def test_add_new_plot():
# Load graphs_one_phase
from bioptim.examples.torque_driven_ocp import track_markers_with_torque_actuators as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
ocp = ocp_module.prepare_ocp(
biorbd_model_path=bioptim_folder + "/models/cube.bioMod",
n_shooting=20,
final_time=0.5,
)
solver = Solver.IPOPT()
solver.set_maximum_iterations(1)
sol = ocp.solve(solver)
# Saving/loading files reset the plot settings to normal
save_name = "test_plot.bo"
ocp.save(sol, save_name)
# Test 1 - Working plot
ocp.add_plot("My New Plot", lambda t, x, u, p: x[0:2, :])
sol.graphs(automatically_organize=False)
# Test 2 - Combine using combine_to is not allowed
ocp, sol = OptimalControlProgram.load(save_name)
with pytest.raises(RuntimeError):
ocp.add_plot("My New Plot",
lambda t, x, u, p: x[0:2, :],
combine_to="NotAllowed")
# Test 3 - Create a completely new plot
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda t, x, u, p: x[0:2, :])
ocp.add_plot("My Second New Plot", lambda t, x, p, u: x[0:2, :])
sol.graphs(automatically_organize=False)
# Test 4 - Combine to the first using fig_name
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda t, x, u, p: x[0:2, :])
ocp.add_plot("My New Plot", lambda t, x, u, p: x[0:2, :])
sol.graphs(automatically_organize=False)
# Add the plot of objectives and constraints to this mess
ocp.add_plot_penalty(CostType.ALL)
sol.graphs(automatically_organize=False)
# Delete the saved file
os.remove(save_name)
def main():
"""
Create and solve a program. Then it saves it using the .bo method, and then using te stand_alone option.
"""
ocp = prepare_ocp(biorbd_model_path="models/pendulum.bioMod",
final_time=1,
n_shooting=100,
n_threads=4)
# --- Solve the program --- #
sol = ocp.solve(show_online_optim=False)
print(f"Time to solve : {sol.real_time_to_optimize}sec")
# --- Print objective cost --- #
print(f"Final objective value : {np.nansum(sol.cost)} \n")
sol.print()
# --- Save the optimal control program and the solution with stand_alone = False --- #
ocp.save(sol,
"pendulum.bo") # you don't have to specify the extension ".bo"
# --- Load the optimal control program and the solution --- #
ocp_load, sol_load = OptimalControlProgram.load("pendulum.bo")
# --- Show results --- #
sol_load.animate()
sol_load.graphs()
# --- Save the optimal control program and the solution with stand_alone = True --- #
ocp.save(sol, f"pendulum_sa.bo", stand_alone=True)
# --- Load the solution saved with stand_alone = True --- #
with open(f"pendulum_sa.bo", "rb") as file:
states, controls, parameters = pickle.load(file)
def save_and_load(sol, ocp, test_solve_of_loaded=False):
file_path = "test.bo"
ocp.save(sol, file_path)
ocp_load, sol_load = OptimalControlProgram.load(file_path)
TestUtils.deep_assert(sol, sol_load)
TestUtils.deep_assert(sol_load, sol)
if test_solve_of_loaded:
sol_from_load = ocp_load.solve()
TestUtils.deep_assert(sol, sol_from_load)
TestUtils.deep_assert(sol_from_load, sol)
TestUtils.deep_assert(ocp_load, ocp)
TestUtils.deep_assert(ocp, ocp_load)
os.remove(file_path)
file_path_bob = "test.bob"
ocp.save_get_data(sol,
file_path_bob,
interpolate_nb_frames=-1,
concatenate=True)
data = Data.get_data(ocp,
sol,
file_path_bob,
interpolate_nb_frames=-1,
concatenate=True)
with open(file_path_bob, "rb") as file:
data_load = pickle.load(file)["data"]
TestUtils.deep_assert(data, data_load)
TestUtils.deep_assert(data_load, data)
os.remove(file_path_bob)
def test_add_new_plot():
# Load graphs_one_phase
PROJECT_FOLDER = Path(__file__).parent / ".."
spec = importlib.util.spec_from_file_location(
"align_markers",
str(PROJECT_FOLDER) +
"/examples/torque_driven_ocp/align_markers_with_torque_actuators.py")
graphs_one_phase = importlib.util.module_from_spec(spec)
spec.loader.exec_module(graphs_one_phase)
ocp = graphs_one_phase.prepare_ocp(
biorbd_model_path=str(PROJECT_FOLDER) +
"/examples/torque_driven_ocp/cube.bioMod",
number_shooting_points=20,
final_time=0.5,
)
sol = ocp.solve(solver_options={"max_iter": 1})
# Saving/loading files reset the plot settings to normal
save_name = "test_plot.bo"
ocp.save(sol, save_name)
# Test 1 - Working plot
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ShowResult(ocp, sol).graphs(automatically_organize=False)
# Test 2 - Combine using combine_to is not allowed
ocp, sol = OptimalControlProgram.load(save_name)
with pytest.raises(RuntimeError):
ocp.add_plot("My New Plot",
lambda x, u, p: x[0:2, :],
combine_to="NotAllowed")
# Test 3 - Create a completely new plot
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ocp.add_plot("My Second New Plot", lambda x, p, u: x[0:2, :])
ShowResult(ocp, sol).graphs(automatically_organize=False)
# Test 4 - Combine to the first using fig_name
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ShowResult(ocp, sol).graphs(automatically_organize=False)
# Delete the saved file
os.remove(save_name)
def test_add_new_plot():
# Load graphs_one_phase
bioptim_folder = TestUtils.bioptim_folder()
graphs = TestUtils.load_module(
bioptim_folder +
"/examples/torque_driven_ocp/track_markers_with_torque_actuators.py")
ocp = graphs.prepare_ocp(
biorbd_model_path=bioptim_folder +
"/examples/torque_driven_ocp/cube.bioMod",
n_shooting=20,
final_time=0.5,
)
sol = ocp.solve(solver_options={"max_iter": 1})
# Saving/loading files reset the plot settings to normal
save_name = "test_plot.bo"
ocp.save(sol, save_name)
# Test 1 - Working plot
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
sol.graphs(automatically_organize=False)
# Test 2 - Combine using combine_to is not allowed
ocp, sol = OptimalControlProgram.load(save_name)
with pytest.raises(RuntimeError):
ocp.add_plot("My New Plot",
lambda x, u, p: x[0:2, :],
combine_to="NotAllowed")
# Test 3 - Create a completely new plot
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ocp.add_plot("My Second New Plot", lambda x, p, u: x[0:2, :])
sol.graphs(automatically_organize=False)
# Test 4 - Combine to the first using fig_name
ocp, sol = OptimalControlProgram.load(save_name)
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
ocp.add_plot("My New Plot", lambda x, u, p: x[0:2, :])
sol.graphs(automatically_organize=False)
# Delete the saved file
os.remove(save_name)
def solve(self,
limit_memory_max_iter,
exact_max_iter,
load_path=None,
force_no_graph=False):
def warm_start_nmpc(ocp, sol):
state, ctrl, param = sol.states, sol.controls, sol.parameters
u_init_guess = InitialGuessList()
x_init_guess = InitialGuessList()
for i in range(ocp.n_phases):
u_init_guess.add(ctrl[i]["all"][:, :-1],
interpolation=InterpolationType.EACH_FRAME)
x_init_guess.add(state[i]["all"],
interpolation=InterpolationType.EACH_FRAME)
time_init_guess = InitialGuess(param["time"], name="time")
ocp.update_initial_guess(x_init=x_init_guess,
u_init=u_init_guess,
param_init=time_init_guess)
ocp.solver.set_lagrange_multiplier(sol)
# Run optimizations
if load_path:
_, sol = OptimalControlProgram.load(load_path)
return sol
else:
sol = None
if limit_memory_max_iter > 0:
sol = self.ocp.solve(
show_online_optim=exact_max_iter == 0
and not force_no_graph,
solver_options={
"linear_solver": "ma57",
"hessian_approximation": "limited-memory",
"max_iter": limit_memory_max_iter,
},
)
if limit_memory_max_iter > 0 and exact_max_iter > 0:
warm_start_nmpc(self.ocp, sol)
if exact_max_iter > 0:
sol = self.ocp.solve(
show_online_optim=True and not force_no_graph,
solver_options={
"linear_solver": "ma57",
"hessian_approximation": "exact",
"max_iter": exact_max_iter,
"warm_start_init_point": "yes",
},
)
return sol
def save_and_load(sol, ocp, test_solve_of_loaded=False):
file_path = "test.bo"
ocp.save(sol, file_path)
ocp_load, sol_load = OptimalControlProgram.load(file_path)
TestUtils.deep_assert(sol, sol_load)
TestUtils.deep_assert(sol_load, sol)
if test_solve_of_loaded:
sol_from_load = ocp_load.solve()
TestUtils.deep_assert(sol, sol_from_load)
TestUtils.deep_assert(sol_from_load, sol)
TestUtils.deep_assert(ocp_load, ocp)
TestUtils.deep_assert(ocp, ocp_load)
os.remove(file_path)
def save_and_load(sol, ocp, test_solve_of_loaded=False):
file_path = "test"
ocp.save(sol, f"{file_path}.bo")
ocp_load, sol_load = OptimalControlProgram.load(f"{file_path}.bo")
TestUtils.deep_assert(sol, sol_load)
TestUtils.deep_assert(sol_load, sol)
if test_solve_of_loaded:
sol_from_load = ocp_load.solve()
TestUtils.deep_assert(sol, sol_from_load)
TestUtils.deep_assert(sol_from_load, sol)
TestUtils.deep_assert(ocp_load, ocp)
TestUtils.deep_assert(ocp, ocp_load)
ocp.save(sol, f"{file_path}_sa.bo", stand_alone=True)
with open(f"{file_path}_sa.bo", "rb") as file:
states, controls, parameters = pickle.load(file)
TestUtils.deep_assert(states, sol.states)
TestUtils.deep_assert(controls, sol.controls)
TestUtils.deep_assert(parameters, sol.parameters)
os.remove(f"{file_path}.bo")
os.remove(f"{file_path}_sa.bo")
sns.set()
from casadi import MX, Function, vertcat, jacobian
from matplotlib import pyplot as plt
import csv
from bioptim import (
OptimalControlProgram,
Data,
ShowResult,
PlotType,
)
# --- Load OCP --- #
biorbd_model = biorbd.Model(
"/home/amedeo/Documents/programmation/Article_Colombe/arm_Belaise_buchanan_de_groote.bioMod"
)
ocp, sol, param = OptimalControlProgram.load(
"/home/amedeo/Documents/programmation/Article_Colombe/results/Q_tracking_with_residual_torque2020-08-06 15:32:25.127113.bo"
)
states, controls, params = Data.get_data(ocp, sol["x"], get_parameters=True)
print(params["shape_factor"])
q = states["q"]
qdot = states["q_dot"]
u = states["muscles"]
x = vertcat(states["q"], states["q_dot"], states["muscles"])
e = controls["muscles"]
nlp = ocp.nlp[0]
# --- Casadi stuff ---#
symbolic_states = MX.sym("x", nlp["nbQ"] + nlp["nbQdot"] + nlp["nbMuscle"], 1)
symbolic_controls = MX.sym("e", nlp["nbMuscle"], 1)
symbolic_length = MX.sym("l", nlp["nbMuscle"], 1)
import time
import sys
import pickle
from bioptim import OptimalControlProgram, ShowResult, Data, Simulate
from up_and_down_bow import xia_model_dynamic, xia_model_configuration, xia_model_fibers, xia_initial_fatigue_at_zero
file_path = "results/xia 5 phases/2020_7_25_upDown.bo"
if len(sys.argv) > 1:
file_path = str(sys.argv[1])
if not isinstance(file_path, str):
t = time.localtime(time.time())
file_path = f"results/{t.tm_year}_{t.tm_mon}_{t.tm_mday}_upDown.bo"
ocp, sol = OptimalControlProgram.load(file_path)
d = Data.get_data(ocp, Simulate.from_solve(ocp, sol, single_shoot=True))
dict = {"data": d}
with open(file_path[:-3] + "_single.bob", "wb") as file:
pickle.dump(dict, file)
if __name__ == "__main__":
"""
Create and solve a program. Then it saves it using the .bob and .bo method
"""
ocp = prepare_ocp(biorbd_model_path="pendulum.bioMod",
final_time=3,
n_shooting=100,
n_threads=4)
# --- Solve the program --- #
tic = time()
sol = ocp.solve(show_online_optim=True)
toc = time() - tic
print(f"Time to solve : {toc}sec")
# --- Print objective cost --- #
print(f"Final objective value : {np.nansum(sol.cost)} \n")
sol.print()
# --- Save the optimal control program and the solution --- #
ocp.save(sol,
"pendulum.bo") # you don't have to specify the extension ".bo"
# --- Load the optimal control program and the solution --- #
ocp_load, sol_load = OptimalControlProgram.load("pendulum.bo")
# --- Show results --- #
sol_load.animate()
sol = ocp.solve(
solver=Solver.IPOPT,
show_online_optim=False,
solver_options={
"tol": 1e-4,
"dual_inf_tol": 1e-4,
"constr_viol_tol": 1e-4,
"compl_inf_tol": 1e-4,
"linear_solver": "ma57",
"max_iter": 500,
"hessian_approximation": "exact",
},
)
if os.path.isfile(
f"solutions/sim_ip_{int(T*1000)}ms_{Ns}sn_{motion}.bo"):
ocp.save(sol,
f"solutions/sim_ip_{int(T*1000)}ms_{Ns}sn_{motion}_1.bo")
else:
ocp.save(sol,
f"solutions/sim_ip_{int(T*1000)}ms_{Ns}sn_{motion}.bo")
if use_BO:
ocp, sol = OptimalControlProgram.load(
f"solutions/sim_ip_{int(T*1000)}ms_{Ns}sn_{motion}.bo")
# --- Show results --- #
result = ShowResult(ocp, sol)
result.graphs()
result.animate()
