) muscle_idx = bio_muscle_names_at_index.index(muscle_model.muscle_name) + + muscle_forces = DynamicsFunctions.get(nlp.states["F_"+muscle_model.muscle_name], states) + muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q) + muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot) + muscle_forces = muscle_forces * muscle_force_length_coeff * muscle_force_velocity_coeff + moment_arm_matrix_for_the_muscle_and_joint = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T muscles_tau += moment_arm_matrix_for_the_muscle_and_joint @ muscle_forces @@ -239,3 +244,43 @@ def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: Fatigue name = "tau" name_tau = ["tau"] ConfigureProblem.configure_new_variable(name, name_tau, ocp, nlp, as_states, as_controls, fatigue=fatigue) + + @staticmethod + def muscle_force_length_coefficient(model, muscle, q): + + b11 = 0.815 + b21 = 1.055 + b31 = 0.162 + b41 = 0.063 + b12 = 0.433 + b22 = 0.717 + b32 = -0.030 + b42 = 0.200 + b13 = 0.100 + b23 = 1.000 + b33 = 0.354 + b43 = 0.0 + + muscle_length = muscle.length(model, q).to_mx() + muscle_optimal_length = muscle.characteristics().optimalLength().to_mx() + norm_length = muscle_length / muscle_optimal_length + + m_FlCE = b11 * exp((-0.5 * ((norm_length - b21) * (norm_length - b21)))/((b31 + b41 * norm_length) * (b31 + b41 * norm_length))) + b12 * exp((-0.5 * ((norm_length - b22) * (norm_length - b22))) / ((b32 + b42 * norm_length) * (b32 + b42 * norm_length))) + b13 * exp((-0.5 * ((norm_length - b23) * (norm_length - b23))) / ((b33 + b43 * norm_length) * (b33 + b43 * norm_length))) + + return m_FlCE + + @staticmethod + def muscle_force_velocity_coefficient(model, muscle, q, qdot): + muscle_velocity = muscle.velocity(model, q, qdot).to_mx() + m_cste_maxShorteningSpeed = 10 + norm_v = muscle_velocity / m_cste_maxShorteningSpeed + + d1 = -0.318 + d2 = -8.149 + d3 = -0.374 + d4 = 0.886 + + m_FvCE = d1 * log((d2 * norm_v + d3) + sqrt((d2 * norm_v + d3) * (d2 * norm_v + d3) + 1)) + d4 + + return m_FvCE +# From 8f07041f5652962de5a76b0a45552f0eac4bf5c5 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 25 Jan 2024 17:51:11 -0500 Subject: [PATCH 14/63] cycling --- ...nsity_optimization_cycling_multi_muscle.py | 27 ++++++++++--------- 1 file changed, 15 insertions(+), 12 deletions(-) diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index 387c914a..d005953e 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -13,11 +13,10 @@ Solver, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequency, FESActuatedBiorbdModelOCP -n_stim = 33 -n_shooting = 10 +n_stim = 30 track_q = [np.array([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1]), [np.array([1.1339, @@ -44,8 +43,8 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() +minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity( + DingModelIntensityFrequency() ) import time @@ -54,15 +53,18 @@ biorbd_model_path="/arm26.bioMod", bound_type="start_end", bound_data=[[65, 38], [65, 38]], - fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")], + fes_muscle_models=[DingModelIntensityFrequency(muscle_name="BIClong"), + DingModelIntensityFrequency(muscle_name="BICshort"), + DingModelIntensityFrequency(muscle_name="TRIlong"), + DingModelIntensityFrequency(muscle_name="TRIlat"), + DingModelIntensityFrequency(muscle_name="TRImed"), + DingModelIntensityFrequency(muscle_name="BRA")], n_stim=n_stim, - n_shooting=10, + 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, @@ -78,6 +80,7 @@ print("--- %s seconds --- SOL" % (time.time() - start_time)) sol.animate() sol.graphs(show_bounds=False) +print(sol.parameters) # Fast OCP : # --- 2.8143112659454346 seconds --- OCP From 6e6a7d02f4558d857492b69eaec18be9183d79f6 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 11:56:03 -0500 Subject: [PATCH 15/63] Comparation between with and without musce force-length relationship --- ...le_force_length_relationship_comparison.py | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 examples/dynamics/muscle_force_length_relationship_comparison.py diff --git a/examples/dynamics/muscle_force_length_relationship_comparison.py b/examples/dynamics/muscle_force_length_relationship_comparison.py new file mode 100644 index 00000000..7c0ec773 --- /dev/null +++ b/examples/dynamics/muscle_force_length_relationship_comparison.py @@ -0,0 +1,54 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work. +Those ocp were build to move the elbow starting from 0 degrees angle. +The stimulation frequency will be set to 10Hz and intensity to 40mA. +No residual torque is allowed. +""" +import matplotlib.pyplot as plt + +from bioptim import Solver + +from cocofest import DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) + +sol_list = [] +muscle_force_length_relationship = [False, True, False, True] +fes_muscle_model = [[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")] + ] + +for i in range(4): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="/arm26_biceps_1dof.bioMod", + bound_type="start", + bound_data=[0], + fes_muscle_models=fes_muscle_model[i], + n_stim=10, + n_shooting=10, + final_time=1, + pulse_duration=0.00026, + pulse_intensity=40, + with_residual_torque=False, + muscle_force_length_relationship=muscle_force_length_relationship[i], + muscle_force_velocity_relationship=False, + use_sx=False, + ) + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) + sol_list.append(sol.merge_phases()) + +plt.title("Comparison between with and without muscle force length relationship for an elbow movement at 10Hz and 40mA or 260us") +plt.plot(sol_list[0].time, sol_list[0].states["q"][0], label="Pulse duration without force length relationship") +plt.plot(sol_list[1].time, sol_list[1].states["q"][0], label="Pulse duration with force length relationship") +plt.plot(sol_list[2].time, sol_list[2].states["q"][0], label="Pulse intensity without force length relationship") +plt.plot(sol_list[3].time, sol_list[3].states["q"][0], label="Pulse intensity with force length relationship") + +plt.xlabel("Time (s)") +plt.ylabel("Angle (rad)") +plt.legend() +plt.show() From d94afcfd744db05e172f3dd8ffa4c1fe438eb55f Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 11:57:24 -0500 Subject: [PATCH 16/63] muscle relationship activation simplification --- cocofest/models/dynamical_model.py | 14 +++++++++++--- cocofest/optimization/fes_ocp_dynamics.py | 4 +++- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 95d1d797..b6ca00f5 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -20,6 +20,8 @@ def __init__( name: str = None, biorbd_path: str = None, muscles_model: DingModelFrequency() = None, + muscle_force_length_relationship: bool = False, + muscle_force_velocity_relationship: bool = False, ): super().__init__(biorbd_path) self._name = name @@ -30,6 +32,9 @@ def __init__( self.muscles_dynamics_model = muscles_model self.bio_stim_model = [self.bio_model] + self.muscles_dynamics_model + self.muscle_force_length_relationship = muscle_force_length_relationship + self.muscle_force_velocity_relationship = muscle_force_velocity_relationship + # ---- Absolutely needed methods ---- # def serialize(self, index: int = 0) -> tuple[Callable, dict]: return self.muscles_dynamics_model[index].serialize() @@ -120,8 +125,12 @@ def muscle_dynamic( muscle_idx = bio_muscle_names_at_index.index(muscle_model.muscle_name) muscle_forces = DynamicsFunctions.get(nlp.states["F_"+muscle_model.muscle_name], states) - muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q) - muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot) + muscle_force_length_coeff = 1 + muscle_force_velocity_coeff = 1 + if nlp.model.muscle_force_length_relationship: + muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q) + if nlp.model.muscle_force_velocity_relationship: + muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot) muscle_forces = muscle_forces * muscle_force_length_coeff * muscle_force_velocity_coeff moment_arm_matrix_for_the_muscle_and_joint = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T @@ -283,4 +292,3 @@ def muscle_force_velocity_coefficient(model, muscle, q, qdot): m_FvCE = d1 * log((d2 * norm_v + d3) + sqrt((d2 * norm_v + d3) * (d2 * norm_v + d3) + 1)) + d4 return m_FvCE -# diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 54df614d..dccb1b13 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -67,6 +67,8 @@ def prepare_ocp( q_tracking: list = None, custom_objective: ObjectiveList = None, with_residual_torque: bool = False, + muscle_force_length_relationship: bool = False, + muscle_force_velocity_relationship: bool = False, use_sx: bool = True, ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), control_type: ControlType = ControlType.CONSTANT, @@ -199,7 +201,7 @@ def prepare_ocp( ) bio_models = [ - FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) + FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models, muscle_force_length_relationship=muscle_force_length_relationship, muscle_force_velocity_relationship=muscle_force_velocity_relationship) for i in range(n_stim) ] From dc51e27f0e3448fadf0952f9ee2d4fef2250f103 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 12:44:17 -0500 Subject: [PATCH 17/63] Enabling muscle fatigue minimization --- cocofest/custom_objectives.py | 20 ++ cocofest/optimization/fes_ocp_dynamics.py | 16 +- examples/dynamics/arm26_biceps_triceps.bioMod | 238 ++++++++++++++++++ ...intensity_optimization_minimize_fatigue.py | 48 ++++ 4 files changed, 320 insertions(+), 2 deletions(-) create mode 100644 examples/dynamics/arm26_biceps_triceps.bioMod create mode 100644 examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index 4275bf85..ee135a88 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -68,3 +68,23 @@ def track_state_from_time_interpolate( return 1 - (force / controller.states[key].cx) else: raise RuntimeError(f"Minimization type {minimization_type} not implemented") + + @staticmethod + def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX: + """ + Minimize the overall muscle fatigue. + This function is quadratic, meaning that it minimizes towards the target. + Targets (default=np.zeros()) and indices (default=all_idx) can be specified. + + Parameters + ---------- + controller: PenaltyController + The penalty node elements + + Returns + ------- + The difference between the two keys + """ + muscle_name_list = controller.model.bio_model.muscle_names + muscle_fatigue = [controller.states["A_"+muscle_name_list[x]].cx for x in range(len(muscle_name_list))] + return sum(muscle_fatigue) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index dccb1b13..dcb63305 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -69,6 +69,7 @@ def prepare_ocp( with_residual_torque: bool = False, muscle_force_length_relationship: bool = False, muscle_force_velocity_relationship: bool = False, + minimize_muscle_fatigue: bool = False, use_sx: bool = True, ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), control_type: ControlType = ControlType.CONSTANT, @@ -215,7 +216,7 @@ def prepare_ocp( ) u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) objective_functions = FESActuatedBiorbdModelOCP._set_objective( - n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef + n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef, minimize_muscle_fatigue ) return OptimalControlProgram( @@ -509,7 +510,7 @@ def _set_controls(bio_models, n_stim, with_residual_torque): return u_bounds, u_init @staticmethod - def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef): + def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef, minimize_muscle_fatigue): # Creates the objective for our problem objective_functions = ObjectiveList() if custom_objective: @@ -560,6 +561,17 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu index=j, ) + if minimize_muscle_fatigue: + for i in range(n_stim): + objective_functions.add( + CustomObjective.minimize_overall_muscle_fatigue, + custom_type=ObjectiveFcn.Mayer, + node=Node.ALL, + quadratic=True, + weight=-1, + phase=i, + ) + return objective_functions @staticmethod diff --git a/examples/dynamics/arm26_biceps_triceps.bioMod b/examples/dynamics/arm26_biceps_triceps.bioMod new file mode 100644 index 00000000..e826ae8a --- /dev/null +++ b/examples/dynamics/arm26_biceps_triceps.bioMod @@ -0,0 +1,238 @@ +version 3 +gravity 0 -9.81 0 + +// SEGMENT DEFINITION + +segment base + // meshfile mesh/ground_ribs.vtp +endsegment + + // Markers + marker target + parent base + position 0.15 0.15 0.17 + endmarker + + +segment r_humerus_translation + parent base + RTinMatrix 1 + RT + 1.0 0.0 0.0 -0.017545 + 0.0 1.0 0.0 -0.007 + 0.0 0.0 1.0 0.17 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus_rotation1 + parent r_humerus_translation + RTinMatrix 1 + RT + 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 + -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 + 0.05894291073968768 0.0 0.9982613551938856 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ -2*pi 2*pi +endsegment + + +segment r_humerus_rotation2 + parent r_humerus_rotation1 +endsegment + +// Segment +segment r_humerus_rotation3 + parent r_humerus_rotation2 + RTinMatrix 1 + RT + 0.0 -0.0588981755023151 0.9982639956056206 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.9982639956056206 0.0588981755023151 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_humerus + parent r_humerus_rotation3 + RTinMatrix 1 + RT + 0.039020807762349605 0.9992383982621836 0.0 0.0 + -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 + 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 + 0.0 0.0 0.0 1.0 + mass 1.8645719999999999 + inertia + 0.01481 0.0 0.0 + 0.0 0.004551 0.0 + 0.0 0.0 0.013193 + com 0 -0.18049599999999999 0 + meshfile mesh/arm_r_humerus.vtp +endsegment + + +segment r_ulna_radius_hand_translation + parent r_humerus + RTinMatrix 1 + RT + 1.0 0.0 0.0 0.0061 + 0.0 1.0 0.0 -0.2904 + 0.0 0.0 1.0 -0.0123 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand_rotation1 + parent r_ulna_radius_hand_translation + RTinMatrix 1 + RT + 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 + 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 + -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 + 0.0 0.0 0.0 1.0 + rotations z + rangesQ 0 pi +endsegment + + +segment r_ulna_radius_hand_rotation2 + parent r_ulna_radius_hand_rotation1 +endsegment + + +segment r_ulna_radius_hand_rotation3 + parent r_ulna_radius_hand_rotation2 + RTinMatrix 1 + RT + 0.0 0.049433130424779516 0.998777435476196 0.0 + 1.0 0.0 0.0 0.0 + 0.0 0.998777435476196 -0.049433130424779516 0.0 + 0.0 0.0 0.0 1.0 +endsegment + + +segment r_ulna_radius_hand + parent r_ulna_radius_hand_rotation3 + RTinMatrix 1 + RT + -0.5953053712684069 0.803499542587912 0.0 0.0 + 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 + 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 + 0.0 0.0 0.0 1.0 + mass 1.5343150000000001 + inertia + 0.019281 0.0 0.0 + 0.0 0.001571 0.0 + 0.0 0.0 0.020062 + com 0 -0.181479 0 + meshfile mesh/arm_r_ulna.vtp +endsegment + + marker COM_hand + parent r_ulna_radius_hand + position 0 -0.181479 0 + endmarker + + + +// MUSCLE DEFINITION + +musclegroup base_to_r_ulna_radius_hand + OriginParent base + InsertionParent r_ulna_radius_hand +endmusclegroup + + muscle TRIlong + Type degrootefatigable + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.053650000000000003 -0.013729999999999999 0.14723 + InsertionPosition -0.021899999999999999 0.010460000000000001 -0.00077999999999999999 + optimalLength 0.13400000000000001 + maximalForce 798.51999999999998 + tendonSlackLength 0.14299999999999999 + pennationAngle 0.20943951 + maxVelocity 10 + + fatigueParameters + Type Xia + fatiguerate 0.01 + recoveryrate 0.002 + developfactor 10 + recoveryfactor 10 + endfatigueparameters + endmuscle + + viapoint TRIlong-P2 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.027140000000000001 -0.11441 -0.0066400000000000001 + endviapoint + viapoint TRIlong-P3 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.03184 -0.22636999999999999 -0.01217 + endviapoint + viapoint TRIlong-P4 + parent r_humerus + muscle TRIlong + musclegroup base_to_r_ulna_radius_hand + position -0.017430000000000001 -0.26756999999999997 -0.01208 + endviapoint + + muscle BIClong + Type degroote + musclegroup base_to_r_ulna_radius_hand + OriginPosition -0.039234999999999999 0.00347 0.14795 + InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 + optimalLength 0.1157 + maximalForce 624.29999999999995 + tendonSlackLength 0.27229999999999999 + pennationAngle 0 + maxVelocity 10 + endmuscle + + viapoint BIClong-P2 + parent base + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position -0.028944999999999999 0.01391 0.15639 + endviapoint + viapoint BIClong-P3 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.021309999999999999 0.017930000000000001 0.010279999999999999 + endviapoint + viapoint BIClong-P4 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.023779999999999999 -0.00511 0.01201 + endviapoint + viapoint BIClong-P5 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01345 -0.02827 0.0013600000000000001 + endviapoint + viapoint BIClong-P6 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01068 -0.077359999999999998 -0.00165 + endviapoint + viapoint BIClong-P7 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.01703 -0.12125 0.00024000000000000001 + endviapoint + viapoint BIClong-P8 + parent r_humerus + muscle BIClong + musclegroup base_to_r_ulna_radius_hand + position 0.022800000000000001 -0.1754 -0.0063 + endviapoint diff --git a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py new file mode 100644 index 00000000..b8b792bc --- /dev/null +++ b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py @@ -0,0 +1,48 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +n_stim = 10 +n_shooting = 10 +objective_functions = ObjectiveList() +objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 90]], + fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong")], + n_stim=n_stim, + n_shooting=10, + final_time=1, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=130, + pulse_intensity_bimapping=False, + with_residual_torque=False, + custom_objective=objective_functions, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=False, + minimize_muscle_fatigue=True, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) From e84fde921b42bd430fef47dde7c7669e718c93ca Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 14:38:09 -0500 Subject: [PATCH 18/63] fix: Corrected a major error in fatigue calculation for pulse duration model --- cocofest/models/ding2007.py | 6 +-- cocofest/models/ding2007_with_fatigue.py | 69 ++++++++++++++++++++---- 2 files changed, 63 insertions(+), 12 deletions(-) diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 5d65db28..f548efd4 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -91,11 +91,11 @@ def system_dynamics( """ r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article - a = self.a_calculation(impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article + a = self.a_calculation(a_scale=self.a_scale, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article f_dot = self.f_dot_fun(cn, f, a, self.tau1_rest, self.km_rest) # Equation n°2 from Ding's 2003 article return vertcat(cn_dot, f_dot) - def a_calculation(self, impulse_time: MX) -> MX: + def a_calculation(self, a_scale: float | MX, impulse_time: MX) -> MX: """ Parameters ---------- @@ -106,7 +106,7 @@ def a_calculation(self, impulse_time: MX) -> MX: ------- The value of scaling factor (unitless) """ - return self.a_scale * (1 - exp(-(impulse_time - self.pd0) / self.pdt)) + return a_scale * (1 - exp(-(impulse_time - self.pd0) / self.pdt)) def set_impulse_duration(self, value: list[MX]): """ diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 8c160168..89ca9344 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -42,19 +42,19 @@ def __init__(self, model_name: str = "ding_2007_with_fatigue", muscle_name: str @property def name_dof(self) -> list[str]: muscle_name = "_" + self.muscle_name if self.muscle_name else "" - return ["Cn"+muscle_name, "F"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] + return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] @property def nb_state(self) -> int: - return 4 + return 5 def standard_rest_values(self) -> np.array: """ Returns ------- - The rested values of Cn, F, Tau1, Km + The rested values of Cn, F, A, Tau1, Km """ - return np.array([[0], [0], [self.tau1_rest], [self.km_rest]]) + return np.array([[0], [0], [self.a_scale], [self.tau1_rest], [self.km_rest]]) def serialize(self) -> tuple[Callable, dict]: # This is where you can serialize your models @@ -81,6 +81,7 @@ def system_dynamics( self, cn: MX, f: MX, + a: MX = None, tau1: MX = None, km: MX = None, t: MX = None, @@ -113,11 +114,27 @@ def system_dynamics( """ r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article - a = self.a_calculation(impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article - f_dot = self.f_dot_fun(cn, f, a, tau1, km) # Equation n°2 from Ding's 2003 article + a_calculated = self.a_calculation(a_scale=a, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article + f_dot = self.f_dot_fun(cn, f, a_calculated, tau1, km) # Equation n°2 from Ding's 2003 article + a_dot = self.a_dot_fun(a, f) tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 from Ding's 2003 article km_dot = self.km_dot_fun(km, f) # Equation n°11 from Ding's 2003 article - return vertcat(cn_dot, f_dot, tau1_dot, km_dot) + return vertcat(cn_dot, f_dot, a_dot, tau1_dot, km_dot) + + def a_dot_fun(self, a: MX, f: MX) -> MX | float: + """ + Parameters + ---------- + a: MX + The previous step value of scaling factor (unitless) + f: MX + The previous step value of force (N) + + Returns + ------- + The value of the derivative scaling factor (unitless) + """ + return -(a - self.a_scale) / self.tau_fat + self.alpha_a * f # Equation n°5 def tau1_dot_fun(self, tau1: MX, f: MX) -> MX | float: """ @@ -200,8 +217,9 @@ def dynamics( dxdt=dxdt_fun( cn=states[0], f=states[1], - tau1=states[2], - km=states[3], + a=states[2], + tau1=states[3], + km=states[4], t=time, t_stim_prev=stim_apparition, impulse_time=impulse_time, @@ -222,11 +240,44 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr """ self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) + @staticmethod + def configure_scaling_factor( + ocp: OptimalControlProgram, + nlp: NonLinearProgram, + as_states: bool, + as_controls: bool, + as_states_dot: bool = False, + muscle_name: str = None, + ): + """ + Configure a new variable of the scaling factor (N/ms) + + Parameters + ---------- + ocp: OptimalControlProgram + A reference to the ocp + nlp: NonLinearProgram + A reference to the phase + as_states: bool + If the generalized coordinates should be a state + as_controls: bool + If the generalized coordinates should be a control + as_states_dot: bool + If the generalized velocities should be a state_dot + """ + muscle_name = "_" + muscle_name if muscle_name else "" + name = "A" + muscle_name + name_a = [name] + ConfigureProblem.configure_new_variable( + name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, + ) + @staticmethod def configure_time_state_force_no_cross_bridge( ocp: OptimalControlProgram, From 2e823415169b3f34769c973970476823146c923c Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 14:38:53 -0500 Subject: [PATCH 19/63] pulse duration optimization to minimize muscle fatigue --- ..._duration_optimization_minimize_fatigue.py | 46 +++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py diff --git a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py new file mode 100644 index 00000000..3d49ccf4 --- /dev/null +++ b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py @@ -0,0 +1,46 @@ +""" +This example will do a 10 stimulation example with Ding's 2007 pulse duration model. +Those ocp were build to move the elbow from 0 to 90 degrees angle. +The stimulation frequency will be set to 10Hz and pulse duration will be optimized to satisfy the motion and to minimize the overall muscle fatigue. +Intensity can be optimized from sensitivity threshold to 600us. No residual torque is allowed. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +n_stim = 10 +n_shooting = 10 +objective_functions = ObjectiveList() +objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) + +minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 90]], + fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong")], + 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, + with_residual_torque=False, + custom_objective=objective_functions, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=False, + minimize_muscle_fatigue=True, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) From 79275e476e0976a995de603c3e8cff32bf45ebd8 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 15:36:45 -0500 Subject: [PATCH 20/63] Adding pulse apparition optimization to reduce muscle fatigue --- cocofest/optimization/fes_ocp.py | 2 +- cocofest/optimization/fes_ocp_dynamics.py | 2 +- .../frequecy_optimization_minimize_fatigue.py | 47 +++++++++++++++++++ 3 files changed, 49 insertions(+), 2 deletions(-) create mode 100644 examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index 84672bca..19b18eac 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -674,7 +674,7 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu for j in range(len(custom_objective[i])): objective_functions.add(custom_objective[i][j]) - if force_fourier_coef is not None: # TODO : Enable multiple force tracking for multiple muscles + if force_fourier_coef is not None: for phase in range(n_stim): for i in range(n_shooting[phase]): objective_functions.add( diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index dcb63305..14632668 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -494,7 +494,7 @@ def _set_controls(bio_models, n_stim, with_residual_torque): # Controls bounds nb_tau = bio_models[0].nb_tau if with_residual_torque: # TODO : ADD SEVERAL INDIVIDUAL FIXED RESIDUAL TORQUE FOR EACH JOINT - tau_min, tau_max, tau_init = [-500] * nb_tau, [500] * nb_tau, [0] * nb_tau + tau_min, tau_max, tau_init = [-50] * nb_tau, [50] * nb_tau, [0] * nb_tau else: tau_min, tau_max, tau_init = [0] * nb_tau, [0] * nb_tau, [0] * nb_tau diff --git a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py new file mode 100644 index 00000000..5110bde6 --- /dev/null +++ b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py @@ -0,0 +1,47 @@ +""" +This example will do a 5 stimulation example with Ding's 2003 frequency model. +Those ocp were build to move the elbow from 0 to 90 degrees angle. +The stimulation apparition will be optimized to satisfy the motion and to minimize the overall muscle fatigue. +Stimulations can occur between 0.01 to 1 second. Residual torque added to help convergence. +""" +import numpy as np + +from bioptim import ( + Node, + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +n_stim = 5 +n_shooting = 10 +objective_functions = ObjectiveList() +objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) +for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10000, quadratic=True, phase=i) + + +ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 90]], + fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong"), + DingModelFrequencyWithFatigue(muscle_name="TRIlong")], + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + time_min=0.01, + time_max=1, + time_bimapping=False, + with_residual_torque=True, + custom_objective=objective_functions, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=False, + minimize_muscle_fatigue=True, +) + +sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) +sol.animate() +sol.graphs(show_bounds=False) From abfeedf1f3c9618cb757c488f8bfb48440343d10 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 15:39:12 -0500 Subject: [PATCH 21/63] black --- cocofest/custom_objectives.py | 2 +- cocofest/models/ding2003.py | 48 ++++-- cocofest/models/ding2003_with_fatigue.py | 45 ++++-- cocofest/models/ding2007.py | 25 ++- cocofest/models/ding2007_with_fatigue.py | 54 +++++-- cocofest/models/dynamical_model.py | 83 +++++++--- cocofest/models/hmed2018.py | 16 +- cocofest/models/hmed2018_with_fatigue.py | 48 ++++-- cocofest/optimization/fes_multi_start.py | 7 +- cocofest/optimization/fes_ocp.py | 6 +- cocofest/optimization/fes_ocp_dynamics.py | 151 ++++++++++++------ ...nsity_optimization_cycling_multi_muscle.py | 45 ++---- .../intensity_optimization_track_q.py | 22 ++- ...nsity_optimization_track_q_multi_muscle.py | 36 ++++- .../frequecy_optimization_minimize_fatigue.py | 17 +- ..._duration_optimization_minimize_fatigue.py | 17 +- ...intensity_optimization_minimize_fatigue.py | 17 +- ...le_force_length_relationship_comparison.py | 21 ++- examples/getting_started/model_integration.py | 8 +- .../muscle_model_identification.py | 7 +- .../getting_started/pulse_mode_example.py | 14 +- .../truncation/summation_truncation_graph.py | 10 +- tests/shard1/test_ivp.py | 30 +++- tests/shard1/test_ocp_build.py | 57 ++++++- tests/shard1/test_ocp_id.py | 34 +++- 25 files changed, 612 insertions(+), 208 deletions(-) diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index ee135a88..93fa2839 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -86,5 +86,5 @@ def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX: The difference between the two keys """ muscle_name_list = controller.model.bio_model.muscle_names - muscle_fatigue = [controller.states["A_"+muscle_name_list[x]].cx for x in range(len(muscle_name_list))] + muscle_fatigue = [controller.states["A_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))] return sum(muscle_fatigue) diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index b4301cf4..3e2c78e7 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -27,7 +27,10 @@ class DingModelFrequency: """ def __init__( - self, model_name: str = "ding2003", muscle_name: str = None, sum_stim_truncation: int = None, + self, + model_name: str = "ding2003", + muscle_name: str = None, + sum_stim_truncation: int = None, ): self._model_name = model_name self._muscle_name = muscle_name @@ -83,7 +86,7 @@ def serialize(self) -> tuple[Callable, dict]: @property def name_dof(self) -> list[str]: muscle_name = "_" + self.muscle_name if self.muscle_name else "" - return ["Cn"+muscle_name, "F"+muscle_name] + return ["Cn" + muscle_name, "F" + muscle_name] @property def nb_state(self) -> int: @@ -98,7 +101,13 @@ def muscle_name(self) -> None | str: return self._muscle_name # ---- Model's dynamics ---- # - def system_dynamics(self, cn: MX, f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None,) -> MX: + def system_dynamics( + self, + cn: MX, + f: MX, + t: MX = None, + t_stim_prev: list[MX] | list[float] = None, + ) -> MX: """ The system dynamics is the function that describes the models. @@ -262,7 +271,14 @@ def dynamics( dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics - return DynamicsEvaluation(dxdt=dxdt_fun(cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition,),) + return DynamicsEvaluation( + dxdt=dxdt_fun( + cn=states[0], + f=states[1], + t=time, + t_stim_prev=stim_apparition, + ), + ) def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgram): """ @@ -275,7 +291,9 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -306,10 +324,16 @@ def configure_ca_troponin_complex( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Cn"+muscle_name + name = "Cn" + muscle_name name_cn = [name] ConfigureProblem.configure_new_variable( - name, name_cn, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_cn, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -338,10 +362,16 @@ def configure_force( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "F"+muscle_name + name = "F" + muscle_name name_f = [name] ConfigureProblem.configure_new_variable( - name, name_f, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_f, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 9cab1034..06aa76db 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -27,7 +27,10 @@ class DingModelFrequencyWithFatigue(DingModelFrequency): """ def __init__( - self, model_name: str = "ding2003_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None, + self, + model_name: str = "ding2003_with_fatigue", + muscle_name: str = None, + sum_stim_truncation: int = None, ): super().__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) self._with_fatigue = True @@ -80,7 +83,7 @@ def serialize(self) -> tuple[Callable, dict]: @property def name_dof(self) -> list[str]: muscle_name = "_" + self.muscle_name if self.muscle_name else "" - return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] + return ["Cn" + muscle_name, "F" + muscle_name, "A" + muscle_name, "Tau1" + muscle_name, "Km" + muscle_name] @property def nb_state(self) -> int: @@ -243,10 +246,14 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -277,10 +284,16 @@ def configure_scaling_factor( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "A"+muscle_name + name = "A" + muscle_name name_a = [name] ConfigureProblem.configure_new_variable( - name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_a, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -309,10 +322,16 @@ def configure_time_state_force_no_cross_bridge( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Tau1"+muscle_name + name = "Tau1" + muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_tau1, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -341,8 +360,14 @@ def configure_cross_bridges( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Km"+muscle_name + name = "Km" + muscle_name name_km = [name] ConfigureProblem.configure_new_variable( - name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_km, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index f548efd4..b6edf059 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -26,7 +26,9 @@ class DingModelPulseDurationFrequency(DingModelFrequency): """ def __init__(self, model_name: str = "ding_2007", muscle_name: str = None, sum_stim_truncation: int = None): - super(DingModelPulseDurationFrequency, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) + super(DingModelPulseDurationFrequency, self).__init__( + model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation + ) self._with_fatigue = False self.impulse_time = None # ---- Custom values for the example ---- # @@ -67,7 +69,12 @@ def serialize(self) -> tuple[Callable, dict]: ) def system_dynamics( - self, cn: MX, f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None, impulse_time: MX = None, + self, + cn: MX, + f: MX, + t: MX = None, + t_stim_prev: list[MX] | list[float] = None, + impulse_time: MX = None, ) -> MX: """ The system dynamics is the function that describes the models. @@ -136,7 +143,7 @@ def get_pulse_duration_parameters(nlp_parameters: ParameterList, muscle_name: st pulse_duration_parameters = vertcat() for j in range(nlp_parameters.mx.shape[0]): if muscle_name: - if "pulse_duration_"+ muscle_name in str(nlp_parameters.mx[j]): + if "pulse_duration_" + muscle_name in str(nlp_parameters.mx[j]): pulse_duration_parameters = vertcat(pulse_duration_parameters, nlp_parameters.mx[j]) elif "pulse_duration" in str(nlp_parameters.mx[j]): pulse_duration_parameters = vertcat(pulse_duration_parameters, nlp_parameters.mx[j]) @@ -191,7 +198,13 @@ def dynamics( dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics return DynamicsEvaluation( - dxdt=dxdt_fun(cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition, impulse_time=impulse_time,), + dxdt=dxdt_fun( + cn=states[0], + f=states[1], + t=time, + t_stim_prev=stim_apparition, + impulse_time=impulse_time, + ), defects=None, ) @@ -206,7 +219,9 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 89ca9344..8c1b0482 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -26,7 +26,9 @@ class DingModelPulseDurationFrequencyWithFatigue(DingModelPulseDurationFrequency Muscle & Nerve: Official Journal of the American Association of Electrodiagnostic Medicine, 36(2), 214-222. """ - def __init__(self, model_name: str = "ding_2007_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None): + def __init__( + self, model_name: str = "ding_2007_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None + ): super(DingModelPulseDurationFrequencyWithFatigue, self).__init__( model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation ) @@ -42,7 +44,7 @@ def __init__(self, model_name: str = "ding_2007_with_fatigue", muscle_name: str @property def name_dof(self) -> list[str]: muscle_name = "_" + self.muscle_name if self.muscle_name else "" - return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] + return ["Cn" + muscle_name, "F" + muscle_name, "A" + muscle_name, "Tau1" + muscle_name, "Km" + muscle_name] @property def nb_state(self) -> int: @@ -238,22 +240,26 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @staticmethod def configure_scaling_factor( - ocp: OptimalControlProgram, - nlp: NonLinearProgram, - as_states: bool, - as_controls: bool, - as_states_dot: bool = False, - muscle_name: str = None, + ocp: OptimalControlProgram, + nlp: NonLinearProgram, + as_states: bool, + as_controls: bool, + as_states_dot: bool = False, + muscle_name: str = None, ): """ Configure a new variable of the scaling factor (N/ms) @@ -275,7 +281,13 @@ def configure_scaling_factor( name = "A" + muscle_name name_a = [name] ConfigureProblem.configure_new_variable( - name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_a, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -304,10 +316,16 @@ def configure_time_state_force_no_cross_bridge( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Tau1"+muscle_name + name = "Tau1" + muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_tau1, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -336,8 +354,14 @@ def configure_cross_bridges( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Km"+muscle_name + name = "Km" + muscle_name name_km = [name] ConfigureProblem.configure_new_variable( - name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_km, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index b6ca00f5..5b79f77f 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -67,7 +67,9 @@ def muscle_dynamic( parameters: MX | SX, stochastic_variables: MX | SX, nlp: NonLinearProgram, - muscle_models: list[DingModelFrequency] | list[DingModelIntensityFrequency] | list[DingModelPulseDurationFrequency], + muscle_models: list[DingModelFrequency] + | list[DingModelIntensityFrequency] + | list[DingModelPulseDurationFrequency], stim_apparition=None, state_name_list=None, ) -> DynamicsEvaluation: @@ -106,34 +108,42 @@ def muscle_dynamic( bio_muscle_names_at_index.append(nlp.model.bio_model.model.muscle(i).name().to_string()) for muscle_model in muscle_models: - muscle_states_idx = [i for i in range(len(state_name_list)) if muscle_model.muscle_name in state_name_list[i]] + muscle_states_idx = [ + i for i in range(len(state_name_list)) if muscle_model.muscle_name in state_name_list[i] + ] muscle_states = vertcat() for i in range(len(muscle_states_idx)): muscle_states = vertcat(muscle_states, states[muscle_states_idx[i]]) muscle_dxdt = muscle_model.dynamics( - time, - muscle_states, - controls, - parameters, - stochastic_variables, - nlp, - stim_apparition, - nlp_dynamics=muscle_model, - ).dxdt + time, + muscle_states, + controls, + parameters, + stochastic_variables, + nlp, + stim_apparition, + nlp_dynamics=muscle_model, + ).dxdt muscle_idx = bio_muscle_names_at_index.index(muscle_model.muscle_name) - muscle_forces = DynamicsFunctions.get(nlp.states["F_"+muscle_model.muscle_name], states) + muscle_forces = DynamicsFunctions.get(nlp.states["F_" + muscle_model.muscle_name], states) muscle_force_length_coeff = 1 muscle_force_velocity_coeff = 1 if nlp.model.muscle_force_length_relationship: - muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q) + muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient( + model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q + ) if nlp.model.muscle_force_velocity_relationship: - muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient(model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot) + muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient( + model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot + ) muscle_forces = muscle_forces * muscle_force_length_coeff * muscle_force_velocity_coeff - moment_arm_matrix_for_the_muscle_and_joint = -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T + moment_arm_matrix_for_the_muscle_and_joint = ( + -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T + ) muscles_tau += moment_arm_matrix_for_the_muscle_and_joint @ muscle_forces dxdt_muscle_list = vertcat(dxdt_muscle_list, muscle_dxdt) @@ -159,20 +169,28 @@ def declare_model_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProg """ state_name_list = [] for muscle_dynamics_model in self.muscles_dynamics_model: - muscle_dynamics_model.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) - state_name_list.append("CN_"+muscle_dynamics_model.muscle_name) - muscle_dynamics_model.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + muscle_dynamics_model.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name + ) + state_name_list.append("CN_" + muscle_dynamics_model.muscle_name) + muscle_dynamics_model.configure_force( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name + ) state_name_list.append("F_" + muscle_dynamics_model.muscle_name) - if "A_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: - muscle_dynamics_model.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + if "A_" + muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + muscle_dynamics_model.configure_scaling_factor( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name + ) state_name_list.append("A_" + muscle_dynamics_model.muscle_name) - if "Tau1_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + if "Tau1_" + muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: muscle_dynamics_model.configure_time_state_force_no_cross_bridge( ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name ) state_name_list.append("Tau1_" + muscle_dynamics_model.muscle_name) - if "Km_"+muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: - muscle_dynamics_model.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name) + if "Km_" + muscle_dynamics_model.muscle_name in muscle_dynamics_model.name_dof: + muscle_dynamics_model.configure_cross_bridges( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=muscle_dynamics_model.muscle_name + ) state_name_list.append("Km_" + muscle_dynamics_model.muscle_name) ConfigureProblem.configure_q(ocp, nlp, as_states=True, as_controls=False) @@ -256,7 +274,6 @@ def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: Fatigue @staticmethod def muscle_force_length_coefficient(model, muscle, q): - b11 = 0.815 b21 = 1.055 b31 = 0.162 @@ -274,7 +291,23 @@ def muscle_force_length_coefficient(model, muscle, q): muscle_optimal_length = muscle.characteristics().optimalLength().to_mx() norm_length = muscle_length / muscle_optimal_length - m_FlCE = b11 * exp((-0.5 * ((norm_length - b21) * (norm_length - b21)))/((b31 + b41 * norm_length) * (b31 + b41 * norm_length))) + b12 * exp((-0.5 * ((norm_length - b22) * (norm_length - b22))) / ((b32 + b42 * norm_length) * (b32 + b42 * norm_length))) + b13 * exp((-0.5 * ((norm_length - b23) * (norm_length - b23))) / ((b33 + b43 * norm_length) * (b33 + b43 * norm_length))) + m_FlCE = ( + b11 + * exp( + (-0.5 * ((norm_length - b21) * (norm_length - b21))) + / ((b31 + b41 * norm_length) * (b31 + b41 * norm_length)) + ) + + b12 + * exp( + (-0.5 * ((norm_length - b22) * (norm_length - b22))) + / ((b32 + b42 * norm_length) * (b32 + b42 * norm_length)) + ) + + b13 + * exp( + (-0.5 * ((norm_length - b23) * (norm_length - b23))) + / ((b33 + b43 * norm_length) * (b33 + b43 * norm_length)) + ) + ) return m_FlCE diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 5fd509ff..3f314e1a 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -27,7 +27,9 @@ class DingModelIntensityFrequency(DingModelFrequency): """ def __init__(self, model_name: str = "hmed2018", muscle_name: str = None, sum_stim_truncation: int = None): - super(DingModelIntensityFrequency, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) + super(DingModelIntensityFrequency, self).__init__( + model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation + ) self._with_fatigue = False # ---- Custom values for the example ---- # # ---- Force models ---- # @@ -204,7 +206,7 @@ def get_intensity_parameters(nlp_parameters: ParameterList, muscle_name: str = N intensity_parameters = vertcat() for j in range(nlp_parameters.mx.shape[0]): if muscle_name: - if "pulse_intensity_"+ muscle_name in str(nlp_parameters.mx[j]): + if "pulse_intensity_" + muscle_name in str(nlp_parameters.mx[j]): intensity_parameters = vertcat(intensity_parameters, nlp_parameters.mx[j]) elif "pulse_intensity" in str(nlp_parameters.mx[j]): intensity_parameters = vertcat(intensity_parameters, nlp_parameters.mx[j]) @@ -264,7 +266,11 @@ def dynamics( return DynamicsEvaluation( dxdt=dxdt_fun( - cn=states[0], f=states[1], t=time, t_stim_prev=stim_apparition, intensity_stim=intensity_stim_prev, + cn=states[0], + f=states[1], + t=time, + t_stim_prev=stim_apparition, + intensity_stim=intensity_stim_prev, ), defects=None, ) @@ -280,7 +286,9 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index a051ecf5..75d78529 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -26,8 +26,12 @@ class DingModelIntensityFrequencyWithFatigue(DingModelIntensityFrequency): Computers in Biology and Medicine, 101, 218-228. """ - def __init__(self, model_name: str = "hmed2018_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None): - super(DingModelIntensityFrequencyWithFatigue, self).__init__(model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation) + def __init__( + self, model_name: str = "hmed2018_with_fatigue", muscle_name: str = None, sum_stim_truncation: int = None + ): + super(DingModelIntensityFrequencyWithFatigue, self).__init__( + model_name=model_name, muscle_name=muscle_name, sum_stim_truncation=sum_stim_truncation + ) self._with_fatigue = True # ---- Fatigue models ---- # self.alpha_a = -4.0 * 10e-7 # Value from Ding's experimentation [1] (s^-2) @@ -39,7 +43,7 @@ def __init__(self, model_name: str = "hmed2018_with_fatigue", muscle_name: str = @property def name_dof(self) -> list[str]: muscle_name = "_" + self.muscle_name if self.muscle_name else "" - return ["Cn"+muscle_name, "F"+muscle_name, "A"+muscle_name, "Tau1"+muscle_name, "Km"+muscle_name] + return ["Cn" + muscle_name, "F" + muscle_name, "A" + muscle_name, "Tau1" + muscle_name, "Km" + muscle_name] @property def nb_state(self) -> int: @@ -242,10 +246,14 @@ def declare_ding_variables(self, ocp: OptimalControlProgram, nlp: NonLinearProgr nlp: NonLinearProgram A reference to the phase """ - self.configure_ca_troponin_complex(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_ca_troponin_complex( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_force(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) self.configure_scaling_factor(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) - self.configure_time_state_force_no_cross_bridge(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) + self.configure_time_state_force_no_cross_bridge( + ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name + ) self.configure_cross_bridges(ocp=ocp, nlp=nlp, as_states=True, as_controls=False, muscle_name=self.muscle_name) stim_apparition = self.get_stim_prev(ocp, nlp) ConfigureProblem.configure_dynamics_function(ocp, nlp, dyn_func=self.dynamics, stim_apparition=stim_apparition) @@ -276,10 +284,16 @@ def configure_scaling_factor( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "A"+muscle_name + name = "A" + muscle_name name_a = [name] ConfigureProblem.configure_new_variable( - name, name_a, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_a, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -308,10 +322,16 @@ def configure_time_state_force_no_cross_bridge( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Tau1"+muscle_name + name = "Tau1" + muscle_name name_tau1 = [name] ConfigureProblem.configure_new_variable( - name, name_tau1, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_tau1, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) @staticmethod @@ -340,8 +360,14 @@ def configure_cross_bridges( If the generalized velocities should be a state_dot """ muscle_name = "_" + muscle_name if muscle_name else "" - name = "Km"+muscle_name + name = "Km" + muscle_name name_km = [name] ConfigureProblem.configure_new_variable( - name, name_km, ocp, nlp, as_states, as_controls, as_states_dot, + name, + name_km, + ocp, + nlp, + as_states, + as_controls, + as_states_dot, ) diff --git a/cocofest/optimization/fes_multi_start.py b/cocofest/optimization/fes_multi_start.py index 793d1673..2359c122 100644 --- a/cocofest/optimization/fes_multi_start.py +++ b/cocofest/optimization/fes_multi_start.py @@ -328,7 +328,12 @@ def construct_filepath(save_path, combinatorial_parameters): " DingModelIntensityFrequency", ) - def save_results(self, sol: Solution, *combinatorial_parameters, **extra_parameters,) -> None: + def save_results( + self, + sol: Solution, + *combinatorial_parameters, + **extra_parameters, + ) -> None: """ Callback of the post_optimization_callback, this can be used to save the results diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index 19b18eac..e5eee3c1 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -454,7 +454,11 @@ def _build_phase_time(final_time, n_stim, pulse_mode, time_min, time_max, time_b else: for i in range(n_stim): constraints.add( - ConstraintFcn.TIME_CONSTRAINT, node=Node.END, min_bound=time_min, max_bound=time_max, phase=i, + ConstraintFcn.TIME_CONSTRAINT, + node=Node.END, + min_bound=time_min, + max_bound=time_max, + phase=i, ) if time_bimapping is True: diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 14632668..66dba370 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -15,7 +15,7 @@ InterpolationType, ObjectiveFcn, OdeSolverBase, - Node + Node, ) from cocofest import ( @@ -75,7 +75,6 @@ def prepare_ocp( control_type: ControlType = ControlType.CONSTANT, n_threads: int = 1, ): - """ This definition prepares the ocp to be solved . @@ -155,7 +154,9 @@ def prepare_ocp( OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) - FESActuatedBiorbdModelOCP._sanity_check_muscle_model(biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models) + FESActuatedBiorbdModelOCP._sanity_check_muscle_model( + biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models + ) n_stim, final_time = OcpFes._build_phase_parameter( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down @@ -170,7 +171,6 @@ def prepare_ocp( for i in range(len(q_tracking[1])): q_fourier_coef.append(OcpFes._build_fourrier_coeff([q_tracking[0], q_tracking[1][i]])) - n_shooting = [n_shooting] * n_stim final_time_phase, constraints, phase_time_bimapping = OcpFes._build_phase_time( final_time=final_time, @@ -180,7 +180,12 @@ def prepare_ocp( time_max=time_max, time_bimapping=time_bimapping, ) - parameters, parameters_bounds, parameters_init, parameter_objectives = FESActuatedBiorbdModelOCP._build_parameters( + ( + parameters, + parameters_bounds, + parameters_init, + parameter_objectives, + ) = FESActuatedBiorbdModelOCP._build_parameters( model=fes_muscle_models, n_stim=n_stim, pulse_duration=pulse_duration, @@ -202,7 +207,13 @@ def prepare_ocp( ) bio_models = [ - FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models, muscle_force_length_relationship=muscle_force_length_relationship, muscle_force_velocity_relationship=muscle_force_velocity_relationship) + FESActuatedBiorbdModel( + name=None, + biorbd_path=biorbd_model_path, + muscles_model=fes_muscle_models, + muscle_force_length_relationship=muscle_force_length_relationship, + muscle_force_velocity_relationship=muscle_force_velocity_relationship, + ) for i in range(n_stim) ] @@ -212,11 +223,21 @@ def prepare_ocp( dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( - bio_models, fes_muscle_models, bound_type, bound_data, n_stim, + bio_models, + fes_muscle_models, + bound_type, + bound_data, + n_stim, ) u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) objective_functions = FESActuatedBiorbdModelOCP._set_objective( - n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef, minimize_muscle_fatigue + n_stim, + n_shooting, + force_fourier_coef, + end_node_tracking, + custom_objective, + q_fourier_coef, + minimize_muscle_fatigue, ) return OptimalControlProgram( @@ -257,18 +278,18 @@ def _declare_dynamics(bio_models, n_stim): @staticmethod def _build_parameters( - model, - n_stim, - pulse_duration, - pulse_duration_min, - pulse_duration_max, - pulse_duration_bimapping, - pulse_duration_similar_for_all_muscles, - pulse_intensity, - pulse_intensity_min, - pulse_intensity_max, - pulse_intensity_bimapping, - pulse_intensity_similar_for_all_muscles, + model, + n_stim, + pulse_duration, + pulse_duration_min, + pulse_duration_max, + pulse_duration_bimapping, + pulse_duration_similar_for_all_muscles, + pulse_intensity, + pulse_intensity_min, + pulse_intensity_max, + pulse_intensity_bimapping, + pulse_intensity_similar_for_all_muscles, ): parameters = ParameterList() parameters_bounds = BoundsList() @@ -277,9 +298,15 @@ def _build_parameters( for i in range(len(model)): if isinstance(model[i], DingModelPulseDurationFrequency): - parameter_name = "pulse_duration" if pulse_duration_similar_for_all_muscles else "pulse_duration" + "_" + model[i].muscle_name - if pulse_duration: #TODO : ADD SEVERAL INDIVIDUAL FIXED PULSE DURATION FOR EACH MUSCLE - if (pulse_duration_similar_for_all_muscles and i == 0) or not pulse_duration_similar_for_all_muscles: + parameter_name = ( + "pulse_duration" + if pulse_duration_similar_for_all_muscles + else "pulse_duration" + "_" + model[i].muscle_name + ) + if pulse_duration: # TODO : ADD SEVERAL INDIVIDUAL FIXED PULSE DURATION FOR EACH MUSCLE + if ( + pulse_duration_similar_for_all_muscles and i == 0 + ) or not pulse_duration_similar_for_all_muscles: parameters.add( parameter_name=parameter_name, function=DingModelPulseDurationFrequency.set_impulse_duration, @@ -302,8 +329,12 @@ def _build_parameters( ) parameters_init[parameter_name] = np.array([pulse_duration] * n_stim) - elif pulse_duration_min and pulse_duration_max: #TODO : ADD SEVERAL MIN MAX PULSE DURATION FOR EACH MUSCLE - if (pulse_duration_similar_for_all_muscles and i == 0) or not pulse_duration_similar_for_all_muscles: + elif ( + pulse_duration_min and pulse_duration_max + ): # TODO : ADD SEVERAL MIN MAX PULSE DURATION FOR EACH MUSCLE + if ( + pulse_duration_similar_for_all_muscles and i == 0 + ) or not pulse_duration_similar_for_all_muscles: parameters_bounds.add( parameter_name, min_bound=[pulse_duration_min], @@ -326,15 +357,20 @@ def _build_parameters( ) if pulse_duration_bimapping: - pass - # parameter_bimapping.add(name="pulse_duration", to_second=[0 for _ in range(n_stim)], to_first=[0]) - # TODO : Fix Bimapping in Bioptim + pass + # parameter_bimapping.add(name="pulse_duration", to_second=[0 for _ in range(n_stim)], to_first=[0]) + # TODO : Fix Bimapping in Bioptim if isinstance(model[i], DingModelIntensityFrequency): - parameter_name = "pulse_intensity" if pulse_intensity_similar_for_all_muscles else "pulse_intensity" + "_" + \ - model[i].muscle_name + parameter_name = ( + "pulse_intensity" + if pulse_intensity_similar_for_all_muscles + else "pulse_intensity" + "_" + model[i].muscle_name + ) if pulse_intensity: # TODO : ADD SEVERAL INDIVIDUAL FIXED PULSE INTENSITY FOR EACH MUSCLE - if (pulse_intensity_similar_for_all_muscles and i == 0) or not pulse_intensity_similar_for_all_muscles: + if ( + pulse_intensity_similar_for_all_muscles and i == 0 + ) or not pulse_intensity_similar_for_all_muscles: parameters.add( parameter_name=parameter_name, function=DingModelIntensityFrequency.set_impulse_intensity, @@ -357,9 +393,12 @@ def _build_parameters( ) parameters_init[parameter_name] = np.array([pulse_intensity] * n_stim) - elif pulse_intensity_min and pulse_intensity_max: #TODO : ADD SEVERAL MIN MAX PULSE INTENSITY FOR EACH MUSCLE + elif ( + pulse_intensity_min and pulse_intensity_max + ): # TODO : ADD SEVERAL MIN MAX PULSE INTENSITY FOR EACH MUSCLE if ( - pulse_intensity_similar_for_all_muscles and i == 0) or not pulse_intensity_similar_for_all_muscles: + pulse_intensity_similar_for_all_muscles and i == 0 + ) or not pulse_intensity_similar_for_all_muscles: parameters_bounds.add( parameter_name, min_bound=[pulse_intensity_min], @@ -414,13 +453,13 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): ) muscle_name = model.muscle_name for i in range(len(variable_bound_list)): - if variable_bound_list[i] == "Cn_"+muscle_name: + if variable_bound_list[i] == "Cn_" + muscle_name: max_bounds[i] = 10 - elif variable_bound_list[i] == "F_"+muscle_name: + elif variable_bound_list[i] == "F_" + muscle_name: max_bounds[i] = 1000 - elif variable_bound_list[i] == "Tau1_"+muscle_name or variable_bound_list[i] == "Km_"+muscle_name: + elif variable_bound_list[i] == "Tau1_" + muscle_name or variable_bound_list[i] == "Km_" + muscle_name: max_bounds[i] = 1 - elif variable_bound_list[i] == "A_"+muscle_name: + elif variable_bound_list[i] == "A_" + muscle_name: min_bounds[i] = 0 starting_bounds_min = np.concatenate((starting_bounds, min_bounds, min_bounds), axis=1) @@ -510,7 +549,15 @@ def _set_controls(bio_models, n_stim, with_residual_torque): return u_bounds, u_init @staticmethod - def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, custom_objective, q_fourier_coef, minimize_muscle_fatigue): + def _set_objective( + n_stim, + n_shooting, + force_fourier_coef, + end_node_tracking, + custom_objective, + q_fourier_coef, + minimize_muscle_fatigue, + ): # Creates the objective for our problem objective_functions = ObjectiveList() if custom_objective: @@ -559,7 +606,7 @@ def _set_objective(n_stim, n_shooting, force_fourier_coef, end_node_tracking, cu weight=1, phase=phase, index=j, - ) + ) if minimize_muscle_fatigue: for i in range(n_stim): @@ -589,25 +636,29 @@ def _sanity_check_bounds(bio_models, bound_type, bound_data): @staticmethod def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): - tested_bio_model = FESActuatedBiorbdModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) + tested_bio_model = FESActuatedBiorbdModel( + name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models + ) fes_muscle_models_name_list = [fes_muscle_models[x].muscle_name for x in range(len(fes_muscle_models))] for biorbd_muscle in tested_bio_model.muscle_names: if biorbd_muscle not in fes_muscle_models_name_list: - raise ValueError(f"The muscle {biorbd_muscle} is not in the fes muscle model " - f"please add it into the fes_muscle_models list by providing the muscle_name =" - f" {biorbd_muscle}") + raise ValueError( + f"The muscle {biorbd_muscle} is not in the fes muscle model " + f"please add it into the fes_muscle_models list by providing the muscle_name =" + f" {biorbd_muscle}" + ) @staticmethod def _sanity_check_fes_models(fes_muscle_models): for i in range(len(fes_muscle_models)): if not isinstance( - fes_muscle_models[i], - DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue, + fes_muscle_models[i], + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue, ): raise TypeError( "model must be a DingModelFrequency, DingModelFrequencyWithFatigue, DingModelPulseDurationFrequency, DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequency, DingModelIntensityFrequencyWithFatigue type" diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index d005953e..78b58d50 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -18,47 +18,36 @@ n_stim = 30 -track_q = [np.array([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1]), - [np.array([1.1339, - 0.9943, - 0.7676, - 0.5757, - 0.4536, - 0.6280, - 1.0292, - 1.0990, - 1.1339]), - np.array([0.6629, - 0.7676, - 1.0641, - 1.3781, - 1.4653, - 1.3781, - 0.9594, - 0.8373, - 0.6629])]] +track_q = [ + np.array([0, 0.125, 0.25, 0.375, 0.5, 0.625, 0.75, 0.875, 1]), + [ + np.array([1.1339, 0.9943, 0.7676, 0.5757, 0.4536, 0.6280, 1.0292, 1.0990, 1.1339]), + np.array([0.6629, 0.7676, 1.0641, 1.3781, 1.4653, 1.3781, 0.9594, 0.8373, 0.6629]), + ], +] objective_functions = ObjectiveList() for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity( - DingModelIntensityFrequency() -) +minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity(DingModelIntensityFrequency()) import time + start_time = time.time() ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26.bioMod", bound_type="start_end", bound_data=[[65, 38], [65, 38]], - fes_muscle_models=[DingModelIntensityFrequency(muscle_name="BIClong"), - DingModelIntensityFrequency(muscle_name="BICshort"), - DingModelIntensityFrequency(muscle_name="TRIlong"), - DingModelIntensityFrequency(muscle_name="TRIlat"), - DingModelIntensityFrequency(muscle_name="TRImed"), - DingModelIntensityFrequency(muscle_name="BRA")], + fes_muscle_models=[ + DingModelIntensityFrequency(muscle_name="BIClong"), + DingModelIntensityFrequency(muscle_name="BICshort"), + DingModelIntensityFrequency(muscle_name="TRIlong"), + DingModelIntensityFrequency(muscle_name="TRIlat"), + DingModelIntensityFrequency(muscle_name="TRImed"), + DingModelIntensityFrequency(muscle_name="BRA"), + ], n_stim=n_stim, n_shooting=5, final_time=1, diff --git a/examples/dynamics/intensity_optimization_track_q.py b/examples/dynamics/intensity_optimization_track_q.py index 05736557..49a05f25 100644 --- a/examples/dynamics/intensity_optimization_track_q.py +++ b/examples/dynamics/intensity_optimization_track_q.py @@ -21,8 +21,26 @@ n_shooting = 10 for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) - objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0], node=Node.ALL, target=np.array([[1.57]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) -objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0], node=Node.ALL, target=np.array([[0]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="q", + index=[0], + node=Node.ALL, + target=np.array([[1.57]] * (n_shooting + 1)).T, + weight=10, + quadratic=True, + phase=i, + ) +objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0], + node=Node.ALL, + target=np.array([[0]] * (n_shooting + 1)).T, + weight=10, + quadratic=True, + phase=i, +) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() diff --git a/examples/dynamics/intensity_optimization_track_q_multi_muscle.py b/examples/dynamics/intensity_optimization_track_q_multi_muscle.py index 9b6e5706..eeed959d 100644 --- a/examples/dynamics/intensity_optimization_track_q_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_track_q_multi_muscle.py @@ -21,8 +21,26 @@ n_shooting = 10 for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) - objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="q", index=[0, 1], node=Node.ALL, target=np.array([[0, 1.57]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) - objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.ALL, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=10, quadratic=True, phase=i) + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="q", + index=[0, 1], + node=Node.ALL, + target=np.array([[0, 1.57]] * (n_shooting + 1)).T, + weight=10, + quadratic=True, + phase=i, + ) + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.ALL, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=10, + quadratic=True, + phase=i, + ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() @@ -32,12 +50,14 @@ biorbd_model_path="/arm26.bioMod", bound_type="start", bound_data=[0, 90], - fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")], + fes_muscle_models=[ + DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA"), + ], n_stim=n_stim, n_shooting=10, final_time=1, diff --git a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py index 5110bde6..f52b2087 100644 --- a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py @@ -18,7 +18,16 @@ n_stim = 5 n_shooting = 10 objective_functions = ObjectiveList() -objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) +objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.END, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=100, + quadratic=True, + phase=n_stim - 1, +) for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10000, quadratic=True, phase=i) @@ -27,8 +36,10 @@ biorbd_model_path="../arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], - fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong"), - DingModelFrequencyWithFatigue(muscle_name="TRIlong")], + fes_muscle_models=[ + DingModelFrequencyWithFatigue(muscle_name="BIClong"), + DingModelFrequencyWithFatigue(muscle_name="TRIlong"), + ], n_stim=n_stim, n_shooting=n_shooting, final_time=1, diff --git a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py index 3d49ccf4..a9f48eb2 100644 --- a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py @@ -18,7 +18,16 @@ n_stim = 10 n_shooting = 10 objective_functions = ObjectiveList() -objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) +objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.END, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=100, + quadratic=True, + phase=n_stim - 1, +) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 @@ -26,8 +35,10 @@ biorbd_model_path="../arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], - fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong")], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], n_stim=n_stim, n_shooting=10, final_time=1, diff --git a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py index b8b792bc..733e0105 100644 --- a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py @@ -18,7 +18,16 @@ n_stim = 10 n_shooting = 10 objective_functions = ObjectiveList() -objective_functions.add(ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], node=Node.END, target=np.array([[0, 0]]*(n_shooting+1)).T, weight=100, quadratic=True, phase=n_stim-1) +objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.END, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=100, + quadratic=True, + phase=n_stim - 1, +) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() @@ -28,8 +37,10 @@ biorbd_model_path="../arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], - fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong")], + fes_muscle_models=[ + DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + ], n_stim=n_stim, n_shooting=10, final_time=1, diff --git a/examples/dynamics/muscle_force_length_relationship_comparison.py b/examples/dynamics/muscle_force_length_relationship_comparison.py index 7c0ec773..b8b245f4 100644 --- a/examples/dynamics/muscle_force_length_relationship_comparison.py +++ b/examples/dynamics/muscle_force_length_relationship_comparison.py @@ -8,7 +8,11 @@ from bioptim import Solver -from cocofest import DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import ( + DingModelPulseDurationFrequencyWithFatigue, + DingModelIntensityFrequencyWithFatigue, + FESActuatedBiorbdModelOCP, +) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( @@ -17,11 +21,12 @@ sol_list = [] muscle_force_length_relationship = [False, True, False, True] -fes_muscle_model = [[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")] - ] +fes_muscle_model = [ + [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], + [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], +] for i in range(4): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( @@ -42,7 +47,9 @@ sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol_list.append(sol.merge_phases()) -plt.title("Comparison between with and without muscle force length relationship for an elbow movement at 10Hz and 40mA or 260us") +plt.title( + "Comparison between with and without muscle force length relationship for an elbow movement at 10Hz and 40mA or 260us" +) plt.plot(sol_list[0].time, sol_list[0].states["q"][0], label="Pulse duration without force length relationship") plt.plot(sol_list[1].time, sol_list[1].states["q"][0], label="Pulse duration with force length relationship") plt.plot(sol_list[2].time, sol_list[2].states["q"][0], label="Pulse intensity without force length relationship") diff --git a/examples/getting_started/model_integration.py b/examples/getting_started/model_integration.py index 5e36616d..703d17cf 100644 --- a/examples/getting_started/model_integration.py +++ b/examples/getting_started/model_integration.py @@ -8,7 +8,13 @@ # --- Build ocp --- # # This problem was build to be integrated and has no objectives nor parameter to optimize. -ivp = IvpFes(model=DingModelFrequencyWithFatigue(), n_stim=10, n_shooting=20, final_time=1, use_sx=True,) +ivp = IvpFes( + model=DingModelFrequencyWithFatigue(), + n_stim=10, + n_shooting=20, + final_time=1, + use_sx=True, +) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) diff --git a/examples/getting_started/muscle_model_identification.py b/examples/getting_started/muscle_model_identification.py index 07c7e1a1..ca7c86b9 100644 --- a/examples/getting_started/muscle_model_identification.py +++ b/examples/getting_started/muscle_model_identification.py @@ -420,7 +420,12 @@ identified_time_list = [] ivp_from_identification = IvpFes( - model=identified_model, n_stim=10, n_shooting=10, final_time=1, use_sx=True, pulse_intensity=pulse_intensity_values, + model=identified_model, + n_stim=10, + n_shooting=10, + final_time=1, + use_sx=True, + pulse_intensity=pulse_intensity_values, ) # Creating the solution from the initial guess diff --git a/examples/getting_started/pulse_mode_example.py b/examples/getting_started/pulse_mode_example.py index 7074c9f3..6bc00297 100644 --- a/examples/getting_started/pulse_mode_example.py +++ b/examples/getting_started/pulse_mode_example.py @@ -12,7 +12,12 @@ # This example shows how to create a problem with doublet pulses. # The stimulation won't be optimized. ivp = IvpFes( - model=DingModelFrequencyWithFatigue(), n_stim=20, n_shooting=10, final_time=1, pulse_mode="Doublet", use_sx=True, + model=DingModelFrequencyWithFatigue(), + n_stim=20, + n_shooting=10, + final_time=1, + pulse_mode="Doublet", + use_sx=True, ) # Creating the solution from the initial guess @@ -39,7 +44,12 @@ # --- Build ocp --- # # This example shows how to create a problem with triplet pulses. ivp = IvpFes( - model=DingModelFrequencyWithFatigue(), n_stim=30, n_shooting=10, final_time=1, pulse_mode="Triplet", use_sx=True, + model=DingModelFrequencyWithFatigue(), + n_stim=30, + n_shooting=10, + final_time=1, + pulse_mode="Triplet", + use_sx=True, ) # Creating the solution from the initial guess diff --git a/examples/sensitivity/truncation/summation_truncation_graph.py b/examples/sensitivity/truncation/summation_truncation_graph.py index 3d2adb0d..95713a7a 100644 --- a/examples/sensitivity/truncation/summation_truncation_graph.py +++ b/examples/sensitivity/truncation/summation_truncation_graph.py @@ -151,9 +151,15 @@ ) elif computation_time_color_bar_scale == "same": - cbar2 = fig.colorbar(im3, ax=axs[1], label="Computation time (s)", ticks=[3.033, 4, 5, 6, 7, 8, 9, 10.038],) + cbar2 = fig.colorbar( + im3, + ax=axs[1], + label="Computation time (s)", + ticks=[3.033, 4, 5, 6, 7, 8, 9, 10.038], + ) cbar2.ax.set_yticklabels( - [3.033, 4, 5, 6, 7, 8, 9, 10.038], style="italic", + [3.033, 4, 5, 6, 7, 8, 9, 10.038], + style="italic", ) axs[0].set_xlabel("Frequency (Hz)") diff --git a/tests/shard1/test_ivp.py b/tests/shard1/test_ivp.py index f210ebce..4aea6463 100644 --- a/tests/shard1/test_ivp.py +++ b/tests/shard1/test_ivp.py @@ -39,7 +39,14 @@ def test_ding2003_ivp(model): @pytest.mark.parametrize("model", [DingModelPulseDurationFrequency(), DingModelPulseDurationFrequencyWithFatigue()]) @pytest.mark.parametrize("pulse_duration", [0.0003, [0.0003, 0.0004, 0.0005]]) def test_ding2007_ivp(model, pulse_duration): - ivp = IvpFes(model=model, n_stim=3, n_shooting=10, final_time=0.3, pulse_duration=pulse_duration, use_sx=True,) + ivp = IvpFes( + model=model, + n_stim=3, + n_shooting=10, + final_time=0.3, + pulse_duration=pulse_duration, + use_sx=True, + ) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -70,7 +77,14 @@ def test_ding2007_ivp(model, pulse_duration): @pytest.mark.parametrize("model", [DingModelIntensityFrequency(), DingModelIntensityFrequencyWithFatigue()]) @pytest.mark.parametrize("pulse_intensity", [50, [50, 60, 70]]) def test_hmed2018_ivp(model, pulse_intensity): - ivp = IvpFes(model=model, n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=pulse_intensity, use_sx=True,) + ivp = IvpFes( + model=model, + n_stim=3, + n_shooting=10, + final_time=0.3, + pulse_intensity=pulse_intensity, + use_sx=True, + ) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -220,7 +234,11 @@ def test_all_ivp_errors(): with pytest.raises(ValueError, match="pulse_intensity list must have the same length as n_stim"): IvpFes( - model=DingModelIntensityFrequency(), n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=[20, 30], + model=DingModelIntensityFrequency(), + n_stim=3, + n_shooting=10, + final_time=0.3, + pulse_intensity=[20, 30], ) pulse_intensity = [20, 30, 0.1] @@ -242,7 +260,11 @@ def test_all_ivp_errors(): with pytest.raises(TypeError, match="pulse_intensity must be int, float or list type"): IvpFes( - model=DingModelIntensityFrequency(), n_stim=3, n_shooting=10, final_time=0.3, pulse_intensity=True, + model=DingModelIntensityFrequency(), + n_stim=3, + n_shooting=10, + final_time=0.3, + pulse_intensity=True, ) with pytest.raises(ValueError, match="ode_solver must be a OdeSolver type"): diff --git a/tests/shard1/test_ocp_build.py b/tests/shard1/test_ocp_build.py index 68abf550..d8388db9 100644 --- a/tests/shard1/test_ocp_build.py +++ b/tests/shard1/test_ocp_build.py @@ -257,7 +257,17 @@ (DingModelFrequencyWithFatigue(), None, None, None, None, None, None, None, None), (DingModelPulseDurationFrequency(), 0.0002, None, None, None, None, None, None, None), (DingModelPulseDurationFrequencyWithFatigue(), 0.0002, None, None, None, None, None, None, None), - (DingModelPulseDurationFrequency(), None, minimum_pulse_duration, 0.0006, False, None, None, None, None,), + ( + DingModelPulseDurationFrequency(), + None, + minimum_pulse_duration, + 0.0006, + False, + None, + None, + None, + None, + ), ( DingModelPulseDurationFrequencyWithFatigue(), None, @@ -272,13 +282,38 @@ # (DingModelPulseDurationFrequency(), None, minimum_pulse_duration, 0.0006, True, None, None, None, None), parameter mapping not yet implemented (DingModelIntensityFrequency(), None, None, None, None, 20, None, None, None), (DingModelIntensityFrequencyWithFatigue(), None, None, None, None, 20, None, None, None), - (DingModelIntensityFrequency(), None, None, None, None, None, minimum_pulse_intensity, 130, False,), - (DingModelIntensityFrequencyWithFatigue(), None, None, None, None, None, minimum_pulse_intensity, 130, False,), + ( + DingModelIntensityFrequency(), + None, + None, + None, + None, + None, + minimum_pulse_intensity, + 130, + False, + ), + ( + DingModelIntensityFrequencyWithFatigue(), + None, + None, + None, + None, + None, + minimum_pulse_intensity, + 130, + False, + ), # (DingModelIntensityFrequency(), None, None, None, None, None, minimum_pulse_intensity, 130, True), parameter mapping not yet implemented ], ) @pytest.mark.parametrize( - "time_min, time_max, time_bimapping", [(None, None, False), (0.01, 0.1, False), (0.01, 0.1, True),], + "time_min, time_max, time_bimapping", + [ + (None, None, False), + (0.01, 0.1, False), + (0.01, 0.1, True), + ], ) @pytest.mark.parametrize("use_sx", [True]) # Later add False @pytest.mark.parametrize( @@ -310,7 +345,11 @@ def test_ocp_building( use_sx, sum_stim_truncation, ): - if (model.model_name == "ding2003" or model.model_name == "ding2003_with_fatigue") and time_min is None and time_max is None: + if ( + (model.model_name == "ding2003" or model.model_name == "ding2003_with_fatigue") + and time_min is None + and time_max is None + ): return model._sum_stim_truncation = sum_stim_truncation @@ -386,7 +425,13 @@ def test_ocp_not_for_optimal_error(): match="This is not an optimal control problem," " add parameter to optimize or use the IvpFes method to build your problem", ): - ocp = OcpFes().prepare_ocp(model=DingModelFrequency(), n_stim=1, n_shooting=10, final_time=1, use_sx=True,) + ocp = OcpFes().prepare_ocp( + model=DingModelFrequency(), + n_stim=1, + n_shooting=10, + final_time=1, + use_sx=True, + ) @pytest.mark.parametrize( diff --git a/tests/shard1/test_ocp_id.py b/tests/shard1/test_ocp_id.py index 652ea784..ad1d279c 100644 --- a/tests/shard1/test_ocp_id.py +++ b/tests/shard1/test_ocp_id.py @@ -117,7 +117,13 @@ ] additional_key_settings = { - "a_rest": {"initial_guess": 1000, "min_bound": 1, "max_bound": 10000, "function": model.set_a_rest, "scaling": 1,}, + "a_rest": { + "initial_guess": 1000, + "min_bound": 1, + "max_bound": 10000, + "function": model.set_a_rest, + "scaling": 1, + }, "km_rest": { "initial_guess": 0.5, "min_bound": 0.001, @@ -132,14 +138,27 @@ "function": model.set_tau1_rest, "scaling": 1000, }, - "tau2": {"initial_guess": 0.5, "min_bound": 0.0001, "max_bound": 1, "function": model.set_tau2, "scaling": 1000,}, + "tau2": { + "initial_guess": 0.5, + "min_bound": 0.0001, + "max_bound": 1, + "function": model.set_tau2, + "scaling": 1000, + }, } def test_ocp_id_ding2003(): # --- Creating the simulated data to identify on --- # # Building the Initial Value Problem - ivp = IvpFes(model=DingModelFrequency(), n_stim=10, n_shooting=10, final_time=1, use_sx=True, extend_last_phase=1,) + ivp = IvpFes( + model=DingModelFrequency(), + n_stim=10, + n_shooting=10, + final_time=1, + use_sx=True, + extend_last_phase=1, + ) # Creating the solution from the initial guess sol_from_initial_guess = Solution.from_initial_guess(ivp, [ivp.x_init, ivp.u_init, ivp.p_init, ivp.s_init]) @@ -402,7 +421,8 @@ def test_all_ocp_id_errors(): def test_all_id_program_errors(): with pytest.raises( - ValueError, match="The given model is not valid and should not be including the fatigue equation in the model", + ValueError, + match="The given model is not valid and should not be including the fatigue equation in the model", ): DingModelFrequencyForceParameterIdentification(model=DingModelFrequencyWithFatigue()) @@ -600,7 +620,8 @@ def test_all_id_program_errors(): ) with pytest.raises( - ValueError, match=re.escape(f"The given model parameters are not valid, only None, int and float are accepted"), + ValueError, + match=re.escape(f"The given model parameters are not valid, only None, int and float are accepted"), ): DingModelFrequencyForceParameterIdentification( model=DingModelFrequency(), @@ -631,7 +652,8 @@ def test_all_id_program_errors(): ) with pytest.raises( - ValueError, match=re.escape(f"The given {'a_rest'} parameter is not valid, it must be given or identified"), + ValueError, + match=re.escape(f"The given {'a_rest'} parameter is not valid, it must be given or identified"), ): DingModelFrequencyForceParameterIdentification( model=DingModelFrequency(), From d4139fa1052543b5ea307628647b869896fc96a8 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 26 Jan 2024 16:07:42 -0500 Subject: [PATCH 22/63] corrected tests according to the pulse duration model fix --- tests/shard1/test_ivp.py | 4 ++-- .../shard1/test_models_dynamics_without_bioptim.py | 13 +++++++------ 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/tests/shard1/test_ivp.py b/tests/shard1/test_ivp.py index 4aea6463..e2a12b2e 100644 --- a/tests/shard1/test_ivp.py +++ b/tests/shard1/test_ivp.py @@ -59,7 +59,7 @@ def test_ding2007_ivp(model, pulse_duration): if model._with_fatigue and isinstance(pulse_duration, list): np.testing.assert_almost_equal(result.states["F"][0][0], 0) np.testing.assert_almost_equal(result.states["F"][0][10], 32.78053644580685) - np.testing.assert_almost_equal(result.states["F"][0][-1], 60.936507876880086) + np.testing.assert_almost_equal(result.states["F"][0][-1], 60.93650724479694) elif model._with_fatigue is False and isinstance(pulse_duration, list): np.testing.assert_almost_equal(result.states["F"][0][0], 0) np.testing.assert_almost_equal(result.states["F"][0][10], 32.48751154425548) @@ -67,7 +67,7 @@ def test_ding2007_ivp(model, pulse_duration): elif model._with_fatigue and isinstance(pulse_duration, float): np.testing.assert_almost_equal(result.states["F"][0][0], 0) np.testing.assert_almost_equal(result.states["F"][0][10], 32.78053644580685) - np.testing.assert_almost_equal(result.states["F"][0][-1], 42.43955858325622) + np.testing.assert_almost_equal(result.states["F"][0][-1], 42.439558210310544) elif model._with_fatigue is False and isinstance(pulse_duration, float): np.testing.assert_almost_equal(result.states["F"][0][0], 0) np.testing.assert_almost_equal(result.states["F"][0][10], 32.48751154425548) diff --git a/tests/shard1/test_models_dynamics_without_bioptim.py b/tests/shard1/test_models_dynamics_without_bioptim.py index 4b4e4ff7..29548a19 100644 --- a/tests/shard1/test_models_dynamics_without_bioptim.py +++ b/tests/shard1/test_models_dynamics_without_bioptim.py @@ -51,15 +51,16 @@ def test_ding2003_dynamics(): def test_ding2007_dynamics(): model = DingModelPulseDurationFrequencyWithFatigue() - assert model.nb_state == 4 + assert model.nb_state == 5 assert model.name_dof == [ "Cn", "F", + "A", "Tau1", "Km", ] np.testing.assert_almost_equal( - model.standard_rest_values(), np.array([[0], [0], [model.tau1_rest], [model.km_rest]]) + model.standard_rest_values(), np.array([[0], [0], [model.a_scale], [model.tau1_rest], [model.km_rest]]) ) np.testing.assert_almost_equal( np.array( @@ -102,11 +103,11 @@ def test_ding2007_dynamics(): np.testing.assert_almost_equal( np.array( model.system_dynamics( - cn=5, f=100, tau1=0.050957, km=0.103, t=0.11, t_stim_prev=[0, 0.1], impulse_time=0.0002 + cn=5, f=100, a=4920, tau1=0.050957, km=0.103, t=0.11, t_stim_prev=[0, 0.1], impulse_time=0.0002 ) ).squeeze(), - np.array(DM([-417.918, -490.511, 0.0210759, 1.9e-05])).squeeze(), - decimal=3, + np.array(DM([-4.179e+02, -4.905e+02, -4.000e-04, 2.108e-02, 1.900e-05])).squeeze(), + decimal=1, ) np.testing.assert_almost_equal(model.exp_time_fun(t=0.1, t_stim_i=0.09), 0.4028903215291327) np.testing.assert_almost_equal(model.ri_fun(r0=1.05, time_between_stim=0.1), 1.0000056342790253) @@ -118,7 +119,7 @@ def test_ding2007_dynamics(): np.testing.assert_almost_equal(model.tau1_dot_fun(tau1=0.060601, f=100), 0.021) np.testing.assert_almost_equal(model.km_dot_fun(km=0.103, f=100), 1.8999999999999998e-05) np.testing.assert_almost_equal( - np.array(model.a_calculation(impulse_time=0.0002)).squeeze(), np.array(DM(1464.4646488)).squeeze() + np.array(model.a_calculation(a_scale=4920, impulse_time=0.0002)).squeeze(), np.array(DM(1464.4646488)).squeeze() ) From ecb3936d82e1891652b3796bc4abf4e41f84bf09 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 29 Jan 2024 14:35:47 -0500 Subject: [PATCH 23/63] Fix : corrected the muscle fatigue calculation sending the force length and force velocity coeff in the model to calculate the muscle fatigue accordingly to muscle force produced --- cocofest/models/ding2003.py | 12 +++++++--- cocofest/models/ding2003_with_fatigue.py | 8 ++++++- cocofest/models/ding2007.py | 8 ++++++- cocofest/models/ding2007_with_fatigue.py | 8 ++++++- cocofest/models/dynamical_model.py | 28 +++++++++++++----------- cocofest/models/hmed2018.py | 8 ++++++- cocofest/models/hmed2018_with_fatigue.py | 8 ++++++- 7 files changed, 59 insertions(+), 21 deletions(-) diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index 3e2c78e7..e5b76f4c 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -107,6 +107,8 @@ def system_dynamics( f: MX, t: MX = None, t_stim_prev: list[MX] | list[float] = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -128,7 +130,7 @@ def system_dynamics( """ r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest) # Equation n°2 + f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 return vertcat(cn_dot, f_dot) def exp_time_fun(self, t: MX, t_stim_i: MX) -> MX | float: @@ -213,7 +215,7 @@ def cn_dot_fun(self, cn: MX, r0: MX | float, t: MX, t_stim_prev: list[MX]) -> MX return (1 / self.tauc) * sum_multiplier - (cn / self.tauc) # Equation n°1 - def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | float) -> MX | float: + def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | float, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1) -> MX | float: """ Parameters ---------- @@ -232,7 +234,7 @@ def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | flo ------- The value of the derivative force (N) """ - return a * (cn / (km + cn)) - (f / (tau1 + self.tau2 * (cn / (km + cn)))) # Equation n°2 + return (a * (cn / (km + cn)) - (f / (tau1 + self.tau2 * (cn / (km + cn))))) * force_length_relationship * force_velocity_relationship # Equation n°2 @staticmethod def dynamics( @@ -244,6 +246,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition=None, nlp_dynamics=None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -277,6 +281,8 @@ def dynamics( f=states[1], t=time, t_stim_prev=stim_apparition, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), ) diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 06aa76db..932a18e6 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -107,6 +107,8 @@ def system_dynamics( km: MX = None, t: MX = None, t_stim_prev: list[MX] | list[float] = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -134,7 +136,7 @@ def system_dynamics( """ r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, a, tau1, km) # Equation n°2 + f_dot = self.f_dot_fun(cn, f, a, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 a_dot = self.a_dot_fun(a, f) # Equation n°5 tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 km_dot = self.km_dot_fun(km, f) # Equation n°11 @@ -195,6 +197,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition=None, nlp_dynamics=None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -231,6 +235,8 @@ def dynamics( km=states[4], t=time, t_stim_prev=stim_apparition, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), defects=None, ) diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index b6edf059..8b5b7978 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -75,6 +75,8 @@ def system_dynamics( t: MX = None, t_stim_prev: list[MX] | list[float] = None, impulse_time: MX = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -99,7 +101,7 @@ def system_dynamics( r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article a = self.a_calculation(a_scale=self.a_scale, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article - f_dot = self.f_dot_fun(cn, f, a, self.tau1_rest, self.km_rest) # Equation n°2 from Ding's 2003 article + f_dot = self.f_dot_fun(cn, f, a, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 from Ding's 2003 article return vertcat(cn_dot, f_dot) def a_calculation(self, a_scale: float | MX, impulse_time: MX) -> MX: @@ -160,6 +162,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition: list[float] = None, nlp_dynamics=None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -204,6 +208,8 @@ def dynamics( t=time, t_stim_prev=stim_apparition, impulse_time=impulse_time, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), defects=None, ) diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 8c1b0482..c4be7f5e 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -89,6 +89,8 @@ def system_dynamics( t: MX = None, t_stim_prev: list[MX] | list[float] = None, impulse_time: MX = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -117,7 +119,7 @@ def system_dynamics( r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article a_calculated = self.a_calculation(a_scale=a, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article - f_dot = self.f_dot_fun(cn, f, a_calculated, tau1, km) # Equation n°2 from Ding's 2003 article + f_dot = self.f_dot_fun(cn, f, a_calculated, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 from Ding's 2003 article a_dot = self.a_dot_fun(a, f) tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 from Ding's 2003 article km_dot = self.km_dot_fun(km, f) # Equation n°11 from Ding's 2003 article @@ -178,6 +180,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition: list[float] = None, nlp_dynamics: NonLinearProgram = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -225,6 +229,8 @@ def dynamics( t=time, t_stim_prev=stim_apparition, impulse_time=impulse_time, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), defects=None, ) diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 5b79f77f..c12f7dda 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -115,20 +115,8 @@ def muscle_dynamic( for i in range(len(muscle_states_idx)): muscle_states = vertcat(muscle_states, states[muscle_states_idx[i]]) - muscle_dxdt = muscle_model.dynamics( - time, - muscle_states, - controls, - parameters, - stochastic_variables, - nlp, - stim_apparition, - nlp_dynamics=muscle_model, - ).dxdt - muscle_idx = bio_muscle_names_at_index.index(muscle_model.muscle_name) - muscle_forces = DynamicsFunctions.get(nlp.states["F_" + muscle_model.muscle_name], states) muscle_force_length_coeff = 1 muscle_force_velocity_coeff = 1 if nlp.model.muscle_force_length_relationship: @@ -139,7 +127,21 @@ def muscle_dynamic( muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient( model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot ) - muscle_forces = muscle_forces * muscle_force_length_coeff * muscle_force_velocity_coeff + + muscle_dxdt = muscle_model.dynamics( + time, + muscle_states, + controls, + parameters, + stochastic_variables, + nlp, + stim_apparition, + nlp_dynamics=muscle_model, + force_length_relationship=muscle_force_length_coeff, + force_velocity_relationship=muscle_force_velocity_coeff, + ).dxdt + + muscle_forces = DynamicsFunctions.get(nlp.states["F_" + muscle_model.muscle_name], states) moment_arm_matrix_for_the_muscle_and_joint = ( -nlp.model.bio_model.model.musclesLengthJacobian(q).to_mx()[muscle_idx, :].T diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 3f314e1a..444e6659 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -78,6 +78,8 @@ def system_dynamics( t: MX = None, t_stim_prev: list[MX] | list[float] = None, intensity_stim: list[MX] | list[float] = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -101,7 +103,7 @@ def system_dynamics( """ r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev, intensity_stim=intensity_stim) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest) # Equation n°2 + f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 return vertcat(cn_dot, f_dot) def cn_dot_fun( @@ -222,6 +224,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition: list[float] = None, nlp_dynamics: NonLinearProgram = None, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -271,6 +275,8 @@ def dynamics( t=time, t_stim_prev=stim_apparition, intensity_stim=intensity_stim_prev, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), defects=None, ) diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 75d78529..e085e891 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -89,6 +89,8 @@ def system_dynamics( t: MX = None, t_stim_prev: list[MX] | list[float] = None, intensity_stim: list[MX] | list[float] = None, + force_length_relationship: float | MX = 1, + force_velocity_relationship: float | MX = 1, ) -> MX: """ The system dynamics is the function that describes the models. @@ -118,7 +120,7 @@ def system_dynamics( """ r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev, intensity_stim=intensity_stim) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, a, tau1, km) # Equation n°2 + f_dot = self.f_dot_fun(cn, f, a, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 a_dot = self.a_dot_fun(a, f) # Equation n°5 tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 km_dot = self.km_dot_fun(km, f) # Equation n°11 @@ -179,6 +181,8 @@ def dynamics( nlp: NonLinearProgram, stim_apparition: list[float] = None, nlp_dynamics: NonLinearProgram = None, + force_length_relationship: float | MX = 1, + force_velocity_relationship: float | MX = 1, ) -> DynamicsEvaluation: """ Functional electrical stimulation dynamic @@ -231,6 +235,8 @@ def dynamics( t=time, t_stim_prev=stim_apparition, intensity_stim=intensity_stim_prev, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, ), defects=None, ) From 06e2b6d2019a3ebbdef9287c414faff75660d426 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 29 Jan 2024 17:12:12 -0500 Subject: [PATCH 24/63] graph for joint angle --- ...le_force_length_relationship_comparison.py | 25 +++++++++---------- 1 file changed, 12 insertions(+), 13 deletions(-) diff --git a/examples/dynamics/muscle_force_length_relationship_comparison.py b/examples/dynamics/muscle_force_length_relationship_comparison.py index b8b245f4..c78b909d 100644 --- a/examples/dynamics/muscle_force_length_relationship_comparison.py +++ b/examples/dynamics/muscle_force_length_relationship_comparison.py @@ -5,6 +5,7 @@ No residual torque is allowed. """ import matplotlib.pyplot as plt +import numpy as np from bioptim import Solver @@ -20,15 +21,15 @@ ) sol_list = [] -muscle_force_length_relationship = [False, True, False, True] +muscle_force_length_relationship = [False, True] fes_muscle_model = [ [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], + # [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], + # [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], ] -for i in range(4): +for i in range(2): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26_biceps_1dof.bioMod", bound_type="start", @@ -37,7 +38,7 @@ n_stim=10, n_shooting=10, final_time=1, - pulse_duration=0.00026, + pulse_duration=0.00025, pulse_intensity=40, with_residual_torque=False, muscle_force_length_relationship=muscle_force_length_relationship[i], @@ -47,15 +48,13 @@ sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol_list.append(sol.merge_phases()) -plt.title( - "Comparison between with and without muscle force length relationship for an elbow movement at 10Hz and 40mA or 260us" -) -plt.plot(sol_list[0].time, sol_list[0].states["q"][0], label="Pulse duration without force length relationship") -plt.plot(sol_list[1].time, sol_list[1].states["q"][0], label="Pulse duration with force length relationship") -plt.plot(sol_list[2].time, sol_list[2].states["q"][0], label="Pulse intensity without force length relationship") -plt.plot(sol_list[3].time, sol_list[3].states["q"][0], label="Pulse intensity with force length relationship") +plt.plot(sol_list[0].time, np.degrees(sol_list[0].states["q"][0]), label="without force length relationship") +plt.plot(sol_list[1].time, np.degrees(sol_list[1].states["q"][0]), label="with force length relationship") plt.xlabel("Time (s)") -plt.ylabel("Angle (rad)") +plt.ylabel("Angle (°)") plt.legend() plt.show() + +joint_overestimation = np.degrees(sol_list[0].states["q"][0][-1]) - np.degrees(sol_list[1].states["q"][0][-1]) +print(f"Joint overestimation: {joint_overestimation} degrees") From 8d8d705db2cb8efc3df260bc4348c74fe2c51d64 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 30 Jan 2024 12:07:36 -0500 Subject: [PATCH 25/63] comparing with and without muscle fatigue minimization --- ...comparing_minimize_fatigue_optimization.py | 95 +++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py diff --git a/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py b/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py new file mode 100644 index 00000000..4f52ad37 --- /dev/null +++ b/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py @@ -0,0 +1,95 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import pickle +import numpy as np +import matplotlib.pyplot as plt + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +get_results = False +make_graphs = True + +if get_results: + n_stim = 10 + n_shooting = 10 + objective_functions = ObjectiveList() + + for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) + + minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() + ) + + minimize_fatigue = [False, True] + pickle_file_list = ["normal.pkl", "minimizing_fatigue.pkl"] + time = [] + states = [] + controls = [] + parameters = [] + for i in range(len(minimize_fatigue)): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 90]], + fes_muscle_models=[ + DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA"), + ], + n_stim=n_stim, + n_shooting=10, + final_time=1, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=80, + pulse_intensity_bimapping=False, + with_residual_torque=False, + custom_objective=objective_functions if i == 0 else None, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + minimize_muscle_fatigue=minimize_fatigue[i], + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.parameters + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open(pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) + + +# if make_graphs: +# with open(r"normal.pkl", "rb") as f: +# data_normal = pickle.load(f) +# +# with open(r"minimizing_fatigue.pkl", "rb") as f: +# data_minimize = pickle.load(f) +# +# plt.plot(data_normal["time"], data_normal["states"]["F_BIClong"][0], label="Normal") +# plt.plot(data_minimize["time"], data_minimize["states"]["F_BIClong"][0], label="Minimizing fatigue") +# plt.legend() +# plt.show() + From 78a69a288ea23496b5159d6e67d5b98be7921897 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 30 Jan 2024 18:58:05 -0500 Subject: [PATCH 26/63] readme --- README.md | 2 ++ docs/arm_flexion.gif | Bin 0 -> 421773 bytes docs/cocofest_logo.png | Bin 525964 -> 1097225 bytes 3 files changed, 2 insertions(+) create mode 100644 docs/arm_flexion.gif diff --git a/README.md b/README.md index 237cb85b..bcd41c5b 100644 --- a/README.md +++ b/README.md @@ -146,3 +146,5 @@ ocp = OcpFes.prepare_ocp() # Citing `Cocofest` is not yet published in a journal. 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znuZ*#c@Xm7cYHcX+0WPvO7&Ced3>MN4%#CG)6NIui>&-c0kLDt%%XH@zWFY+^ilQ) zVx|%Brl)<;Ay_bVH8w-iaXGg7qtqx*koHXHEYYv{F7TUS2+*q3r&pEi4rQw!8+)o}z=%SVkraao@71InOXik7GlANZxZ8l80G*JuG^W!Phqm7mD5+*Q8DRvMINZaJfK==V1B# z`&a1y|F-;BPey;(YrvLJH1H+eKP;94v0CMuFQMX3h~)XVyb#FvH{^Li5D6?Q zv}TZK`|9Ke#djyk!!(=_8n+2T8eDcBtqOP52#1L!P&WKeLDvJDHQ!G4O3(yi0h{@h z`XRksKId-Jzq-Re`}udY;6_PteTR+HRgS9}aW aefF25BgE!`A5&nTkDQdUWSzK4$o~VQ3~m Date: Tue, 30 Jan 2024 19:00:09 -0500 Subject: [PATCH 27/63] . --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bcd41c5b..b4824ac1 100644 --- a/README.md +++ b/README.md @@ -147,4 +147,4 @@ ocp = OcpFes.prepare_ocp() `Cocofest` is not yet published in a journal. But if you use `Cocofest` in your research, please kindly cite this package by giving the repository link. -![](https://github.com/Kev1CO/cocofest/tree/main/docs/arm_flexion.gif) +![](https://github.com/Kev1CO/cocofest/tree/dynamics_2/docs/arm_flexion.gif) From 83365e78031b1c02a88d35d980dd01b5856894ec Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 30 Jan 2024 19:02:44 -0500 Subject: [PATCH 28/63] adding a gif --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b4824ac1..a49f53ea 100644 --- a/README.md +++ b/README.md @@ -147,4 +147,4 @@ ocp = OcpFes.prepare_ocp() `Cocofest` is not yet published in a journal. But if you use `Cocofest` in your research, please kindly cite this package by giving the repository link. -![](https://github.com/Kev1CO/cocofest/tree/dynamics_2/docs/arm_flexion.gif) +![](docs/arm_flexion.gif) From 23862ee9f3a58170a0c308cd46fb2ec905297db4 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Wed, 31 Jan 2024 11:27:03 -0500 Subject: [PATCH 29/63] doc string --- cocofest/custom_objectives.py | 4 +- cocofest/models/ding2003.py | 30 ++++++++-- cocofest/models/ding2003_with_fatigue.py | 22 +++++++- cocofest/models/ding2007.py | 22 ++++++-- cocofest/models/ding2007_with_fatigue.py | 29 ++++++++-- cocofest/models/dynamical_model.py | 56 +++++++++++++++++-- cocofest/models/hmed2018.py | 20 +++++-- cocofest/models/hmed2018_with_fatigue.py | 27 +++++++-- cocofest/optimization/fes_ocp_dynamics.py | 34 ++++++++--- ...nsity_optimization_cycling_multi_muscle.py | 17 ------ 10 files changed, 200 insertions(+), 61 deletions(-) diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index 93fa2839..3b151d9a 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -73,8 +73,6 @@ def track_state_from_time_interpolate( def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX: """ Minimize the overall muscle fatigue. - This function is quadratic, meaning that it minimizes towards the target. - Targets (default=np.zeros()) and indices (default=all_idx) can be specified. Parameters ---------- @@ -83,7 +81,7 @@ def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX: Returns ------- - The difference between the two keys + The sum of each force scaling factor """ muscle_name_list = controller.model.bio_model.muscle_names muscle_fatigue = [controller.states["A_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))] diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index e5b76f4c..c205303d 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -100,6 +100,10 @@ def model_name(self) -> None | str: def muscle_name(self) -> None | str: return self._muscle_name + @property + def with_fatigue(self): + return self._with_fatigue + # ---- Model's dynamics ---- # def system_dynamics( self, @@ -123,6 +127,10 @@ def system_dynamics( The current time at which the dynamics is evaluated (ms) t_stim_prev: list[MX] The time list of the previous stimulations (ms) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -229,6 +237,10 @@ def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | flo The previous step value of time_state_force_no_cross_bridge (ms) km: MX | float The previous step value of cross_bridges (unitless) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -245,7 +257,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition=None, - nlp_dynamics=None, + fes_model=None, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: @@ -268,12 +280,18 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelFrequency + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format """ - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( @@ -328,6 +346,8 @@ def configure_ca_troponin_complex( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Cn" + muscle_name @@ -366,6 +386,8 @@ def configure_force( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "F" + muscle_name @@ -401,7 +423,3 @@ def get_stim_prev(ocp: OptimalControlProgram, nlp: NonLinearProgram) -> list[flo if not isinstance(t_stim_prev[0], (MX, float)): t_stim_prev = [ocp.node_time(phase_idx=i, node_idx=0, type="mx") for i in range(nlp.phase_idx + 1)] return t_stim_prev - - @property - def with_fatigue(self): - return self._with_fatigue diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 932a18e6..02377111 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -1,6 +1,6 @@ from typing import Callable -from casadi import MX, exp, vertcat +from casadi import MX, vertcat import numpy as np from bioptim import ( @@ -129,6 +129,10 @@ def system_dynamics( The current time at which the dynamics is evaluated (ms) t_stim_prev: list[MX] The time list of the previous stimulations (ms) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -196,7 +200,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition=None, - nlp_dynamics=None, + fes_model=None, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: @@ -219,12 +223,18 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelFrequencyWithFatigue + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format """ - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( @@ -288,6 +298,8 @@ def configure_scaling_factor( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "A" + muscle_name @@ -326,6 +338,8 @@ def configure_time_state_force_no_cross_bridge( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Tau1" + muscle_name @@ -364,6 +378,8 @@ def configure_cross_bridges( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Km" + muscle_name diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 8b5b7978..1c0238b7 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -93,6 +93,10 @@ def system_dynamics( The time list of the previous stimulations (ms) impulse_time: MX The pulsation duration of the current stimulation (ms) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -108,6 +112,8 @@ def a_calculation(self, a_scale: float | MX, impulse_time: MX) -> MX: """ Parameters ---------- + a_scale: float | MX + The scaling factor of the current stimulation (unitless) impulse_time: MX The pulsation duration of the current stimulation (s) @@ -137,6 +143,8 @@ def get_pulse_duration_parameters(nlp_parameters: ParameterList, muscle_name: st ---------- nlp_parameters: ParameterList The nlp list parameter + muscle_name: str + The muscle name Returns ------- @@ -161,7 +169,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, - nlp_dynamics=None, + fes_model=None, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: @@ -184,14 +192,20 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelPulseDurationFrequency + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format """ pulse_duration_parameters = ( nlp.model.get_pulse_duration_parameters(nlp.parameters) - if nlp_dynamics is None - else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) + if fes_model is None + else fes_model.get_pulse_duration_parameters(nlp.parameters, muscle_name=fes_model.muscle_name) ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -199,7 +213,7 @@ def dynamics( else: impulse_time = pulse_duration_parameters[nlp.phase_idx] - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index c4be7f5e..5e192e05 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -1,6 +1,6 @@ from typing import Callable -from casadi import MX, vertcat, exp +from casadi import MX, vertcat import numpy as np from bioptim import ( @@ -8,7 +8,6 @@ DynamicsEvaluation, NonLinearProgram, OptimalControlProgram, - ParameterList, ) from .ding2007 import DingModelPulseDurationFrequency @@ -101,6 +100,8 @@ def system_dynamics( The value of the ca_troponin_complex (unitless) f: MX The value of the force (N) + a: MX + The value of the scaling factor (unitless) tau1: MX The value of the time_state_force_no_cross_bridge (ms) km: MX @@ -111,6 +112,10 @@ def system_dynamics( The time list of the previous stimulations (ms) impulse_time: MX The pulsation duration of the current stimulation (ms) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -179,7 +184,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, - nlp_dynamics: NonLinearProgram = None, + fes_model=None, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: @@ -202,14 +207,20 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelPulseDurationFrequencyWithFatigue + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format """ pulse_duration_parameters = ( nlp.model.get_pulse_duration_parameters(nlp.parameters) - if nlp_dynamics is None - else nlp_dynamics.get_pulse_duration_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) + if fes_model is None + else fes_model.get_pulse_duration_parameters(nlp.parameters, muscle_name=fes_model.muscle_name) ) if pulse_duration_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -217,7 +228,7 @@ def dynamics( else: impulse_time = pulse_duration_parameters[nlp.phase_idx] - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( @@ -282,6 +293,8 @@ def configure_scaling_factor( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "A" + muscle_name @@ -320,6 +333,8 @@ def configure_time_state_force_no_cross_bridge( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Tau1" + muscle_name @@ -358,6 +373,8 @@ def configure_cross_bridges( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Km" + muscle_name diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index c12f7dda..cc21797b 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -11,7 +11,13 @@ FatigueList, ) -from cocofest import DingModelFrequency, DingModelIntensityFrequency, DingModelPulseDurationFrequency +from cocofest import (DingModelFrequency, + DingModelFrequencyWithFatigue, + DingModelPulseDurationFrequency, + DingModelPulseDurationFrequencyWithFatigue, + DingModelIntensityFrequency, + DingModelIntensityFrequencyWithFatigue, + ) class FESActuatedBiorbdModel(BiorbdModel): @@ -68,8 +74,11 @@ def muscle_dynamic( stochastic_variables: MX | SX, nlp: NonLinearProgram, muscle_models: list[DingModelFrequency] + | list[DingModelFrequencyWithFatigue] + | list[DingModelPulseDurationFrequency] + | list[DingModelPulseDurationFrequencyWithFatigue] | list[DingModelIntensityFrequency] - | list[DingModelPulseDurationFrequency], + | list[DingModelIntensityFrequencyWithFatigue], stim_apparition=None, state_name_list=None, ) -> DynamicsEvaluation: @@ -90,7 +99,12 @@ def muscle_dynamic( The stochastic variables of the system nlp: NonLinearProgram A reference to the phase - + muscle_models: list[DingModelFrequency] | list[DingModelIntensityFrequency] | list[DingModelPulseDurationFrequency] | list[DingModelFrequencyWithFatigue] | list[DingModelIntensityFrequencyWithFatigue] | list[DingModelPulseDurationFrequencyWithFatigue] + The list of the muscle models + stim_apparition: list[float] + The stimulations apparition time list (s) + state_name_list: list[str] + The states names list Returns ------- The derivative of the states in the tuple[MX | SX] format @@ -136,7 +150,7 @@ def muscle_dynamic( stochastic_variables, nlp, stim_apparition, - nlp_dynamics=muscle_model, + fes_model=muscle_model, force_length_relationship=muscle_force_length_coeff, force_velocity_relationship=muscle_force_velocity_coeff, ).dxdt @@ -276,6 +290,22 @@ def configure_tau(ocp, nlp, as_states: bool, as_controls: bool, fatigue: Fatigue @staticmethod def muscle_force_length_coefficient(model, muscle, q): + """ + Muscle force length coefficient from HillDeGroote + + Parameters + ---------- + model: BiorbdModel + The biorbd model + muscle: MX + The muscle + q: MX + The generalized coordinates + + Returns + ------- + The muscle force length coefficient + """ b11 = 0.815 b21 = 1.055 b31 = 0.162 @@ -315,6 +345,24 @@ def muscle_force_length_coefficient(model, muscle, q): @staticmethod def muscle_force_velocity_coefficient(model, muscle, q, qdot): + """ + Muscle force velocity coefficient from HillDeGroote + + Parameters + ---------- + model: BiorbdModel + The biorbd model + muscle: MX + The muscle + q: MX + The generalized coordinates + qdot: MX + The generalized velocities + + Returns + ------- + The muscle force velocity coefficient + """ muscle_velocity = muscle.velocity(model, q, qdot).to_mx() m_cste_maxShorteningSpeed = 10 norm_v = muscle_velocity / m_cste_maxShorteningSpeed diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 444e6659..04980960 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -96,6 +96,10 @@ def system_dynamics( The time list of the previous stimulations (ms) intensity_stim: list[MX] The pulsation intensity of the current stimulation (mA) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -200,6 +204,8 @@ def get_intensity_parameters(nlp_parameters: ParameterList, muscle_name: str = N ---------- nlp_parameters: ParameterList The nlp list parameter + muscle_name: str + The muscle name Returns ------- @@ -223,7 +229,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, - nlp_dynamics: NonLinearProgram = None, + fes_model: NonLinearProgram = None, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1, ) -> DynamicsEvaluation: @@ -246,6 +252,12 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelIntensityFrequency + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format @@ -255,8 +267,8 @@ def dynamics( ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase intensity_parameters = ( nlp.model.get_intensity_parameters(nlp.parameters) - if nlp_dynamics is None - else nlp_dynamics.get_intensity_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) + if fes_model is None + else fes_model.get_intensity_parameters(nlp.parameters, muscle_name=fes_model.muscle_name) ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -266,7 +278,7 @@ def dynamics( for i in range(nlp.phase_idx + 1): intensity_stim_prev.append(intensity_parameters[i]) - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index e085e891..3f7bc827 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -1,6 +1,6 @@ from typing import Callable -from casadi import MX, vertcat, tanh +from casadi import MX, vertcat import numpy as np from bioptim import ( @@ -8,7 +8,6 @@ DynamicsEvaluation, NonLinearProgram, OptimalControlProgram, - ParameterList, ) from .hmed2018 import DingModelIntensityFrequency @@ -113,6 +112,10 @@ def system_dynamics( The time list of the previous stimulations (ms) intensity_stim: list[MX] The pulsation intensity of the current stimulation (mA) + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- @@ -180,7 +183,7 @@ def dynamics( stochastic_variables: MX, nlp: NonLinearProgram, stim_apparition: list[float] = None, - nlp_dynamics: NonLinearProgram = None, + fes_model=None, force_length_relationship: float | MX = 1, force_velocity_relationship: float | MX = 1, ) -> DynamicsEvaluation: @@ -203,6 +206,12 @@ def dynamics( A reference to the phase stim_apparition: list[float] The time list of the previous stimulations (s) + fes_model: DingModelIntensityFrequencyWithFatigue + The current phase fes model + force_length_relationship: MX | float + The force length relationship value (unitless) + force_velocity_relationship: MX | float + The force velocity relationship value (unitless) Returns ------- The derivative of the states in the tuple[MX] format @@ -212,8 +221,8 @@ def dynamics( ) # Every stimulation intensity before the current phase, i.e.: the intensity of each phase intensity_parameters = ( nlp.model.get_intensity_parameters(nlp.parameters) - if nlp_dynamics is None - else nlp_dynamics.get_intensity_parameters(nlp.parameters, muscle_name=nlp_dynamics.muscle_name) + if fes_model is None + else fes_model.get_intensity_parameters(nlp.parameters, muscle_name=fes_model.muscle_name) ) if intensity_parameters.shape[0] == 1: # check if pulse duration is mapped @@ -223,7 +232,7 @@ def dynamics( for i in range(nlp.phase_idx + 1): intensity_stim_prev.append(intensity_parameters[i]) - dxdt_fun = nlp_dynamics.system_dynamics if nlp_dynamics else nlp.model.system_dynamics + dxdt_fun = fes_model.system_dynamics if fes_model else nlp.model.system_dynamics return DynamicsEvaluation( dxdt=dxdt_fun( @@ -288,6 +297,8 @@ def configure_scaling_factor( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "A" + muscle_name @@ -326,6 +337,8 @@ def configure_time_state_force_no_cross_bridge( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Tau1" + muscle_name @@ -364,6 +377,8 @@ def configure_cross_bridges( If the generalized coordinates should be a control as_states_dot: bool If the generalized velocities should be a state_dot + muscle_name: str + The muscle name """ muscle_name = "_" + muscle_name if muscle_name else "" name = "Km" + muscle_name diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 66dba370..356bbee9 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -76,11 +76,17 @@ def prepare_ocp( n_threads: int = 1, ): """ - This definition prepares the ocp to be solved + This definition prepares the dynamics ocp to be solved . Attributes ---------- - fes_muscle_models: DingModelFrequency | DingModelFrequencyWithFatigue | DingModelPulseDurationFrequency | DingModelPulseDurationFrequencyWithFatigue | DingModelIntensityFrequency | DingModelIntensityFrequencyWithFatigue + biorbd_model_path: str + The bioMod file path + bound_type: str + The bound type to use (start, end, start_end) + bound_data: list + The data to use for the bound + fes_muscle_models: list[DingModelFrequency] | list[DingModelFrequencyWithFatigue] | list[DingModelPulseDurationFrequency] | list[DingModelPulseDurationFrequencyWithFatigue] | list[DingModelIntensityFrequency] | list[DingModelIntensityFrequencyWithFatigue] The fes model type used for the ocp n_stim: int Number of stimulation that will occur during the ocp, it is as well refer as phases @@ -88,10 +94,6 @@ def prepare_ocp( Number of shooting point for each individual phases final_time: float Refers to the final time of the ocp - force_tracking: list[np.ndarray, np.ndarray] - List of time and associated force to track during ocp optimisation - end_node_tracking: int | float - Force objective value to reach at the last node time_min: int | float Minimum time for a phase time_max: int | float @@ -114,12 +116,28 @@ def prepare_ocp( Maximum pulse intensity for a phase pulse_intensity_bimapping: bool Set pulse intensity constant among phases + force_tracking: list[np.ndarray, np.ndarray] + List of time and associated force to track during ocp optimisation + end_node_tracking: int | float + Force objective value to reach at the last node + q_tracking: list + List of time and associated q to track during ocp optimisation custom_objective: list[Objective] Additional objective for the system - ode_solver: OdeSolver - The ode solver to use + with_residual_torque: bool + If residual torque is used + muscle_force_length_relationship: bool + If the force length relationship is used + muscle_force_velocity_relationship: bool + If the force velocity relationship is used + minimize_muscle_fatigue: bool + Minimize the muscle fatigue use_sx: bool The nature of the casadi variables. MX are used if False. + ode_solver: OdeSolver + The ode solver to use + control_type: ControlType + The type of control to use n_threads: int The number of thread to use while solving (multi-threading if > 1) """ diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index 78b58d50..20083a95 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -7,7 +7,6 @@ import numpy as np from bioptim import ( - Node, ObjectiveFcn, ObjectiveList, Solver, @@ -33,9 +32,6 @@ minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity(DingModelIntensityFrequency()) -import time - -start_time = time.time() ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="/arm26.bioMod", bound_type="start_end", @@ -62,21 +58,8 @@ q_tracking=track_q, use_sx=True, ) -print("--- %s seconds --- OCP" % (time.time() - start_time)) -start_time = time.time() sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) -print("--- %s seconds --- SOL" % (time.time() - start_time)) sol.animate() sol.graphs(show_bounds=False) print(sol.parameters) - -# Fast OCP : -# --- 2.8143112659454346 seconds --- OCP -# --- 55.290322065353394 seconds --- SOL -# 106 1.7460726e+03 - -# Slow OCP : -# --- 84.57249999046326 seconds --- OCP -# --- 56.183839321136475 seconds --- SOL -# 106 1.7460726e+03 From 41316883bac24e2fec1c395a6b817329565b3892 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 1 Feb 2024 14:33:27 -0500 Subject: [PATCH 30/63] reaching example --- cocofest/optimization/fes_ocp_dynamics.py | 13 ++ examples/dynamics/arm26.bioMod | 5 + examples/dynamics/reaching_task.py | 165 ++++++++++++++++++++++ 3 files changed, 183 insertions(+) create mode 100644 examples/dynamics/reaching_task.py diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 356bbee9..f5709097 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -3,6 +3,7 @@ from bioptim import ( BoundsList, ControlType, + ConstraintList, DynamicsList, InitialGuessList, Objective, @@ -66,6 +67,7 @@ def prepare_ocp( end_node_tracking: int | float = None, q_tracking: list = None, custom_objective: ObjectiveList = None, + custom_constraint: ConstraintList = None, with_residual_torque: bool = False, muscle_force_length_relationship: bool = False, muscle_force_velocity_relationship: bool = False, @@ -218,6 +220,8 @@ def prepare_ocp( pulse_intensity_similar_for_all_muscles=pulse_intensity_similar_for_all_muscles, ) + constraints = FESActuatedBiorbdModelOCP._set_constraints(constraints, custom_constraint) + if len(constraints) == 0 and len(parameters) == 0: raise ValueError( "This is not an optimal control problem," @@ -446,6 +450,15 @@ def _build_parameters( return parameters, parameters_bounds, parameters_init, parameter_objectives + @staticmethod + def _set_constraints(constraints, custom_constraint): + if custom_constraint: + for i in range(len(custom_constraint)): + if custom_constraint[i]: + for j in range(len(custom_constraint[i])): + constraints.add(custom_constraint[i][j]) + return constraints + @staticmethod def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): # ---- STATE BOUNDS REPRESENTATION ---- # diff --git a/examples/dynamics/arm26.bioMod b/examples/dynamics/arm26.bioMod index ed03d03c..10461eec 100644 --- a/examples/dynamics/arm26.bioMod +++ b/examples/dynamics/arm26.bioMod @@ -12,6 +12,11 @@ endsegment parent base position 0.15 0.15 0.17 endmarker + + marker reaching_target + parent base + position 0.45 -0.05 0.167 + endmarker segment r_humerus_translation diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py new file mode 100644 index 00000000..3b15713e --- /dev/null +++ b/examples/dynamics/reaching_task.py @@ -0,0 +1,165 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import pickle +import numpy as np +import matplotlib.pyplot as plt + +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + ConstraintList, + ConstraintFcn, + Solver, + Node, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +get_results = True +make_graphs = False + +if get_results: + n_stim = 10 + n_shooting = 10 + objective_functions = ObjectiveList() + + fes_muscle_models = [[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA")], + [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")]] + + for i in range(n_stim): + objective_functions.add( + ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, + key="tau", + weight=1, + quadratic=True, + phase=i, + ) + objective_functions.add( + ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=6, + node=Node.END, + weight=10000, + ) + + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.END, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=100, + quadratic=True, + phase=6, + ) + + minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() + ) + + minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 + + pickle_file_list = ["pulse_duration.pkl", "pulse_intensity.pkl"] + time = [] + states = [] + controls = [] + parameters = [] + for i in range(len(pickle_file_list)): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 5]], + fes_muscle_models=fes_muscle_models[i], + 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_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=80, + pulse_intensity_bimapping=False, + with_residual_torque=True, + custom_objective=objective_functions, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) #.merge_phases() + sol.animate() + sol.graphs(show_bounds=False) + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.parameters + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open(pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) + + +if make_graphs: + with open(r"normal.pkl", "rb") as f: + data_normal = pickle.load(f) + + with open(r"minimizing_fatigue.pkl", "rb") as f: + data_minimize = pickle.load(f) + + muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] + muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] + fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) + counter = 0 + for i in range(3): + for j in range(2): + axs[i][j].set_xlim(left=0, right=1) + axs[i][j].set_ylim(bottom=0, top=190) + + axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] + + if i == 0 and j == 0: + axs[i][j].plot(data_normal["time"], data_normal["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Normal") + axs[i][j].plot(data_minimize["time"], data_minimize["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing fatigue") + else: + axs[i][j].plot(data_normal["time"], data_normal["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize["time"], data_minimize["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + counter += 1 + + plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 75, 150], yticklabels=[0, 75, 150]) + + + fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) + fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) + + # fig.legend() + # fig.tight_layout() + plt.show() + From 5c9bfe0c9e4b764b253ca818391411153adc1a92 Mon Sep 17 00:00:00 2001 From: kevin Date: Fri, 2 Feb 2024 11:06:34 -0500 Subject: [PATCH 31/63] condition modification --- examples/dynamics/reaching_task.py | 105 +++++++++++++++-------------- 1 file changed, 56 insertions(+), 49 deletions(-) diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index 3b15713e..ad143092 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -9,6 +9,7 @@ import matplotlib.pyplot as plt from bioptim import ( + Axis, ObjectiveFcn, ObjectiveList, ConstraintList, @@ -23,8 +24,8 @@ make_graphs = False if get_results: - n_stim = 10 - n_shooting = 10 + n_stim = 28 + n_shooting = 5 objective_functions = ObjectiveList() fes_muscle_models = [[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), @@ -44,17 +45,19 @@ objective_functions.add( ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", - weight=1, + weight=100000, quadratic=True, phase=i, ) - objective_functions.add( - ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, + + constraint = ConstraintList() + constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=6, + phase=19, node=Node.END, - weight=10000, + axes=[Axis.X, Axis.Y] ) objective_functions.add( @@ -63,9 +66,9 @@ index=[0, 1], node=Node.END, target=np.array([[0, 0]] * (n_shooting + 1)).T, - weight=100, + weight=1000, quadratic=True, - phase=6, + phase=19, ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( @@ -74,50 +77,55 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 - pickle_file_list = ["pulse_duration.pkl", "pulse_intensity.pkl"] + pickle_file_list = ["pulse_duration_normal.pkl", "pulse_intensity_normal.pkl"] time = [] states = [] controls = [] parameters = [] for i in range(len(pickle_file_list)): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start_end", - bound_data=[[0, 5], [0, 5]], - fes_muscle_models=fes_muscle_models[i], - 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_intensity_min=minimum_pulse_intensity, - pulse_intensity_max=80, - pulse_intensity_bimapping=False, - with_residual_torque=True, - custom_objective=objective_functions, - muscle_force_length_relationship=True, - muscle_force_velocity_relationship=True, - use_sx=False, - ) - - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) #.merge_phases() - sol.animate() - sol.graphs(show_bounds=False) - time = sol.time - states = sol.states - controls = sol.controls - parameters = sol.parameters - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open(pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) + if i == 0: + pass + else: + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 5]], + fes_muscle_models=fes_muscle_models[i], + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1.4, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=80, + pulse_intensity_bimapping=False, + with_residual_torque=True, + custom_objective=objective_functions, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + minimize_muscle_fatigue=False, + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() + # sol.animate() + # sol.graphs(show_bounds=False) + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.parameters + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open(pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) if make_graphs: @@ -155,7 +163,6 @@ plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], yticks=[0, 75, 150], yticklabels=[0, 75, 150]) - fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) From 9705377c4fe24b55c7bc9d22d51f09d6f55f80ae Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 2 Feb 2024 11:23:03 -0500 Subject: [PATCH 32/63] working on minimization strategies --- cocofest/custom_objectives.py | 18 +++ cocofest/optimization/fes_ocp_dynamics.py | 11 ++ examples/dynamics/reaching_task.py | 150 +++++++++++++--------- 3 files changed, 121 insertions(+), 58 deletions(-) diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index 3b151d9a..de8c4ee7 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -86,3 +86,21 @@ def minimize_overall_muscle_fatigue(controller: PenaltyController) -> MX: muscle_name_list = controller.model.bio_model.muscle_names muscle_fatigue = [controller.states["A_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))] return sum(muscle_fatigue) + + @staticmethod + def minimize_overall_muscle_force_production(controller: PenaltyController) -> MX: + """ + Minimize the overall muscle force production. + + Parameters + ---------- + controller: PenaltyController + The penalty node elements + + Returns + ------- + The sum of each force + """ + muscle_name_list = controller.model.bio_model.muscle_names + muscle_force = [controller.states["F_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))] + return sum(muscle_force) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index f5709097..989d3b04 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -650,6 +650,17 @@ def _set_objective( phase=i, ) + # if minimize_muscle_fatigue: + # for i in range(n_stim): + # objective_functions.add( + # CustomObjective.minimize_overall_muscle_force, + # custom_type=ObjectiveFcn.Mayer, + # node=Node.ALL, + # quadratic=True, + # weight=1, + # phase=i, + # ) + return objective_functions @staticmethod diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index ad143092..b6a23e4d 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -23,23 +23,54 @@ get_results = True make_graphs = False +# Fiber type proportion from [1] +biceps_fiber_type_2_proportion = 0.607 +triceps_fiber_type_2_proportion = 0.465 +brachioradialis_fiber_type_2_proportion = 0.457 + +biceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong") +biceps_long_duration.alpha_a = biceps_long_duration.alpha_a * biceps_fiber_type_2_proportion +biceps_short_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort") +biceps_short_duration.alpha_a = biceps_short_duration.alpha_a * biceps_fiber_type_2_proportion +triceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong") +triceps_long_duration.alpha_a = triceps_long_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat") +triceps_lat_duration.alpha_a = triceps_lat_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_med_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed") +triceps_med_duration.alpha_a = triceps_med_duration.alpha_a * triceps_fiber_type_2_proportion +brachioradialis_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA") +brachioradialis_duration.alpha_a = brachioradialis_duration.alpha_a * brachioradialis_fiber_type_2_proportion + +biceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong") +biceps_long_intensity.alpha_a = biceps_long_intensity.alpha_a * biceps_fiber_type_2_proportion +biceps_short_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort") +biceps_short_intensity.alpha_a = biceps_short_intensity.alpha_a * biceps_fiber_type_2_proportion +triceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong") +triceps_long_intensity.alpha_a = triceps_long_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat") +triceps_lat_intensity.alpha_a = triceps_lat_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_med_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed") +triceps_med_intensity.alpha_a = triceps_med_intensity.alpha_a * triceps_fiber_type_2_proportion +brachioradialis_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BRA") +brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion + if get_results: - n_stim = 28 + n_stim = 20 n_shooting = 5 objective_functions = ObjectiveList() - fes_muscle_models = [[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA")], - [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")]] + fes_muscle_models = [[biceps_long_duration, + biceps_short_duration, + triceps_long_duration, + triceps_lat_duration, + triceps_med_duration, + brachioradialis_duration], + [biceps_long_intensity, + biceps_short_intensity, + triceps_long_intensity, + triceps_lat_intensity, + triceps_med_intensity, + brachioradialis_intensity]] for i in range(n_stim): objective_functions.add( @@ -55,7 +86,7 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=19, + phase=9, node=Node.END, axes=[Axis.X, Axis.Y] ) @@ -68,7 +99,7 @@ target=np.array([[0, 0]] * (n_shooting + 1)).T, weight=1000, quadratic=True, - phase=19, + phase=9, ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( @@ -83,49 +114,47 @@ controls = [] parameters = [] for i in range(len(pickle_file_list)): - if i == 0: - pass - else: - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start_end", - bound_data=[[0, 5], [0, 5]], - fes_muscle_models=fes_muscle_models[i], - n_stim=n_stim, - n_shooting=n_shooting, - final_time=1.4, - pulse_duration_min=minimum_pulse_duration, - pulse_duration_max=0.0006, - pulse_duration_bimapping=False, - pulse_intensity_min=minimum_pulse_intensity, - pulse_intensity_max=80, - pulse_intensity_bimapping=False, - with_residual_torque=True, - custom_objective=objective_functions, - custom_constraint=constraint, - muscle_force_length_relationship=True, - muscle_force_velocity_relationship=True, - minimize_muscle_fatigue=False, - use_sx=False, - ) - - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() - # sol.animate() - # sol.graphs(show_bounds=False) - time = sol.time - states = sol.states - controls = sol.controls - parameters = sol.parameters - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open(pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start_end", + bound_data=[[0, 130], [0, 130]], + fes_muscle_models=fes_muscle_models[i], + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=80, + pulse_intensity_bimapping=False, + with_residual_torque=True, + custom_objective=objective_functions, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + minimize_muscle_fatigue=True, + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) #.merge_phases() + sol.animate() + sol.graphs(show_bounds=False) + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.parameters + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open(pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) if make_graphs: @@ -170,3 +199,8 @@ # fig.tight_layout() plt.show() + + +# [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). +# Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. +# Journal of biomechanics, 38(12), 2451-2459. From ea43ae7e24466e18bcd40c848c521ee4cfa21482 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Tue, 6 Feb 2024 10:35:06 -0500 Subject: [PATCH 33/63] for computational tests --- cocofest/optimization/fes_ocp_dynamics.py | 5 ++ examples/dynamics/reaching_task.py | 59 ++++++++++++++++++++--- 2 files changed, 57 insertions(+), 7 deletions(-) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 989d3b04..ceaff6ac 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -537,6 +537,11 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") + # if bound_type == "all": + # q_x_bounds = bound_data[i] + + q_x_bounds.min[0] = 0 + if i == 0: if bound_type == "start_end": for j in range(bio_models[i].nb_q): diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index b6a23e4d..b6f29259 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -28,35 +28,57 @@ triceps_fiber_type_2_proportion = 0.465 brachioradialis_fiber_type_2_proportion = 0.457 +# PCSA (cm²) from [2] +biceps_pcsa = 12.7 +triceps_pcsa = 28.3 +brachioradialis_pcsa = 11.6 + +biceps_a_rest_proportion = 12.7 / 28.3 +triceps_a_rest_proportion = 1 +brachioradialis_a_rest_proportion = 11.6 / 28.3 + biceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong") biceps_long_duration.alpha_a = biceps_long_duration.alpha_a * biceps_fiber_type_2_proportion +biceps_long_duration.a_rest = biceps_long_duration.a_rest * biceps_a_rest_proportion biceps_short_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort") biceps_short_duration.alpha_a = biceps_short_duration.alpha_a * biceps_fiber_type_2_proportion +biceps_short_duration.a_rest = biceps_short_duration.a_rest * biceps_a_rest_proportion triceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong") triceps_long_duration.alpha_a = triceps_long_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_long_duration.a_rest = triceps_long_duration.a_rest * triceps_a_rest_proportion triceps_lat_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat") triceps_lat_duration.alpha_a = triceps_lat_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_duration.a_rest = triceps_lat_duration.a_rest * triceps_a_rest_proportion triceps_med_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed") triceps_med_duration.alpha_a = triceps_med_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_med_duration.a_rest = triceps_med_duration.a_rest * triceps_a_rest_proportion brachioradialis_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA") brachioradialis_duration.alpha_a = brachioradialis_duration.alpha_a * brachioradialis_fiber_type_2_proportion +brachioradialis_duration.a_rest = brachioradialis_duration.a_rest * brachioradialis_a_rest_proportion biceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong") biceps_long_intensity.alpha_a = biceps_long_intensity.alpha_a * biceps_fiber_type_2_proportion +biceps_long_intensity.a_rest = biceps_long_intensity.a_rest * biceps_a_rest_proportion biceps_short_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort") biceps_short_intensity.alpha_a = biceps_short_intensity.alpha_a * biceps_fiber_type_2_proportion +biceps_short_intensity.a_rest = biceps_short_intensity.a_rest * biceps_a_rest_proportion triceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong") triceps_long_intensity.alpha_a = triceps_long_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_long_intensity.a_rest = triceps_long_intensity.a_rest * triceps_a_rest_proportion triceps_lat_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat") triceps_lat_intensity.alpha_a = triceps_lat_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_intensity.a_rest = triceps_lat_intensity.a_rest * triceps_a_rest_proportion triceps_med_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed") triceps_med_intensity.alpha_a = triceps_med_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_med_intensity.a_rest = triceps_med_intensity.a_rest * triceps_a_rest_proportion brachioradialis_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BRA") brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion +brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion + if get_results: - n_stim = 20 - n_shooting = 5 + n_stim = 100 + n_shooting = 1 objective_functions = ObjectiveList() fes_muscle_models = [[biceps_long_duration, @@ -86,20 +108,40 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=9, + phase=24, node=Node.END, axes=[Axis.X, Axis.Y] ) + # objective_functions.add( + # ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, + # node=Node.ALL, + # first_marker="COM_hand", + # second_marker="reaching_target", + # weight=1000, + # quadratic=True, + # phase=24, + # ) + # + # objective_functions.add( + # ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, + # node=Node.ALL, + # first_marker="COM_hand", + # second_marker="reaching_target", + # weight=1000, + # quadratic=True, + # phase=25, + # ) + objective_functions.add( ObjectiveFcn.Mayer.MINIMIZE_STATE, key="qdot", index=[0, 1], - node=Node.END, + node=Node.ALL, target=np.array([[0, 0]] * (n_shooting + 1)).T, weight=1000, quadratic=True, - phase=9, + phase=25, ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( @@ -122,7 +164,7 @@ fes_muscle_models=fes_muscle_models[i], n_stim=n_stim, n_shooting=n_shooting, - final_time=1, + final_time=2, pulse_duration_min=minimum_pulse_duration, pulse_duration_max=0.0006, pulse_duration_bimapping=False, @@ -200,7 +242,10 @@ plt.show() - # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. # Journal of biomechanics, 38(12), 2451-2459. + +# [2] Klein, C. S., Allman, B. L., Marsh, G. D., & Rice, C. L. (2002). +# Muscle size, strength, and bone geometry in the upper limbs of young and old men. +# The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M455-M459. From a53f35f451f82c9e4ab27c6f47c2418ef3395543 Mon Sep 17 00:00:00 2001 From: kevin Date: Tue, 6 Feb 2024 15:36:49 -0500 Subject: [PATCH 34/63] . --- cocofest/optimization/fes_ocp_dynamics.py | 26 ++-- examples/dynamics/arm26.bioMod | 2 +- examples/dynamics/reaching_task.py | 157 ++++++++++------------ 3 files changed, 91 insertions(+), 94 deletions(-) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index ceaff6ac..9062e7dd 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -72,6 +72,7 @@ def prepare_ocp( muscle_force_length_relationship: bool = False, muscle_force_velocity_relationship: bool = False, minimize_muscle_fatigue: bool = False, + minimize_muscle_force: bool = False, use_sx: bool = True, ode_solver: OdeSolverBase = OdeSolver.RK4(n_integration_steps=1), control_type: ControlType = ControlType.CONSTANT, @@ -134,6 +135,8 @@ def prepare_ocp( If the force velocity relationship is used minimize_muscle_fatigue: bool Minimize the muscle fatigue + minimize_muscle_force: bool + Minimize the muscle force use_sx: bool The nature of the casadi variables. MX are used if False. ode_solver: OdeSolver @@ -260,6 +263,7 @@ def prepare_ocp( custom_objective, q_fourier_coef, minimize_muscle_fatigue, + minimize_muscle_force, ) return OptimalControlProgram( @@ -541,6 +545,7 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): # q_x_bounds = bound_data[i] q_x_bounds.min[0] = 0 + q_x_bounds.max[1] = 2.6166666666666667 if i == 0: if bound_type == "start_end": @@ -593,6 +598,7 @@ def _set_objective( custom_objective, q_fourier_coef, minimize_muscle_fatigue, + minimize_muscle_force, ): # Creates the objective for our problem objective_functions = ObjectiveList() @@ -655,16 +661,16 @@ def _set_objective( phase=i, ) - # if minimize_muscle_fatigue: - # for i in range(n_stim): - # objective_functions.add( - # CustomObjective.minimize_overall_muscle_force, - # custom_type=ObjectiveFcn.Mayer, - # node=Node.ALL, - # quadratic=True, - # weight=1, - # phase=i, - # ) + if minimize_muscle_force: + for i in range(n_stim): + objective_functions.add( + CustomObjective.minimize_overall_muscle_force_production, + custom_type=ObjectiveFcn.Mayer, + node=Node.ALL, + quadratic=True, + weight=1, + phase=i, + ) return objective_functions diff --git a/examples/dynamics/arm26.bioMod b/examples/dynamics/arm26.bioMod index 10461eec..7a43d116 100644 --- a/examples/dynamics/arm26.bioMod +++ b/examples/dynamics/arm26.bioMod @@ -15,7 +15,7 @@ endsegment marker reaching_target parent base - position 0.45 -0.05 0.167 + position 0.40 -0.05 0.167 endmarker diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index b6f29259..6cf190db 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -75,96 +75,86 @@ brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion - +pickle_file_list = ["minimize_muscle_force.pkl", "minimize_muscle_fatigue.pkl"] if get_results: - n_stim = 100 - n_shooting = 1 - objective_functions = ObjectiveList() - - fes_muscle_models = [[biceps_long_duration, - biceps_short_duration, - triceps_long_duration, - triceps_lat_duration, - triceps_med_duration, - brachioradialis_duration], - [biceps_long_intensity, - biceps_short_intensity, - triceps_long_intensity, - triceps_lat_intensity, - triceps_med_intensity, - brachioradialis_intensity]] - - for i in range(n_stim): + for i in range(len(pickle_file_list)): + n_stim = 40 + n_shooting = 2 + objective_functions = ObjectiveList() + + fes_muscle_models = [[biceps_long_duration, + biceps_short_duration, + triceps_long_duration, + triceps_lat_duration, + triceps_med_duration, + brachioradialis_duration], + [biceps_long_intensity, + biceps_short_intensity, + triceps_long_intensity, + triceps_lat_intensity, + triceps_med_intensity, + brachioradialis_intensity]] + + for j in range(n_stim): + objective_functions.add( + ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, + key="tau", + weight=100000, + quadratic=True, + phase=j, + ) + + constraint = ConstraintList() + constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=19, + node=Node.END, + axes=[Axis.X, Axis.Y] + ) + objective_functions.add( - ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, - key="tau", - weight=100000, + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.ALL, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=1000, quadratic=True, - phase=i, + phase=20, ) - constraint = ConstraintList() - constraint.add( - ConstraintFcn.SUPERIMPOSE_MARKERS, - first_marker="COM_hand", - second_marker="reaching_target", - phase=24, - node=Node.END, - axes=[Axis.X, Axis.Y] - ) - - # objective_functions.add( - # ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, - # node=Node.ALL, - # first_marker="COM_hand", - # second_marker="reaching_target", - # weight=1000, - # quadratic=True, - # phase=24, - # ) - # - # objective_functions.add( - # ObjectiveFcn.Mayer.SUPERIMPOSE_MARKERS, - # node=Node.ALL, - # first_marker="COM_hand", - # second_marker="reaching_target", - # weight=1000, - # quadratic=True, - # phase=25, - # ) - - objective_functions.add( - ObjectiveFcn.Mayer.MINIMIZE_STATE, - key="qdot", - index=[0, 1], - node=Node.ALL, - target=np.array([[0, 0]] * (n_shooting + 1)).T, - weight=1000, - quadratic=True, - phase=25, - ) - - minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() - ) - - minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 - - pickle_file_list = ["pulse_duration_normal.pkl", "pulse_intensity_normal.pkl"] - time = [] - states = [] - controls = [] - parameters = [] - for i in range(len(pickle_file_list)): + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0, 1], + node=Node.ALL, + target=np.array([[0, 0]] * (n_shooting + 1)).T, + weight=1000, + quadratic=True, + phase=21, + ) + + minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() + ) + + minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 + + time = [] + states = [] + controls = [] + parameters = [] ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="arm26.bioMod", bound_type="start_end", - bound_data=[[0, 130], [0, 130]], - fes_muscle_models=fes_muscle_models[i], + bound_data=[[0, 150], [0, 150]], + fes_muscle_models=fes_muscle_models[0], n_stim=n_stim, n_shooting=n_shooting, - final_time=2, + final_time=1, pulse_duration_min=minimum_pulse_duration, pulse_duration_max=0.0006, pulse_duration_bimapping=False, @@ -176,13 +166,14 @@ custom_constraint=constraint, muscle_force_length_relationship=True, muscle_force_velocity_relationship=True, - minimize_muscle_fatigue=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=1000)) #.merge_phases() - sol.animate() - sol.graphs(show_bounds=False) + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() + # sol.animate() + # sol.graphs(show_bounds=False) time = sol.time states = sol.states controls = sol.controls From f7427ea3efe430a88597aca5b10bc83684bb06f2 Mon Sep 17 00:00:00 2001 From: kevin Date: Tue, 6 Feb 2024 17:11:23 -0500 Subject: [PATCH 35/63] trying to remove residual torque --- examples/dynamics/reaching_task.py | 34 +++++++++++++++--------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index 6cf190db..4033804d 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -78,7 +78,7 @@ pickle_file_list = ["minimize_muscle_force.pkl", "minimize_muscle_fatigue.pkl"] if get_results: for i in range(len(pickle_file_list)): - n_stim = 40 + n_stim = 60 n_shooting = 2 objective_functions = ObjectiveList() @@ -95,21 +95,21 @@ triceps_med_intensity, brachioradialis_intensity]] - for j in range(n_stim): - objective_functions.add( - ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, - key="tau", - weight=100000, - quadratic=True, - phase=j, - ) + # for j in range(n_stim): + # objective_functions.add( + # ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, + # key="tau", + # weight=100000, + # quadratic=True, + # phase=j, + # ) constraint = ConstraintList() constraint.add( ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=19, + phase=29, node=Node.END, axes=[Axis.X, Axis.Y] ) @@ -122,7 +122,7 @@ target=np.array([[0, 0]] * (n_shooting + 1)).T, weight=1000, quadratic=True, - phase=20, + phase=30, ) objective_functions.add( @@ -133,7 +133,7 @@ target=np.array([[0, 0]] * (n_shooting + 1)).T, weight=1000, quadratic=True, - phase=21, + phase=31, ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( @@ -154,14 +154,14 @@ fes_muscle_models=fes_muscle_models[0], n_stim=n_stim, n_shooting=n_shooting, - final_time=1, + final_time=2, pulse_duration_min=minimum_pulse_duration, pulse_duration_max=0.0006, pulse_duration_bimapping=False, pulse_intensity_min=minimum_pulse_intensity, pulse_intensity_max=80, pulse_intensity_bimapping=False, - with_residual_torque=True, + with_residual_torque=False, custom_objective=objective_functions, custom_constraint=constraint, muscle_force_length_relationship=True, @@ -171,9 +171,9 @@ use_sx=False, ) - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() - # sol.animate() - # sol.graphs(show_bounds=False) + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) #.merge_phases() + sol.animate() + sol.graphs(show_bounds=False) time = sol.time states = sol.states controls = sol.controls From f17b1ac554a04476cde67e5ae60045267dc88611 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 11:07:20 -0500 Subject: [PATCH 36/63] changing initial guess --- cocofest/optimization/fes_ocp_dynamics.py | 3 +- .../reaching_task_intensity_optimization.py | 261 ++++++++++++++++++ ...aching_task_pulse_duration_optimization.py | 176 ++++++++++++ 3 files changed, 439 insertions(+), 1 deletion(-) create mode 100644 examples/dynamics/reaching_task/reaching_task_intensity_optimization.py create mode 100644 examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 9062e7dd..b4166362 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -367,7 +367,8 @@ def _build_parameters( max_bound=[pulse_duration_max], interpolation=InterpolationType.CONSTANT, ) - parameters_init[parameter_name] = np.array([0] * n_stim) + pulse_duration_avg = (pulse_duration_max + pulse_duration_min) / 2 + parameters_init[parameter_name] = np.array([pulse_duration_avg] * n_stim) parameters.add( parameter_name=parameter_name, function=DingModelPulseDurationFrequency.set_impulse_duration, diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py new file mode 100644 index 00000000..b08dab3d --- /dev/null +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -0,0 +1,261 @@ +""" +This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +""" +import pickle +import numpy as np +import matplotlib.pyplot as plt + +from bioptim import ( + Axis, + ObjectiveFcn, + ObjectiveList, + ConstraintList, + ConstraintFcn, + Solver, + Node, +) + +from cocofest import DingModelIntensityFrequencyWithFatigue, DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +get_results = False +make_graphs = True + +# Fiber type proportion from [1] +biceps_fiber_type_2_proportion = 0.607 +triceps_fiber_type_2_proportion = 0.465 +brachioradialis_fiber_type_2_proportion = 0.457 + +# PCSA (cm²) from [2] +biceps_pcsa = 12.7 +triceps_pcsa = 28.3 +brachioradialis_pcsa = 11.6 + +biceps_a_rest_proportion = 12.7 / 28.3 +triceps_a_rest_proportion = 1 +brachioradialis_a_rest_proportion = 11.6 / 28.3 + +biceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong") +biceps_long_duration.alpha_a = biceps_long_duration.alpha_a * biceps_fiber_type_2_proportion +biceps_long_duration.a_rest = biceps_long_duration.a_rest * biceps_a_rest_proportion +biceps_short_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort") +biceps_short_duration.alpha_a = biceps_short_duration.alpha_a * biceps_fiber_type_2_proportion +biceps_short_duration.a_rest = biceps_short_duration.a_rest * biceps_a_rest_proportion +triceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong") +triceps_long_duration.alpha_a = triceps_long_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_long_duration.a_rest = triceps_long_duration.a_rest * triceps_a_rest_proportion +triceps_lat_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat") +triceps_lat_duration.alpha_a = triceps_lat_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_duration.a_rest = triceps_lat_duration.a_rest * triceps_a_rest_proportion +triceps_med_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed") +triceps_med_duration.alpha_a = triceps_med_duration.alpha_a * triceps_fiber_type_2_proportion +triceps_med_duration.a_rest = triceps_med_duration.a_rest * triceps_a_rest_proportion +brachioradialis_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA") +brachioradialis_duration.alpha_a = brachioradialis_duration.alpha_a * brachioradialis_fiber_type_2_proportion +brachioradialis_duration.a_rest = brachioradialis_duration.a_rest * brachioradialis_a_rest_proportion + +biceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong") +biceps_long_intensity.alpha_a = biceps_long_intensity.alpha_a * biceps_fiber_type_2_proportion +biceps_long_intensity.a_rest = biceps_long_intensity.a_rest * biceps_a_rest_proportion +biceps_short_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort") +biceps_short_intensity.alpha_a = biceps_short_intensity.alpha_a * biceps_fiber_type_2_proportion +biceps_short_intensity.a_rest = biceps_short_intensity.a_rest * biceps_a_rest_proportion +triceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong") +triceps_long_intensity.alpha_a = triceps_long_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_long_intensity.a_rest = triceps_long_intensity.a_rest * triceps_a_rest_proportion +triceps_lat_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat") +triceps_lat_intensity.alpha_a = triceps_lat_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_lat_intensity.a_rest = triceps_lat_intensity.a_rest * triceps_a_rest_proportion +triceps_med_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed") +triceps_med_intensity.alpha_a = triceps_med_intensity.alpha_a * triceps_fiber_type_2_proportion +triceps_med_intensity.a_rest = triceps_med_intensity.a_rest * triceps_a_rest_proportion +brachioradialis_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BRA") +brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion +brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion + +# pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] +pickle_file_list = ["minimize_muscle_force.pkl"] +if get_results: + for i in range(len(pickle_file_list)): + n_stim = 40 + n_shooting = 2 + objective_functions = ObjectiveList() + + fes_muscle_models = [[biceps_long_duration, + biceps_short_duration, + triceps_long_duration, + triceps_lat_duration, + triceps_med_duration, + brachioradialis_duration], + [biceps_long_intensity, + biceps_short_intensity, + triceps_long_intensity, + triceps_lat_intensity, + triceps_med_intensity, + brachioradialis_intensity]] + + # for j in range(n_stim): + # objective_functions.add( + # ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, + # key="tau", + # weight=100000, + # quadratic=True, + # phase=j, + # ) + + constraint = ConstraintList() + constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=39, + node=Node.END, + axes=[Axis.X, Axis.Y] + ) + + # objective_functions.add( + # ObjectiveFcn.Mayer.MINIMIZE_STATE, + # key="qdot", + # index=[0, 1], + # node=Node.ALL, + # target=np.array([[0, 0]] * (n_shooting + 1)).T, + # weight=1000, + # quadratic=True, + # phase=20, + # ) + # + # objective_functions.add( + # ObjectiveFcn.Mayer.MINIMIZE_STATE, + # key="qdot", + # index=[0, 1], + # node=Node.ALL, + # target=np.array([[0, 0]] * (n_shooting + 1)).T, + # weight=1000, + # quadratic=True, + # phase=21, + # ) + + minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() + ) + + minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 + + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models[0], + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=80, + pulse_intensity_bimapping=False, + with_residual_torque=False, + custom_objective=objective_functions, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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=100000)).merge_phases() + # sol.animate() + # sol.graphs(show_bounds=False) + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.parameters + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open(pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) + + +if make_graphs: + with open(r"minimize_muscle_force.pkl", "rb") as f: + data_minimize_force = pickle.load(f) + + with open(r"minimize_muscle_fatigue.pkl", "rb") as f: + data_minimize_fatigue = pickle.load(f) + + muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] + muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] + fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) + counter = 0 + for i in range(3): + for j in range(2): + axs[i][j].set_xlim(left=0, right=1) + axs[i][j].set_ylim(bottom=0, top=300) + + axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] + + if i == 0 and j == 0: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing force") + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing fatigue") + else: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + counter += 1 + + # axs[3][0].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][0], ms=4, + # linewidth=5.0) + # axs[3][0].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][0], ms=4, + # linewidth=5.0) + # + # axs[3][1].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][1], ms=4, + # linewidth=5.0) + # axs[3][1].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][1], ms=4, + # linewidth=5.0) + + plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) + + fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) + fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) + + # fig.legend() + # fig.tight_layout() + plt.show() + + +# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] +# a_sum = 0 +# for key_a in a_list: +# a_sum += data_minimize_force["states"][key_a][0][-1] + + + + +# [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). +# Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. +# Journal of biomechanics, 38(12), 2451-2459. + +# [2] Klein, C. S., Allman, B. L., Marsh, G. D., & Rice, C. L. (2002). +# Muscle size, strength, and bone geometry in the upper limbs of young and old men. +# The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M455-M459. diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py new file mode 100644 index 00000000..11519862 --- /dev/null +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -0,0 +1,176 @@ +""" +This example will do a pulse duration optimization to either minimize overall muscle force or muscle fatigue +for a reaching task. Those ocp were build to move from starting position (arm: 0°, elbow: 5°) to a target position +defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. +The files will contain the time, states, controls and parameters of the ocp. +If the files already exist, it is possible to create graphs of the force for each muscle. +""" +import pickle +import matplotlib.pyplot as plt + +from bioptim import ( + Axis, + ConstraintList, + ConstraintFcn, + Solver, + Node, +) + +from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +get_results = True +make_graphs = False + +# Fiber type proportion from [1] +biceps_fiber_type_2_proportion = 0.607 +triceps_fiber_type_2_proportion = 0.465 +brachioradialis_fiber_type_2_proportion = 0.457 +alpha_a_proportion_list = [biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion] + +# PCSA (cm²) from [2] +biceps_pcsa = 12.7 +triceps_pcsa = 28.3 +brachioradialis_pcsa = 11.6 + +biceps_a_rest_proportion = 12.7 / 28.3 +triceps_a_rest_proportion = 1 +brachioradialis_a_rest_proportion = 11.6 / 28.3 +a_rest_proportion_list = [biceps_a_rest_proportion, + biceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + brachioradialis_a_rest_proportion] + +fes_muscle_models = [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA")] + +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_rest = fes_muscle_models[i].a_rest * a_rest_proportion_list[i] + +minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 +pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] +n_stim = 40 +n_shooting = 5 + +constraint = ConstraintList() +constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=n_stim-1, + node=Node.END, + axes=[Axis.X, Axis.Y] +) + +if get_results: + for i in range(len(pickle_file_list)): + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + with_residual_torque=False, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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() + # sol.animate() + # sol.graphs(show_bounds=False) + time = sol.time + states = sol.states + controls = sol.controls + parameters = sol.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) + + +if make_graphs: + with open(r"minimize_muscle_force.pkl", "rb") as f: + data_minimize_force = pickle.load(f) + + with open(r"minimize_muscle_fatigue.pkl", "rb") as f: + data_minimize_fatigue = pickle.load(f) + + muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] + muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] + fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) + counter = 0 + for i in range(3): + for j in range(2): + axs[i][j].set_xlim(left=0, right=1) + axs[i][j].set_ylim(bottom=0, top=300) + + axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] + + if i == 0 and j == 0: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing force") + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing fatigue") + else: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + counter += 1 + + plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) + + fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) + fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) + + # fig.legend() + # fig.tight_layout() + plt.show() + + +# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] +# a_sum = 0 +# for key_a in a_list: +# a_sum += data_minimize_force["states"][key_a][0][-1] + +# [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). +# Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. +# Journal of biomechanics, 38(12), 2451-2459. + +# [2] Klein, C. S., Allman, B. L., Marsh, G. D., & Rice, C. L. (2002). +# Muscle size, strength, and bone geometry in the upper limbs of young and old men. +# The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M455-M459. From 86d0ed5f13e8be06025ff8758986ed52f1a720dc Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 11:08:20 -0500 Subject: [PATCH 37/63] reaching task refactor --- examples/dynamics/reaching_task/make_gaph.py | 75 ++++++ .../reaching_task_frequency_optimization.py | 123 +++++++++ .../reaching_task_intensity_optimization.py | 235 ++++-------------- ...aching_task_pulse_duration_optimization.py | 72 +----- 4 files changed, 258 insertions(+), 247 deletions(-) create mode 100644 examples/dynamics/reaching_task/make_gaph.py create mode 100644 examples/dynamics/reaching_task/reaching_task_frequency_optimization.py diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py new file mode 100644 index 00000000..96a64588 --- /dev/null +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -0,0 +1,75 @@ +""" +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. +The available graphs are: frequency, duration, intensity +""" + +import pickle +import matplotlib.pyplot as plt + +chosen_graph_to_plot = "duration" + +frequency_path = [r"/result_file/pulse_apparition_minimize_muscle_force.pkl", + r"/result_file/pulse_apparition_minimize_muscle_fatigue.pkl"] + +duration_path = [r"/result_file/pulse_duration_minimize_muscle_force.pkl", + r"/result_file/pulse_duration_minimize_muscle_fatigue.pkl"] + +intensity_path = [r"/result_file/pulse_intensity_minimize_muscle_force.pkl", + r"/result_file/pulse_intensity_minimize_muscle_fatigue.pkl"] + +chosen_graph_to_plot_path = (frequency_path if chosen_graph_to_plot == "frequency" else + duration_path if chosen_graph_to_plot == "duration" else + intensity_path if chosen_graph_to_plot == "duration" else + None) + +if chosen_graph_to_plot_path is None: + raise ValueError("The chosen graph to plot is not valid") + + +with open(chosen_graph_to_plot_path[0], "rb") as f: + data_minimize_force = pickle.load(f) + +with open(chosen_graph_to_plot_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"] +muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] +fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) +index = 0 +for i in range(3): + for j in range(2): + axs[i][j].set_xlim(left=0, right=1) + axs[i][j].set_ylim(bottom=0, top=300) + + axs[i][j].text(.025, .975, f'{muscle_names[index]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] + + if i == 0 and j == 0: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0, + label="Minimizing force") + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0, + label="Minimizing fatigue") + else: + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0) + index += 1 + +plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) + +fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) +fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) + +# fig.legend() +# fig.tight_layout() +plt.show() + + +# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] +# a_sum = 0 +# for key_a in a_list: +# a_sum += data_minimize_force["states"][key_a][0][-1] \ No newline at end of file diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py new file mode 100644 index 00000000..ce45fdc5 --- /dev/null +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -0,0 +1,123 @@ +""" +This example will do a pulse apparition optimization to either minimize overall muscle force or muscle fatigue +for a reaching task. Those ocp were build to move from starting position (arm: 0°, elbow: 5°) to a target position +defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. +The files will contain the time, states, controls and parameters of the ocp. +""" +import pickle + +from bioptim import ( + Axis, + ConstraintList, + ConstraintFcn, + Solver, + Node, +) + +from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP + +get_results = True +make_graphs = False + +# Fiber type proportion from [1] +biceps_fiber_type_2_proportion = 0.607 +triceps_fiber_type_2_proportion = 0.465 +brachioradialis_fiber_type_2_proportion = 0.457 +alpha_a_proportion_list = [biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion] + +# PCSA (cm²) from [2] +biceps_pcsa = 12.7 +triceps_pcsa = 28.3 +brachioradialis_pcsa = 11.6 + +biceps_a_rest_proportion = 12.7 / 28.3 +triceps_a_rest_proportion = 1 +brachioradialis_a_rest_proportion = 11.6 / 28.3 +a_rest_proportion_list = [biceps_a_rest_proportion, + biceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + brachioradialis_a_rest_proportion] + +fes_muscle_models = [DingModelFrequencyWithFatigue(muscle_name="BIClong"), + DingModelFrequencyWithFatigue(muscle_name="BICshort"), + DingModelFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelFrequencyWithFatigue(muscle_name="TRImed"), + DingModelFrequencyWithFatigue(muscle_name="BRA")] + +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_rest = fes_muscle_models[i].a_rest * a_rest_proportion_list[i] + +pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] +n_stim = 40 +n_shooting = 5 + +constraint = ConstraintList() +constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=n_stim-1, + node=Node.END, + axes=[Axis.X, Axis.Y] +) + +if get_results: + for i in range(len(pickle_file_list)): + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=False, + with_residual_torque=False, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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 + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open("/result_file/pulse_apparition_" + pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) + + +# [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). +# Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. +# Journal of biomechanics, 38(12), 2451-2459. + +# [2] Klein, C. S., Allman, B. L., Marsh, G. D., & Rice, C. L. (2002). +# Muscle size, strength, and bone geometry in the upper limbs of young and old men. +# The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M455-M459. diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index b08dab3d..6baa5d2a 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -1,32 +1,34 @@ """ -This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. +This example will do a pulse intensity optimization to either minimize overall muscle force or muscle fatigue +for a reaching task. Those ocp were build to move from starting position (arm: 0°, elbow: 5°) to a target position +defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. +The files will contain the time, states, controls and parameters of the ocp. """ import pickle -import numpy as np -import matplotlib.pyplot as plt from bioptim import ( Axis, - ObjectiveFcn, - ObjectiveList, ConstraintList, ConstraintFcn, Solver, Node, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP -get_results = False -make_graphs = True +get_results = True +make_graphs = False # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 brachioradialis_fiber_type_2_proportion = 0.457 +alpha_a_proportion_list = [biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion] # PCSA (cm²) from [2] biceps_pcsa = 12.7 @@ -36,113 +38,43 @@ biceps_a_rest_proportion = 12.7 / 28.3 triceps_a_rest_proportion = 1 brachioradialis_a_rest_proportion = 11.6 / 28.3 +a_rest_proportion_list = [biceps_a_rest_proportion, + biceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + brachioradialis_a_rest_proportion] + +fes_muscle_models = [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")] + +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_rest = fes_muscle_models[i].a_rest * a_rest_proportion_list[i] + +minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() +) +pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] +n_stim = 40 +n_shooting = 5 + +constraint = ConstraintList() +constraint.add( + ConstraintFcn.SUPERIMPOSE_MARKERS, + first_marker="COM_hand", + second_marker="reaching_target", + phase=n_stim-1, + node=Node.END, + axes=[Axis.X, Axis.Y] +) -biceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong") -biceps_long_duration.alpha_a = biceps_long_duration.alpha_a * biceps_fiber_type_2_proportion -biceps_long_duration.a_rest = biceps_long_duration.a_rest * biceps_a_rest_proportion -biceps_short_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort") -biceps_short_duration.alpha_a = biceps_short_duration.alpha_a * biceps_fiber_type_2_proportion -biceps_short_duration.a_rest = biceps_short_duration.a_rest * biceps_a_rest_proportion -triceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong") -triceps_long_duration.alpha_a = triceps_long_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_long_duration.a_rest = triceps_long_duration.a_rest * triceps_a_rest_proportion -triceps_lat_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat") -triceps_lat_duration.alpha_a = triceps_lat_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_lat_duration.a_rest = triceps_lat_duration.a_rest * triceps_a_rest_proportion -triceps_med_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed") -triceps_med_duration.alpha_a = triceps_med_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_med_duration.a_rest = triceps_med_duration.a_rest * triceps_a_rest_proportion -brachioradialis_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA") -brachioradialis_duration.alpha_a = brachioradialis_duration.alpha_a * brachioradialis_fiber_type_2_proportion -brachioradialis_duration.a_rest = brachioradialis_duration.a_rest * brachioradialis_a_rest_proportion - -biceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong") -biceps_long_intensity.alpha_a = biceps_long_intensity.alpha_a * biceps_fiber_type_2_proportion -biceps_long_intensity.a_rest = biceps_long_intensity.a_rest * biceps_a_rest_proportion -biceps_short_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort") -biceps_short_intensity.alpha_a = biceps_short_intensity.alpha_a * biceps_fiber_type_2_proportion -biceps_short_intensity.a_rest = biceps_short_intensity.a_rest * biceps_a_rest_proportion -triceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong") -triceps_long_intensity.alpha_a = triceps_long_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_long_intensity.a_rest = triceps_long_intensity.a_rest * triceps_a_rest_proportion -triceps_lat_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat") -triceps_lat_intensity.alpha_a = triceps_lat_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_lat_intensity.a_rest = triceps_lat_intensity.a_rest * triceps_a_rest_proportion -triceps_med_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed") -triceps_med_intensity.alpha_a = triceps_med_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_med_intensity.a_rest = triceps_med_intensity.a_rest * triceps_a_rest_proportion -brachioradialis_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BRA") -brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion -brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion - -# pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] -pickle_file_list = ["minimize_muscle_force.pkl"] if get_results: for i in range(len(pickle_file_list)): - n_stim = 40 - n_shooting = 2 - objective_functions = ObjectiveList() - - fes_muscle_models = [[biceps_long_duration, - biceps_short_duration, - triceps_long_duration, - triceps_lat_duration, - triceps_med_duration, - brachioradialis_duration], - [biceps_long_intensity, - biceps_short_intensity, - triceps_long_intensity, - triceps_lat_intensity, - triceps_med_intensity, - brachioradialis_intensity]] - - # for j in range(n_stim): - # objective_functions.add( - # ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, - # key="tau", - # weight=100000, - # quadratic=True, - # phase=j, - # ) - - constraint = ConstraintList() - constraint.add( - ConstraintFcn.SUPERIMPOSE_MARKERS, - first_marker="COM_hand", - second_marker="reaching_target", - phase=39, - node=Node.END, - axes=[Axis.X, Axis.Y] - ) - - # objective_functions.add( - # ObjectiveFcn.Mayer.MINIMIZE_STATE, - # key="qdot", - # index=[0, 1], - # node=Node.ALL, - # target=np.array([[0, 0]] * (n_shooting + 1)).T, - # weight=1000, - # quadratic=True, - # phase=20, - # ) - # - # objective_functions.add( - # ObjectiveFcn.Mayer.MINIMIZE_STATE, - # key="qdot", - # index=[0, 1], - # node=Node.ALL, - # target=np.array([[0, 0]] * (n_shooting + 1)).T, - # weight=1000, - # quadratic=True, - # phase=21, - # ) - - minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() - ) - - minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 - time = [] states = [] controls = [] @@ -152,18 +84,14 @@ biorbd_model_path="arm26.bioMod", bound_type="start", bound_data=[0, 5], - fes_muscle_models=fes_muscle_models[0], + fes_muscle_models=fes_muscle_models, n_stim=n_stim, n_shooting=n_shooting, final_time=1, - pulse_duration_min=minimum_pulse_duration, - pulse_duration_max=0.0006, - pulse_duration_bimapping=False, pulse_intensity_min=minimum_pulse_intensity, pulse_intensity_max=80, pulse_intensity_bimapping=False, with_residual_torque=False, - custom_objective=objective_functions, custom_constraint=constraint, muscle_force_length_relationship=True, muscle_force_velocity_relationship=True, @@ -172,9 +100,7 @@ use_sx=False, ) - sol = ocp.solve(Solver.IPOPT(_max_iter=100000)).merge_phases() - # sol.animate() - # sol.graphs(show_bounds=False) + sol = ocp.solve(Solver.IPOPT(_max_iter=10000)).merge_phases() time = sol.time states = sol.states controls = sol.controls @@ -187,71 +113,10 @@ "parameters": parameters, } - with open(pickle_file_list[i], "wb") as file: + with open("/result_file/pulse_intensity_" + pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) -if make_graphs: - with open(r"minimize_muscle_force.pkl", "rb") as f: - data_minimize_force = pickle.load(f) - - with open(r"minimize_muscle_fatigue.pkl", "rb") as f: - data_minimize_fatigue = pickle.load(f) - - muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] - muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] - fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) - counter = 0 - for i in range(3): - for j in range(2): - axs[i][j].set_xlim(left=0, right=1) - axs[i][j].set_ylim(bottom=0, top=300) - - axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] - - if i == 0 and j == 0: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing force") - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing fatigue") - else: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - counter += 1 - - # axs[3][0].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][0], ms=4, - # linewidth=5.0) - # axs[3][0].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][0], ms=4, - # linewidth=5.0) - # - # axs[3][1].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][1], ms=4, - # linewidth=5.0) - # axs[3][1].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][1], ms=4, - # linewidth=5.0) - - plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) - - fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) - fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) - - # fig.legend() - # fig.tight_layout() - plt.show() - - -# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] -# a_sum = 0 -# for key_a in a_list: -# a_sum += data_minimize_force["states"][key_a][0][-1] - - - - # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. # Journal of biomechanics, 38(12), 2451-2459. diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index 11519862..0d177ad5 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -3,10 +3,8 @@ for a reaching task. Those ocp were build to move from starting position (arm: 0°, elbow: 5°) to a target position defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. The files will contain the time, states, controls and parameters of the ocp. -If the files already exist, it is possible to create graphs of the force for each muscle. """ import pickle -import matplotlib.pyplot as plt from bioptim import ( Axis, @@ -37,15 +35,15 @@ triceps_pcsa = 28.3 brachioradialis_pcsa = 11.6 -biceps_a_rest_proportion = 12.7 / 28.3 -triceps_a_rest_proportion = 1 -brachioradialis_a_rest_proportion = 11.6 / 28.3 -a_rest_proportion_list = [biceps_a_rest_proportion, - biceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - brachioradialis_a_rest_proportion] +biceps_a_scale_proportion = 12.7 / 28.3 +triceps_a_scale_proportion = 1 +brachioradialis_a_scale_proportion = 11.6 / 28.3 +a_scale_proportion_list = [biceps_a_scale_proportion, + biceps_a_scale_proportion, + triceps_a_scale_proportion, + triceps_a_scale_proportion, + triceps_a_scale_proportion, + brachioradialis_a_scale_proportion] fes_muscle_models = [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), @@ -56,7 +54,7 @@ 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_rest = fes_muscle_models[i].a_rest * a_rest_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"] @@ -101,8 +99,6 @@ ) sol = ocp.solve(Solver.IPOPT(_max_iter=10000)).merge_phases() - # sol.animate() - # sol.graphs(show_bounds=False) time = sol.time states = sol.states controls = sol.controls @@ -119,54 +115,6 @@ pickle.dump(dictionary, file) -if make_graphs: - with open(r"minimize_muscle_force.pkl", "rb") as f: - data_minimize_force = pickle.load(f) - - with open(r"minimize_muscle_fatigue.pkl", "rb") as f: - data_minimize_fatigue = pickle.load(f) - - muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] - muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] - fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) - counter = 0 - for i in range(3): - for j in range(2): - axs[i][j].set_xlim(left=0, right=1) - axs[i][j].set_ylim(bottom=0, top=300) - - axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", 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] - - if i == 0 and j == 0: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing force") - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing fatigue") - else: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - counter += 1 - - plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) - - fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) - fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) - - # fig.legend() - # fig.tight_layout() - plt.show() - - -# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] -# a_sum = 0 -# for key_a in a_list: -# a_sum += data_minimize_force["states"][key_a][0][-1] - # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. # Journal of biomechanics, 38(12), 2451-2459. From 4d1c0164801cefa9ea9dab8724cc5a2f569ea22f Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 11:09:24 -0500 Subject: [PATCH 38/63] Auto stash before merge of "dynamics_2" and "origin/dynamics_2" --- examples/dynamics/reaching_task.py | 101 +++++++++++++++++------------ 1 file changed, 60 insertions(+), 41 deletions(-) diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index 4033804d..b7140b07 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -20,8 +20,8 @@ from cocofest import DingModelIntensityFrequencyWithFatigue, DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP -get_results = True -make_graphs = False +get_results = False +make_graphs = True # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 @@ -75,7 +75,8 @@ brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion -pickle_file_list = ["minimize_muscle_force.pkl", "minimize_muscle_fatigue.pkl"] +# pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] +pickle_file_list = ["minimize_muscle_force.pkl"] if get_results: for i in range(len(pickle_file_list)): n_stim = 60 @@ -109,32 +110,32 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=29, + phase=39, node=Node.END, axes=[Axis.X, Axis.Y] ) - objective_functions.add( - ObjectiveFcn.Mayer.MINIMIZE_STATE, - key="qdot", - index=[0, 1], - node=Node.ALL, - target=np.array([[0, 0]] * (n_shooting + 1)).T, - weight=1000, - quadratic=True, - phase=30, - ) - - objective_functions.add( - ObjectiveFcn.Mayer.MINIMIZE_STATE, - key="qdot", - index=[0, 1], - node=Node.ALL, - target=np.array([[0, 0]] * (n_shooting + 1)).T, - weight=1000, - quadratic=True, - phase=31, - ) + # objective_functions.add( + # ObjectiveFcn.Mayer.MINIMIZE_STATE, + # key="qdot", + # index=[0, 1], + # node=Node.ALL, + # target=np.array([[0, 0]] * (n_shooting + 1)).T, + # weight=1000, + # quadratic=True, + # phase=20, + # ) + # + # objective_functions.add( + # ObjectiveFcn.Mayer.MINIMIZE_STATE, + # key="qdot", + # index=[0, 1], + # node=Node.ALL, + # target=np.array([[0, 0]] * (n_shooting + 1)).T, + # weight=1000, + # quadratic=True, + # phase=21, + # ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() @@ -149,8 +150,8 @@ ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="arm26.bioMod", - bound_type="start_end", - bound_data=[[0, 150], [0, 150]], + bound_type="start", + bound_data=[0, 5], fes_muscle_models=fes_muscle_models[0], n_stim=n_stim, n_shooting=n_shooting, @@ -171,9 +172,9 @@ use_sx=False, ) - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) #.merge_phases() - sol.animate() - sol.graphs(show_bounds=False) + sol = ocp.solve(Solver.IPOPT(_max_iter=100000)).merge_phases() + # sol.animate() + # sol.graphs(show_bounds=False) time = sol.time states = sol.states controls = sol.controls @@ -191,11 +192,11 @@ if make_graphs: - with open(r"normal.pkl", "rb") as f: - data_normal = pickle.load(f) + with open(r"minimize_muscle_force.pkl", "rb") as f: + data_minimize_force = pickle.load(f) - with open(r"minimizing_fatigue.pkl", "rb") as f: - data_minimize = pickle.load(f) + with open(r"minimize_muscle_fatigue.pkl", "rb") as f: + data_minimize_fatigue = pickle.load(f) muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] @@ -204,7 +205,7 @@ for i in range(3): for j in range(2): axs[i][j].set_xlim(left=0, right=1) - axs[i][j].set_ylim(bottom=0, top=190) + axs[i][j].set_ylim(bottom=0, top=300) axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", weight='bold', font="Times New Roman") @@ -213,17 +214,27 @@ [label.set_fontsize(14) for label in labels] if i == 0 and j == 0: - axs[i][j].plot(data_normal["time"], data_normal["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Normal") - axs[i][j].plot(data_minimize["time"], data_minimize["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, + label="Minimizing force") + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, label="Minimizing fatigue") else: - axs[i][j].plot(data_normal["time"], data_normal["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - axs[i][j].plot(data_minimize["time"], data_minimize["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) counter += 1 + # axs[3][0].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][0], ms=4, + # linewidth=5.0) + # axs[3][0].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][0], ms=4, + # linewidth=5.0) + # + # axs[3][1].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][1], ms=4, + # linewidth=5.0) + # axs[3][1].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][1], ms=4, + # linewidth=5.0) + plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 75, 150], yticklabels=[0, 75, 150]) + yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) @@ -233,6 +244,14 @@ plt.show() +# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] +# a_sum = 0 +# for key_a in a_list: +# a_sum += data_minimize_force["states"][key_a][0][-1] + + + + # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. # Journal of biomechanics, 38(12), 2451-2459. From dec2a00c4b5727f1dab03645d606617acb1513a4 Mon Sep 17 00:00:00 2001 From: kevin Date: Thu, 8 Feb 2024 11:23:45 -0500 Subject: [PATCH 39/63] corrected path and cleaning --- .../reaching_task_frequency_optimization.py | 86 +++++++++---------- .../reaching_task_intensity_optimization.py | 86 +++++++++---------- ...aching_task_pulse_duration_optimization.py | 86 +++++++++---------- 3 files changed, 123 insertions(+), 135 deletions(-) diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py index ce45fdc5..b9593d94 100644 --- a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -16,9 +16,6 @@ from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP -get_results = True -make_graphs = False - # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 @@ -70,48 +67,47 @@ axes=[Axis.X, Axis.Y] ) -if get_results: - for i in range(len(pickle_file_list)): - time = [] - states = [] - controls = [] - parameters = [] - - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start", - bound_data=[0, 5], - fes_muscle_models=fes_muscle_models, - n_stim=n_stim, - n_shooting=n_shooting, - final_time=1, - time_min=0.01, - time_max=0.1, - time_bimapping=False, - with_residual_torque=False, - custom_constraint=constraint, - muscle_force_length_relationship=True, - muscle_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 - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open("/result_file/pulse_apparition_" + pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) +for i in range(len(pickle_file_list)): + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + time_min=0.01, + time_max=0.1, + time_bimapping=False, + with_residual_torque=False, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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 + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open("/result_file/pulse_apparition_" + pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index 6baa5d2a..fb7e4653 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -16,9 +16,6 @@ from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP -get_results = True -make_graphs = False - # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 @@ -73,48 +70,47 @@ axes=[Axis.X, Axis.Y] ) -if get_results: - for i in range(len(pickle_file_list)): - time = [] - states = [] - controls = [] - parameters = [] - - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start", - bound_data=[0, 5], - fes_muscle_models=fes_muscle_models, - 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, - with_residual_torque=False, - custom_constraint=constraint, - muscle_force_length_relationship=True, - muscle_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 - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open("/result_file/pulse_intensity_" + pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) +for i in range(len(pickle_file_list)): + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models, + 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, + with_residual_torque=False, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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 + + dictionary = { + "time": time, + "states": states, + "controls": controls, + "parameters": parameters, + } + + with open("/result_file/pulse_intensity_" + pickle_file_list[i], "wb") as file: + pickle.dump(dictionary, file) # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index 0d177ad5..a51dd0c0 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -16,9 +16,6 @@ from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP -get_results = True -make_graphs = False - # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 @@ -71,48 +68,47 @@ axes=[Axis.X, Axis.Y] ) -if get_results: - for i in range(len(pickle_file_list)): - time = [] - states = [] - controls = [] - parameters = [] - - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start", - bound_data=[0, 5], - fes_muscle_models=fes_muscle_models, - n_stim=n_stim, - n_shooting=n_shooting, - final_time=1, - pulse_duration_min=minimum_pulse_duration, - pulse_duration_max=0.0006, - pulse_duration_bimapping=False, - with_residual_torque=False, - custom_constraint=constraint, - muscle_force_length_relationship=True, - muscle_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 - - 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) +for i in range(len(pickle_file_list)): + time = [] + states = [] + controls = [] + parameters = [] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../arm26.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=fes_muscle_models, + n_stim=n_stim, + n_shooting=n_shooting, + final_time=1, + pulse_duration_min=minimum_pulse_duration, + pulse_duration_max=0.0006, + pulse_duration_bimapping=False, + with_residual_torque=False, + custom_constraint=constraint, + muscle_force_length_relationship=True, + muscle_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 + + 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) # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). From 4596c392874965d82a488eafd7b79893e2a90466 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 11:33:26 -0500 Subject: [PATCH 40/63] black --- cocofest/models/ding2003.py | 27 ++++- cocofest/models/ding2003_with_fatigue.py | 10 +- cocofest/models/ding2007.py | 10 +- cocofest/models/ding2007_with_fatigue.py | 10 +- cocofest/models/dynamical_model.py | 15 +-- cocofest/models/hmed2018.py | 10 +- cocofest/models/hmed2018_with_fatigue.py | 10 +- ...comparing_minimize_fatigue_optimization.py | 3 +- examples/dynamics/reaching_task.py | 98 +++++++++++++------ examples/dynamics/reaching_task/make_gaph.py | 95 +++++++++++++----- .../reaching_task_frequency_optimization.py | 50 +++++----- .../reaching_task_intensity_optimization.py | 50 +++++----- ...aching_task_pulse_duration_optimization.py | 50 +++++----- .../test_models_dynamics_without_bioptim.py | 2 +- 14 files changed, 303 insertions(+), 137 deletions(-) diff --git a/cocofest/models/ding2003.py b/cocofest/models/ding2003.py index c205303d..df9e9c4c 100644 --- a/cocofest/models/ding2003.py +++ b/cocofest/models/ding2003.py @@ -138,7 +138,15 @@ def system_dynamics( """ r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 + f_dot = self.f_dot_fun( + cn, + f, + self.a_rest, + self.tau1_rest, + self.km_rest, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 return vertcat(cn_dot, f_dot) def exp_time_fun(self, t: MX, t_stim_i: MX) -> MX | float: @@ -223,7 +231,16 @@ def cn_dot_fun(self, cn: MX, r0: MX | float, t: MX, t_stim_prev: list[MX]) -> MX return (1 / self.tauc) * sum_multiplier - (cn / self.tauc) # Equation n°1 - def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | float, force_length_relationship: MX | float = 1, force_velocity_relationship: MX | float = 1) -> MX | float: + def f_dot_fun( + self, + cn: MX, + f: MX, + a: MX | float, + tau1: MX | float, + km: MX | float, + force_length_relationship: MX | float = 1, + force_velocity_relationship: MX | float = 1, + ) -> MX | float: """ Parameters ---------- @@ -246,7 +263,11 @@ def f_dot_fun(self, cn: MX, f: MX, a: MX | float, tau1: MX | float, km: MX | flo ------- The value of the derivative force (N) """ - return (a * (cn / (km + cn)) - (f / (tau1 + self.tau2 * (cn / (km + cn))))) * force_length_relationship * force_velocity_relationship # Equation n°2 + return ( + (a * (cn / (km + cn)) - (f / (tau1 + self.tau2 * (cn / (km + cn))))) + * force_length_relationship + * force_velocity_relationship + ) # Equation n°2 @staticmethod def dynamics( diff --git a/cocofest/models/ding2003_with_fatigue.py b/cocofest/models/ding2003_with_fatigue.py index 02377111..546ab5cc 100644 --- a/cocofest/models/ding2003_with_fatigue.py +++ b/cocofest/models/ding2003_with_fatigue.py @@ -140,7 +140,15 @@ def system_dynamics( """ r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, a, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 + f_dot = self.f_dot_fun( + cn, + f, + a, + tau1, + km, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 a_dot = self.a_dot_fun(a, f) # Equation n°5 tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 km_dot = self.km_dot_fun(km, f) # Equation n°11 diff --git a/cocofest/models/ding2007.py b/cocofest/models/ding2007.py index 1c0238b7..040992c8 100644 --- a/cocofest/models/ding2007.py +++ b/cocofest/models/ding2007.py @@ -105,7 +105,15 @@ def system_dynamics( r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article a = self.a_calculation(a_scale=self.a_scale, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article - f_dot = self.f_dot_fun(cn, f, a, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 from Ding's 2003 article + f_dot = self.f_dot_fun( + cn, + f, + a, + self.tau1_rest, + self.km_rest, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 from Ding's 2003 article return vertcat(cn_dot, f_dot) def a_calculation(self, a_scale: float | MX, impulse_time: MX) -> MX: diff --git a/cocofest/models/ding2007_with_fatigue.py b/cocofest/models/ding2007_with_fatigue.py index 5e192e05..74308415 100644 --- a/cocofest/models/ding2007_with_fatigue.py +++ b/cocofest/models/ding2007_with_fatigue.py @@ -124,7 +124,15 @@ def system_dynamics( r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev) # Equation n°1 from Ding's 2003 article a_calculated = self.a_calculation(a_scale=a, impulse_time=impulse_time) # Equation n°3 from Ding's 2007 article - f_dot = self.f_dot_fun(cn, f, a_calculated, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 from Ding's 2003 article + f_dot = self.f_dot_fun( + cn, + f, + a_calculated, + tau1, + km, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 from Ding's 2003 article a_dot = self.a_dot_fun(a, f) tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 from Ding's 2003 article km_dot = self.km_dot_fun(km, f) # Equation n°11 from Ding's 2003 article diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index cc21797b..eb8c3788 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -11,13 +11,14 @@ FatigueList, ) -from cocofest import (DingModelFrequency, - DingModelFrequencyWithFatigue, - DingModelPulseDurationFrequency, - DingModelPulseDurationFrequencyWithFatigue, - DingModelIntensityFrequency, - DingModelIntensityFrequencyWithFatigue, - ) +from cocofest import ( + DingModelFrequency, + DingModelFrequencyWithFatigue, + DingModelPulseDurationFrequency, + DingModelPulseDurationFrequencyWithFatigue, + DingModelIntensityFrequency, + DingModelIntensityFrequencyWithFatigue, +) class FESActuatedBiorbdModel(BiorbdModel): diff --git a/cocofest/models/hmed2018.py b/cocofest/models/hmed2018.py index 04980960..d223da83 100644 --- a/cocofest/models/hmed2018.py +++ b/cocofest/models/hmed2018.py @@ -107,7 +107,15 @@ def system_dynamics( """ r0 = self.km_rest + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev, intensity_stim=intensity_stim) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, self.a_rest, self.tau1_rest, self.km_rest, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 + f_dot = self.f_dot_fun( + cn, + f, + self.a_rest, + self.tau1_rest, + self.km_rest, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 return vertcat(cn_dot, f_dot) def cn_dot_fun( diff --git a/cocofest/models/hmed2018_with_fatigue.py b/cocofest/models/hmed2018_with_fatigue.py index 3f7bc827..335a960a 100644 --- a/cocofest/models/hmed2018_with_fatigue.py +++ b/cocofest/models/hmed2018_with_fatigue.py @@ -123,7 +123,15 @@ def system_dynamics( """ r0 = km + self.r0_km_relationship # Simplification cn_dot = self.cn_dot_fun(cn, r0, t, t_stim_prev=t_stim_prev, intensity_stim=intensity_stim) # Equation n°1 - f_dot = self.f_dot_fun(cn, f, a, tau1, km, force_length_relationship=force_length_relationship, force_velocity_relationship=force_velocity_relationship) # Equation n°2 + f_dot = self.f_dot_fun( + cn, + f, + a, + tau1, + km, + force_length_relationship=force_length_relationship, + force_velocity_relationship=force_velocity_relationship, + ) # Equation n°2 a_dot = self.a_dot_fun(a, f) # Equation n°5 tau1_dot = self.tau1_dot_fun(tau1, f) # Equation n°9 km_dot = self.km_dot_fun(km, f) # Equation n°11 diff --git a/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py b/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py index 4f52ad37..6f80e793 100644 --- a/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py +++ b/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py @@ -75,7 +75,7 @@ "states": states, "controls": controls, "parameters": parameters, - } + } with open(pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) @@ -92,4 +92,3 @@ # plt.plot(data_minimize["time"], data_minimize["states"]["F_BIClong"][0], label="Minimizing fatigue") # plt.legend() # plt.show() - diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py index b7140b07..b8a495d4 100644 --- a/examples/dynamics/reaching_task.py +++ b/examples/dynamics/reaching_task.py @@ -18,7 +18,11 @@ Node, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import ( + DingModelIntensityFrequencyWithFatigue, + DingModelPulseDurationFrequencyWithFatigue, + FESActuatedBiorbdModelOCP, +) get_results = False make_graphs = True @@ -83,18 +87,24 @@ n_shooting = 2 objective_functions = ObjectiveList() - fes_muscle_models = [[biceps_long_duration, - biceps_short_duration, - triceps_long_duration, - triceps_lat_duration, - triceps_med_duration, - brachioradialis_duration], - [biceps_long_intensity, - biceps_short_intensity, - triceps_long_intensity, - triceps_lat_intensity, - triceps_med_intensity, - brachioradialis_intensity]] + fes_muscle_models = [ + [ + biceps_long_duration, + biceps_short_duration, + triceps_long_duration, + triceps_lat_duration, + triceps_med_duration, + brachioradialis_duration, + ], + [ + biceps_long_intensity, + biceps_short_intensity, + triceps_long_intensity, + triceps_lat_intensity, + triceps_med_intensity, + brachioradialis_intensity, + ], + ] # for j in range(n_stim): # objective_functions.add( @@ -112,7 +122,7 @@ second_marker="reaching_target", phase=39, node=Node.END, - axes=[Axis.X, Axis.Y] + axes=[Axis.X, Axis.Y], ) # objective_functions.add( @@ -185,7 +195,7 @@ "states": states, "controls": controls, "parameters": parameters, - } + } with open(pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) @@ -207,20 +217,49 @@ axs[i][j].set_xlim(left=0, right=1) axs[i][j].set_ylim(bottom=0, top=300) - axs[i][j].text(.025, .975, f'{muscle_names[counter]}', transform=axs[i][j].transAxes, ha="left", va="top", weight='bold', font="Times New Roman") + axs[i][j].text( + 0.025, + 0.975, + f"{muscle_names[counter]}", + transform=axs[i][j].transAxes, + ha="left", + va="top", + 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] if i == 0 and j == 0: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing force") - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0, - label="Minimizing fatigue") + axs[i][j].plot( + data_minimize_force["time"], + data_minimize_force["states"][muscle_keys[counter]][0], + ms=4, + linewidth=5.0, + label="Minimizing force", + ) + axs[i][j].plot( + data_minimize_fatigue["time"], + data_minimize_fatigue["states"][muscle_keys[counter]][0], + ms=4, + linewidth=5.0, + label="Minimizing fatigue", + ) else: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][muscle_keys[counter]][0], ms=4, linewidth=5.0) + axs[i][j].plot( + data_minimize_force["time"], + data_minimize_force["states"][muscle_keys[counter]][0], + ms=4, + linewidth=5.0, + ) + axs[i][j].plot( + data_minimize_fatigue["time"], + data_minimize_fatigue["states"][muscle_keys[counter]][0], + ms=4, + linewidth=5.0, + ) counter += 1 # axs[3][0].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][0], ms=4, @@ -233,11 +272,16 @@ # axs[3][1].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][1], ms=4, # linewidth=5.0) - plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) + plt.setp( + axs, + xticks=[0, 0.25, 0.5, 0.75, 1], + xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 100, 200, 300], + yticklabels=[0, 100, 200, 300], + ) - fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) - fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) + fig.supxlabel("Time (s)", font="Times New Roman", fontsize=14) + fig.supylabel("Force (N)", font="Times New Roman", fontsize=14) # fig.legend() # fig.tight_layout() @@ -250,8 +294,6 @@ # a_sum += data_minimize_force["states"][key_a][0][-1] - - # [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). # Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. # Journal of biomechanics, 38(12), 2451-2459. diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index 96a64588..87ea36eb 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -9,19 +9,30 @@ chosen_graph_to_plot = "duration" -frequency_path = [r"/result_file/pulse_apparition_minimize_muscle_force.pkl", - r"/result_file/pulse_apparition_minimize_muscle_fatigue.pkl"] - -duration_path = [r"/result_file/pulse_duration_minimize_muscle_force.pkl", - r"/result_file/pulse_duration_minimize_muscle_fatigue.pkl"] - -intensity_path = [r"/result_file/pulse_intensity_minimize_muscle_force.pkl", - r"/result_file/pulse_intensity_minimize_muscle_fatigue.pkl"] - -chosen_graph_to_plot_path = (frequency_path if chosen_graph_to_plot == "frequency" else - duration_path if chosen_graph_to_plot == "duration" else - intensity_path if chosen_graph_to_plot == "duration" else - None) +frequency_path = [ + r"/result_file/pulse_apparition_minimize_muscle_force.pkl", + r"/result_file/pulse_apparition_minimize_muscle_fatigue.pkl", +] + +duration_path = [ + r"/result_file/pulse_duration_minimize_muscle_force.pkl", + r"/result_file/pulse_duration_minimize_muscle_fatigue.pkl", +] + +intensity_path = [ + r"/result_file/pulse_intensity_minimize_muscle_force.pkl", + r"/result_file/pulse_intensity_minimize_muscle_fatigue.pkl", +] + +chosen_graph_to_plot_path = ( + frequency_path + if chosen_graph_to_plot == "frequency" + else duration_path + if chosen_graph_to_plot == "duration" + else intensity_path + if chosen_graph_to_plot == "duration" + else None +) if chosen_graph_to_plot_path is None: raise ValueError("The chosen graph to plot is not valid") @@ -42,27 +53,61 @@ axs[i][j].set_xlim(left=0, right=1) axs[i][j].set_ylim(bottom=0, top=300) - axs[i][j].text(.025, .975, f'{muscle_names[index]}', transform=axs[i][j].transAxes, ha="left", va="top", weight='bold', font="Times New Roman") + axs[i][j].text( + 0.025, + 0.975, + f"{muscle_names[index]}", + transform=axs[i][j].transAxes, + ha="left", + va="top", + 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] if i == 0 and j == 0: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0, - label="Minimizing force") - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0, - label="Minimizing fatigue") + axs[i][j].plot( + data_minimize_force["time"], + data_minimize_force["states"][force_muscle_keys[index]][0], + ms=4, + linewidth=5.0, + label="Minimizing force", + ) + axs[i][j].plot( + data_minimize_fatigue["time"], + data_minimize_fatigue["states"][force_muscle_keys[index]][0], + ms=4, + linewidth=5.0, + label="Minimizing fatigue", + ) else: - axs[i][j].plot(data_minimize_force["time"], data_minimize_force["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0) - axs[i][j].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"][force_muscle_keys[index]][0], ms=4, linewidth=5.0) + axs[i][j].plot( + data_minimize_force["time"], + data_minimize_force["states"][force_muscle_keys[index]][0], + ms=4, + linewidth=5.0, + ) + axs[i][j].plot( + data_minimize_fatigue["time"], + data_minimize_fatigue["states"][force_muscle_keys[index]][0], + ms=4, + linewidth=5.0, + ) index += 1 -plt.setp(axs, xticks=[0, 0.25, 0.5, 0.75, 1], xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], yticklabels=[0, 100, 200, 300]) +plt.setp( + axs, + xticks=[0, 0.25, 0.5, 0.75, 1], + xticklabels=[0, 0.25, 0.5, 0.75, 1], + yticks=[0, 100, 200, 300], + yticklabels=[0, 100, 200, 300], +) -fig.supxlabel('Time (s)', font="Times New Roman", fontsize=14) -fig.supylabel('Force (N)', font="Times New Roman", fontsize=14) +fig.supxlabel("Time (s)", font="Times New Roman", fontsize=14) +fig.supylabel("Force (N)", font="Times New Roman", fontsize=14) # fig.legend() # fig.tight_layout() @@ -72,4 +117,4 @@ # a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] # a_sum = 0 # for key_a in a_list: -# a_sum += data_minimize_force["states"][key_a][0][-1] \ No newline at end of file +# a_sum += data_minimize_force["states"][key_a][0][-1] diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py index b9593d94..e549ac33 100644 --- a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -20,12 +20,14 @@ biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 brachioradialis_fiber_type_2_proportion = 0.457 -alpha_a_proportion_list = [biceps_fiber_type_2_proportion, - biceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - brachioradialis_fiber_type_2_proportion] +alpha_a_proportion_list = [ + biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion, +] # PCSA (cm²) from [2] biceps_pcsa = 12.7 @@ -35,19 +37,23 @@ biceps_a_rest_proportion = 12.7 / 28.3 triceps_a_rest_proportion = 1 brachioradialis_a_rest_proportion = 11.6 / 28.3 -a_rest_proportion_list = [biceps_a_rest_proportion, - biceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - brachioradialis_a_rest_proportion] - -fes_muscle_models = [DingModelFrequencyWithFatigue(muscle_name="BIClong"), - DingModelFrequencyWithFatigue(muscle_name="BICshort"), - DingModelFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelFrequencyWithFatigue(muscle_name="TRImed"), - DingModelFrequencyWithFatigue(muscle_name="BRA")] +a_rest_proportion_list = [ + biceps_a_rest_proportion, + biceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + brachioradialis_a_rest_proportion, +] + +fes_muscle_models = [ + DingModelFrequencyWithFatigue(muscle_name="BIClong"), + DingModelFrequencyWithFatigue(muscle_name="BICshort"), + DingModelFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelFrequencyWithFatigue(muscle_name="TRImed"), + DingModelFrequencyWithFatigue(muscle_name="BRA"), +] 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] @@ -62,9 +68,9 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=n_stim-1, + phase=n_stim - 1, node=Node.END, - axes=[Axis.X, Axis.Y] + axes=[Axis.X, Axis.Y], ) for i in range(len(pickle_file_list)): @@ -104,7 +110,7 @@ "states": states, "controls": controls, "parameters": parameters, - } + } with open("/result_file/pulse_apparition_" + pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index fb7e4653..8810a3d6 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -20,12 +20,14 @@ biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 brachioradialis_fiber_type_2_proportion = 0.457 -alpha_a_proportion_list = [biceps_fiber_type_2_proportion, - biceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - brachioradialis_fiber_type_2_proportion] +alpha_a_proportion_list = [ + biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion, +] # PCSA (cm²) from [2] biceps_pcsa = 12.7 @@ -35,19 +37,23 @@ biceps_a_rest_proportion = 12.7 / 28.3 triceps_a_rest_proportion = 1 brachioradialis_a_rest_proportion = 11.6 / 28.3 -a_rest_proportion_list = [biceps_a_rest_proportion, - biceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - triceps_a_rest_proportion, - brachioradialis_a_rest_proportion] - -fes_muscle_models = [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BRA")] +a_rest_proportion_list = [ + biceps_a_rest_proportion, + biceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + triceps_a_rest_proportion, + brachioradialis_a_rest_proportion, +] + +fes_muscle_models = [ + DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), + DingModelIntensityFrequencyWithFatigue(muscle_name="BRA"), +] 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] @@ -65,9 +71,9 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=n_stim-1, + phase=n_stim - 1, node=Node.END, - axes=[Axis.X, Axis.Y] + axes=[Axis.X, Axis.Y], ) for i in range(len(pickle_file_list)): @@ -107,7 +113,7 @@ "states": states, "controls": controls, "parameters": parameters, - } + } with open("/result_file/pulse_intensity_" + pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index a51dd0c0..0b06512c 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -20,12 +20,14 @@ biceps_fiber_type_2_proportion = 0.607 triceps_fiber_type_2_proportion = 0.465 brachioradialis_fiber_type_2_proportion = 0.457 -alpha_a_proportion_list = [biceps_fiber_type_2_proportion, - biceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - triceps_fiber_type_2_proportion, - brachioradialis_fiber_type_2_proportion] +alpha_a_proportion_list = [ + biceps_fiber_type_2_proportion, + biceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + triceps_fiber_type_2_proportion, + brachioradialis_fiber_type_2_proportion, +] # PCSA (cm²) from [2] biceps_pcsa = 12.7 @@ -35,19 +37,23 @@ biceps_a_scale_proportion = 12.7 / 28.3 triceps_a_scale_proportion = 1 brachioradialis_a_scale_proportion = 11.6 / 28.3 -a_scale_proportion_list = [biceps_a_scale_proportion, - biceps_a_scale_proportion, - triceps_a_scale_proportion, - triceps_a_scale_proportion, - triceps_a_scale_proportion, - brachioradialis_a_scale_proportion] - -fes_muscle_models = [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed"), - DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA")] +a_scale_proportion_list = [ + biceps_a_scale_proportion, + biceps_a_scale_proportion, + triceps_a_scale_proportion, + triceps_a_scale_proportion, + triceps_a_scale_proportion, + brachioradialis_a_scale_proportion, +] + +fes_muscle_models = [ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA"), +] 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] @@ -63,9 +69,9 @@ ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=n_stim-1, + phase=n_stim - 1, node=Node.END, - axes=[Axis.X, Axis.Y] + axes=[Axis.X, Axis.Y], ) for i in range(len(pickle_file_list)): @@ -105,7 +111,7 @@ "states": states, "controls": controls, "parameters": parameters, - } + } with open("/result_file/pulse_duration_" + pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) diff --git a/tests/shard1/test_models_dynamics_without_bioptim.py b/tests/shard1/test_models_dynamics_without_bioptim.py index 29548a19..4c8c7786 100644 --- a/tests/shard1/test_models_dynamics_without_bioptim.py +++ b/tests/shard1/test_models_dynamics_without_bioptim.py @@ -106,7 +106,7 @@ def test_ding2007_dynamics(): cn=5, f=100, a=4920, tau1=0.050957, km=0.103, t=0.11, t_stim_prev=[0, 0.1], impulse_time=0.0002 ) ).squeeze(), - np.array(DM([-4.179e+02, -4.905e+02, -4.000e-04, 2.108e-02, 1.900e-05])).squeeze(), + np.array(DM([-4.179e02, -4.905e02, -4.000e-04, 2.108e-02, 1.900e-05])).squeeze(), decimal=1, ) np.testing.assert_almost_equal(model.exp_time_fun(t=0.1, t_stim_i=0.09), 0.4028903215291327) From c389101ff5c787b3a195dea2058240d780784d09 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 14:26:29 -0500 Subject: [PATCH 41/63] Answering to assossiated pr commantes --- cocofest/optimization/fes_ocp.py | 4 +- cocofest/optimization/fes_ocp_dynamics.py | 183 +- ...timization_musculoskeletal_dynamic_2dof.py | 2 +- ...nsity_optimization_cycling_multi_muscle.py | 2 +- ...> intensity_optimization_hold_position.py} | 22 +- ...ptimization_hold_position_multi_muscle.py} | 2 +- ...comparing_minimize_fatigue_optimization.py | 94 - .../frequecy_optimization_minimize_fatigue.py | 5 +- ..._duration_optimization_minimize_fatigue.py | 2 +- ...intensity_optimization_minimize_fatigue.py | 2 +- ...orce_velocity_relationships_comparison.py} | 28 +- examples/dynamics/reaching_task.py | 303 - .../reaching_task_frequency_optimization.py | 2 +- .../reaching_task_intensity_optimization.py | 2 +- ...aching_task_pulse_duration_optimization.py | 2 +- .../result_file/minimize_muscle_fatigue.pkl | Bin 0 -> 28108 bytes .../result_file/minimize_muscle_force.pkl | Bin 0 -> 28108 bytes examples/getting_started/arm26_biceps.bioMod | 256 - .../getting_started/arm26_biceps_1dof.bioMod | 254 - ...cy_optimization_musculoskeletal_dynamic.py | 4 +- .../getting_started/mesh/arm_r_humerus.vtp | 1228 -- examples/getting_started/mesh/arm_r_ulna.vtp | 842 -- examples/getting_started/mesh/ground_ribs.vtp | 10998 ---------------- ...on_optimization_musculoskeletal_dynamic.py | 4 +- ...ty_optimization_musculoskeletal_dynamic.py | 4 +- .../{dynamics => msk_models}/arm26.bioMod | 0 .../arm26_biceps.bioMod | 0 .../arm26_biceps_1dof.bioMod | 0 .../arm26_biceps_triceps.bioMod | 0 .../arm26_cycling.bioMod | 0 .../mesh/arm_r_humerus.vtp | 0 .../mesh/arm_r_ulna.vtp | 0 .../mesh/ground_ribs.vtp | 0 33 files changed, 190 insertions(+), 14055 deletions(-) rename examples/dynamics/{intensity_optimization_track_q.py => intensity_optimization_hold_position.py} (84%) rename examples/dynamics/{intensity_optimization_track_q_multi_muscle.py => intensity_optimization_hold_position_multi_muscle.py} (97%) delete mode 100644 examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py rename examples/dynamics/{muscle_force_length_relationship_comparison.py => muscle_force_length_and_force_velocity_relationships_comparison.py} (52%) delete mode 100644 examples/dynamics/reaching_task.py create mode 100644 examples/dynamics/reaching_task/result_file/minimize_muscle_fatigue.pkl create mode 100644 examples/dynamics/reaching_task/result_file/minimize_muscle_force.pkl delete mode 100644 examples/getting_started/arm26_biceps.bioMod delete mode 100644 examples/getting_started/arm26_biceps_1dof.bioMod delete mode 100644 examples/getting_started/mesh/arm_r_humerus.vtp delete mode 100644 examples/getting_started/mesh/arm_r_ulna.vtp delete mode 100644 examples/getting_started/mesh/ground_ribs.vtp rename examples/{dynamics => msk_models}/arm26.bioMod (100%) rename examples/{dynamics => msk_models}/arm26_biceps.bioMod (100%) rename examples/{dynamics => msk_models}/arm26_biceps_1dof.bioMod (100%) rename examples/{dynamics => msk_models}/arm26_biceps_triceps.bioMod (100%) rename examples/{dynamics => msk_models}/arm26_cycling.bioMod (100%) rename examples/{dynamics => msk_models}/mesh/arm_r_humerus.vtp (100%) rename examples/{dynamics => msk_models}/mesh/arm_r_ulna.vtp (100%) rename examples/{dynamics => msk_models}/mesh/ground_ribs.vtp (100%) diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index e5eee3c1..32d9e1f4 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -159,7 +159,7 @@ def prepare_ocp( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down ) - force_fourier_coef = None if force_tracking is None else OcpFes._build_fourrier_coeff(force_tracking) + force_fourier_coef = None if force_tracking is None else OcpFes._build_fourier_coeff(force_tracking) end_node_tracking = end_node_tracking models = [model] * n_stim n_shooting = [n_shooting] * n_stim @@ -434,7 +434,7 @@ def _sanity_check_frequency(n_stim, final_time, frequency, round_down): raise TypeError("round_down must be bool type") @staticmethod - def _build_fourrier_coeff(force_tracking): + def _build_fourier_coeff(force_tracking): return FourierSeries().compute_real_fourier_coeffs(force_tracking[0], force_tracking[1], 50) @staticmethod diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index b4166362..7c0139de 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -89,7 +89,12 @@ def prepare_ocp( The bound type to use (start, end, start_end) bound_data: list The data to use for the bound - fes_muscle_models: list[DingModelFrequency] | list[DingModelFrequencyWithFatigue] | list[DingModelPulseDurationFrequency] | list[DingModelPulseDurationFrequencyWithFatigue] | list[DingModelIntensityFrequency] | list[DingModelIntensityFrequencyWithFatigue] + fes_muscle_models: list[DingModelFrequency] + | list[DingModelFrequencyWithFatigue] + | list[DingModelPulseDurationFrequency] + | list[DingModelPulseDurationFrequencyWithFatigue] + | list[DingModelIntensityFrequency] + | list[DingModelIntensityFrequencyWithFatigue] The fes model type used for the ocp n_stim: int Number of stimulation that will occur during the ocp, it is as well refer as phases @@ -146,7 +151,7 @@ def prepare_ocp( n_threads: int The number of thread to use while solving (multi-threading if > 1) """ - # TODO : MAKE A NEW SANITY CHECK FOR THE FES MUSCLE MODEL + OcpFes._sanity_check( model=fes_muscle_models[0], n_stim=n_stim, @@ -165,15 +170,26 @@ def prepare_ocp( pulse_intensity_min=pulse_intensity_min, pulse_intensity_max=pulse_intensity_max, pulse_intensity_bimapping=pulse_intensity_bimapping, - force_tracking=force_tracking, - end_node_tracking=end_node_tracking, custom_objective=custom_objective, use_sx=use_sx, ode_solver=ode_solver, n_threads=n_threads, ) - FESActuatedBiorbdModelOCP._sanity_check_fes_models(fes_muscle_models) + FESActuatedBiorbdModelOCP._sanity_check_fes_models_inputs( + biorbd_model_path=biorbd_model_path, + bound_type=bound_type, + bound_data=bound_data, + fes_muscle_models=fes_muscle_models, + force_tracking=force_tracking, + end_node_tracking=end_node_tracking, + q_tracking=q_tracking, + with_residual_torque=with_residual_torque, + muscle_force_length_relationship=muscle_force_length_relationship, + muscle_force_velocity_relationship=muscle_force_velocity_relationship, + minimize_muscle_fatigue=minimize_muscle_fatigue, + minimize_muscle_force=minimize_muscle_force, + ) OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) @@ -185,14 +201,15 @@ def prepare_ocp( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down ) - # NOT AVAILABLE FOR MULTI MUSCLE - force_fourier_coef = None if force_tracking is None else OcpFes._build_fourrier_coeff(force_tracking) - end_node_tracking = end_node_tracking + force_fourier_coef = [] if force_tracking else None + if force_tracking: + for i in range(len(force_tracking[1])): + force_fourier_coef.append(OcpFes._build_fourier_coeff([force_tracking[0], force_tracking[1][i]])) q_fourier_coef = [] if q_tracking else None if q_tracking: for i in range(len(q_tracking[1])): - q_fourier_coef.append(OcpFes._build_fourrier_coeff([q_tracking[0], q_tracking[1][i]])) + q_fourier_coef.append(OcpFes._build_fourier_coeff([q_tracking[0], q_tracking[1][i]])) n_shooting = [n_shooting] * n_stim final_time_phase, constraints, phase_time_bimapping = OcpFes._build_phase_time( @@ -255,6 +272,7 @@ def prepare_ocp( n_stim, ) u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) + muscle_force_key = ["F_" + fes_muscle_models[i].muscle_name for i in range(len(fes_muscle_models))] objective_functions = FESActuatedBiorbdModelOCP._set_objective( n_stim, n_shooting, @@ -264,6 +282,7 @@ def prepare_ocp( q_fourier_coef, minimize_muscle_fatigue, minimize_muscle_force, + muscle_force_key, ) return OptimalControlProgram( @@ -450,7 +469,9 @@ def _build_parameters( if pulse_intensity_bimapping: pass - # parameter_bimapping.add(name="pulse_intensity", to_second=[0 for _ in range(n_stim)], to_first=[0]) + # parameter_bimapping.add(name="pulse_intensity", + # to_second=[0 for _ in range(n_stim)], + # to_first=[0]) # TODO : Fix Bimapping in Bioptim return parameters, parameters_bounds, parameters_init, parameter_objectives @@ -542,12 +563,6 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") - # if bound_type == "all": - # q_x_bounds = bound_data[i] - - q_x_bounds.min[0] = 0 - q_x_bounds.max[1] = 2.6166666666666667 - if i == 0: if bound_type == "start_end": for j in range(bio_models[i].nb_q): @@ -600,6 +615,7 @@ def _set_objective( q_fourier_coef, minimize_muscle_fatigue, minimize_muscle_force, + muscle_force_key, ): # Creates the objective for our problem objective_functions = ObjectiveList() @@ -609,29 +625,30 @@ def _set_objective( for j in range(len(custom_objective[i])): objective_functions.add(custom_objective[i][j]) - if force_fourier_coef is not None: # TODO : Enable multiple force tracking for multiple muscles - for phase in range(n_stim): - for i in range(n_shooting[phase]): - objective_functions.add( - CustomObjective.track_state_from_time, - custom_type=ObjectiveFcn.Mayer, - node=i, - fourier_coeff=force_fourier_coef, - key="F", - quadratic=True, - weight=1, - phase=phase, - ) + if force_fourier_coef is not None: + for j in range(len(muscle_force_key)): + for phase in range(n_stim): + for i in range(n_shooting[phase]): + objective_functions.add( + CustomObjective.track_state_from_time, + custom_type=ObjectiveFcn.Mayer, + node=i, + fourier_coeff=force_fourier_coef[j], + key=muscle_force_key[j], + quadratic=True, + weight=1, + phase=phase, + ) - if end_node_tracking: - if isinstance(end_node_tracking, int | float): + if end_node_tracking is not None: + for j in range(len(muscle_force_key)): objective_functions.add( ObjectiveFcn.Mayer.MINIMIZE_STATE, node=Node.END, - key="F", + key=muscle_force_key[j], quadratic=True, weight=1, - target=end_node_tracking, + target=end_node_tracking[j], phase=n_stim - 1, ) @@ -703,7 +720,45 @@ def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): ) @staticmethod - def _sanity_check_fes_models(fes_muscle_models): + def _sanity_check_fes_models_inputs( + biorbd_model_path, + bound_type, + bound_data, + fes_muscle_models, + force_tracking, + end_node_tracking, + q_tracking, + with_residual_torque, + muscle_force_length_relationship, + muscle_force_velocity_relationship, + minimize_muscle_fatigue, + minimize_muscle_force, + ): + if not isinstance(biorbd_model_path, str): + raise TypeError("biorbd_model_path should be a string") + + if bound_type: + tested_bio_model = FESActuatedBiorbdModel( + name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models + ) + if not isinstance(bound_type, str) or bound_type not in ["start", "end", "start_end"]: + raise ValueError("bound_type should be a string and should be equal to start, end or start_end") + if not isinstance(bound_data, list): + raise TypeError("bound_data should be a list") + if bound_type == "start_end": + if len(bound_data) != tested_bio_model.nb_q: + raise ValueError(f"bound_data should be a list of {tested_bio_model.nb_q} elements") + if not isinstance(bound_data[0], list) or not isinstance(bound_data[1], list): + raise TypeError("bound_data should be a list of two list") + if len(bound_data[0]) != len(bound_data[1]): + raise ValueError("bound_data should be a list of two list with the same size") + if bound_type == "start" or bound_type == "end": + if len(bound_data) != tested_bio_model.nb_q: + raise ValueError(f"bound_data should be a list of {tested_bio_model.nb_q} element") + for i in range(len(bound_data)): + if not isinstance(bound_data[i], int | float): + raise TypeError(f"bound data index {i}: {bound_data[i]} should be an int or float") + for i in range(len(fes_muscle_models)): if not isinstance( fes_muscle_models[i], @@ -715,5 +770,63 @@ def _sanity_check_fes_models(fes_muscle_models): | DingModelIntensityFrequencyWithFatigue, ): raise TypeError( - "model must be a DingModelFrequency, DingModelFrequencyWithFatigue, DingModelPulseDurationFrequency, DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequency, DingModelIntensityFrequencyWithFatigue type" + "model must be a DingModelFrequency," + " DingModelFrequencyWithFatigue," + " DingModelPulseDurationFrequency," + " DingModelPulseDurationFrequencyWithFatigue," + " DingModelIntensityFrequency," + " DingModelIntensityFrequencyWithFatigue type" ) + + if force_tracking: + if isinstance(force_tracking, list): + if not isinstance(force_tracking[0], np.ndarray): + raise TypeError(f"force_tracking index 0: {force_tracking[0]} must be np.ndarray type") + if not isinstance(force_tracking[1], list): + raise TypeError(f"force_tracking index 1: {force_tracking[1]} must be list type") + if len(force_tracking[1]) != len(fes_muscle_models): + raise ValueError( + "force_tracking index 1 list must have the same size as the number of muscles in fes_muscle_models" + ) + if len(force_tracking[0]) != len(force_tracking[1]) or len(force_tracking) != 2: + raise ValueError("force_tracking time and force argument must be the same length") + else: + raise TypeError(f"force_tracking: {force_tracking} must be list type") + + if end_node_tracking: + if not isinstance(end_node_tracking, list): + raise TypeError(f"force_tracking: {end_node_tracking} must be list type") + if len(end_node_tracking) != len(fes_muscle_models): + raise ValueError( + "end_node_tracking list must have the same size as the number of muscles in fes_muscle_models" + ) + for i in range(len(end_node_tracking)): + if not isinstance(end_node_tracking[i], int | float): + raise TypeError(f"end_node_tracking index {i}: {end_node_tracking[i]} must be int or float type") + + if q_tracking: + if not isinstance(q_tracking, list) and len(q_tracking) != 2: + raise TypeError("q_tracking should be a list of size 2") + tested_bio_model = FESActuatedBiorbdModel( + name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models + ) + if len(q_tracking[0]) != 1: + raise ValueError("q_tracking[0] should be a list of size 1") + if len(q_tracking[1]) != tested_bio_model.nb_q: + raise ValueError("q_tracking[1] should have the same size as the number of generalized coordinates") + for i in range(tested_bio_model.nb_q): + if len(q_tracking[0][0]) != len(q_tracking[1][i]): + raise ValueError("q_tracking[0] and q_tracking[1] should have the same size") + + list_to_check = [ + with_residual_torque, + muscle_force_length_relationship, + muscle_force_velocity_relationship, + minimize_muscle_fatigue, + minimize_muscle_force, + ] + + for i in range(len(list_to_check)): + if list_to_check[i]: + if not isinstance(list_to_check[i], bool): + raise TypeError(f"{list_to_check[i]} should be a boolean") diff --git a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py index 3bd87a67..6ca3a2fc 100644 --- a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py +++ b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py @@ -20,7 +20,7 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps.bioMod", + biorbd_model_path="../msk_models/arm26_biceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 120]], fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong")], diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index 20083a95..6efa3067 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -33,7 +33,7 @@ minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity(DingModelIntensityFrequency()) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26.bioMod", + biorbd_model_path="../msk_models/arm26.bioMod", bound_type="start_end", bound_data=[[65, 38], [65, 38]], fes_muscle_models=[ diff --git a/examples/dynamics/intensity_optimization_track_q.py b/examples/dynamics/intensity_optimization_hold_position.py similarity index 84% rename from examples/dynamics/intensity_optimization_track_q.py rename to examples/dynamics/intensity_optimization_hold_position.py index 49a05f25..92a83a1b 100644 --- a/examples/dynamics/intensity_optimization_track_q.py +++ b/examples/dynamics/intensity_optimization_hold_position.py @@ -31,23 +31,23 @@ quadratic=True, phase=i, ) -objective_functions.add( - ObjectiveFcn.Mayer.MINIMIZE_STATE, - key="qdot", - index=[0], - node=Node.ALL, - target=np.array([[0]] * (n_shooting + 1)).T, - weight=10, - quadratic=True, - phase=i, -) + objective_functions.add( + ObjectiveFcn.Mayer.MINIMIZE_STATE, + key="qdot", + index=[0], + node=Node.ALL, + target=np.array([[0]] * (n_shooting + 1)).T, + weight=10, + quadratic=True, + phase=i, + ) minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( DingModelIntensityFrequencyWithFatigue() ) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1dof.bioMod", + biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start", bound_data=[90], fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], diff --git a/examples/dynamics/intensity_optimization_track_q_multi_muscle.py b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py similarity index 97% rename from examples/dynamics/intensity_optimization_track_q_multi_muscle.py rename to examples/dynamics/intensity_optimization_hold_position_multi_muscle.py index eeed959d..44395964 100644 --- a/examples/dynamics/intensity_optimization_track_q_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py @@ -47,7 +47,7 @@ ) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26.bioMod", + biorbd_model_path="../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 90], fes_muscle_models=[ diff --git a/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py b/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py deleted file mode 100644 index 6f80e793..00000000 --- a/examples/dynamics/minimize_fatigue/comparing_minimize_fatigue_optimization.py +++ /dev/null @@ -1,94 +0,0 @@ -""" -This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. -""" -import pickle -import numpy as np -import matplotlib.pyplot as plt - -from bioptim import ( - ObjectiveFcn, - ObjectiveList, - Solver, -) - -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP - -get_results = False -make_graphs = True - -if get_results: - n_stim = 10 - n_shooting = 10 - objective_functions = ObjectiveList() - - for i in range(n_stim): - objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10, quadratic=True, phase=i) - - minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() - ) - - minimize_fatigue = [False, True] - pickle_file_list = ["normal.pkl", "minimizing_fatigue.pkl"] - time = [] - states = [] - controls = [] - parameters = [] - for i in range(len(minimize_fatigue)): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26.bioMod", - bound_type="start_end", - bound_data=[[0, 5], [0, 90]], - fes_muscle_models=[ - DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat"), - DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed"), - DingModelIntensityFrequencyWithFatigue(muscle_name="BRA"), - ], - n_stim=n_stim, - n_shooting=10, - final_time=1, - pulse_intensity_min=minimum_pulse_intensity, - pulse_intensity_max=80, - pulse_intensity_bimapping=False, - with_residual_torque=False, - custom_objective=objective_functions if i == 0 else None, - muscle_force_length_relationship=True, - muscle_force_velocity_relationship=True, - minimize_muscle_fatigue=minimize_fatigue[i], - use_sx=False, - ) - - sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() - time = sol.time - states = sol.states - controls = sol.controls - parameters = sol.parameters - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open(pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) - - -# if make_graphs: -# with open(r"normal.pkl", "rb") as f: -# data_normal = pickle.load(f) -# -# with open(r"minimizing_fatigue.pkl", "rb") as f: -# data_minimize = pickle.load(f) -# -# plt.plot(data_normal["time"], data_normal["states"]["F_BIClong"][0], label="Normal") -# plt.plot(data_minimize["time"], data_minimize["states"]["F_BIClong"][0], label="Minimizing fatigue") -# plt.legend() -# plt.show() diff --git a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py index f52b2087..daa3b82e 100644 --- a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py @@ -1,4 +1,7 @@ """ +/!\ This example is not functional yet. /!\ +/!\ It is a work in progress as biceps and triceps can not be stimulated seperatly /!\ + This example will do a 5 stimulation example with Ding's 2003 frequency model. Those ocp were build to move the elbow from 0 to 90 degrees angle. The stimulation apparition will be optimized to satisfy the motion and to minimize the overall muscle fatigue. @@ -33,7 +36,7 @@ ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26_biceps_triceps.bioMod", + biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], fes_muscle_models=[ diff --git a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py index a9f48eb2..f266fe8b 100644 --- a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py @@ -32,7 +32,7 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26_biceps_triceps.bioMod", + biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], fes_muscle_models=[ diff --git a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py index 733e0105..40ec5fe7 100644 --- a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py @@ -34,7 +34,7 @@ ) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26_biceps_triceps.bioMod", + biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], fes_muscle_models=[ diff --git a/examples/dynamics/muscle_force_length_relationship_comparison.py b/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py similarity index 52% rename from examples/dynamics/muscle_force_length_relationship_comparison.py rename to examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py index c78b909d..2ddef919 100644 --- a/examples/dynamics/muscle_force_length_relationship_comparison.py +++ b/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py @@ -1,9 +1,8 @@ """ -This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work. -Those ocp were build to move the elbow starting from 0 degrees angle. -The stimulation frequency will be set to 10Hz and intensity to 40mA. -No residual torque is allowed. +This example is used to compare the effect of the muscle force-length and force-velocity relationships +on the joint angle. """ + import matplotlib.pyplot as plt import numpy as np @@ -11,45 +10,34 @@ from cocofest import ( DingModelPulseDurationFrequencyWithFatigue, - DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP, ) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 -minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() -) sol_list = [] muscle_force_length_relationship = [False, True] -fes_muscle_model = [ - [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], - [DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], - # [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], - # [DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], -] for i in range(2): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1dof.bioMod", + biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start", bound_data=[0], - fes_muscle_models=fes_muscle_model[i], + fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], n_stim=10, n_shooting=10, final_time=1, pulse_duration=0.00025, - pulse_intensity=40, with_residual_torque=False, muscle_force_length_relationship=muscle_force_length_relationship[i], - muscle_force_velocity_relationship=False, + muscle_force_velocity_relationship=muscle_force_length_relationship[i], use_sx=False, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) sol_list.append(sol.merge_phases()) -plt.plot(sol_list[0].time, np.degrees(sol_list[0].states["q"][0]), label="without force length relationship") -plt.plot(sol_list[1].time, np.degrees(sol_list[1].states["q"][0]), label="with force length relationship") +plt.plot(sol_list[0].time, np.degrees(sol_list[0].states["q"][0]), label="without relationships") +plt.plot(sol_list[1].time, np.degrees(sol_list[1].states["q"][0]), label="with relationships") plt.xlabel("Time (s)") plt.ylabel("Angle (°)") diff --git a/examples/dynamics/reaching_task.py b/examples/dynamics/reaching_task.py deleted file mode 100644 index b8a495d4..00000000 --- a/examples/dynamics/reaching_task.py +++ /dev/null @@ -1,303 +0,0 @@ -""" -This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 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. -""" -import pickle -import numpy as np -import matplotlib.pyplot as plt - -from bioptim import ( - Axis, - ObjectiveFcn, - ObjectiveList, - ConstraintList, - ConstraintFcn, - Solver, - Node, -) - -from cocofest import ( - DingModelIntensityFrequencyWithFatigue, - DingModelPulseDurationFrequencyWithFatigue, - FESActuatedBiorbdModelOCP, -) - -get_results = False -make_graphs = True - -# Fiber type proportion from [1] -biceps_fiber_type_2_proportion = 0.607 -triceps_fiber_type_2_proportion = 0.465 -brachioradialis_fiber_type_2_proportion = 0.457 - -# PCSA (cm²) from [2] -biceps_pcsa = 12.7 -triceps_pcsa = 28.3 -brachioradialis_pcsa = 11.6 - -biceps_a_rest_proportion = 12.7 / 28.3 -triceps_a_rest_proportion = 1 -brachioradialis_a_rest_proportion = 11.6 / 28.3 - -biceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong") -biceps_long_duration.alpha_a = biceps_long_duration.alpha_a * biceps_fiber_type_2_proportion -biceps_long_duration.a_rest = biceps_long_duration.a_rest * biceps_a_rest_proportion -biceps_short_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BICshort") -biceps_short_duration.alpha_a = biceps_short_duration.alpha_a * biceps_fiber_type_2_proportion -biceps_short_duration.a_rest = biceps_short_duration.a_rest * biceps_a_rest_proportion -triceps_long_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong") -triceps_long_duration.alpha_a = triceps_long_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_long_duration.a_rest = triceps_long_duration.a_rest * triceps_a_rest_proportion -triceps_lat_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlat") -triceps_lat_duration.alpha_a = triceps_lat_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_lat_duration.a_rest = triceps_lat_duration.a_rest * triceps_a_rest_proportion -triceps_med_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRImed") -triceps_med_duration.alpha_a = triceps_med_duration.alpha_a * triceps_fiber_type_2_proportion -triceps_med_duration.a_rest = triceps_med_duration.a_rest * triceps_a_rest_proportion -brachioradialis_duration = DingModelPulseDurationFrequencyWithFatigue(muscle_name="BRA") -brachioradialis_duration.alpha_a = brachioradialis_duration.alpha_a * brachioradialis_fiber_type_2_proportion -brachioradialis_duration.a_rest = brachioradialis_duration.a_rest * brachioradialis_a_rest_proportion - -biceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong") -biceps_long_intensity.alpha_a = biceps_long_intensity.alpha_a * biceps_fiber_type_2_proportion -biceps_long_intensity.a_rest = biceps_long_intensity.a_rest * biceps_a_rest_proportion -biceps_short_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BICshort") -biceps_short_intensity.alpha_a = biceps_short_intensity.alpha_a * biceps_fiber_type_2_proportion -biceps_short_intensity.a_rest = biceps_short_intensity.a_rest * biceps_a_rest_proportion -triceps_long_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong") -triceps_long_intensity.alpha_a = triceps_long_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_long_intensity.a_rest = triceps_long_intensity.a_rest * triceps_a_rest_proportion -triceps_lat_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlat") -triceps_lat_intensity.alpha_a = triceps_lat_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_lat_intensity.a_rest = triceps_lat_intensity.a_rest * triceps_a_rest_proportion -triceps_med_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="TRImed") -triceps_med_intensity.alpha_a = triceps_med_intensity.alpha_a * triceps_fiber_type_2_proportion -triceps_med_intensity.a_rest = triceps_med_intensity.a_rest * triceps_a_rest_proportion -brachioradialis_intensity = DingModelIntensityFrequencyWithFatigue(muscle_name="BRA") -brachioradialis_intensity.alpha_a = brachioradialis_intensity.alpha_a * brachioradialis_fiber_type_2_proportion -brachioradialis_intensity.a_rest = brachioradialis_intensity.a_rest * brachioradialis_a_rest_proportion - -# pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] -pickle_file_list = ["minimize_muscle_force.pkl"] -if get_results: - for i in range(len(pickle_file_list)): - n_stim = 60 - n_shooting = 2 - objective_functions = ObjectiveList() - - fes_muscle_models = [ - [ - biceps_long_duration, - biceps_short_duration, - triceps_long_duration, - triceps_lat_duration, - triceps_med_duration, - brachioradialis_duration, - ], - [ - biceps_long_intensity, - biceps_short_intensity, - triceps_long_intensity, - triceps_lat_intensity, - triceps_med_intensity, - brachioradialis_intensity, - ], - ] - - # for j in range(n_stim): - # objective_functions.add( - # ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, - # key="tau", - # weight=100000, - # quadratic=True, - # phase=j, - # ) - - constraint = ConstraintList() - constraint.add( - ConstraintFcn.SUPERIMPOSE_MARKERS, - first_marker="COM_hand", - second_marker="reaching_target", - phase=39, - node=Node.END, - axes=[Axis.X, Axis.Y], - ) - - # objective_functions.add( - # ObjectiveFcn.Mayer.MINIMIZE_STATE, - # key="qdot", - # index=[0, 1], - # node=Node.ALL, - # target=np.array([[0, 0]] * (n_shooting + 1)).T, - # weight=1000, - # quadratic=True, - # phase=20, - # ) - # - # objective_functions.add( - # ObjectiveFcn.Mayer.MINIMIZE_STATE, - # key="qdot", - # index=[0, 1], - # node=Node.ALL, - # target=np.array([[0, 0]] * (n_shooting + 1)).T, - # weight=1000, - # quadratic=True, - # phase=21, - # ) - - minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( - DingModelIntensityFrequencyWithFatigue() - ) - - minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 - - time = [] - states = [] - controls = [] - parameters = [] - - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26.bioMod", - bound_type="start", - bound_data=[0, 5], - fes_muscle_models=fes_muscle_models[0], - n_stim=n_stim, - n_shooting=n_shooting, - final_time=2, - pulse_duration_min=minimum_pulse_duration, - pulse_duration_max=0.0006, - pulse_duration_bimapping=False, - pulse_intensity_min=minimum_pulse_intensity, - pulse_intensity_max=80, - pulse_intensity_bimapping=False, - with_residual_torque=False, - custom_objective=objective_functions, - custom_constraint=constraint, - muscle_force_length_relationship=True, - muscle_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=100000)).merge_phases() - # sol.animate() - # sol.graphs(show_bounds=False) - time = sol.time - states = sol.states - controls = sol.controls - parameters = sol.parameters - - dictionary = { - "time": time, - "states": states, - "controls": controls, - "parameters": parameters, - } - - with open(pickle_file_list[i], "wb") as file: - pickle.dump(dictionary, file) - - -if make_graphs: - with open(r"minimize_muscle_force.pkl", "rb") as f: - data_minimize_force = pickle.load(f) - - with open(r"minimize_muscle_fatigue.pkl", "rb") as f: - data_minimize_fatigue = pickle.load(f) - - muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] - muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] - fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) - counter = 0 - for i in range(3): - for j in range(2): - axs[i][j].set_xlim(left=0, right=1) - axs[i][j].set_ylim(bottom=0, top=300) - - axs[i][j].text( - 0.025, - 0.975, - f"{muscle_names[counter]}", - transform=axs[i][j].transAxes, - ha="left", - va="top", - 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] - - if i == 0 and j == 0: - axs[i][j].plot( - data_minimize_force["time"], - data_minimize_force["states"][muscle_keys[counter]][0], - ms=4, - linewidth=5.0, - label="Minimizing force", - ) - axs[i][j].plot( - data_minimize_fatigue["time"], - data_minimize_fatigue["states"][muscle_keys[counter]][0], - ms=4, - linewidth=5.0, - label="Minimizing fatigue", - ) - else: - axs[i][j].plot( - data_minimize_force["time"], - data_minimize_force["states"][muscle_keys[counter]][0], - ms=4, - linewidth=5.0, - ) - axs[i][j].plot( - data_minimize_fatigue["time"], - data_minimize_fatigue["states"][muscle_keys[counter]][0], - ms=4, - linewidth=5.0, - ) - counter += 1 - - # axs[3][0].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][0], ms=4, - # linewidth=5.0) - # axs[3][0].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][0], ms=4, - # linewidth=5.0) - # - # axs[3][1].plot(data_minimize_force["time"], data_minimize_force["states"]["q"][1], ms=4, - # linewidth=5.0) - # axs[3][1].plot(data_minimize_fatigue["time"], data_minimize_fatigue["states"]["q"][1], ms=4, - # linewidth=5.0) - - plt.setp( - axs, - xticks=[0, 0.25, 0.5, 0.75, 1], - xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], - yticklabels=[0, 100, 200, 300], - ) - - fig.supxlabel("Time (s)", font="Times New Roman", fontsize=14) - fig.supylabel("Force (N)", font="Times New Roman", fontsize=14) - - # fig.legend() - # fig.tight_layout() - plt.show() - - -# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] -# a_sum = 0 -# for key_a in a_list: -# a_sum += data_minimize_force["states"][key_a][0][-1] - - -# [1] Dahmane, R., Djordjevič, S., Šimunič, B., & Valenčič, V. (2005). -# Spatial fiber type distribution in normal human muscle: histochemical and tensiomyographical evaluation. -# Journal of biomechanics, 38(12), 2451-2459. - -# [2] Klein, C. S., Allman, B. L., Marsh, G. D., & Rice, C. L. (2002). -# Muscle size, strength, and bone geometry in the upper limbs of young and old men. -# The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57(7), M455-M459. diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py index e549ac33..4256d29e 100644 --- a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -80,7 +80,7 @@ parameters = [] ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26.bioMod", + biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 5], fes_muscle_models=fes_muscle_models, diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index 8810a3d6..013bbac0 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -83,7 +83,7 @@ parameters = [] ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26.bioMod", + biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 5], fes_muscle_models=fes_muscle_models, diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index 0b06512c..7b84af48 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -81,7 +81,7 @@ parameters = [] ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../arm26.bioMod", + biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 5], 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-9.81 0 - -// SEGMENT DEFINITION - -segment base - meshfile mesh/ground_ribs.vtp - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - // Markers - marker target - parent base - position 0.15 0.15 0.17 - endmarker - marker r_acromion - parent base - position -0.01256 0.04 0.17 - endmarker - - -segment r_humerus_translation - parent base - RTinMatrix 1 - RT - 1.0 0.0 0.0 -0.017545 - 0.0 1.0 0.0 -0.007 - 0.0 0.0 1.0 0.17 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation1 - parent r_humerus_translation - RTinMatrix 1 - RT - 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 - -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 - 0.05894291073968768 0.0 0.9982613551938856 0.0 - 0.0 0.0 0.0 1.0 - rotations z - rangesQ -1 pi - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation2 - parent r_humerus_rotation1 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation3 - parent r_humerus_rotation2 - RTinMatrix 1 - RT - 0.0 -0.0588981755023151 0.9982639956056206 0.0 - 1.0 0.0 0.0 0.0 - 0.0 0.9982639956056206 0.0588981755023151 0.0 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus - parent r_humerus_rotation3 - RTinMatrix 1 - RT - 0.039020807762349605 0.9992383982621836 0.0 0.0 - -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 - 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 - 0.0 0.0 0.0 1.0 - mass 1.8645719999999999 - inertia - 0.01481 0.0 0.0 - 0.0 0.004551 0.0 - 0.0 0.0 0.013193 - com 0 -0.18049599999999999 0 - meshfile mesh/arm_r_humerus.vtp -endsegment - - // Markers - marker r_humerus_epicondyle - parent r_humerus - position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 - endmarker - marker COM_arm - parent r_humerus - position 0 -0.18049599999999999 0 - endmarker - - -segment r_ulna_radius_hand_translation - parent r_humerus - RTinMatrix 1 - RT - 1.0 0.0 0.0 0.0061 - 0.0 1.0 0.0 -0.2904 - 0.0 0.0 1.0 -0.0123 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation1 - parent r_ulna_radius_hand_translation - RTinMatrix 1 - RT - 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 - 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 - -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 - 0.0 0.0 0.0 1.0 - rotations z - rangesQ 0 pi - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation2 - parent r_ulna_radius_hand_rotation1 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation3 - parent r_ulna_radius_hand_rotation2 - RTinMatrix 1 - RT - 0.0 0.049433130424779516 0.998777435476196 0.0 - 1.0 0.0 0.0 0.0 - 0.0 0.998777435476196 -0.049433130424779516 0.0 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand - parent r_ulna_radius_hand_rotation3 - RTinMatrix 1 - RT - -0.5953053712684069 0.803499542587912 0.0 0.0 - 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 - 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 - 0.0 0.0 0.0 1.0 - mass 1.5343150000000001 - inertia - 0.019281 0.0 0.0 - 0.0 0.001571 0.0 - 0.0 0.0 0.020062 - com 0 -0.181479 0 - meshfile mesh/arm_r_ulna.vtp -endsegment - - // Markers - marker r_radius_styloid - parent r_ulna_radius_hand - position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 - endmarker - marker COM_hand - parent r_ulna_radius_hand - position 0 -0.181479 0 - endmarker - - - -// MUSCLE DEFINITION - -// base > r_ulna_radius_hand -musclegroup base_to_r_ulna_radius_hand - OriginParent base - InsertionParent r_ulna_radius_hand -endmusclegroup - - muscle BIClong - Type degroote - statetype degroote - musclegroup base_to_r_ulna_radius_hand - OriginPosition -0.039234999999999999 0.00347 0.14795 - InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 - optimalLength 0.1157 - maximalForce 624.29999999999995 - tendonSlackLength 0.27229999999999999 - pennationAngle 0 - maxVelocity 10 - endmuscle - - viapoint BIClong-P2 - parent base - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position -0.028944999999999999 0.01391 0.15639 - endviapoint - viapoint BIClong-P3 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.021309999999999999 0.017930000000000001 0.010279999999999999 - endviapoint - viapoint BIClong-P4 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.023779999999999999 -0.00511 0.01201 - endviapoint - viapoint BIClong-P5 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01345 -0.02827 0.0013600000000000001 - endviapoint - viapoint BIClong-P6 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01068 -0.077359999999999998 -0.00165 - endviapoint - viapoint BIClong-P7 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01703 -0.12125 0.00024000000000000001 - endviapoint - viapoint BIClong-P8 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.022800000000000001 -0.1754 -0.0063 - endviapoint diff --git a/examples/getting_started/arm26_biceps_1dof.bioMod b/examples/getting_started/arm26_biceps_1dof.bioMod deleted file mode 100644 index 21f518c5..00000000 --- a/examples/getting_started/arm26_biceps_1dof.bioMod +++ /dev/null @@ -1,254 +0,0 @@ -version 3 -gravity 0 -9.81 0 - -// SEGMENT DEFINITION - -segment base - meshfile mesh/ground_ribs.vtp - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - // Markers - marker target - parent base - position 0.15 0.15 0.17 - endmarker - marker r_acromion - parent base - position -0.01256 0.04 0.17 - endmarker - - -segment r_humerus_translation - parent base - RTinMatrix 1 - RT - 1.0 0.0 0.0 -0.017545 - 0.0 1.0 0.0 -0.007 - 0.0 0.0 1.0 0.17 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation1 - parent r_humerus_translation - RTinMatrix 1 - RT - 0.9975010776109747 0.039020807762349584 -0.058898019716436364 0.0 - -0.038952964437603196 0.9992383982621832 0.0022999999889266845 0.0 - 0.05894291073968768 0.0 0.9982613551938856 0.0 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation2 - parent r_humerus_rotation1 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus_rotation3 - parent r_humerus_rotation2 - RTinMatrix 1 - RT - 0.0 -0.0588981755023151 0.9982639956056206 0.0 - 1.0 0.0 0.0 0.0 - 0.0 0.9982639956056206 0.0588981755023151 0.0 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_humerus - parent r_humerus_rotation3 - RTinMatrix 1 - RT - 0.039020807762349605 0.9992383982621836 0.0 0.0 - -0.11754676602826802 0.004590265714620227 0.9930567391931666 0.0 - 0.9923004254548464 -0.03874987611716229 0.11763635808301447 0.0 - 0.0 0.0 0.0 1.0 - mass 1.8645719999999999 - inertia - 0.01481 0.0 0.0 - 0.0 0.004551 0.0 - 0.0 0.0 0.013193 - com 0 -0.18049599999999999 0 - meshfile mesh/arm_r_humerus.vtp -endsegment - - // Markers - marker r_humerus_epicondyle - parent r_humerus - position 0.0050000000000000001 -0.29039999999999999 0.029999999999999999 - endmarker - marker COM_arm - parent r_humerus - position 0 -0.18049599999999999 0 - endmarker - - -segment r_ulna_radius_hand_translation - parent r_humerus - RTinMatrix 1 - RT - 1.0 0.0 0.0 0.0061 - 0.0 1.0 0.0 -0.2904 - 0.0 0.0 1.0 -0.0123 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation1 - parent r_ulna_radius_hand_translation - RTinMatrix 1 - RT - 0.801979522152563 -0.5953053712684071 0.04940000998917986 0.0 - 0.5941792022021661 0.8034995425879125 0.036600009991983457 0.0 - -0.06148106796684942 3.469446951953614e-18 0.9981082497813831 0.0 - 0.0 0.0 0.0 1.0 - rotations z - rangesQ 0 pi - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation2 - parent r_ulna_radius_hand_rotation1 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand_rotation3 - parent r_ulna_radius_hand_rotation2 - RTinMatrix 1 - RT - 0.0 0.049433130424779516 0.998777435476196 0.0 - 1.0 0.0 0.0 0.0 - 0.0 0.998777435476196 -0.049433130424779516 0.0 - 0.0 0.0 0.0 1.0 - inertia - 0 0 0 - 0 0 0 - 0 0 0 -endsegment - - -segment r_ulna_radius_hand - parent r_ulna_radius_hand_rotation3 - RTinMatrix 1 - RT - -0.5953053712684069 0.803499542587912 0.0 0.0 - 0.08898397360606149 0.06592740211634747 0.9938487963928239 0.0 - 0.7985570533031812 0.5916435267212894 -0.11074551868375905 0.0 - 0.0 0.0 0.0 1.0 - mass 1.5343150000000001 - inertia - 0.019281 0.0 0.0 - 0.0 0.001571 0.0 - 0.0 0.0 0.020062 - com 0 -0.181479 0 - meshfile mesh/arm_r_ulna.vtp -endsegment - - // Markers - marker r_radius_styloid - parent r_ulna_radius_hand - position -0.0011000000000000001 -0.23558999999999999 0.094299999999999995 - endmarker - marker COM_hand - parent r_ulna_radius_hand - position 0 -0.181479 0 - endmarker - - - -// MUSCLE DEFINITION - -// base > r_ulna_radius_hand -musclegroup base_to_r_ulna_radius_hand - OriginParent base - InsertionParent r_ulna_radius_hand -endmusclegroup - - muscle BIClong - Type degroote - statetype degroote - musclegroup base_to_r_ulna_radius_hand - OriginPosition -0.039234999999999999 0.00347 0.14795 - InsertionPosition 0.0075100000000000002 -0.048390000000000002 0.02179 - optimalLength 0.1157 - maximalForce 624.29999999999995 - tendonSlackLength 0.27229999999999999 - pennationAngle 0 - maxVelocity 10 - endmuscle - - viapoint BIClong-P2 - parent base - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position -0.028944999999999999 0.01391 0.15639 - endviapoint - viapoint BIClong-P3 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.021309999999999999 0.017930000000000001 0.010279999999999999 - endviapoint - viapoint BIClong-P4 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.023779999999999999 -0.00511 0.01201 - endviapoint - viapoint BIClong-P5 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01345 -0.02827 0.0013600000000000001 - endviapoint - viapoint BIClong-P6 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01068 -0.077359999999999998 -0.00165 - endviapoint - viapoint BIClong-P7 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.01703 -0.12125 0.00024000000000000001 - endviapoint - viapoint BIClong-P8 - parent r_humerus - muscle BIClong - musclegroup base_to_r_ulna_radius_hand - position 0.022800000000000001 -0.1754 -0.0063 - endviapoint diff --git a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py index dd474668..b96ab8eb 100644 --- a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -20,7 +20,7 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="arm26_biceps_1dof.bioMod", + biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], fes_muscle_models=[DingModelFrequencyWithFatigue(muscle_name="BIClong")], @@ -32,6 +32,8 @@ time_bimapping=True, custom_objective=objective_functions, with_residual_torque=True, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/getting_started/mesh/arm_r_humerus.vtp 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16131 16134 16137 16140 16143 16146 16149 16152 16155 16158 16161 16164 16167 16170 16173 16176 16179 16182 16185 16188 16191 16194 16197 16200 16203 16206 16209 16212 16215 16218 16221 16224 16227 16230 16233 16236 16239 16242 16245 16248 16251 16254 16257 16260 16263 16266 16269 16272 16275 16278 16281 16284 16287 16290 16293 16296 16299 16302 16305 16308 16311 16314 16317 16320 16323 16326 - - - - - diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py index 88cd7504..750254bc 100644 --- a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -21,7 +21,7 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1dof.bioMod", + biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], @@ -36,6 +36,8 @@ pulse_duration_bimapping=False, custom_objective=objective_functions, with_residual_torque=True, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index e7331b0a..b17956e9 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -24,7 +24,7 @@ ) ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="/arm26_biceps_1dof.bioMod", + biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], fes_muscle_models=[DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong")], @@ -39,6 +39,8 @@ pulse_intensity_bimapping=False, custom_objective=objective_functions, with_residual_torque=True, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, ) sol = ocp.solve(Solver.IPOPT(_max_iter=1000)) diff --git a/examples/dynamics/arm26.bioMod b/examples/msk_models/arm26.bioMod similarity index 100% rename from examples/dynamics/arm26.bioMod rename to examples/msk_models/arm26.bioMod diff --git a/examples/dynamics/arm26_biceps.bioMod b/examples/msk_models/arm26_biceps.bioMod similarity index 100% rename from examples/dynamics/arm26_biceps.bioMod rename to examples/msk_models/arm26_biceps.bioMod diff --git a/examples/dynamics/arm26_biceps_1dof.bioMod b/examples/msk_models/arm26_biceps_1dof.bioMod similarity index 100% rename from examples/dynamics/arm26_biceps_1dof.bioMod rename to examples/msk_models/arm26_biceps_1dof.bioMod diff --git a/examples/dynamics/arm26_biceps_triceps.bioMod b/examples/msk_models/arm26_biceps_triceps.bioMod similarity index 100% rename from examples/dynamics/arm26_biceps_triceps.bioMod rename to examples/msk_models/arm26_biceps_triceps.bioMod diff --git a/examples/dynamics/arm26_cycling.bioMod b/examples/msk_models/arm26_cycling.bioMod similarity index 100% rename from examples/dynamics/arm26_cycling.bioMod rename to examples/msk_models/arm26_cycling.bioMod diff --git a/examples/dynamics/mesh/arm_r_humerus.vtp b/examples/msk_models/mesh/arm_r_humerus.vtp similarity index 100% rename from examples/dynamics/mesh/arm_r_humerus.vtp rename to examples/msk_models/mesh/arm_r_humerus.vtp diff --git a/examples/dynamics/mesh/arm_r_ulna.vtp b/examples/msk_models/mesh/arm_r_ulna.vtp similarity index 100% rename from examples/dynamics/mesh/arm_r_ulna.vtp rename to examples/msk_models/mesh/arm_r_ulna.vtp diff --git a/examples/dynamics/mesh/ground_ribs.vtp b/examples/msk_models/mesh/ground_ribs.vtp similarity index 100% rename from examples/dynamics/mesh/ground_ribs.vtp rename to examples/msk_models/mesh/ground_ribs.vtp From fb2243b1aef6906d721e9dd6523eb2930184a413 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 14:31:42 -0500 Subject: [PATCH 42/63] new black version application --- cocofest/custom_objectives.py | 1 + cocofest/integration/ivp_fes.py | 42 +++++++++++-------- cocofest/models/dynamical_model.py | 14 ++++--- .../optimization/fes_identification_ocp.py | 14 ++++--- cocofest/optimization/fes_ocp.py | 14 ++++--- cocofest/optimization/fes_ocp_dynamics.py | 14 ++++--- data_process/force_from_sensor.py | 4 +- ...nsity_optimization_cycling_multi_muscle.py | 1 + .../intensity_optimization_hold_position.py | 1 + ...optimization_hold_position_multi_muscle.py | 1 + .../frequecy_optimization_minimize_fatigue.py | 1 + ..._duration_optimization_minimize_fatigue.py | 1 + ...intensity_optimization_minimize_fatigue.py | 1 + examples/dynamics/reaching_task/make_gaph.py | 10 ++--- .../reaching_task_frequency_optimization.py | 1 + .../reaching_task_intensity_optimization.py | 1 + ...aching_task_pulse_duration_optimization.py | 1 + .../force_tracking_parameter_optimization.py | 1 + examples/getting_started/multi_start.py | 1 + .../pulse_intensity_optimization.py | 1 + .../getting_started/pulse_mode_example.py | 1 + 21 files changed, 76 insertions(+), 50 deletions(-) diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index de8c4ee7..7c010cea 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -2,6 +2,7 @@ This custom objective is to enable the tracking of a curve by a state at all node. Used for sample data control problems such as functional electro stimulation """ + import numpy as np from casadi import MX, SX diff --git a/cocofest/integration/ivp_fes.py b/cocofest/integration/ivp_fes.py index 1930aaaa..f76a20a1 100644 --- a/cocofest/integration/ivp_fes.py +++ b/cocofest/integration/ivp_fes.py @@ -57,12 +57,14 @@ class IvpFes(OptimalControlProgram): def __init__( self, - model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue, + model: ( + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue + ), n_stim: int = None, n_shooting: int = None, final_time: float = None, @@ -257,12 +259,14 @@ def build_initial_guess_from_ocp(self, ocp): @classmethod def from_frequency_and_final_time( cls, - model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue, + model: ( + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue + ), n_shooting: int, final_time: float, frequency: int | float = None, @@ -298,12 +302,14 @@ def from_frequency_and_final_time( @classmethod def from_frequency_and_n_stim( cls, - model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue, + model: ( + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue + ), n_stim: int, n_shooting: int, frequency: int | float = None, diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index eb8c3788..1a7039fe 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -74,12 +74,14 @@ def muscle_dynamic( parameters: MX | SX, stochastic_variables: MX | SX, nlp: NonLinearProgram, - muscle_models: list[DingModelFrequency] - | list[DingModelFrequencyWithFatigue] - | list[DingModelPulseDurationFrequency] - | list[DingModelPulseDurationFrequencyWithFatigue] - | list[DingModelIntensityFrequency] - | list[DingModelIntensityFrequencyWithFatigue], + muscle_models: ( + list[DingModelFrequency] + | list[DingModelFrequencyWithFatigue] + | list[DingModelPulseDurationFrequency] + | list[DingModelPulseDurationFrequencyWithFatigue] + | list[DingModelIntensityFrequency] + | list[DingModelIntensityFrequencyWithFatigue] + ), stim_apparition=None, state_name_list=None, ) -> DynamicsEvaluation: diff --git a/cocofest/optimization/fes_identification_ocp.py b/cocofest/optimization/fes_identification_ocp.py index ea0c85d2..8b05ccd8 100644 --- a/cocofest/optimization/fes_identification_ocp.py +++ b/cocofest/optimization/fes_identification_ocp.py @@ -34,12 +34,14 @@ def __init__(self): @staticmethod def prepare_ocp( - model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue = None, + model: ( + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue + ) = None, n_shooting: list[int] = None, final_time_phase: tuple | list = None, pulse_duration: int | float | list = None, diff --git a/cocofest/optimization/fes_ocp.py b/cocofest/optimization/fes_ocp.py index 32d9e1f4..d84e2970 100644 --- a/cocofest/optimization/fes_ocp.py +++ b/cocofest/optimization/fes_ocp.py @@ -48,12 +48,14 @@ class OcpFes: @staticmethod def prepare_ocp( - model: DingModelFrequency - | DingModelFrequencyWithFatigue - | DingModelPulseDurationFrequency - | DingModelPulseDurationFrequencyWithFatigue - | DingModelIntensityFrequency - | DingModelIntensityFrequencyWithFatigue = None, + model: ( + DingModelFrequency + | DingModelFrequencyWithFatigue + | DingModelPulseDurationFrequency + | DingModelPulseDurationFrequencyWithFatigue + | DingModelIntensityFrequency + | DingModelIntensityFrequencyWithFatigue + ) = None, n_stim: int = None, n_shooting: int = None, final_time: int | float = None, diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 7c0139de..b323d086 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -38,12 +38,14 @@ def prepare_ocp( biorbd_model_path: str, bound_type: str = None, bound_data: list = None, - fes_muscle_models: list[DingModelFrequency] - | list[DingModelFrequencyWithFatigue] - | list[DingModelPulseDurationFrequency] - | list[DingModelPulseDurationFrequencyWithFatigue] - | list[DingModelIntensityFrequency] - | list[DingModelIntensityFrequencyWithFatigue] = None, + fes_muscle_models: ( + list[DingModelFrequency] + | list[DingModelFrequencyWithFatigue] + | list[DingModelPulseDurationFrequency] + | list[DingModelPulseDurationFrequencyWithFatigue] + | list[DingModelIntensityFrequency] + | list[DingModelIntensityFrequencyWithFatigue] + ) = None, n_stim: int = None, n_shooting: int = None, final_time: int | float = None, diff --git a/data_process/force_from_sensor.py b/data_process/force_from_sensor.py index ada1ddca..66bedac5 100644 --- a/data_process/force_from_sensor.py +++ b/data_process/force_from_sensor.py @@ -61,9 +61,7 @@ def __init__( muscle_name = ( muscle_name if isinstance(muscle_name, str) - else muscle_name[i] - if isinstance(muscle_name, list) - else "biceps" + else muscle_name[i] if isinstance(muscle_name, list) else "biceps" ) dictionary = {"time": self.time, muscle_name: self.all_biceps_force_vector, "stim_time": self.stim_time} with open(save_pickle_path, "wb") as file: diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index 6efa3067..f977eefc 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -4,6 +4,7 @@ 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. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/intensity_optimization_hold_position.py b/examples/dynamics/intensity_optimization_hold_position.py index 92a83a1b..7e75cfaf 100644 --- a/examples/dynamics/intensity_optimization_hold_position.py +++ b/examples/dynamics/intensity_optimization_hold_position.py @@ -4,6 +4,7 @@ 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. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py index 44395964..1c721fd1 100644 --- a/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py @@ -4,6 +4,7 @@ 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. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py index daa3b82e..262cf4ee 100644 --- a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py @@ -7,6 +7,7 @@ The stimulation apparition will be optimized to satisfy the motion and to minimize the overall muscle fatigue. Stimulations can occur between 0.01 to 1 second. Residual torque added to help convergence. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py index f266fe8b..54542b54 100644 --- a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py @@ -4,6 +4,7 @@ The stimulation frequency will be set to 10Hz and pulse duration will be optimized to satisfy the motion and to minimize the overall muscle fatigue. Intensity can be optimized from sensitivity threshold to 600us. No residual torque is allowed. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py index 40ec5fe7..ed068496 100644 --- a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py @@ -4,6 +4,7 @@ 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. """ + import numpy as np from bioptim import ( diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index 87ea36eb..a5f58f34 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -27,11 +27,11 @@ chosen_graph_to_plot_path = ( frequency_path if chosen_graph_to_plot == "frequency" - else duration_path - if chosen_graph_to_plot == "duration" - else intensity_path - if chosen_graph_to_plot == "duration" - else None + else ( + duration_path + if chosen_graph_to_plot == "duration" + else intensity_path if chosen_graph_to_plot == "duration" else None + ) ) if chosen_graph_to_plot_path is None: diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py index 4256d29e..38baf051 100644 --- a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -4,6 +4,7 @@ defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. The files will contain the time, states, controls and parameters of the ocp. """ + import pickle from bioptim import ( diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index 013bbac0..562e4f7b 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -4,6 +4,7 @@ defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. The files will contain the time, states, controls and parameters of the ocp. """ + import pickle from bioptim import ( diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index 7b84af48..d816fc1d 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -4,6 +4,7 @@ defined in the bioMod file. At the end of the simulation 2 files will be created, one for each optimization. The files will contain the time, states, controls and parameters of the ocp. """ + import pickle from bioptim import ( diff --git a/examples/getting_started/force_tracking_parameter_optimization.py b/examples/getting_started/force_tracking_parameter_optimization.py index 394069d0..100e812f 100644 --- a/examples/getting_started/force_tracking_parameter_optimization.py +++ b/examples/getting_started/force_tracking_parameter_optimization.py @@ -2,6 +2,7 @@ This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 intensity work This ocp was build to match a force curve across all optimization. """ + import matplotlib.pyplot as plt import numpy as np diff --git a/examples/getting_started/multi_start.py b/examples/getting_started/multi_start.py index 514a4123..7790bfae 100644 --- a/examples/getting_started/multi_start.py +++ b/examples/getting_started/multi_start.py @@ -2,6 +2,7 @@ An example of how to use multi-start to find local minima from stimulation parameter. This example is a variation of the fes frequency in examples/frequency_optimization.py. """ + import shutil from cocofest import DingModelFrequencyWithFatigue, FunctionalElectricStimulationMultiStart diff --git a/examples/getting_started/pulse_intensity_optimization.py b/examples/getting_started/pulse_intensity_optimization.py index 18899373..f3ff3d2a 100644 --- a/examples/getting_started/pulse_intensity_optimization.py +++ b/examples/getting_started/pulse_intensity_optimization.py @@ -2,6 +2,7 @@ This example will do a 10 stimulation example with Ding's 2003 frequency model associated to Bakir's 2022 work. This ocp was build to match a force value of 200N at the end of the last node. """ + import numpy as np from cocofest import DingModelIntensityFrequency, OcpFes diff --git a/examples/getting_started/pulse_mode_example.py b/examples/getting_started/pulse_mode_example.py index 6bc00297..fd4e74f5 100644 --- a/examples/getting_started/pulse_mode_example.py +++ b/examples/getting_started/pulse_mode_example.py @@ -2,6 +2,7 @@ This example will do a 10 stimulation example using doublets and triplets. The example model is the Ding2003 frequency model. """ + import numpy as np import matplotlib.pyplot as plt from bioptim import Solution, Shooting, SolutionIntegrator From afbe2ce4bca1167997cf5bea63d082fdf740df82 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 17:35:16 -0500 Subject: [PATCH 43/63] adding test for dynamical problems --- cocofest/optimization/fes_ocp_dynamics.py | 63 +-- tests/shard2/test_fes_dynamics.py | 588 ++++++++++++++++++++++ 2 files changed, 622 insertions(+), 29 deletions(-) create mode 100644 tests/shard2/test_fes_dynamics.py diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index b323d086..03a87f67 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -66,7 +66,7 @@ def prepare_ocp( pulse_intensity_bimapping: bool = False, pulse_intensity_similar_for_all_muscles: bool = False, force_tracking: list = None, - end_node_tracking: int | float = None, + end_node_tracking: list = None, q_tracking: list = None, custom_objective: ObjectiveList = None, custom_constraint: ConstraintList = None, @@ -261,10 +261,6 @@ def prepare_ocp( for i in range(n_stim) ] - FESActuatedBiorbdModelOCP._sanity_check_bounds( - bio_models=bio_models, bound_type=bound_type, bound_data=bound_data - ) - dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( bio_models, @@ -561,6 +557,11 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): for i in range(bio_models[0].nb_q): start_bounds.append(3.14 / (180 / bound_data[i]) if bound_data[i] != 0 else 0) + elif bound_type == "end": + end_bounds = [] + for i in range(bio_models[0].nb_q): + end_bounds.append(3.14 / (180 / bound_data[i]) if bound_data[i] != 0 else 0) + for i in range(n_stim): q_x_bounds = bio_models[i].bounds_from_ranges("q") qdot_x_bounds = bio_models[i].bounds_from_ranges("qdot") @@ -578,6 +579,9 @@ def _set_bounds(bio_models, fes_muscle_models, bound_type, bound_data, n_stim): if bound_type == "start_end": for j in range(bio_models[i].nb_q): q_x_bounds[j, [-1]] = end_bounds[j] + elif bound_type == "end": + for j in range(bio_models[i].nb_q): + q_x_bounds[j, [-1]] = end_bounds[j] x_bounds.add(key="q", bounds=q_x_bounds, phase=i) x_bounds.add(key="qdot", bounds=qdot_x_bounds, phase=i) @@ -694,19 +698,6 @@ def _set_objective( return objective_functions - @staticmethod - def _sanity_check_bounds(bio_models, bound_type, bound_data): - for i in range(bio_models[0].nb_q): - if bound_type == "start_end": - if not isinstance(bound_data, list): - raise TypeError("The bound data should be a list of two elements") - if len(bound_data) != 2: - raise ValueError("The bound data should be a list of two elements, start and end position") - if not isinstance(bound_data[0], list) or not isinstance(bound_data[1], list): - raise TypeError("The start and end position should be a list") - if len(bound_data[0]) != bio_models[0].nb_q or len(bound_data[1]) != bio_models[0].nb_q: - raise ValueError("The start and end position should be a list of size nb_q") - @staticmethod def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): tested_bio_model = FESActuatedBiorbdModel( @@ -748,12 +739,15 @@ def _sanity_check_fes_models_inputs( if not isinstance(bound_data, list): raise TypeError("bound_data should be a list") if bound_type == "start_end": - if len(bound_data) != tested_bio_model.nb_q: - raise ValueError(f"bound_data should be a list of {tested_bio_model.nb_q} elements") - if not isinstance(bound_data[0], list) or not isinstance(bound_data[1], list): + if len(bound_data) != 2 or not isinstance(bound_data[0], list) or not isinstance(bound_data[1], list): raise TypeError("bound_data should be a list of two list") - if len(bound_data[0]) != len(bound_data[1]): - raise ValueError("bound_data should be a list of two list with the same size") + if len(bound_data[0]) != tested_bio_model.nb_q or len(bound_data[1]) != tested_bio_model.nb_q: + raise ValueError(f"bound_data should be a list of {tested_bio_model.nb_q} elements") + for i in range(len(bound_data[0])): + if not isinstance(bound_data[0][i], int | float) or not isinstance(bound_data[1][i], int | float): + raise TypeError( + f"bound data index {i}: {bound_data[0][i]} and {bound_data[1][i]} should be an int or float" + ) if bound_type == "start" or bound_type == "end": if len(bound_data) != tested_bio_model.nb_q: raise ValueError(f"bound_data should be a list of {tested_bio_model.nb_q} element") @@ -782,6 +776,8 @@ def _sanity_check_fes_models_inputs( if force_tracking: if isinstance(force_tracking, list): + if len(force_tracking) != 2: + raise ValueError("force_tracking must of size 2") if not isinstance(force_tracking[0], np.ndarray): raise TypeError(f"force_tracking index 0: {force_tracking[0]} must be np.ndarray type") if not isinstance(force_tracking[1], list): @@ -790,8 +786,9 @@ def _sanity_check_fes_models_inputs( raise ValueError( "force_tracking index 1 list must have the same size as the number of muscles in fes_muscle_models" ) - if len(force_tracking[0]) != len(force_tracking[1]) or len(force_tracking) != 2: - raise ValueError("force_tracking time and force argument must be the same length") + for i in range(len(force_tracking[1])): + if len(force_tracking[0]) != len(force_tracking[1][i]): + raise ValueError("force_tracking time and force argument must be the same length") else: raise TypeError(f"force_tracking: {force_tracking} must be list type") @@ -812,12 +809,12 @@ def _sanity_check_fes_models_inputs( tested_bio_model = FESActuatedBiorbdModel( name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models ) - if len(q_tracking[0]) != 1: - raise ValueError("q_tracking[0] should be a list of size 1") + if not isinstance(q_tracking[0], list): + raise ValueError("q_tracking[0] should be a list") if len(q_tracking[1]) != tested_bio_model.nb_q: raise ValueError("q_tracking[1] should have the same size as the number of generalized coordinates") for i in range(tested_bio_model.nb_q): - if len(q_tracking[0][0]) != len(q_tracking[1][i]): + if len(q_tracking[0]) != len(q_tracking[1][i]): raise ValueError("q_tracking[0] and q_tracking[1] should have the same size") list_to_check = [ @@ -828,7 +825,15 @@ def _sanity_check_fes_models_inputs( minimize_muscle_force, ] + list_to_check_name = [ + "with_residual_torque", + "muscle_force_length_relationship", + "muscle_force_velocity_relationship", + "minimize_muscle_fatigue", + "minimize_muscle_force", + ] + for i in range(len(list_to_check)): if list_to_check[i]: if not isinstance(list_to_check[i], bool): - raise TypeError(f"{list_to_check[i]} should be a boolean") + raise TypeError(f"{list_to_check_name[i]} should be a boolean") diff --git a/tests/shard2/test_fes_dynamics.py b/tests/shard2/test_fes_dynamics.py new file mode 100644 index 00000000..e3f8d9c1 --- /dev/null +++ b/tests/shard2/test_fes_dynamics.py @@ -0,0 +1,588 @@ +import re +import pytest + +import numpy as np +from bioptim import ( + ObjectiveFcn, + ObjectiveList, + Solver, +) + +from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP + + +def test_multi_muscle_fes_dynamics(): + objective_functions = ObjectiveList() + n_stim = 10 + for i in range(n_stim): + objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) + + minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, 120]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + 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, + custom_objective=objective_functions, + with_residual_torque=True, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() + + np.testing.assert_almost_equal(sol.cost, 2.64645e-08) + np.testing.assert_almost_equal( + sol.parameters["pulse_duration_BIClong"], + np.array( + [ + [0.00059638], + [0.00059498], + [0.00059357], + [0.00059024], + [0.00058198], + [0.00054575], + [0.00014772], + [0.00015474], + [0.00018023], + [0.00029466], + ] + ), + ) + np.testing.assert_almost_equal( + sol.parameters["pulse_duration_TRIlong"], + np.array( + [ + [0.00015802], + [0.00015879], + [0.00052871], + [0.00055611], + [0.00028161], + [0.00013942], + [0.00014098], + [0.00014026], + [0.00014371], + [0.00019614], + ] + ), + ) + + np.testing.assert_almost_equal(sol.states["q"][0][0], 0) + np.testing.assert_almost_equal(sol.states["q"][0][-1], 0) + np.testing.assert_almost_equal(sol.states["q"][1][0], 0.08722222222222223) + np.testing.assert_almost_equal(sol.states["q"][1][-1], 2.0933333333333333) + np.testing.assert_almost_equal(sol.states["F_BIClong"][0][-1], 33.20686595) + np.testing.assert_almost_equal(sol.states["F_TRIlong"][0][-1], 18.36373478) + + +def test_fes_models_inputs_sanity_check_errors(): + with pytest.raises( + TypeError, + match=re.escape("biorbd_model_path should be a string"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path=5, + bound_type="start_end", + bound_data=[[0, 5], [0, 120]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + ValueError, + match=re.escape("bound_type should be a string and should be equal to start, end or start_end"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="hello", + bound_data=[[0, 5], [0, 120]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape("bound_data should be a list"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data="[[0, 5], [0, 120]]", + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + ValueError, + match=re.escape(f"bound_data should be a list of {2} elements"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5, 7], [0, 120, 150]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape(f"bound_data should be a list of two list"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=["[0, 5]", [0, 120]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + ValueError, + match=re.escape(f"bound_data should be a list of {2} elements"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5, 7], [0, 120, 150]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape(f"bound data index {1}: {5} and {'120'} should be an int or float"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start_end", + bound_data=[[0, 5], [0, "120"]], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + ValueError, + match=re.escape(f"bound_data should be a list of {2} element"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5, 10], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape(f"bound data index {1}: {'5'} should be an int or float"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="end", + bound_data=[0, "5"], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape( + "model must be a DingModelFrequency," + " DingModelFrequencyWithFatigue," + " DingModelPulseDurationFrequency," + " DingModelPulseDurationFrequencyWithFatigue," + " DingModelIntensityFrequency," + " DingModelIntensityFrequencyWithFatigue type" + ), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + "DingModelPulseDurationFrequencyWithFatigue(muscle_name='TRIlong')", + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) + + with pytest.raises( + TypeError, + match=re.escape(f"force_tracking: {'hello'} must be list type"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking="hello", + ) + + with pytest.raises( + ValueError, + match=re.escape("force_tracking must of size 2"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="end", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking=["hello"], + ) + + with pytest.raises( + TypeError, + match=re.escape(f"force_tracking index 0: {'hello'} must be np.ndarray type"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking=["hello", [1, 2, 3]], + ) + + with pytest.raises( + TypeError, + match=re.escape(f"force_tracking index 1: {'[1, 2, 3]'} must be list type"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking=[np.array([1, 2, 3]), "[1, 2, 3]"], + ) + + with pytest.raises( + ValueError, + match=re.escape( + "force_tracking index 1 list must have the same size as the number of muscles in fes_muscle_models" + ), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking=[np.array([1, 2, 3]), [[1, 2, 3], [1, 2, 3], [1, 2, 3]]], + ) + + with pytest.raises( + ValueError, + match=re.escape("force_tracking time and force argument must be the same length"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + force_tracking=[np.array([1, 2, 3]), [[1, 2, 3], [1, 2]]], + ) + + with pytest.raises( + TypeError, + match=re.escape(f"force_tracking: {'hello'} must be list type"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + end_node_tracking="hello", + ) + + with pytest.raises( + ValueError, + match=re.escape("end_node_tracking list must have the same size as the number of muscles in fes_muscle_models"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + end_node_tracking=[2, 3, 4], + ) + + with pytest.raises( + TypeError, + match=re.escape(f"end_node_tracking index {1}: {'hello'} must be int or float type"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + end_node_tracking=[2, "hello"], + ) + + with pytest.raises( + TypeError, + match=re.escape("q_tracking should be a list of size 2"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + q_tracking="hello", + ) + + with pytest.raises( + ValueError, + match=re.escape("q_tracking[0] should be a list"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + q_tracking=["hello", [1, 2, 3]], + ) + + with pytest.raises( + ValueError, + match=re.escape("q_tracking[1] should have the same size as the number of generalized coordinates"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + q_tracking=[[1, 2, 3], [1, 2, 3]], + ) + + with pytest.raises( + ValueError, + match=re.escape("q_tracking[0] and q_tracking[1] should have the same size"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + q_tracking=[[1, 2, 3], [[1, 2, 3], [4, 5]]], + ) + + with pytest.raises( + TypeError, + match=re.escape(f"{'with_residual_torque'} should be a boolean"), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong"), + DingModelPulseDurationFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + with_residual_torque="hello", + ) + + +def test_fes_muscle_models_sanity_check_errors(): + with pytest.raises( + ValueError, + match=re.escape( + f"The muscle {'TRIlong'} is not in the fes muscle model " + f"please add it into the fes_muscle_models list by providing the muscle_name =" + f" {'TRIlong'}" + ), + ): + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], + n_stim=1, + n_shooting=10, + final_time=1, + pulse_duration_min=0.0003, + pulse_duration_max=0.0006, + ) From bcc3d14fb3182ca22f2d64a522d3c2e9d3f595c9 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 17:58:40 -0500 Subject: [PATCH 44/63] enabling biomod file access in online tests --- examples/msk_models/init.py | 0 tests/shard2/test_fes_dynamics.py | 56 +++++++++++++++++-------------- 2 files changed, 31 insertions(+), 25 deletions(-) create mode 100644 examples/msk_models/init.py diff --git a/examples/msk_models/init.py b/examples/msk_models/init.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/shard2/test_fes_dynamics.py b/tests/shard2/test_fes_dynamics.py index e3f8d9c1..b12d7a7c 100644 --- a/tests/shard2/test_fes_dynamics.py +++ b/tests/shard2/test_fes_dynamics.py @@ -1,5 +1,6 @@ import re import pytest +import os import numpy as np from bioptim import ( @@ -10,6 +11,11 @@ from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from examples.msk_models import init as ocp_module + +biomodel_folder = os.path.dirname(ocp_module.__file__) +biorbd_model_path = biomodel_folder + "/arm26_biceps_triceps.bioMod" + def test_multi_muscle_fes_dynamics(): objective_functions = ObjectiveList() @@ -19,7 +25,7 @@ def test_multi_muscle_fes_dynamics(): minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5], [0, 120]], fes_muscle_models=[ @@ -110,7 +116,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("bound_type should be a string and should be equal to start, end or start_end"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="hello", bound_data=[[0, 5], [0, 120]], fes_muscle_models=[ @@ -129,7 +135,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("bound_data should be a list"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data="[[0, 5], [0, 120]]", fes_muscle_models=[ @@ -148,7 +154,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound_data should be a list of {2} elements"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5, 7], [0, 120, 150]], fes_muscle_models=[ @@ -167,7 +173,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound_data should be a list of two list"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=["[0, 5]", [0, 120]], fes_muscle_models=[ @@ -186,7 +192,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound_data should be a list of {2} elements"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5, 7], [0, 120, 150]], fes_muscle_models=[ @@ -205,7 +211,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound data index {1}: {5} and {'120'} should be an int or float"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5], [0, "120"]], fes_muscle_models=[ @@ -224,7 +230,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound_data should be a list of {2} element"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5, 10], fes_muscle_models=[ @@ -243,7 +249,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"bound data index {1}: {'5'} should be an int or float"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="end", bound_data=[0, "5"], fes_muscle_models=[ @@ -269,7 +275,7 @@ def test_fes_models_inputs_sanity_check_errors(): ), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -288,7 +294,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"force_tracking: {'hello'} must be list type"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -308,7 +314,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("force_tracking must of size 2"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="end", bound_data=[0, 5], fes_muscle_models=[ @@ -328,7 +334,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"force_tracking index 0: {'hello'} must be np.ndarray type"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -348,7 +354,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"force_tracking index 1: {'[1, 2, 3]'} must be list type"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -370,7 +376,7 @@ def test_fes_models_inputs_sanity_check_errors(): ), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -390,7 +396,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("force_tracking time and force argument must be the same length"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -410,7 +416,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"force_tracking: {'hello'} must be list type"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -430,7 +436,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("end_node_tracking list must have the same size as the number of muscles in fes_muscle_models"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -450,7 +456,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"end_node_tracking index {1}: {'hello'} must be int or float type"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -470,7 +476,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("q_tracking should be a list of size 2"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -490,7 +496,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("q_tracking[0] should be a list"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -510,7 +516,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("q_tracking[1] should have the same size as the number of generalized coordinates"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -530,7 +536,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape("q_tracking[0] and q_tracking[1] should have the same size"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -550,7 +556,7 @@ def test_fes_models_inputs_sanity_check_errors(): match=re.escape(f"{'with_residual_torque'} should be a boolean"), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[ @@ -576,7 +582,7 @@ def test_fes_muscle_models_sanity_check_errors(): ), ): ocp = FESActuatedBiorbdModelOCP.prepare_ocp( - biorbd_model_path="../../examples/msk_models/arm26_biceps_triceps.bioMod", + biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], fes_muscle_models=[DingModelPulseDurationFrequencyWithFatigue(muscle_name="BIClong")], From ce9a13f2b41a22480de6bdbe4f5c3383abfba46b Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 18:13:13 -0500 Subject: [PATCH 45/63] to correspond to online tests --- tests/shard2/test_fes_dynamics.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/shard2/test_fes_dynamics.py b/tests/shard2/test_fes_dynamics.py index b12d7a7c..0392d8c0 100644 --- a/tests/shard2/test_fes_dynamics.py +++ b/tests/shard2/test_fes_dynamics.py @@ -87,8 +87,8 @@ def test_multi_muscle_fes_dynamics(): np.testing.assert_almost_equal(sol.states["q"][0][-1], 0) np.testing.assert_almost_equal(sol.states["q"][1][0], 0.08722222222222223) np.testing.assert_almost_equal(sol.states["q"][1][-1], 2.0933333333333333) - np.testing.assert_almost_equal(sol.states["F_BIClong"][0][-1], 33.20686595) - np.testing.assert_almost_equal(sol.states["F_TRIlong"][0][-1], 18.36373478) + np.testing.assert_almost_equal(sol.states["F_BIClong"][0][-1], 33.206865, decimal=6) + np.testing.assert_almost_equal(sol.states["F_TRIlong"][0][-1], 18.363734, decimal=6) def test_fes_models_inputs_sanity_check_errors(): From 4756af1405a92bf1859281782ee418cb6efeca50 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 8 Feb 2024 19:04:52 -0500 Subject: [PATCH 46/63] adding one more test for multiple force tracking with intensity optim --- tests/shard2/test_fes_dynamics.py | 85 ++++++++++++++++++++++++++++++- 1 file changed, 83 insertions(+), 2 deletions(-) diff --git a/tests/shard2/test_fes_dynamics.py b/tests/shard2/test_fes_dynamics.py index 0392d8c0..ee4b6bdd 100644 --- a/tests/shard2/test_fes_dynamics.py +++ b/tests/shard2/test_fes_dynamics.py @@ -9,7 +9,11 @@ Solver, ) -from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import ( + DingModelPulseDurationFrequencyWithFatigue, + DingModelIntensityFrequencyWithFatigue, + FESActuatedBiorbdModelOCP, +) from examples.msk_models import init as ocp_module @@ -17,7 +21,7 @@ biorbd_model_path = biomodel_folder + "/arm26_biceps_triceps.bioMod" -def test_multi_muscle_fes_dynamics(): +def test_pulse_duration_multi_muscle_fes_dynamics(): objective_functions = ObjectiveList() n_stim = 10 for i in range(n_stim): @@ -91,6 +95,83 @@ def test_multi_muscle_fes_dynamics(): np.testing.assert_almost_equal(sol.states["F_TRIlong"][0][-1], 18.363734, decimal=6) +def test_pulse_intensity_multi_muscle_fes_dynamics(): + n_stim = 10 + minimum_pulse_intensity = DingModelIntensityFrequencyWithFatigue.min_pulse_intensity( + DingModelIntensityFrequencyWithFatigue() + ) + track_forces = [ + np.array([0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]), + [np.array([0, 10, 40, 90, 140, 80, 50, 10, 0, 0, 0]), np.array([0, 0, 0, 10, 40, 90, 140, 80, 50, 10, 0])], + ] + + ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + biorbd_model_path=biorbd_model_path, + bound_type="start", + bound_data=[0, 5], + fes_muscle_models=[ + DingModelIntensityFrequencyWithFatigue(muscle_name="BIClong"), + DingModelIntensityFrequencyWithFatigue(muscle_name="TRIlong"), + ], + n_stim=n_stim, + n_shooting=5, + final_time=1, + pulse_intensity_min=minimum_pulse_intensity, + pulse_intensity_max=130, + pulse_intensity_bimapping=False, + force_tracking=track_forces, + with_residual_torque=False, + muscle_force_length_relationship=True, + muscle_force_velocity_relationship=True, + use_sx=False, + ) + + sol = ocp.solve(Solver.IPOPT(_max_iter=1000)).merge_phases() + + np.testing.assert_almost_equal(sol.cost, 6666357.331403, decimal=6) + np.testing.assert_almost_equal( + sol.parameters["pulse_intensity_BIClong"], + np.array( + [ + [130.00000125], + [130.00000126], + [130.00000128], + [130.00000128], + [130.00000121], + [50.86019267], + [17.02854916], + [17.02854916], + [17.02854917], + [17.0285492], + ] + ), + ) + np.testing.assert_almost_equal( + sol.parameters["pulse_intensity_TRIlong"], + np.array( + [ + [45.78076277], + [25.84044302], + [59.54858653], + [130.00000124], + [130.00000128], + [130.00000128], + [130.00000122], + [76.08547779], + [17.02854916], + [22.72956196], + ] + ), + ) + + np.testing.assert_almost_equal(sol.states["q"][0][0], 0) + np.testing.assert_almost_equal(sol.states["q"][0][-1], -0.35378857156156907) + np.testing.assert_almost_equal(sol.states["q"][1][0], 0.08722222222222223) + np.testing.assert_almost_equal(sol.states["q"][1][-1], -7.097935277992009e-10) + np.testing.assert_almost_equal(sol.states["F_BIClong"][0][-1], 47.408594, decimal=6) + np.testing.assert_almost_equal(sol.states["F_TRIlong"][0][-1], 29.131785, decimal=6) + + def test_fes_models_inputs_sanity_check_errors(): with pytest.raises( TypeError, From bed920496b56d8d8f5d8a65f3c33f2d88debef83 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 9 Feb 2024 11:28:03 -0500 Subject: [PATCH 47/63] correction --- .../reaching_task_pulse_duration_optimization.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index d816fc1d..b8a2c4df 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -75,6 +75,16 @@ axes=[Axis.X, Axis.Y], ) +force_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] +for force_key in force_keys: + constraint.add( + ConstraintFcn.TRACK_STATE, + key=force_key, + phase=n_stim - 1, + node=Node.END, + target=0, + ) + for i in range(len(pickle_file_list)): time = [] states = [] From e6e1bbd6b0a3504a7a5fc785c1401c452a7363ba Mon Sep 17 00:00:00 2001 From: kevin Date: Sat, 10 Feb 2024 12:55:48 -0500 Subject: [PATCH 48/63] testing new simulation conditions --- cocofest/custom_objectives.py | 2 +- ...aching_task_pulse_duration_optimization.py | 28 +++++-------------- 2 files changed, 8 insertions(+), 22 deletions(-) diff --git a/cocofest/custom_objectives.py b/cocofest/custom_objectives.py index 7c010cea..58f4c2cc 100644 --- a/cocofest/custom_objectives.py +++ b/cocofest/custom_objectives.py @@ -103,5 +103,5 @@ def minimize_overall_muscle_force_production(controller: PenaltyController) -> M The sum of each force """ muscle_name_list = controller.model.bio_model.muscle_names - muscle_force = [controller.states["F_" + muscle_name_list[x]].cx for x in range(len(muscle_name_list))] + muscle_force = [controller.states["F_" + muscle_name_list[x]].cx ** 3 for x in range(len(muscle_name_list))] return sum(muscle_force) diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index b8a2c4df..83f48345 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -62,43 +62,29 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 pickle_file_list = ["minimize_muscle_fatigue.pkl", "minimize_muscle_force.pkl"] -n_stim = 40 -n_shooting = 5 +n_stim = 60 +n_shooting = 2 constraint = ConstraintList() constraint.add( ConstraintFcn.SUPERIMPOSE_MARKERS, first_marker="COM_hand", second_marker="reaching_target", - phase=n_stim - 1, + phase=39, node=Node.END, axes=[Axis.X, Axis.Y], ) -force_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] -for force_key in force_keys: - constraint.add( - ConstraintFcn.TRACK_STATE, - key=force_key, - phase=n_stim - 1, - node=Node.END, - target=0, - ) - for i in range(len(pickle_file_list)): - time = [] - states = [] - controls = [] - parameters = [] ocp = FESActuatedBiorbdModelOCP.prepare_ocp( biorbd_model_path="../../msk_models/arm26.bioMod", - bound_type="start", - bound_data=[0, 5], + bound_type="start_end", + bound_data=[[0, 5], [0, 5]], fes_muscle_models=fes_muscle_models, n_stim=n_stim, n_shooting=n_shooting, - final_time=1, + final_time=1.5, pulse_duration_min=minimum_pulse_duration, pulse_duration_max=0.0006, pulse_duration_bimapping=False, @@ -124,7 +110,7 @@ "parameters": parameters, } - with open("/result_file/pulse_duration_" + pickle_file_list[i], "wb") as file: + with open("result_file/pulse_duration_" + pickle_file_list[i], "wb") as file: pickle.dump(dictionary, file) From 232a6690fe1400fd1f0dd05efb12ca686e40664f Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Mon, 12 Feb 2024 14:24:45 -0500 Subject: [PATCH 49/63] graphic modification --- examples/dynamics/reaching_task/make_gaph.py | 244 ++++++++++++++++--- 1 file changed, 207 insertions(+), 37 deletions(-) diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index a5f58f34..7b8e17e6 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -1,42 +1,34 @@ """ 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. -The available graphs are: frequency, duration, intensity +The available graphs are: duration, intensity """ import pickle import matplotlib.pyplot as plt +import numpy as np chosen_graph_to_plot = "duration" -frequency_path = [ - r"/result_file/pulse_apparition_minimize_muscle_force.pkl", - r"/result_file/pulse_apparition_minimize_muscle_fatigue.pkl", -] - duration_path = [ - r"/result_file/pulse_duration_minimize_muscle_force.pkl", - r"/result_file/pulse_duration_minimize_muscle_fatigue.pkl", + r"result_file/pulse_duration_minimize_muscle_force.pkl", + r"result_file/pulse_duration_minimize_muscle_fatigue.pkl", ] intensity_path = [ - r"/result_file/pulse_intensity_minimize_muscle_force.pkl", - r"/result_file/pulse_intensity_minimize_muscle_fatigue.pkl", + r"result_file/pulse_intensity_minimize_muscle_force.pkl", + r"result_file/pulse_intensity_minimize_muscle_fatigue.pkl", ] chosen_graph_to_plot_path = ( - frequency_path - if chosen_graph_to_plot == "frequency" - else ( duration_path if chosen_graph_to_plot == "duration" - else intensity_path if chosen_graph_to_plot == "duration" else None + else intensity_path if chosen_graph_to_plot == "intensity" else None ) -) -if chosen_graph_to_plot_path is None: - raise ValueError("The chosen graph to plot is not valid") +if chosen_graph_to_plot_path is None: + raise ValueError("The chosen graph to plot is not available") with open(chosen_graph_to_plot_path[0], "rb") as f: data_minimize_force = pickle.load(f) @@ -46,15 +38,18 @@ force_muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] -fig, axs = plt.subplots(3, 2, figsize=(5, 3), sharex=True, sharey=True, constrained_layout=True) +muscle_title_x_postiton = [0.55, 0.5, 0.56, 0.62, 0.55, 0.73] +fig, axs = plt.subplots(3, 3, figsize=(5, 3), constrained_layout=True) index = 0 -for i in range(3): - for j in range(2): - axs[i][j].set_xlim(left=0, right=1) - axs[i][j].set_ylim(bottom=0, top=300) + +# Force across time +for i in range(2): + for j in range(3): + axs[i][j].set_xlim(left=0, right=1.5) + axs[i][j].set_ylim(bottom=0, top=250) axs[i][j].text( - 0.025, + muscle_title_x_postiton[index], 0.975, f"{muscle_names[index]}", transform=axs[i][j].transAxes, @@ -68,6 +63,30 @@ [label.set_fontname("Times New Roman") for label in labels] [label.set_fontsize(14) for label in labels] + axs[i][j].scatter( + 1, + 0, + color="red" + ) + + 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 == 0 and j == 0: axs[i][j].plot( data_minimize_force["time"], @@ -98,23 +117,174 @@ ) index += 1 +# Joint angle across time +q_names = ["q_arm", "q_elbow"] +q_x_position = [0.65, 0.57] +for i in range(2): + axs[2][i].set_xlim(left=0, right=1.5) + axs[2][i].set_ylim(bottom=-1, top=2.5) + + axs[2][i].text( + q_x_position[i], + 0.975, + f"{q_names[i]}", + transform=axs[2][i].transAxes, + ha="left", + va="top", + weight="bold", + font="Times New Roman", + ) + + labels = axs[2][i].get_xticklabels() + axs[2][i].get_yticklabels() + [label.set_fontname("Times New Roman") for label in labels] + [label.set_fontsize(14) for label in labels] + + axs[2][i].scatter( + 1, + -2, + color="red" + ) + + if i == 0: + plt.setp( + axs[2][i], + xticks=[0, 0.5, 1, 1.5], + xticklabels=[0, 0.5, 1, 1.5], + yticks=[-2, -1, 0, 1, 2], + yticklabels=[-2, -1, 0, 1, 2], + ) + + else: + plt.setp( + axs[2][i], + xticks=[0, 0.5, 1, 1.5], + xticklabels=[0, 0.5, 1, 1.5], + yticks=[-1, 0, 1, 2], + yticklabels=[], + ) + + axs[2][i].plot( + data_minimize_force["time"], + data_minimize_force["states"]["q"][i], + ms=4, + linewidth=5.0, + ) + axs[2][i].plot( + data_minimize_fatigue["time"], + data_minimize_fatigue["states"]["q"][i], + ms=4, + linewidth=5.0, + ) + +# fatigue across time +axs[2][2].set_xlim(left=0, right=1.5) +axs[2][2].set_ylim(bottom=-6, top=0) + +axs[2][2].text( + 0.3, + 0.975, + f"{'Scaling factor'}", + transform=axs[2][2].transAxes, + ha="left", + va="top", + weight="bold", + font="Times New Roman", +) + +labels = axs[2][2].get_xticklabels() + axs[2][2].get_yticklabels() +[label.set_fontname("Times New Roman") for label in labels] +[label.set_fontsize(14) for label in labels] + +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][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][0][i] + a_fatigue_sum += data_minimize_fatigue["states"][key_a][0][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 = [] +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) + plt.setp( - axs, - xticks=[0, 0.25, 0.5, 0.75, 1], - xticklabels=[0, 0.25, 0.5, 0.75, 1], - yticks=[0, 100, 200, 300], - yticklabels=[0, 100, 200, 300], + axs[2][2], + xticks=[0, 0.5, 1, 1.5], + xticklabels=[0, 0.5, 1, 1.5], + yticks=[0, -2, -4, -6], + yticklabels=[0, 2, 4, 6], ) -fig.supxlabel("Time (s)", font="Times New Roman", fontsize=14) -fig.supylabel("Force (N)", font="Times New Roman", fontsize=14) +axs[2][2].plot(data_minimize_force["time"], a_force_diff_list, ms=4, linewidth=5.0) +axs[2][2].plot(data_minimize_force["time"], a_fatigue_diff_list, ms=4, linewidth=5.0) -# fig.legend() -# fig.tight_layout() -plt.show() +axs[2][2].scatter( + 1, + -6, + color="red" +) + +axs[2][2].text( + 0, + 1.15, + f"{'-1e-4'}", + transform=axs[2][2].transAxes, + ha="left", + va="top", + fontsize=10, +) + +# fig.delaxes(axs[2][2]) +# Figure labels +axs[1][0].text( + -0.5, + 1.85, + f"{'Force (N)'}", + transform=axs[1][0].transAxes, + ha="left", + va="top", + weight="bold", + font="Times New Roman", + fontsize=14, + rotation=90, +) -# a_list = ["A_BIClong", "A_BICshort", "A_TRIlong", "A_TRIlat", "A_TRImed", "A_BRA"] -# a_sum = 0 -# for key_a in a_list: -# a_sum += data_minimize_force["states"][key_a][0][-1] +axs[2][0].text( + -0.5, + 1.18, + "Joint angle \n (rad)", + transform=axs[2][0].transAxes, + ha="left", + va="top", + weight="bold", + font="Times New Roman", + fontsize=14, + rotation=90, +) + +axs[2][1].text( + 0.225, + -0.4, + "Time (s)", + transform=axs[2][1].transAxes, + ha="left", + va="top", + weight="bold", + font="Times New Roman", + fontsize=14, +) + +fig.tight_layout(pad=4.0) +plt.subplots_adjust(left=0.130, right=0.975, top=0.975, bottom=0.155) +plt.show() From 417c92006cadab4d373f57fb35b3a67c6ef42743 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 14:00:11 -0500 Subject: [PATCH 50/63] Renaming class --- cocofest/__init__.py | 4 +- cocofest/models/dynamical_model.py | 6 +- cocofest/optimization/fes_ocp_dynamics.py | 28 +++++----- ...timization_musculoskeletal_dynamic_2dof.py | 4 +- ...nsity_optimization_cycling_multi_muscle.py | 4 +- .../intensity_optimization_hold_position.py | 4 +- ...optimization_hold_position_multi_muscle.py | 4 +- .../frequecy_optimization_minimize_fatigue.py | 4 +- ..._duration_optimization_minimize_fatigue.py | 4 +- ...intensity_optimization_minimize_fatigue.py | 4 +- ...force_velocity_relationships_comparison.py | 4 +- .../reaching_task_frequency_optimization.py | 4 +- .../reaching_task_intensity_optimization.py | 4 +- ...aching_task_pulse_duration_optimization.py | 4 +- ...cy_optimization_musculoskeletal_dynamic.py | 4 +- ...on_optimization_musculoskeletal_dynamic.py | 4 +- ...ty_optimization_musculoskeletal_dynamic.py | 4 +- tests/shard2/test_fes_dynamics.py | 56 +++++++++---------- 18 files changed, 75 insertions(+), 75 deletions(-) diff --git a/cocofest/__init__.py b/cocofest/__init__.py index 66234015..340e53e1 100644 --- a/cocofest/__init__.py +++ b/cocofest/__init__.py @@ -5,11 +5,11 @@ from .models.ding2007_with_fatigue import DingModelPulseDurationFrequencyWithFatigue from .models.hmed2018 import DingModelIntensityFrequency from .models.hmed2018_with_fatigue import DingModelIntensityFrequencyWithFatigue -from .models.dynamical_model import FESActuatedBiorbdModel +from .models.dynamical_model import FesMskModel from .optimization.fes_multi_start import FunctionalElectricStimulationMultiStart from .optimization.fes_ocp import OcpFes from .optimization.fes_identification_ocp import OcpFesId -from .optimization.fes_ocp_dynamics import FESActuatedBiorbdModelOCP +from .optimization.fes_ocp_dynamics import OcpFesMsk from .integration.ivp_fes import IvpFes from .fourier_approx import FourierSeries from .read_data import ExtractData diff --git a/cocofest/models/dynamical_model.py b/cocofest/models/dynamical_model.py index 1a7039fe..1a36e688 100644 --- a/cocofest/models/dynamical_model.py +++ b/cocofest/models/dynamical_model.py @@ -21,7 +21,7 @@ ) -class FESActuatedBiorbdModel(BiorbdModel): +class FesMskModel(BiorbdModel): def __init__( self, name: str = None, @@ -137,11 +137,11 @@ def muscle_dynamic( muscle_force_length_coeff = 1 muscle_force_velocity_coeff = 1 if nlp.model.muscle_force_length_relationship: - muscle_force_length_coeff = FESActuatedBiorbdModel.muscle_force_length_coefficient( + muscle_force_length_coeff = FesMskModel.muscle_force_length_coefficient( model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q ) if nlp.model.muscle_force_velocity_relationship: - muscle_force_velocity_coeff = FESActuatedBiorbdModel.muscle_force_velocity_coefficient( + muscle_force_velocity_coeff = FesMskModel.muscle_force_velocity_coefficient( model=nlp.model.bio_model.model, muscle=nlp.model.bio_model.model.muscle(muscle_idx), q=q, qdot=qdot ) diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 03a87f67..587ed529 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -27,12 +27,12 @@ DingModelIntensityFrequency, DingModelIntensityFrequencyWithFatigue, OcpFes, - FESActuatedBiorbdModel, + FesMskModel, CustomObjective, ) -class FESActuatedBiorbdModelOCP: +class OcpFesMsk: @staticmethod def prepare_ocp( biorbd_model_path: str, @@ -178,7 +178,7 @@ def prepare_ocp( n_threads=n_threads, ) - FESActuatedBiorbdModelOCP._sanity_check_fes_models_inputs( + OcpFesMsk._sanity_check_fes_models_inputs( biorbd_model_path=biorbd_model_path, bound_type=bound_type, bound_data=bound_data, @@ -195,7 +195,7 @@ def prepare_ocp( OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) - FESActuatedBiorbdModelOCP._sanity_check_muscle_model( + OcpFesMsk._sanity_check_muscle_model( biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models ) @@ -227,7 +227,7 @@ def prepare_ocp( parameters_bounds, parameters_init, parameter_objectives, - ) = FESActuatedBiorbdModelOCP._build_parameters( + ) = OcpFesMsk._build_parameters( model=fes_muscle_models, n_stim=n_stim, pulse_duration=pulse_duration, @@ -242,7 +242,7 @@ def prepare_ocp( pulse_intensity_similar_for_all_muscles=pulse_intensity_similar_for_all_muscles, ) - constraints = FESActuatedBiorbdModelOCP._set_constraints(constraints, custom_constraint) + constraints = OcpFesMsk._set_constraints(constraints, custom_constraint) if len(constraints) == 0 and len(parameters) == 0: raise ValueError( @@ -251,7 +251,7 @@ def prepare_ocp( ) bio_models = [ - FESActuatedBiorbdModel( + FesMskModel( name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models, @@ -261,17 +261,17 @@ def prepare_ocp( for i in range(n_stim) ] - dynamics = FESActuatedBiorbdModelOCP._declare_dynamics(bio_models, n_stim) - x_bounds, x_init = FESActuatedBiorbdModelOCP._set_bounds( + dynamics = OcpFesMsk._declare_dynamics(bio_models, n_stim) + x_bounds, x_init = OcpFesMsk._set_bounds( bio_models, fes_muscle_models, bound_type, bound_data, n_stim, ) - u_bounds, u_init = FESActuatedBiorbdModelOCP._set_controls(bio_models, n_stim, with_residual_torque) + u_bounds, u_init = OcpFesMsk._set_controls(bio_models, n_stim, with_residual_torque) muscle_force_key = ["F_" + fes_muscle_models[i].muscle_name for i in range(len(fes_muscle_models))] - objective_functions = FESActuatedBiorbdModelOCP._set_objective( + objective_functions = OcpFesMsk._set_objective( n_stim, n_shooting, force_fourier_coef, @@ -700,7 +700,7 @@ def _set_objective( @staticmethod def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): - tested_bio_model = FESActuatedBiorbdModel( + tested_bio_model = FesMskModel( name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models ) fes_muscle_models_name_list = [fes_muscle_models[x].muscle_name for x in range(len(fes_muscle_models))] @@ -731,7 +731,7 @@ def _sanity_check_fes_models_inputs( raise TypeError("biorbd_model_path should be a string") if bound_type: - tested_bio_model = FESActuatedBiorbdModel( + tested_bio_model = FesMskModel( name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models ) if not isinstance(bound_type, str) or bound_type not in ["start", "end", "start_end"]: @@ -806,7 +806,7 @@ def _sanity_check_fes_models_inputs( if q_tracking: if not isinstance(q_tracking, list) and len(q_tracking) != 2: raise TypeError("q_tracking should be a list of size 2") - tested_bio_model = FESActuatedBiorbdModel( + tested_bio_model = FesMskModel( name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models ) if not isinstance(q_tracking[0], list): diff --git a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py index 6ca3a2fc..d4643037 100644 --- a/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py +++ b/examples/dynamics/frequency_optimization_musculoskeletal_dynamic_2dof.py @@ -11,7 +11,7 @@ Solver, ) -from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -19,7 +19,7 @@ for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 120]], diff --git a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py index f977eefc..7026fcce 100644 --- a/examples/dynamics/intensity_optimization_cycling_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_cycling_multi_muscle.py @@ -13,7 +13,7 @@ Solver, ) -from cocofest import DingModelIntensityFrequency, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequency, OcpFesMsk n_stim = 30 @@ -33,7 +33,7 @@ minimum_pulse_intensity = DingModelIntensityFrequency.min_pulse_intensity(DingModelIntensityFrequency()) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26.bioMod", bound_type="start_end", bound_data=[[65, 38], [65, 38]], diff --git a/examples/dynamics/intensity_optimization_hold_position.py b/examples/dynamics/intensity_optimization_hold_position.py index 7e75cfaf..499ac17f 100644 --- a/examples/dynamics/intensity_optimization_hold_position.py +++ b/examples/dynamics/intensity_optimization_hold_position.py @@ -14,7 +14,7 @@ Solver, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -47,7 +47,7 @@ DingModelIntensityFrequencyWithFatigue() ) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start", bound_data=[90], diff --git a/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py index 1c721fd1..d487c667 100644 --- a/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py +++ b/examples/dynamics/intensity_optimization_hold_position_multi_muscle.py @@ -14,7 +14,7 @@ Solver, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -47,7 +47,7 @@ DingModelIntensityFrequencyWithFatigue() ) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 90], diff --git a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py index 262cf4ee..68f1266f 100644 --- a/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/frequecy_optimization_minimize_fatigue.py @@ -17,7 +17,7 @@ Solver, ) -from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelFrequencyWithFatigue, OcpFesMsk n_stim = 5 n_shooting = 10 @@ -36,7 +36,7 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=10000, quadratic=True, phase=i) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], diff --git a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py index 54542b54..5a155479 100644 --- a/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_duration_optimization_minimize_fatigue.py @@ -14,7 +14,7 @@ Solver, ) -from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk n_stim = 10 n_shooting = 10 @@ -32,7 +32,7 @@ minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], diff --git a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py index ed068496..f4ec8a82 100644 --- a/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py +++ b/examples/dynamics/minimize_fatigue/pulse_intensity_optimization_minimize_fatigue.py @@ -14,7 +14,7 @@ Solver, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk n_stim = 10 n_shooting = 10 @@ -34,7 +34,7 @@ DingModelIntensityFrequencyWithFatigue() ) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26_biceps_triceps.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 90]], diff --git a/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py b/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py index 2ddef919..42e9a0f7 100644 --- a/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py +++ b/examples/dynamics/muscle_force_length_and_force_velocity_relationships_comparison.py @@ -10,7 +10,7 @@ from cocofest import ( DingModelPulseDurationFrequencyWithFatigue, - FESActuatedBiorbdModelOCP, + OcpFesMsk, ) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 @@ -19,7 +19,7 @@ muscle_force_length_relationship = [False, True] for i in range(2): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start", bound_data=[0], diff --git a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py index 38baf051..103dcec5 100644 --- a/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_frequency_optimization.py @@ -15,7 +15,7 @@ Node, ) -from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelFrequencyWithFatigue, OcpFesMsk # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 @@ -80,7 +80,7 @@ controls = [] parameters = [] - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 5], diff --git a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py index 562e4f7b..537fa84f 100644 --- a/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_intensity_optimization.py @@ -15,7 +15,7 @@ Node, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 @@ -83,7 +83,7 @@ controls = [] parameters = [] - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start", bound_data=[0, 5], diff --git a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py index 83f48345..0b96d643 100644 --- a/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py +++ b/examples/dynamics/reaching_task/reaching_task_pulse_duration_optimization.py @@ -15,7 +15,7 @@ Node, ) -from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk # Fiber type proportion from [1] biceps_fiber_type_2_proportion = 0.607 @@ -77,7 +77,7 @@ for i in range(len(pickle_file_list)): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../../msk_models/arm26.bioMod", bound_type="start_end", bound_data=[[0, 5], [0, 5]], diff --git a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py index b96ab8eb..217716ea 100644 --- a/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/frequency_optimization_musculoskeletal_dynamic.py @@ -11,7 +11,7 @@ Solver, ) -from cocofest import DingModelFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -19,7 +19,7 @@ for i in range(n_stim): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], diff --git a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py index 750254bc..0288ed18 100644 --- a/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_duration_optimization_musculoskeletal_dynamic.py @@ -11,7 +11,7 @@ Solver, ) -from cocofest import DingModelPulseDurationFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelPulseDurationFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -20,7 +20,7 @@ objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], diff --git a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py index b17956e9..9106166c 100644 --- a/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py +++ b/examples/getting_started/pulse_intensity_optimization_musculoskeletal_dynamic.py @@ -11,7 +11,7 @@ Solver, ) -from cocofest import DingModelIntensityFrequencyWithFatigue, FESActuatedBiorbdModelOCP +from cocofest import DingModelIntensityFrequencyWithFatigue, OcpFesMsk objective_functions = ObjectiveList() @@ -23,7 +23,7 @@ DingModelIntensityFrequencyWithFatigue() ) -ocp = FESActuatedBiorbdModelOCP.prepare_ocp( +ocp = OcpFesMsk.prepare_ocp( biorbd_model_path="../msk_models/arm26_biceps_1dof.bioMod", bound_type="start_end", bound_data=[[5], [120]], diff --git a/tests/shard2/test_fes_dynamics.py b/tests/shard2/test_fes_dynamics.py index ee4b6bdd..b4753b9e 100644 --- a/tests/shard2/test_fes_dynamics.py +++ b/tests/shard2/test_fes_dynamics.py @@ -12,7 +12,7 @@ from cocofest import ( DingModelPulseDurationFrequencyWithFatigue, DingModelIntensityFrequencyWithFatigue, - FESActuatedBiorbdModelOCP, + OcpFesMsk, ) from examples.msk_models import init as ocp_module @@ -28,7 +28,7 @@ def test_pulse_duration_multi_muscle_fes_dynamics(): objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="tau", weight=1, quadratic=True, phase=i) minimum_pulse_duration = DingModelPulseDurationFrequencyWithFatigue().pd0 - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5], [0, 120]], @@ -105,7 +105,7 @@ def test_pulse_intensity_multi_muscle_fes_dynamics(): [np.array([0, 10, 40, 90, 140, 80, 50, 10, 0, 0, 0]), np.array([0, 0, 0, 10, 40, 90, 140, 80, 50, 10, 0])], ] - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -177,7 +177,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape("biorbd_model_path should be a string"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=5, bound_type="start_end", bound_data=[[0, 5], [0, 120]], @@ -196,7 +196,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("bound_type should be a string and should be equal to start, end or start_end"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="hello", bound_data=[[0, 5], [0, 120]], @@ -215,7 +215,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape("bound_data should be a list"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data="[[0, 5], [0, 120]]", @@ -234,7 +234,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape(f"bound_data should be a list of {2} elements"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5, 7], [0, 120, 150]], @@ -253,7 +253,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"bound_data should be a list of two list"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=["[0, 5]", [0, 120]], @@ -272,7 +272,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape(f"bound_data should be a list of {2} elements"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5, 7], [0, 120, 150]], @@ -291,7 +291,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"bound data index {1}: {5} and {'120'} should be an int or float"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start_end", bound_data=[[0, 5], [0, "120"]], @@ -310,7 +310,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape(f"bound_data should be a list of {2} element"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5, 10], @@ -329,7 +329,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"bound data index {1}: {'5'} should be an int or float"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="end", bound_data=[0, "5"], @@ -355,7 +355,7 @@ def test_fes_models_inputs_sanity_check_errors(): " DingModelIntensityFrequencyWithFatigue type" ), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -374,7 +374,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"force_tracking: {'hello'} must be list type"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -394,7 +394,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("force_tracking must of size 2"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="end", bound_data=[0, 5], @@ -414,7 +414,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"force_tracking index 0: {'hello'} must be np.ndarray type"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -434,7 +434,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"force_tracking index 1: {'[1, 2, 3]'} must be list type"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -456,7 +456,7 @@ def test_fes_models_inputs_sanity_check_errors(): "force_tracking index 1 list must have the same size as the number of muscles in fes_muscle_models" ), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -476,7 +476,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("force_tracking time and force argument must be the same length"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -496,7 +496,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"force_tracking: {'hello'} must be list type"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -516,7 +516,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("end_node_tracking list must have the same size as the number of muscles in fes_muscle_models"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -536,7 +536,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"end_node_tracking index {1}: {'hello'} must be int or float type"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -556,7 +556,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape("q_tracking should be a list of size 2"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -576,7 +576,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("q_tracking[0] should be a list"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -596,7 +596,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("q_tracking[1] should have the same size as the number of generalized coordinates"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -616,7 +616,7 @@ def test_fes_models_inputs_sanity_check_errors(): ValueError, match=re.escape("q_tracking[0] and q_tracking[1] should have the same size"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -636,7 +636,7 @@ def test_fes_models_inputs_sanity_check_errors(): TypeError, match=re.escape(f"{'with_residual_torque'} should be a boolean"), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], @@ -662,7 +662,7 @@ def test_fes_muscle_models_sanity_check_errors(): f" {'TRIlong'}" ), ): - ocp = FESActuatedBiorbdModelOCP.prepare_ocp( + ocp = OcpFesMsk.prepare_ocp( biorbd_model_path=biorbd_model_path, bound_type="start", bound_data=[0, 5], From 486fbf1e3408503e84ef12671a2f27ff1a2f53dc Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 14:53:01 -0500 Subject: [PATCH 51/63] moving logo in readme --- README.md | 14 ++++++-------- 1 file changed, 6 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index a49f53ea..70abca22 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,15 @@ -

- logo -

+[//]: # () + # COCOFEST `Cocofest` : Custom Optimal Control Optimization for Functional Electrical Stimulation, is an optimal control program (OCP) package for functional electrical stimulation (FES). It is based on the [bioptim](https://github.com/pyomeca/bioptim) framework for the optimal control construction. Bioptim uses [biorbd](https://github.com/pyomeca/biorbd) a biomechanics library and benefits from the powerful algorithmic diff provided by [CasADi](https://web.casadi.org/). -To solve the OCP, the robust solver [Ipopt](https://github.com/coin-or/Ipopt) has been implemented. +To solve the OCP, the robust solver [Ipopt](https://github.com/coin-or/Ipopt) has been implemented. + +
+
## Status From 633b1e95e95fd96f02eb5b0109413ec3fe5e6084 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 14:53:45 -0500 Subject: [PATCH 52/63] same --- README.md | 3 --- 1 file changed, 3 deletions(-) diff --git a/README.md b/README.md index 70abca22..d7205128 100644 --- a/README.md +++ b/README.md @@ -8,9 +8,6 @@ It is based on the [bioptim](https://github.com/pyomeca/bioptim) framework for t Bioptim uses [biorbd](https://github.com/pyomeca/biorbd) a biomechanics library and benefits from the powerful algorithmic diff provided by [CasADi](https://web.casadi.org/). To solve the OCP, the robust solver [Ipopt](https://github.com/coin-or/Ipopt) has been implemented. -
-
- ## Status | Type | Status | From 51bbb296daf542e0558f3bf2feabb77feba7aa29 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 15:56:36 -0500 Subject: [PATCH 53/63] removing read data and multistart file --- cocofest/__init__.py | 2 - cocofest/optimization/fes_multi_start.py | 548 ----------------------- cocofest/read_data.py | 54 --- environment.yml | 1 - 4 files changed, 605 deletions(-) delete mode 100644 cocofest/optimization/fes_multi_start.py delete mode 100644 cocofest/read_data.py diff --git a/cocofest/__init__.py b/cocofest/__init__.py index 340e53e1..998c81bb 100644 --- a/cocofest/__init__.py +++ b/cocofest/__init__.py @@ -6,13 +6,11 @@ from .models.hmed2018 import DingModelIntensityFrequency from .models.hmed2018_with_fatigue import DingModelIntensityFrequencyWithFatigue from .models.dynamical_model import FesMskModel -from .optimization.fes_multi_start import FunctionalElectricStimulationMultiStart from .optimization.fes_ocp import OcpFes from .optimization.fes_identification_ocp import OcpFesId from .optimization.fes_ocp_dynamics import OcpFesMsk from .integration.ivp_fes import IvpFes from .fourier_approx import FourierSeries -from .read_data import ExtractData from .identification.ding2003_force_parameter_identification import DingModelFrequencyForceParameterIdentification from .identification.ding2007_force_parameter_identification import ( DingModelPulseDurationFrequencyForceParameterIdentification, diff --git a/cocofest/optimization/fes_multi_start.py b/cocofest/optimization/fes_multi_start.py deleted file mode 100644 index 2359c122..00000000 --- a/cocofest/optimization/fes_multi_start.py +++ /dev/null @@ -1,548 +0,0 @@ -import os - -import numpy as np -import pickle - -from bioptim import Solver, MultiStart, Solution -from cocofest import DingModelFrequency, DingModelPulseDurationFrequency, DingModelIntensityFrequency -from ..read_data import ExtractData -from .fes_ocp import OcpFes - - -class FunctionalElectricStimulationMultiStart(MultiStart): - """ - The main class to define a multi start program. This class prepares the full multi start and gives all - the needed parameters to solve multiple functional electrical stimulation ocp - - Attributes - ---------- - model: list[DingModelFrequency | DingModelPulseDurationFrequency| DingModelIntensityFrequency] - The model type used for the ocp - n_stim: list[int] - Number of stimulation that will occur during the ocp, it is as well refer as phases - n_shooting: list[int] - Number of shooting point for each individual phases - final_time: list[float] - Refers to the final time of the ocp - frequency list[int]: - Frequency of stimulation apparition - force_tracking: list[list[np.ndarray, np.ndarray]] - List of time and associated force to track during ocp optimisation - end_node_tracking: list[int] | list[float] - Force objective value to reach at the last node - time_min: list[int] | list[float] - Minimum time for a phase - time_max: list[int] | list[float] - Maximum time for a phase - time_bimapping: list[bool] - Set phase time constant - pulse_duration: list[int | float] - Setting a chosen pulse time among phases - pulse_duration_min: list[int | float] - Minimum pulse time for a phase - pulse_duration_max: list[int | float] - Maximum pulse time for a phase - pulse_duration_bimapping: list[bool] - Set pulse time constant among phases - pulse_intensity: list[int | float] - Setting a chosen pulse intensity among phases - pulse_intensity_min: list[int | float] - Minimum pulse intensity for a phase - pulse_intensity_max: list[int | float] - Maximum pulse intensity for a phase - pulse_intensity_bimapping: list[bool] - Set pulse intensity constant among phases - **kwargs: - objective: list[Objective] - Additional objective for the system - ode_solver: list[OdeSolver] - The ode solver to use - use_sx: list[bool] - The nature of the casadi variables. MX are used if False. - n_threads: list[int] - The number of thread to use while solving (multi-threading if > 1) - - Example - ---------- - combinatorial_parameters = {"model": list[model1, model2, model3], - "n_stim": list[n_stim1, n_stim2], - "force_tracking": list[force_tracking1, force_tracking2]} - - 3 model, 2 n_stim, 2 force_tracking different so 3 x 2 x 2 = 12 different ocp run in the multi start - All cases : - case 1 : model1 + n_stim1 + force_tracking1 - case 2 : model1 + n_stim1 + force_tracking2 - case 3 : model1 + n_stim2 + force_tracking1 - case 4 : model1 + n_stim2 + force_tracking2 - case 5 : model2 + n_stim1 + force_tracking1 - case 6 : model2 + n_stim1 + force_tracking2 - case 7 : model2 + n_stim2 + force_tracking1 - case 8 : model2 + n_stim2 + force_tracking2 - case 9 : model3 + n_stim1 + force_tracking1 - case 10 : model3 + n_stim1 + force_tracking2 - case 11 : model3 + n_stim2 + force_tracking1 - case 12 : model3 + n_stim2 + force_tracking2 - """ - - def __init__( - self, - methode: str = None, - model: list[DingModelFrequency | DingModelPulseDurationFrequency | DingModelIntensityFrequency] = None, - n_stim: list[int] | list[None] = None, - n_shooting: list[int] = None, - final_time: list[float] | list[None] = None, - frequency: list[int] | list[None] = None, - force_tracking: list[list[np.ndarray, np.ndarray]] | list[None] = None, - end_node_tracking: list[int] | list[float] | list[None] = None, - time_min: list[int] | list[float] | list[None] = None, - time_max: list[int] | list[float] | list[None] = None, - time_bimapping: list[bool] | list[None] = None, - pulse_duration: list[int] | list[float] | list[None] = None, - pulse_duration_min: list[int] | list[float] | list[None] = None, - pulse_duration_max: list[int] | list[float] | list[None] = None, - pulse_duration_bimapping: list[bool] | list[None] = None, - pulse_intensity: list[int] | list[float] | list[None] = None, - pulse_intensity_min: list[int] | list[float] | list[None] = None, - pulse_intensity_max: list[int] | list[float] | list[None] = None, - pulse_intensity_bimapping: list[bool] | list[None] = None, - kwargs_fes: dict = None, - **kwargs, - ): - self.methode = methode - # --- Prepare the multi-start and run it --- # - combinatorial_parameters = { - "model": [None] if model is None else model, - "n_stim": [None] if n_stim is None else n_stim, - "n_shooting": [None] if n_shooting is None else n_shooting, - "final_time": [None] if final_time is None else final_time, - "frequency": [None] if frequency is None else frequency, - "force_tracking": [None] if force_tracking is None else force_tracking, - "end_node_tracking": [None] if end_node_tracking is None else end_node_tracking, - "time_min": [None] if time_min is None else time_min, - "time_max": [None] if time_max is None else time_max, - "time_bimapping": [None] if time_bimapping is None else time_bimapping, - "pulse_duration": [None] if pulse_duration is None else pulse_duration, - "pulse_duration_min": [None] if pulse_duration_min is None else pulse_duration_min, - "pulse_duration_max": [None] if pulse_duration_max is None else pulse_duration_max, - "pulse_duration_bimapping": [None] if pulse_duration_bimapping is None else pulse_duration_bimapping, - "pulse_intensity": [None] if pulse_intensity is None else pulse_intensity, - "pulse_intensity_min": [None] if pulse_intensity_min is None else pulse_intensity_min, - "pulse_intensity_max": [None] if pulse_intensity_max is None else pulse_intensity_max, - "pulse_intensity_bimapping": [None] if pulse_intensity_bimapping is None else pulse_intensity_bimapping, - } - - if "path_folder" in kwargs: - save_folder = kwargs["path_folder"] - else: - save_folder = "./multiprocess_results" - - n_pools = 1 - if not isinstance(save_folder, str): - raise ValueError("save_folder must be a str") - if not os.path.exists(save_folder): - os.mkdir(save_folder) - if "max_iter" in kwargs: - if not isinstance(kwargs["max_iter"], int): - raise ValueError("max_iter must be an int") - else: - kwargs["max_iter"] = 1000 - - if kwargs_fes is not None: - for kwarg in kwargs_fes: - if isinstance(kwarg, list): - raise ValueError("Kwargs are not combinatorial parameters, kwarg fix across all multi_ocp") - - self.kwarg_fes = kwargs_fes - - super().__init__( - combinatorial_parameters=combinatorial_parameters, - prepare_ocp_callback=self.prepare_ocp, - post_optimization_callback=(self.save_results, {"save_folder": save_folder}), - should_solve_callback=(self.should_solve, {"save_folder": save_folder}), - solver=Solver.IPOPT(_max_iter=kwargs["max_iter"]), - n_pools=n_pools, - ) - - @staticmethod - def construct_filepath(save_path, combinatorial_parameters): - ( - model, - n_stim, - n_shooting, - final_time, - frequency, - force_tracking, - end_node_tracking, - time_min, - time_max, - time_bimapping, - pulse_duration, - pulse_duration_min, - pulse_duration_max, - pulse_duration_bimapping, - pulse_intensity, - pulse_intensity_min, - pulse_intensity_max, - pulse_intensity_bimapping, - ) = combinatorial_parameters - - if force_tracking is None: - force_tracking_state = False - else: - force_tracking_state = True - if end_node_tracking is None: - end_node_state = False - else: - end_node_state = True - if time_min or time_max is None: - time_parameter = False - else: - time_parameter = True - - if frequency is None: - frequency = n_stim / final_time - - file_list = [ - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{frequency}_HZ_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{frequency}_HZ_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{frequency}_HZ_and_{pulse_duration_min}_min_{pulse_duration_max}_max_pulse_duration_bimapped{pulse_duration_bimapping}.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{frequency}_HZ_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{frequency}_HZ_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelPulseDurationFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{frequency}_HZ_and_{pulse_duration}_pulse_duration.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{frequency}_HZ_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{frequency}_HZ_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{frequency}_HZ_and_{pulse_intensity_min}_min_{pulse_intensity_max}_max_pulse_intensity_bimapped{pulse_intensity_bimapping}.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{frequency}_HZ_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{frequency}_HZ_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelIntensityFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{frequency}_HZ_and_{pulse_intensity}_pulse_intensity.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{time_min}_min_{time_max}_max_time_bimapped{time_bimapping}.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_force_tracking_{frequency}_HZ.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{end_node_tracking}N_end_node_tracking_{frequency}_HZ.pkl", - f"{save_path}/DingModelFrequency_multi_start_{n_stim}_stimulation_{n_shooting}_node_shooting_{frequency}_HZ.pkl", - ] - - if isinstance(model, DingModelPulseDurationFrequency): - if pulse_duration_min or pulse_duration_max is None: - pulse_duration_parameter = False - else: - pulse_duration_parameter = True - - if pulse_duration_parameter is True: - if time_parameter is True: - if force_tracking_state is True: - return file_list[0] - elif end_node_state is True: - return file_list[1] - else: - return file_list[2] - else: - if force_tracking_state is True: - return file_list[3] - elif end_node_state is True: - return file_list[4] - else: - return file_list[5] - else: - if time_parameter is True: - if force_tracking_state is True: - return file_list[6] - elif end_node_state is True: - return file_list[7] - else: - return file_list[8] - else: - if force_tracking_state is True: - return file_list[9] - elif end_node_state is True: - return file_list[10] - else: - return file_list[11] - elif isinstance(model, DingModelIntensityFrequency): - if pulse_duration_min or pulse_duration_max is None: - pulse_intensity_parameter = False - else: - pulse_intensity_parameter = True - - if pulse_intensity_parameter is True: - if time_parameter is True: - if force_tracking_state is True: - return file_list[12] - elif end_node_state is True: - return file_list[13] - else: - return file_list[14] - else: - if force_tracking_state is True: - return file_list[15] - elif end_node_state is True: - return file_list[16] - else: - return file_list[17] - else: - if time_parameter is True: - if force_tracking_state is True: - return file_list[18] - elif end_node_state is True: - return file_list[19] - else: - return file_list[20] - else: - if force_tracking_state is True: - return file_list[21] - elif end_node_state is True: - return file_list[22] - else: - return file_list[23] - elif isinstance(model, DingModelFrequency): - if time_parameter is True: - if force_tracking_state is True: - return file_list[24] - elif end_node_state is True: - return file_list[25] - else: - return file_list[26] - else: - if force_tracking_state is True: - return file_list[27] - elif end_node_state is True: - return file_list[28] - else: - return file_list[29] - else: - raise ValueError( - "Wrong model type, either DingModelFrequency, DingModelPulseDurationFrequency," - " DingModelIntensityFrequency", - ) - - def save_results( - self, - sol: Solution, - *combinatorial_parameters, - **extra_parameters, - ) -> None: - """ - Callback of the post_optimization_callback, this can be used to save the results - - Parameters - ---------- - sol: Solution - The solution to the ocp at the current pool - combinatorial_parameters: - The current values of the combinatorial_parameters being treated - extra_parameters: - All the non-combinatorial parameters sent by the user - """ - ( - model, - n_stim, - n_shooting, - final_time, - frequency, - force_tracking, - end_node_tracking, - time_min, - time_max, - time_bimapping, - pulse_duration, - pulse_duration_min, - pulse_duration_max, - pulse_duration_bimapping, - pulse_intensity, - pulse_intensity_min, - pulse_intensity_max, - pulse_intensity_bimapping, - ) = combinatorial_parameters - - save_folder = extra_parameters["save_folder"] - - file_path = self.construct_filepath(save_folder, combinatorial_parameters) - merged_phase = sol.merge_phases() - states = merged_phase.states - time = merged_phase.time - phase_time = [] - for i in range(len(sol.time)): - phase_time.append(sol.time[i][0]) - states["time"] = time - states["cost"] = sol.cost - states["computation_time"] = sol.real_time_to_optimize - states["parameters"] = sol.parameters - states["phase_time"] = np.array(phase_time) - states["status"] = sol.status - states["model"] = np.array([str(model)]) - states["n_stim"] = np.array([n_stim]) - states["n_shooting"] = np.array([n_shooting]) - states["force_tracking"] = force_tracking - states["end_node_tracking"] = np.array([end_node_tracking]) - states["time_min"] = np.array([time_min]) - states["time_max"] = np.array([time_max]) - states["time_bimapping"] = np.array([time_bimapping]) - states["pulse_duration"] = np.array([pulse_duration]) - states["pulse_duration_min"] = np.array([pulse_duration_min]) - states["pulse_duration_max"] = np.array([pulse_duration_max]) - states["pulse_duration_bimapping"] = np.array([pulse_duration_bimapping]) - states["pulse_intensity"] = np.array([pulse_intensity]) - states["pulse_intensity_min"] = np.array([pulse_intensity_min]) - states["pulse_intensity_max"] = np.array([pulse_intensity_max]) - states["pulse_intensity_bimapping"] = np.array([pulse_intensity_bimapping]) - - with open(file_path, "wb") as file: - pickle.dump(states, file) - - def should_solve(self, *combinatorial_parameters, **extra_parameters): - """ - Callback of the should_solve_callback, this allows the user to instruct bioptim - - Parameters - ---------- - combinatorial_parameters: - The current values of the combinatorial_parameters being treated - extra_parameters: - All the non-combinatorial parameters sent by the user - """ - - save_folder = extra_parameters["save_folder"] - - file_path = self.construct_filepath(save_folder, combinatorial_parameters) - return not os.path.exists(file_path) - - def prepare_ocp( - self, - model: DingModelFrequency | DingModelPulseDurationFrequency | DingModelIntensityFrequency = None, - n_stim: int = None, - n_shooting: int = None, - final_time: int | float = None, - frequency: int = None, - force_tracking: list[np.ndarray, np.ndarray] = None, - end_node_tracking: int | float = None, - time_min: list[int] | list[float] = None, - time_max: list[int] | list[float] = None, - time_bimapping: bool = None, - pulse_duration: int | float = None, - pulse_duration_min: int | float = None, - pulse_duration_max: int | float = None, - pulse_duration_bimapping: bool = None, - pulse_intensity: int | float = None, - pulse_intensity_min: int | float = None, - pulse_intensity_max: int | float = None, - pulse_intensity_bimapping: bool = None, - ): - if self.methode is None or self.methode == "standard": - ocp = OcpFes.prepare_ocp( - model=model, - n_stim=n_stim, - n_shooting=n_shooting, - final_time=final_time, - force_tracking=force_tracking, - end_node_tracking=end_node_tracking, - time_min=time_min, - time_max=time_max, - time_bimapping=time_bimapping, - pulse_duration=pulse_duration, - pulse_duration_min=pulse_duration_min, - pulse_duration_max=pulse_duration_max, - pulse_duration_bimapping=pulse_duration_bimapping, - pulse_intensity=pulse_intensity, - pulse_intensity_min=pulse_intensity_min, - pulse_intensity_max=pulse_intensity_max, - pulse_intensity_bimapping=pulse_intensity_bimapping, - use_sx=True, - ) - - elif self.methode == "from_frequency_and_final_time": - ocp = OcpFes.prepare_ocp( - model=model, - n_shooting=n_shooting, - final_time=final_time, - force_tracking=force_tracking, - end_node_tracking=end_node_tracking, - round_down=True, - frequency=frequency, - time_min=time_min, - time_max=time_max, - time_bimapping=time_bimapping, - pulse_duration=pulse_duration, - pulse_duration_min=pulse_duration_min, - pulse_duration_max=pulse_duration_max, - pulse_duration_bimapping=pulse_duration_bimapping, - pulse_intensity=pulse_intensity, - pulse_intensity_min=pulse_intensity_min, - pulse_intensity_max=pulse_intensity_max, - pulse_intensity_bimapping=pulse_intensity_bimapping, - use_sx=True, - ) - - elif self.methode == "from_frequency_and_n_stim": - ocp = OcpFes.prepare_ocp( - model=model, - n_shooting=n_shooting, - n_stim=n_stim, - force_tracking=force_tracking, - end_node_tracking=end_node_tracking, - frequency=frequency, - time_min=time_min, - time_max=time_max, - time_bimapping=time_bimapping, - pulse_duration=pulse_duration, - pulse_duration_min=pulse_duration_min, - pulse_duration_max=pulse_duration_max, - pulse_duration_bimapping=pulse_duration_bimapping, - pulse_intensity=pulse_intensity, - pulse_intensity_min=pulse_intensity_min, - pulse_intensity_max=pulse_intensity_max, - pulse_intensity_bimapping=pulse_intensity_bimapping, - use_sx=True, - ) - - else: - raise ValueError( - "method should be either None or standard for an ocp build with n_stim and final_time", - " from_frequency_and_final_time for an ocp build with frequency and final_time", - " of from_frequency_and_n_stim for an ocp build with frequency and n_stim", - ) - - return ocp - - -if __name__ == "__main__": - time, force = ExtractData.load_data("../../examples/data/hand_cycling_force.bio") - force = force - force[0] - force = [time, force] - - a = FunctionalElectricStimulationMultiStart( - methode="standard", - model=[DingModelFrequency()], - n_stim=[10], - n_shooting=[20], - final_time=[1], - frequency=[None], - force_tracking=[None], - end_node_tracking=[270], - time_min=[0.01], - time_max=[0.1], - time_bimapping=[True], - pulse_duration=[None], - pulse_duration_min=[None], - pulse_duration_max=[None], - pulse_duration_bimapping=[False], - pulse_intensity=[None], - pulse_intensity_min=[None], - pulse_intensity_max=[None], - pulse_intensity_bimapping=[None], - path_folder="./for_test", - ) - - a.solve() diff --git a/cocofest/read_data.py b/cocofest/read_data.py deleted file mode 100644 index 2a6311fc..00000000 --- a/cocofest/read_data.py +++ /dev/null @@ -1,54 +0,0 @@ -import csv -from math import ceil -import matplotlib.pyplot as plt -import numpy as np - -from biosiglive import load - - -class ExtractData: - @staticmethod - def data(path: str) -> np.array: - datas = [] - with open(path, "r") as file: - reader = csv.reader(file, delimiter="\t") - for row in reader: - row_bis = [float(i) for i in row] - datas.append(row_bis) - return np.array(datas) - - @staticmethod - def time_force(datas: np.array, time_start: int | float, time_end: int | float) -> np.array: - closest_start = min(datas[:, 20], key=lambda x: abs(x - time_start)) - closest_end = min(datas[:, 20], key=lambda x: abs(x - time_end)) - idx_start = np.where(datas[:, 20] == closest_start)[0][0] - idx_end = np.where(datas[:, 20] == closest_end)[0][0] - force = np.array(np.sqrt(datas[:, 21] ** 2 + datas[:, 22] ** 2 + datas[:, 23] ** 2))[idx_start:idx_end] - time = datas[:, 20][idx_start:idx_end] - time = time - time[0] - return time, force - - @staticmethod - def load_data(path: str) -> np.array: - data = load(path) - - force = data["f_est"][17, :100] - min(data["f_est"][17, :100]) - time = np.linspace(0, 1, 100) - return time, force - - @staticmethod - def plot_data(path: str): - data = load(path) - n_line = 4 - - if data["f_est"].shape[0] <= n_line: - n_column = 1 - else: - n_column = ceil(data["f_est"].shape[0] / n_line) - - for i in range(data["f_est"].shape[0]): - plt.subplot(n_line, n_column, i + 1) - plt.plot(data["f_est"][i, :100], color="b") - plt.grid() - plt.legend() - plt.show() diff --git a/environment.yml b/environment.yml index 08f4f450..ea9f527b 100644 --- a/environment.yml +++ b/environment.yml @@ -4,7 +4,6 @@ channels: - conda-forge dependencies: - biorbd = 1.10.2 -- biosiglive - bioviz - python-graphviz - pyqtgraph From 62808e18a59314c23315d4c9d7fe27cf8d81ea4e Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 16:19:58 -0500 Subject: [PATCH 54/63] Removing last multistart examples --- examples/getting_started/multi_start.py | 31 ----------------------- tests/shard1/test_ocp_build.py | 33 ------------------------- 2 files changed, 64 deletions(-) delete mode 100644 examples/getting_started/multi_start.py diff --git a/examples/getting_started/multi_start.py b/examples/getting_started/multi_start.py deleted file mode 100644 index 7790bfae..00000000 --- a/examples/getting_started/multi_start.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -An example of how to use multi-start to find local minima from stimulation parameter. -This example is a variation of the fes frequency in examples/frequency_optimization.py. -""" - -import shutil - -from cocofest import DingModelFrequencyWithFatigue, FunctionalElectricStimulationMultiStart - -# --- Build multi start --- # -# This multi start was build to match a force value of 270N at the end of the last node. -# The stimulation will be optimized between 0.01 to 0.1 seconds and are equally spaced (a fixed frequency). -# Plus the number of stimulation will be different at each optimization 10 to 15 in this example. -save_folder = "./temporary" -fes_multi_start = FunctionalElectricStimulationMultiStart( - model=[DingModelFrequencyWithFatigue()], - n_stim=[10, 11, 12, 13, 14, 15], - n_shooting=[20], - final_time=[1], - end_node_tracking=[270], - time_min=[0.01], - time_max=[0.1], - time_bimapping=[True], - use_sx=[True], - path_folder=save_folder, -) -# --- Solve each program --- # -sol = fes_multi_start.solve() - -# --- Delete the solutions ---# -shutil.rmtree(save_folder) diff --git a/tests/shard1/test_ocp_build.py b/tests/shard1/test_ocp_build.py index d8388db9..a51c3e55 100644 --- a/tests/shard1/test_ocp_build.py +++ b/tests/shard1/test_ocp_build.py @@ -5,7 +5,6 @@ import numpy as np from cocofest import ( - FunctionalElectricStimulationMultiStart, DingModelFrequency, DingModelFrequencyWithFatigue, DingModelPulseDurationFrequency, @@ -434,38 +433,6 @@ def test_ocp_not_for_optimal_error(): ) -@pytest.mark.parametrize( - "force_tracking, end_node_tracking", [(init_force_tracking, None), (None, init_end_node_tracking)] -) -@pytest.mark.parametrize("min_pulse_duration, min_pulse_intensity", [(minimum_pulse_duration, minimum_pulse_intensity)]) -def test_multi_start_building(force_tracking, end_node_tracking, min_pulse_duration, min_pulse_intensity): - multi_start = FunctionalElectricStimulationMultiStart( - methode="standard", - model=[DingModelFrequency(), DingModelPulseDurationFrequency(), DingModelIntensityFrequency()], - n_stim=[10], - n_shooting=[20], - final_time=[1], - frequency=[None], - force_tracking=[force_tracking], - end_node_tracking=[end_node_tracking], - time_min=[0.01], - time_max=[0.1], - time_bimapping=[False], - pulse_duration=[None], - pulse_duration_min=[minimum_pulse_duration], - pulse_duration_max=[0.0006], - pulse_duration_bimapping=[None], - pulse_intensity=[None], - pulse_intensity_min=[minimum_pulse_intensity], - pulse_intensity_max=[130], - pulse_intensity_bimapping=[None], - path_folder="./temp", - ) - - # --- Delete the temp file ---# - shutil.rmtree("./temp") - - def test_ding2007_build(): min_duration = DingModelPulseDurationFrequency().pd0 ocp = OcpFes().prepare_ocp( From 65d7c455667a4994ad06ad1067ecbe5b15b1af14 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 17:10:28 -0500 Subject: [PATCH 55/63] Readme update --- README.md | 66 ++++++++++++++++++++++++++++++++++++------------------- 1 file changed, 43 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index d7205128..b9d3a94a 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,3 @@ -[//]: # () # COCOFEST @@ -32,11 +31,11 @@ To solve the OCP, the robust solver [Ipopt](https://github.com/coin-or/Ipopt) ha [Create your own FES OCP](#create-your-own-fes-ocp) [Examples](#examples) +- [Musculoskeletal model driven by FES models](#musculoskeletal-model-driven-by-FES-models)
Other functionalities -- [With fatigue](#with-fatigue) - [Initial value problem](#initital-value-problem) - [Summation truncation](#summation-truncation) @@ -51,11 +50,36 @@ Cloning the repository is the first step to be able to use the package. ## Dependencies `Cocofest` relies on several libraries. -Based on `bioptim`, the user is invited to directly download the framework from anaconda or from the [sources](https://github.com/pyomeca/bioptim) by cloning the repository +Follows the steps to install everything you need to use `Cocofest`. +
+First, you need to create a new conda environment ```bash -conda install -c conda-forge bioptim +conda create -n YOUR_ENV_NAME python=3.10 ``` -The other [bioptim dependencies](https://github.com/pyomeca/bioptim#dependencies) must be installed as well. + +Then, activate the environment +```bash +conda activate YOUR_ENV_NAME +``` + +This step will allow you to install the dependencies in the environment much quicker +```bash: +conda install -cconda-forge conda-libmamba-solver +``` + +After, install the dependencies +```bash +conda install numpy matplotlib pytest casadi biorbd -cconda-forge --solver=libmamba +``` + +Finally, install the bioptim setup.py file located in your cocofest/external/bioptim folder +```bash +cd /external/bioptim +python setup.py install +``` + +You got everything you need to use `Cocofest`! + # Available FES models The available FES models are likely to increase so stay tune. @@ -91,7 +115,7 @@ the minimum and maximum time between two stimulation pulse, the time bimapping (If True, will act like a frequency at constant pulse interval). ```python -ocp = OcpFes.prepare_ocp(..., +ocp = OcpFes().prepare_ocp(..., n_stim=10, n_shooting=20, ...,) @@ -105,41 +129,37 @@ result = ocp.solve() # Examples You can find all the available examples in the [examples](https://github.com/Kev1CO/cocofest/tree/main/examples) file. +## Musculoskeletal model driven by FES models +The following example is a musculoskeletal model driven by the Ding2007 FES model. +The objective function is to reach a 90° forearm position and 0° arm position at the movement end. +The stimulation last 1s and the stimulation frequency is 10Hz. +The optimized parameter are each stimulation pulse width. -# Other functionalities + -## With fatigue -It is possible to compute the models with their fatigue equations or not. -For example, the "ding2003" model can be use with fatigue DingModelFrequencyWithFatigue or without DingModelFrequency. -If no fatigue is applied, the fatigue equation will not be added to the model and the muscle force will remain -constant during the simulation regardless of the previous stimulation appearance. - -```python -ocp = OcpFes.prepare_ocp(model=DingModelFrequencyWithFatigue()) -``` +# Other functionalities ## Initital value problem You can also compute the models form initial value problem. For that, use the IvpFes class to build the computed problem. ```python -ocp = IvpFes( - ding_model=DingModelFrequency(), -) +ocp = IvpFes(model=DingModelFrequency(), ...) ``` ## Summation truncation The summation truncation is an integer parameter that can be added to the model. It will truncate the stimulation apparition list used for the calcium summation. -The integer number defines the stimulation number to keep for this summation. +The integer number defines the stimulation number to keep prior this summation calculation (e.g only the 5 past stimulation will be included). ```python -ocp = OcpFes.prepare_ocp() +ocp = OcpFes().prepare_ocp(model=DingModelFrequency(sum_stim_truncation=5)) ``` # Citing `Cocofest` is not yet published in a journal. -But if you use `Cocofest` in your research, please kindly cite this package by giving the repository link. +But if you use `Cocofest` in your research, please kindly cite this zenodo link [10.5281/zenodo.10427934](https://doi.org/10.5281/zenodo.10427934). -![](docs/arm_flexion.gif) +# Acknowledgements +The Cocofest [logo](docs/cocofest_logo.png) has been design by [MaxMV](https://www.instagram.com/max_mv3/) From cfbeeee0235f2af5a9f4d70154bea8923df0cdc0 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 17:12:43 -0500 Subject: [PATCH 56/63] centering gif --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b9d3a94a..7da14e3d 100644 --- a/README.md +++ b/README.md @@ -135,7 +135,11 @@ The objective function is to reach a 90° forearm position and 0° arm position The stimulation last 1s and the stimulation frequency is 10Hz. The optimized parameter are each stimulation pulse width. - +

+ +

+ + # Other functionalities From 48d3154355e848082be01d2d2e92877bc93b1cca Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 17:13:28 -0500 Subject: [PATCH 57/63] . --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7da14e3d..7b0683c3 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ The stimulation last 1s and the stimulation frequency is 10Hz. The optimized parameter are each stimulation pulse width.

- +

From 720fbf2617dd57ca5602840b18e95f3383b1df46 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Thu, 15 Feb 2024 18:32:25 -0500 Subject: [PATCH 58/63] Finalizing graph and data for minimize fatigue --- cocofest/optimization/fes_ocp_dynamics.py | 17 ++++---- examples/dynamics/reaching_task/make_gaph.py | 40 +++++------------- .../result_file/minimize_muscle_fatigue.pkl | Bin 28108 -> 0 bytes .../result_file/minimize_muscle_force.pkl | Bin 28108 -> 0 bytes ...pulse_duration_minimize_muscle_fatigue.pkl | Bin 0 -> 40948 bytes .../pulse_duration_minimize_muscle_force.pkl | Bin 0 -> 40948 bytes 6 files changed, 18 insertions(+), 39 deletions(-) delete mode 100644 examples/dynamics/reaching_task/result_file/minimize_muscle_fatigue.pkl delete mode 100644 examples/dynamics/reaching_task/result_file/minimize_muscle_force.pkl create mode 100644 examples/dynamics/reaching_task/result_file/pulse_duration_minimize_muscle_fatigue.pkl create mode 100644 examples/dynamics/reaching_task/result_file/pulse_duration_minimize_muscle_force.pkl diff --git a/cocofest/optimization/fes_ocp_dynamics.py b/cocofest/optimization/fes_ocp_dynamics.py index 587ed529..b7a9290c 100644 --- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -686,15 +686,14 @@ def _set_objective( ) if minimize_muscle_force: - for i in range(n_stim): - objective_functions.add( - CustomObjective.minimize_overall_muscle_force_production, - custom_type=ObjectiveFcn.Mayer, - node=Node.ALL, - quadratic=True, - weight=1, - phase=i, - ) + objective_functions.add( + CustomObjective.minimize_overall_muscle_force_production, + custom_type=ObjectiveFcn.Mayer, + node=Node.END, + quadratic=True, + weight=1, + phase=n_stim - 1, + ) return objective_functions diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index 7b8e17e6..44b574c3 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -63,12 +63,6 @@ [label.set_fontname("Times New Roman") for label in labels] [label.set_fontsize(14) for label in labels] - axs[i][j].scatter( - 1, - 0, - color="red" - ) - if j == 0: plt.setp( axs[i][j], @@ -139,12 +133,6 @@ [label.set_fontname("Times New Roman") for label in labels] [label.set_fontsize(14) for label in labels] - axs[2][i].scatter( - 1, - -2, - color="red" - ) - if i == 0: plt.setp( axs[2][i], @@ -178,7 +166,7 @@ # fatigue across time axs[2][2].set_xlim(left=0, right=1.5) -axs[2][2].set_ylim(bottom=-6, top=0) +# axs[2][2].set_ylim(bottom=-0.63, top=0) axs[2][2].text( 0.3, @@ -213,38 +201,30 @@ 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) -plt.setp( - axs[2][2], - xticks=[0, 0.5, 1, 1.5], - xticklabels=[0, 0.5, 1, 1.5], - yticks=[0, -2, -4, -6], - yticklabels=[0, 2, 4, 6], -) - -axs[2][2].plot(data_minimize_force["time"], a_force_diff_list, ms=4, linewidth=5.0) -axs[2][2].plot(data_minimize_force["time"], a_fatigue_diff_list, ms=4, linewidth=5.0) + 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) -axs[2][2].scatter( - 1, - -6, - color="red" -) +axs[2][2].plot(data_minimize_force["time"], fatigue_minimization_percentage_gain_list, ms=4, linewidth=5.0, color="green") axs[2][2].text( 0, 1.15, - f"{'-1e-4'}", + f"{'%'}", transform=axs[2][2].transAxes, ha="left", va="top", fontsize=10, ) -# fig.delaxes(axs[2][2]) +plt.setp( + axs[2][2], + xticks=[0, 0.5, 1, 1.5], + xticklabels=[0, 0.5, 1, 1.5], +) # Figure labels axs[1][0].text( diff --git a/examples/dynamics/reaching_task/result_file/minimize_muscle_fatigue.pkl 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--- a/cocofest/optimization/fes_ocp_dynamics.py +++ b/cocofest/optimization/fes_ocp_dynamics.py @@ -195,9 +195,7 @@ def prepare_ocp( OcpFes._sanity_check_frequency(n_stim=n_stim, final_time=final_time, frequency=frequency, round_down=round_down) - OcpFesMsk._sanity_check_muscle_model( - biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models - ) + OcpFesMsk._sanity_check_muscle_model(biorbd_model_path=biorbd_model_path, fes_muscle_models=fes_muscle_models) n_stim, final_time = OcpFes._build_phase_parameter( n_stim=n_stim, final_time=final_time, frequency=frequency, pulse_mode=pulse_mode, round_down=round_down @@ -699,9 +697,7 @@ def _set_objective( @staticmethod def _sanity_check_muscle_model(biorbd_model_path, fes_muscle_models): - tested_bio_model = FesMskModel( - name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models - ) + tested_bio_model = FesMskModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) fes_muscle_models_name_list = [fes_muscle_models[x].muscle_name for x in range(len(fes_muscle_models))] for biorbd_muscle in tested_bio_model.muscle_names: if biorbd_muscle not in fes_muscle_models_name_list: @@ -730,9 +726,7 @@ def _sanity_check_fes_models_inputs( raise TypeError("biorbd_model_path should be a string") if bound_type: - tested_bio_model = FesMskModel( - name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models - ) + tested_bio_model = FesMskModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) if not isinstance(bound_type, str) or bound_type not in ["start", "end", "start_end"]: raise ValueError("bound_type should be a string and should be equal to start, end or start_end") if not isinstance(bound_data, list): @@ -805,9 +799,7 @@ def _sanity_check_fes_models_inputs( if q_tracking: if not isinstance(q_tracking, list) and len(q_tracking) != 2: raise TypeError("q_tracking should be a list of size 2") - tested_bio_model = FesMskModel( - name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models - ) + tested_bio_model = FesMskModel(name=None, biorbd_path=biorbd_model_path, muscles_model=fes_muscle_models) if not isinstance(q_tracking[0], list): raise ValueError("q_tracking[0] should be a list") if len(q_tracking[1]) != tested_bio_model.nb_q: diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index 44b574c3..f87de069 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -21,10 +21,10 @@ ] chosen_graph_to_plot_path = ( - duration_path - if chosen_graph_to_plot == "duration" - else intensity_path if chosen_graph_to_plot == "intensity" else None - ) + duration_path + if chosen_graph_to_plot == "duration" + else intensity_path if chosen_graph_to_plot == "intensity" else None +) if chosen_graph_to_plot_path is None: @@ -39,7 +39,7 @@ force_muscle_keys = ["F_BIClong", "F_BICshort", "F_TRIlong", "F_TRIlat", "F_TRImed", "F_BRA"] muscle_names = ["BIClong", "BICshort", "TRIlong", "TRIlat", "TRImed", "BRA"] muscle_title_x_postiton = [0.55, 0.5, 0.56, 0.62, 0.55, 0.73] -fig, axs = plt.subplots(3, 3, figsize=(5, 3), constrained_layout=True) +fig, axs = plt.subplots(3, 3, figsize=(5, 3), constrained_layout=True) index = 0 # Force across time @@ -203,12 +203,16 @@ 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) + 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) + 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 + ) -axs[2][2].plot(data_minimize_force["time"], fatigue_minimization_percentage_gain_list, ms=4, linewidth=5.0, color="green") +axs[2][2].plot( + data_minimize_force["time"], fatigue_minimization_percentage_gain_list, ms=4, linewidth=5.0, color="green" +) axs[2][2].text( 0, From ada97bbb1957387c990f308f67611ab20477f2fa Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 16 Feb 2024 11:57:59 -0500 Subject: [PATCH 60/63] readme update --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b0683c3..609b51d1 100644 --- a/README.md +++ b/README.md @@ -78,7 +78,7 @@ cd /external/bioptim python setup.py install ``` -You got everything you need to use `Cocofest`! +You are now ready to use `Cocofest`! # Available FES models From ff00cb5c857c6c3b080f3308bcdb231364c06209 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 16 Feb 2024 12:01:09 -0500 Subject: [PATCH 61/63] cleanning --- examples/dynamics/reaching_task/make_gaph.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index f87de069..2cb0a76b 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -1,12 +1,11 @@ """ 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. -The available graphs are: duration, intensity +The available graphs are: duration """ import pickle import matplotlib.pyplot as plt -import numpy as np chosen_graph_to_plot = "duration" @@ -15,15 +14,10 @@ r"result_file/pulse_duration_minimize_muscle_fatigue.pkl", ] -intensity_path = [ - r"result_file/pulse_intensity_minimize_muscle_force.pkl", - r"result_file/pulse_intensity_minimize_muscle_fatigue.pkl", -] - chosen_graph_to_plot_path = ( duration_path if chosen_graph_to_plot == "duration" - else intensity_path if chosen_graph_to_plot == "intensity" else None + else None ) @@ -166,7 +160,6 @@ # fatigue across time axs[2][2].set_xlim(left=0, right=1.5) -# axs[2][2].set_ylim(bottom=-0.63, top=0) axs[2][2].text( 0.3, From 17fb9d07b958a2dd7ce7f1c58982d358bda66474 Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 16 Feb 2024 12:02:10 -0500 Subject: [PATCH 62/63] black --- examples/dynamics/reaching_task/make_gaph.py | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/examples/dynamics/reaching_task/make_gaph.py b/examples/dynamics/reaching_task/make_gaph.py index 2cb0a76b..81c515e8 100644 --- a/examples/dynamics/reaching_task/make_gaph.py +++ b/examples/dynamics/reaching_task/make_gaph.py @@ -14,11 +14,7 @@ r"result_file/pulse_duration_minimize_muscle_fatigue.pkl", ] -chosen_graph_to_plot_path = ( - duration_path - if chosen_graph_to_plot == "duration" - else None -) +chosen_graph_to_plot_path = duration_path if chosen_graph_to_plot == "duration" else None if chosen_graph_to_plot_path is None: From bc81f3c6263dfb5be43da65b17a5ba31cb2315bb Mon Sep 17 00:00:00 2001 From: Kevin CO Date: Fri, 16 Feb 2024 12:09:05 -0500 Subject: [PATCH 63/63] adding new issue template --- .github/ISSUE_TEMPLATE/enhancement_report.md | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/enhancement_report.md diff --git a/.github/ISSUE_TEMPLATE/enhancement_report.md b/.github/ISSUE_TEMPLATE/enhancement_report.md new file mode 100644 index 00000000..b7e26aee --- /dev/null +++ b/.github/ISSUE_TEMPLATE/enhancement_report.md @@ -0,0 +1,17 @@ +--- +name: Enhancement report +about: Create a report to help us improve +title: '' +labels: '' +assignees: '' + +--- + +### Subject of the enhancement +Give a title to your enhancement here. + +### Proposed enhancement +Describe your enhancement here. + +### Additional information +Any additional information, like libraries versions, etc.