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.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():
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(options_ipopt={"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: 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: 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: x[0:2, :])
ocp.add_plot("My Second New Plot", lambda x, 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: x[0:2, :])
ocp.add_plot("My New Plot", lambda x, u: x[0:2, :])
ShowResult(ocp, sol).graphs(automatically_organize=False)
# Delete the saved file
os.remove(save_name)
def save_and_load(sol, ocp, test_solve_of_loaded=False):
ocp.save(sol, "test.bo")
ocp_load, sol_load = OptimalControlProgram.load("test.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)
os.remove("test.bo")
OptimalControlProgram,
Data,
ShowResult,
)
mat_contents = sio.loadmat("./data/excitations.mat")
excitations_ref = mat_contents["excitations"]
excitations_ref = excitations_ref[:, :]
mat_contents = sio.loadmat("./data/markers.mat")
marker_ref = mat_contents["markers"]
marker_ref = marker_ref[:, 4:, :]
mat_contents = sio.loadmat("./data/x_init.mat")
x_init = mat_contents["x_init"][: 2 * 6, :]
ocp, sol = OptimalControlProgram.load("activation_driven_ipopt.bo")
states, controls = Data.get_data(ocp, sol["x"])
biorbd_model = biorbd.Model("arm_Belaise.bioMod")
q = states["q"]
qdot = states["q_dot"]
x = vertcat(states["q"], states["q_dot"])
u = controls["muscles"]
nlp = ocp.nlp[0]
result = ShowResult(ocp, sol)
# result.animate()
# result.graphs()
with open("sol_marker_activation_tracking_acados.bob", 'rb' ) as file :
data = pickle.load(file)
states_ac = data['data'][0]
ocp = prepare_ocp(biorbd_model_path="pendulum.bioMod",
final_time=3,
number_shooting_points=100,
nb_threads=4)
# --- Solve the program --- #
tic = time()
sol = ocp.solve(show_online_optim=False)
toc = time() - tic
print(f"Time to solve : {toc}sec")
# --- Save result of get_data --- #
ocp.save_get_data(
sol, "pendulum.bob") # you don't have to specify the extension ".bob"
# --- Load result of get_data --- #
with open("pendulum.bob", "rb") as file:
data = pickle.load(file)["data"]
# --- 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 --- #
result = ShowResult(ocp_load, sol_load)
result.graphs()
result.animate()
import time
import sys
import pickle
from biorbd_optim 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)
import numpy as np
import biorbd
import seaborn as sns ; sns.set()
from casadi import MX, Function, vertcat, jacobian
from matplotlib import pyplot as plt
import csv
from biorbd_optim 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)
symbolic_force = MX.sym("f", nlp["nbMuscle"], 1)
symbolic_tsl = MX.sym("tsl", nlp["nbMuscle"], 1)
final_time,
X_init,
U_init,
X_bounds,
U_bounds,
objective_functions,
constraints,
)
if __name__ == "__main__":
for initial_guess in InterpolationType:
print(f"Solving problem using {initial_guess} initial guess")
ocp = prepare_ocp("cube.bioMod",
number_shooting_points=30,
final_time=2,
initial_guess=initial_guess)
sol = ocp.solve()
print("\n")
# Save the last ocp
ocp.save(sol, "cube_ocp_sol")
ocp_loaded, sol_loaded = OptimalControlProgram.load("cube_ocp_sol.bo")
# Rerun the loaded OCP
sol_loaded_and_reran = ocp_loaded.solve()
# Print the last solution
result_plot = ShowResult(ocp, sol)
result_plot.graphs()
"../models/jumper1contacts.bioMod",
"../models/jumper1contacts.bioMod",
"../models/jumper1contacts.bioMod",
"../models/jumper2contacts.bioMod",
)
# time_min = [0.1, 0.3, 0.2, 0.1, 0.1]
# time_max = [0.4, 0.6, 2, 0.4, 0.4]
phase_time = [0.2, 0.4, 1, 0.3, 0.3]
number_shooting_points = [20, 20, 20, 20, 20]
tic = time()
run_and_save_ocp(model_path,
phase_time=phase_time,
number_shooting_points=number_shooting_points)
ocp, sol = OptimalControlProgram.load("../Results/jumper5phases_sol.bo")
# ocp = prepare_ocp(model_path=model_path, phase_time=phase_time, number_shooting_points=number_shooting_points, use_symmetry=True)
# sol = ocp.solve(show_online_optim=False, options_ipopt={"hessian_approximation": "limited-memory", "max_iter": 2})
# --- Show results --- #
# param = Data.get_data(ocp, sol["x"], get_states=False, get_controls=False, get_parameters=True)
# print(
# f"The optimized phases times are: {param['time'][0, 0]}s, {param['time'][1, 0]}s, {param['time'][2, 0]}s, {param['time'][3, 0]}s and {param['time'][4, 0]}s."
# )
toc = time() - tic
print(f"Time to solve : {toc}sec")
result = ShowResult(ocp, sol)
result.graphs()
