InterpolationType.CONSTANT,
)
muscle_states_init = InitialConditions(
[muscle_activated_init] * ocp.nlp[0]["model"].nbMuscles() +
[muscle_fatigued_init] * ocp.nlp[0]["model"].nbMuscles() +
[muscle_resting_init] * ocp.nlp[0]["model"].nbMuscles(),
InterpolationType.CONSTANT,
)
X.concatenate(muscle_states_init)
# --- Simulate --- #
sol_simulate = Simulate.from_controls_and_initial_states(ocp,
X,
U,
single_shoot=True)
# --- Save for biorbdviz --- #
OptimalControlProgram.save_get_data(ocp,
sol_simulate,
f"results/simulate.bob",
interpolate_nb_frames=100)
# --- Graph --- #
result_single = ShowResult(ocp, sol_simulate)
result_single.graphs()
# todo multiphase
print("ok")
# result_single.animate()
# --- Solve the program --- #
tic = time.time()
sol, sol_iterations = ocp.solve(
show_online_optim=True,
return_iterations=True,
solver_options={
"tol": 1e-4,
"max_iter": 3000,
"ipopt.hessian_approximation": "limited-memory"
},
)
toc = time.time() - tic
print(f"Time to solve : {toc}sec")
t = time.localtime(time.time())
date = f"{t.tm_year}_{t.tm_mon}_{t.tm_mday}"
OptimalControlProgram.save(ocp, sol, f"results/{date}_xiaModel.bo")
OptimalControlProgram.save_get_data(ocp,
sol,
f"results/{date}_xiaModel.bob",
sol_iterations=sol_iterations)
OptimalControlProgram.save_get_data(
ocp,
sol,
f"results/{date}_xiaModel_interpolate.bob",
interpolate_nb_frames=100)
# --- Show results --- #
result = ShowResult(ocp, sol)
result.graphs()
