diff --git a/bioptim/examples/getting_started/pendulum.py b/bioptim/examples/getting_started/pendulum.py index fb1e7c790..3c6aa34a9 100644 --- a/bioptim/examples/getting_started/pendulum.py +++ b/bioptim/examples/getting_started/pendulum.py @@ -152,10 +152,14 @@ def main(): # --- Solve the ocp. Please note that online graphics only works with the Linux operating system --- # sol = ocp.solve(Solver.IPOPT(show_online_optim=platform.system() == "Linux")) - # --- Show the results (graph or animation) --- # + # --- Show the results graph --- # sol.print_cost() # sol.graphs(show_bounds=True, save_name="results.png") - sol.animate(n_frames=100) + + # --- Animate the solution --- # + viewer = "bioviz" + # viewer = "pyorerun" + sol.animate(n_frames=0, viewer=viewer, show_now=True) # # --- Saving the solver's output after the optimization --- # # Here is an example of how we recommend to save the solution. Please note that sol.ocp is not picklable and that sol will be loaded using the current bioptim version, not the version at the time of the generation of the results. diff --git a/bioptim/examples/holonomic_constraints/two_pendulums.py b/bioptim/examples/holonomic_constraints/two_pendulums.py index 911009945..a25cfc0ae 100644 --- a/bioptim/examples/holonomic_constraints/two_pendulums.py +++ b/bioptim/examples/holonomic_constraints/two_pendulums.py @@ -6,7 +6,6 @@ import matplotlib.pyplot as plt import numpy as np - from casadi import MX, Function from bioptim import ( @@ -213,11 +212,21 @@ def main(): # --- Show results --- # q, qdot, qddot, lambdas = compute_all_states(sol, bio_model) - import bioviz + viewer = "pyorerun" + if viewer == "bioviz": + import bioviz + + viz = bioviz.Viz(model_path) + viz.load_movement(q) + viz.exec() + + if viewer == "pyorerun": + import pyorerun + + viz = pyorerun.PhaseRerun(t_span=np.concatenate(sol.decision_time()).squeeze()) + viz.add_animated_model(pyorerun.BiorbdModel(model_path), q=q) - viz = bioviz.Viz(model_path) - viz.load_movement(q) - viz.exec() + viz.rerun("double_pendulum") time = sol.decision_time(to_merge=SolutionMerge.NODES) plt.title("Lagrange multipliers of the holonomic constraint") diff --git a/bioptim/examples/moving_horizon_estimation/mhe.py b/bioptim/examples/moving_horizon_estimation/mhe.py index ea16f0e99..36955f42d 100644 --- a/bioptim/examples/moving_horizon_estimation/mhe.py +++ b/bioptim/examples/moving_horizon_estimation/mhe.py @@ -15,11 +15,12 @@ from copy import copy -import matplotlib.pyplot as plt -from scipy.integrate import solve_ivp +import biorbd_casadi as biorbd import casadi as cas +import matplotlib.pyplot as plt import numpy as np -import biorbd_casadi as biorbd +from scipy.integrate import solve_ivp + from bioptim import ( BioModel, BiorbdModel, @@ -232,12 +233,12 @@ def target(i: int): sol = mhe.solve(update_functions, **get_solver_options(solver)) sol_states = sol.decision_states(to_merge=SolutionMerge.NODES) - print("ACADOS with Bioptim") + print(f"{solver} with Bioptim") print(f"Window size of MHE : {window_duration} s.") print(f"New measurement every : {1 / n_shoot_per_second} s.") print(f"Average time per iteration of MHE : {sol.solver_time_to_optimize / (n_frames_total - 1)} s.") print(f"Average real time per iteration of MHE : {sol.real_time_to_optimize / (n_frames_total - 1)} s.") - print(f"Norm of the error on q = {np.linalg.norm(states[:bio_model.nb_q, :n_frames_total] - sol_states['q'])}") + print(f"Norm of the error on q = {np.linalg.norm(states[:bio_model.nb_q, :n_frames_total + 1] - sol_states['q'])}") markers_estimated = states_to_markers(bio_model, sol_states["q"]) @@ -256,7 +257,7 @@ def target(i: int): plt.figure() plt.plot(sol_states["q"].T, "--", label="States estimate (q)") plt.plot(sol_states["qdot"].T, "--", label="States estimate (qdot)") - plt.plot(sol_states[:, :n_frames_total].T, label="State truth") + plt.plot(states[:, :n_frames_total].T, label="State truth") plt.legend() plt.show() diff --git a/bioptim/examples/moving_horizon_estimation/models/cart_pendulum.bioMod b/bioptim/examples/moving_horizon_estimation/models/cart_pendulum.bioMod index 26cfed4b2..1035aafbe 100644 --- a/bioptim/examples/moving_horizon_estimation/models/cart_pendulum.bioMod +++ b/bioptim/examples/moving_horizon_estimation/models/cart_pendulum.bioMod @@ -41,7 +41,7 @@ segment Arm 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Markers diff --git a/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.STL b/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.STL new file mode 100755 index 000000000..fb6f4bc66 Binary files /dev/null and b/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.STL differ diff --git a/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.vtp b/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.vtp deleted file mode 100644 index 36b98aa9b..000000000 --- a/bioptim/examples/moving_horizon_estimation/models/mesh/pendulum.vtp +++ /dev/null @@ -1,1973 +0,0 @@ - - - - - - - - - - - 0.014999999664723873 0.014999999664723873 -0.8999999761581421 -0.014999999664723873 0.014999999664723873 -0.8999999761581421 - 0.014999999664723873 0.014999999664723873 -2.220446049250313e-16 -0.014999999664723873 0.014999999664723873 -2.220446049250313e-16 - 0.014999999664723873 -0.014999999664723873 -0.8999999761581421 0.014999999664723873 -0.014999999664723873 -2.220446049250313e-16 - 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5799 5802 5805 5808 5811 5814 - 5817 5820 5823 5826 5829 5832 - 5835 5838 5841 5844 5847 5850 - 5853 5856 5859 5862 5865 5868 - 5871 5874 5877 5880 5883 5886 - 5889 5892 5895 5898 5901 5904 - 5907 5910 5913 5916 5919 5922 - 5925 5928 5931 5934 5937 5940 - 5943 5946 5949 5952 5955 5958 - 5961 5964 5967 5970 5973 5976 - 5979 5982 5985 5988 5991 5994 - 5997 6000 6003 6006 6009 6012 - 6015 6018 6021 6024 6027 6030 - 6033 6036 6039 6042 6045 6048 - 6051 6054 6057 6060 6063 6066 - 6069 6072 6075 6078 6081 6084 - 6087 6090 6093 6096 6099 6102 - 6105 6108 6111 6114 6117 6120 - 6123 6126 6129 6132 6135 6138 - 6141 6144 6147 6150 6153 6156 - 6159 6162 6165 6168 6171 6174 - 6177 6180 6183 6186 6189 6192 - 6195 6198 6201 6204 6207 6210 - 6213 6216 6219 6222 6225 6228 - 6231 6234 6237 6240 6243 6246 - 6249 6252 6255 6258 6261 6264 - 6267 6270 6273 6276 6279 6282 - 6285 6288 - - - - - diff --git a/bioptim/examples/muscle_driven_with_contact/muscle_activations_contacts_tracker.py b/bioptim/examples/muscle_driven_with_contact/muscle_activations_contacts_tracker.py index 53cb7300f..223a91c3f 100644 --- a/bioptim/examples/muscle_driven_with_contact/muscle_activations_contacts_tracker.py +++ b/bioptim/examples/muscle_driven_with_contact/muscle_activations_contacts_tracker.py @@ -1,14 +1,14 @@ """ -# TODO: Remove all the examples/muscle_driven_with_contact and make sure everything is properly tested All the examples in muscle_driven_with_contact are merely to show some dynamics and prepare some OCP for the tests. It is not really relevant and will be removed when unitary tests for the dynamics will be implemented """ import importlib.util -from pathlib import Path import platform +from pathlib import Path import numpy as np + from bioptim import ( BiorbdModel, OptimalControlProgram, @@ -21,6 +21,7 @@ OdeSolver, Solver, SolutionMerge, + Node, ) # Load track_segment_on_rt @@ -41,8 +42,12 @@ def prepare_ocp( # Add objective functions objective_functions = ObjectiveList() - objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTROL, key="muscles", target=muscle_activations_ref) - objective_functions.add(ObjectiveFcn.Lagrange.TRACK_CONTACT_FORCES, target=contact_forces_ref) + objective_functions.add( + ObjectiveFcn.Lagrange.TRACK_CONTROL, key="muscles", target=muscle_activations_ref, node=Node.ALL_SHOOTING + ) + objective_functions.add( + ObjectiveFcn.Lagrange.TRACK_CONTACT_FORCES, target=contact_forces_ref, node=Node.ALL_SHOOTING + ) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="qdot", weight=0.001) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_STATE, key="q", weight=0.001) objective_functions.add(ObjectiveFcn.Lagrange.MINIMIZE_CONTROL, key="muscles", weight=0.001) @@ -53,40 +58,38 @@ def prepare_ocp( dynamics.add(DynamicsFcn.MUSCLE_DRIVEN, with_residual_torque=True, with_contact=True) # Path constraint - n_q = bio_model.nb_q - n_qdot = n_q - pose_at_first_node = [0, 0, -0.75, 0.75] + q_at_first_node = [0, 0, -0.75, 0.75] # Initialize x_bounds x_bounds = BoundsList() - x_bounds.add(bounds=bio_model.bounds_from_ranges(["q", "qdot"])) - x_bounds[0][:, 0] = pose_at_first_node + [0] * n_qdot + x_bounds["q"] = bio_model.bounds_from_ranges("q") + x_bounds["qdot"] = bio_model.bounds_from_ranges("qdot") + x_bounds["q"][:, 0] = q_at_first_node # Initial guess x_init = InitialGuessList() - x_init.add(pose_at_first_node + [0] * n_qdot) + x_init["q"] = q_at_first_node # Define control path constraint u_bounds = BoundsList() - u_bounds.add( - [tau_min] * bio_model.nb_tau + [activation_min] * bio_model.nb_muscles, - [tau_max] * bio_model.nb_tau + [activation_max] * bio_model.nb_muscles, - ) + u_bounds["tau"] = ([tau_min] * bio_model.nb_tau, [tau_max] * bio_model.nb_tau) + u_bounds["muscles"] = ([activation_min] * bio_model.nb_muscles, [activation_max] * bio_model.nb_muscles) u_init = InitialGuessList() - u_init.add([tau_init] * bio_model.nb_tau + [activation_init] * bio_model.nb_muscles) + u_init["tau"] = [tau_init] * bio_model.nb_tau + u_init["muscles"] = [activation_init] * bio_model.nb_muscles # ------------- # return OptimalControlProgram( - bio_model, - dynamics, - n_shooting, - phase_time, - x_init, - u_init, - x_bounds, - u_bounds, + bio_model=bio_model, + dynamics=dynamics, + n_shooting=n_shooting, + phase_time=phase_time, + x_init=x_init, + u_init=u_init, + x_bounds=x_bounds, + u_bounds=u_bounds, objective_functions=objective_functions, ode_solver=ode_solver, ) @@ -112,9 +115,13 @@ def main(): controls = sol.decision_controls(to_merge=SolutionMerge.NODES) q, qdot, tau, mus = states["q"], states["qdot"], controls["tau"], controls["muscles"] - x = np.concatenate((q, qdot)) - u = np.concatenate((tau, mus)) - contact_forces_ref = np.array(ocp_to_track.nlp[0].contact_forces_func(x[:, :-1], u[:, :-1], [])) + x = np.concatenate((q, qdot), axis=0) + u = np.concatenate((tau, mus), axis=0) + contact_forces_ref = ( + np.array([ocp_to_track.nlp[0].contact_forces_func([], x[:, i], u[:, i], [], [], []) for i in range(ns)]) + .squeeze() + .T + ) muscle_activations_ref = mus # Track these data @@ -122,7 +129,7 @@ def main(): biorbd_model_path=model_path, phase_time=final_time, n_shooting=ns, - muscle_activations_ref=muscle_activations_ref[:, :-1], + muscle_activations_ref=muscle_activations_ref, contact_forces_ref=contact_forces_ref, ) diff --git a/bioptim/examples/torque_driven_ocp/example_minimize_segment_velocity.py b/bioptim/examples/torque_driven_ocp/example_minimize_segment_velocity.py index fa775feab..477234106 100644 --- a/bioptim/examples/torque_driven_ocp/example_minimize_segment_velocity.py +++ b/bioptim/examples/torque_driven_ocp/example_minimize_segment_velocity.py @@ -5,6 +5,7 @@ """ import numpy as np + from bioptim import ( BiorbdModel, OptimalControlProgram, diff --git a/bioptim/examples/torque_driven_ocp/example_multi_biorbd_model.py b/bioptim/examples/torque_driven_ocp/example_multi_biorbd_model.py index d35fbc2c5..7d9b58b46 100644 --- a/bioptim/examples/torque_driven_ocp/example_multi_biorbd_model.py +++ b/bioptim/examples/torque_driven_ocp/example_multi_biorbd_model.py @@ -85,16 +85,19 @@ def main(): sol.graphs(show_bounds=True) - # --- Show results --- # - show_solution_animation = False - if show_solution_animation: - states = sol.decision_states(to_merge=SolutionMerge.NODES) - q = states["q"] - import bioviz - - b = bioviz.Viz("models/triple_pendulum_both_inertia.bioMod") - b.load_movement(q) - b.exec() + # --- Animate results with bioviz --- # + # show_solution_animation = False + # if show_solution_animation: + # states = sol.decision_states(to_merge=SolutionMerge.NODES) + # q = states["q"] + # import bioviz + # + # b = bioviz.Viz("models/triple_pendulum_both_inertia.bioMod") + # b.load_movement(q) + # b.exec() + + # --- Animate results with pyorerun --- # + sol.animate(viewer="pyorerun") if __name__ == "__main__": diff --git a/bioptim/examples/torque_driven_ocp/example_quaternions.py b/bioptim/examples/torque_driven_ocp/example_quaternions.py index bff8d44b6..2978704da 100644 --- a/bioptim/examples/torque_driven_ocp/example_quaternions.py +++ b/bioptim/examples/torque_driven_ocp/example_quaternions.py @@ -7,9 +7,10 @@ import platform -import numpy as np import biorbd_casadi as biorbd +import numpy as np from casadi import MX, Function + from bioptim import ( BiorbdModel, OptimalControlProgram, diff --git a/bioptim/examples/torque_driven_ocp/models/double_pendulum.bioMod b/bioptim/examples/torque_driven_ocp/models/double_pendulum.bioMod index 0b14a07d6..db3e969a8 100644 --- a/bioptim/examples/torque_driven_ocp/models/double_pendulum.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/double_pendulum.bioMod @@ -10,7 +10,7 @@ segment Seg1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 @@ -38,7 +38,7 @@ segment Seg2 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 3 diff --git a/bioptim/examples/torque_driven_ocp/models/double_pendulum_both_inertia.bioMod b/bioptim/examples/torque_driven_ocp/models/double_pendulum_both_inertia.bioMod index 5771c40de..48961b19b 100644 --- a/bioptim/examples/torque_driven_ocp/models/double_pendulum_both_inertia.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/double_pendulum_both_inertia.bioMod @@ -11,7 +11,7 @@ segment Seg1_1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 1 0 0 endsegment @@ -40,7 +40,7 @@ segment Seg2_1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 1 0 0 endsegment @@ -66,7 +66,7 @@ segment Seg1_2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 0 0 1 endsegment @@ -95,7 +95,7 @@ segment Seg2_2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 0 0 1 endsegment diff --git a/bioptim/examples/torque_driven_ocp/models/double_pendulum_modified_inertia.bioMod b/bioptim/examples/torque_driven_ocp/models/double_pendulum_modified_inertia.bioMod index 4e938b933..27f5e3951 100644 --- a/bioptim/examples/torque_driven_ocp/models/double_pendulum_modified_inertia.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/double_pendulum_modified_inertia.bioMod @@ -10,7 +10,7 @@ segment Seg1 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 @@ -38,7 +38,7 @@ segment Seg2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 3 diff --git a/bioptim/examples/torque_driven_ocp/models/double_pendulum_with_translations.bioMod b/bioptim/examples/torque_driven_ocp/models/double_pendulum_with_translations.bioMod index 2d6a6eedb..fc452fcda 100644 --- a/bioptim/examples/torque_driven_ocp/models/double_pendulum_with_translations.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/double_pendulum_with_translations.bioMod @@ -12,7 +12,7 @@ segment Seg1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 @@ -40,7 +40,7 @@ segment Seg2 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 3 diff --git a/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.STL b/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.STL new file mode 100755 index 000000000..fb6f4bc66 Binary files /dev/null and b/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.STL differ diff --git a/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.vtp b/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.vtp deleted file mode 100644 index 36b98aa9b..000000000 --- a/bioptim/examples/torque_driven_ocp/models/mesh/pendulum.vtp +++ /dev/null @@ -1,1973 +0,0 @@ - - - - - - - - - - - 0.014999999664723873 0.014999999664723873 -0.8999999761581421 -0.014999999664723873 0.014999999664723873 -0.8999999761581421 - 0.014999999664723873 0.014999999664723873 -2.220446049250313e-16 -0.014999999664723873 0.014999999664723873 -2.220446049250313e-16 - 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6267 6270 6273 6276 6279 6282 - 6285 6288 - - - - - diff --git a/bioptim/examples/torque_driven_ocp/models/pendulum.bioMod b/bioptim/examples/torque_driven_ocp/models/pendulum.bioMod index 35ae9c3da..ee849a3c4 100644 --- a/bioptim/examples/torque_driven_ocp/models/pendulum.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/pendulum.bioMod @@ -12,7 +12,7 @@ segment Seg1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 diff --git a/bioptim/examples/torque_driven_ocp/models/pendulum_with_passive_torque.bioMod b/bioptim/examples/torque_driven_ocp/models/pendulum_with_passive_torque.bioMod index 8c22b6750..5e096a929 100644 --- a/bioptim/examples/torque_driven_ocp/models/pendulum_with_passive_torque.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/pendulum_with_passive_torque.bioMod @@ -12,7 +12,7 @@ segment Seg1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 diff --git a/bioptim/examples/torque_driven_ocp/models/triple_pendulum.bioMod b/bioptim/examples/torque_driven_ocp/models/triple_pendulum.bioMod index e2f097ff5..444626f70 100644 --- a/bioptim/examples/torque_driven_ocp/models/triple_pendulum.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/triple_pendulum.bioMod @@ -10,7 +10,7 @@ segment Seg1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 @@ -38,7 +38,7 @@ segment Seg2 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 3 @@ -66,7 +66,7 @@ segment Seg3 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 5 diff --git a/bioptim/examples/torque_driven_ocp/models/triple_pendulum_both_inertia.bioMod b/bioptim/examples/torque_driven_ocp/models/triple_pendulum_both_inertia.bioMod index b095d3282..2f05417d6 100644 --- a/bioptim/examples/torque_driven_ocp/models/triple_pendulum_both_inertia.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/triple_pendulum_both_inertia.bioMod @@ -11,7 +11,7 @@ segment Seg1_1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 1 0 0 endsegment @@ -40,7 +40,7 @@ segment Seg2_1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 1 0 0 endsegment @@ -69,7 +69,7 @@ segment Seg3_1 0.0000 0.0335 -0.0032 0.0000 -0.0032 0.0090 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 1 0 0 endsegment @@ -95,7 +95,7 @@ segment Seg1_2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 0 0 1 endsegment @@ -124,7 +124,7 @@ segment Seg2_2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 0 0 1 endsegment @@ -153,7 +153,7 @@ segment Seg3_2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL meshcolor 0 0 1 endsegment diff --git a/bioptim/examples/torque_driven_ocp/models/triple_pendulum_modified_inertia.bioMod b/bioptim/examples/torque_driven_ocp/models/triple_pendulum_modified_inertia.bioMod index a35fc517a..b6d15be8d 100644 --- a/bioptim/examples/torque_driven_ocp/models/triple_pendulum_modified_inertia.bioMod +++ b/bioptim/examples/torque_driven_ocp/models/triple_pendulum_modified_inertia.bioMod @@ -10,7 +10,7 @@ segment Seg1 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 1 @@ -38,7 +38,7 @@ segment Seg2 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 3 @@ -66,7 +66,7 @@ segment Seg3 0.0000 0.03 -0.0032 0.0000 -0.0032 0.02 com -0.0005 0.0688 -0.9542 - meshfile mesh/pendulum.vtp + meshfile mesh/pendulum.STL endsegment // Marker 5 diff --git a/bioptim/examples/torque_driven_ocp/torque_driven_free_floating_base.py b/bioptim/examples/torque_driven_ocp/torque_driven_free_floating_base.py index 4a6a53a65..73250949e 100644 --- a/bioptim/examples/torque_driven_ocp/torque_driven_free_floating_base.py +++ b/bioptim/examples/torque_driven_ocp/torque_driven_free_floating_base.py @@ -4,9 +4,10 @@ This specific problem generates one somersault in straight position with 1 twist. """ -import numpy as np import platform +import numpy as np + from bioptim import ( BiorbdModel, OptimalControlProgram, @@ -17,7 +18,6 @@ ObjectiveList, ObjectiveFcn, InterpolationType, - BiMappingList, Solver, Node, ) diff --git a/bioptim/models/biorbd/biorbd_model.py b/bioptim/models/biorbd/biorbd_model.py index 3586d3c14..5ddb09224 100644 --- a/bioptim/models/biorbd/biorbd_model.py +++ b/bioptim/models/biorbd/biorbd_model.py @@ -7,7 +7,7 @@ GeneralizedAcceleration, ) from casadi import SX, MX, vertcat, horzcat, norm_fro -from typing import Callable, Any +from typing import Callable from ..utils import _var_mapping, bounds_from_ranges from ...limits.path_conditions import Bounds @@ -733,62 +733,17 @@ def partitioned_forward_dynamics(self, q_u, qdot_u, tau, external_forces=None, f @staticmethod def animate( ocp, - solution: "SolutionData", + solution, show_now: bool = True, - tracked_markers: list[np.ndarray] = None, - **kwargs: Any, - ) -> None | list: - try: - import bioviz - except ModuleNotFoundError: - raise RuntimeError("bioviz must be install to animate the model") - - check_version(bioviz, "2.0.0", "2.4.0") - - if "q_roots" in solution and "q_joints" in solution: - states = np.vstack((solution["q_roots"], solution["q_joints"])) - else: - states = solution["q"] - - if not isinstance(states, (list, tuple)): - states = [states] - - if tracked_markers is None: - tracked_markers = [None] * len(states) - - all_bioviz = [] - for idx_phase, data in enumerate(states): - if not isinstance(ocp.nlp[idx_phase].model, BiorbdModel): - raise NotImplementedError("Animation is only implemented for biorbd models") - - # This calls each of the function that modify the internal dynamic model based on the parameters - nlp = ocp.nlp[idx_phase] - - # noinspection PyTypeChecker - biorbd_model: BiorbdModel = nlp.model - - all_bioviz.append(bioviz.Viz(biorbd_model.path, **kwargs)) - all_bioviz[-1].load_movement(ocp.nlp[idx_phase].variable_mappings["q"].to_second.map(solution["q"])) + show_tracked_markers: bool = False, + viewer: str = "pyorerun", + n_frames: int = 0, + **kwargs, + ): + from .viewer_bioviz import animate_with_bioviz_for_loop + from .viewer_pyorerun import animate_with_pyorerun - if "q_roots" in solution and "q_joints" in solution: - # TODO: Fix the mapping for this case - raise NotImplementedError("Mapping is not implemented for this case") - q = data - else: - q = ocp.nlp[idx_phase].variable_mappings["q"].to_second.map(data) - all_bioviz[-1].load_movement(q) - - if tracked_markers[idx_phase] is not None: - all_bioviz[-1].load_experimental_markers(tracked_markers[idx_phase]) - - if show_now: - b_is_visible = [True] * len(all_bioviz) - while sum(b_is_visible): - for i, b in enumerate(all_bioviz): - if b.vtk_window.is_active: - b.update() - else: - b_is_visible[i] = False - return None - else: - return all_bioviz + if viewer == "bioviz": + return animate_with_bioviz_for_loop(ocp, solution, show_now, show_tracked_markers, n_frames, **kwargs) + if viewer == "pyorerun": + return animate_with_pyorerun(ocp, solution, show_now, show_tracked_markers, **kwargs) diff --git a/bioptim/models/biorbd/multi_biorbd_model.py b/bioptim/models/biorbd/multi_biorbd_model.py index e682dfa87..cfa44c39e 100644 --- a/bioptim/models/biorbd/multi_biorbd_model.py +++ b/bioptim/models/biorbd/multi_biorbd_model.py @@ -1,19 +1,12 @@ -from typing import Callable, Any - import biorbd_casadi as biorbd -from biorbd_casadi import ( - GeneralizedCoordinates, - GeneralizedVelocity, -) from casadi import SX, MX, vertcat +from typing import Callable, Any -from ...misc.utils import check_version +from .biorbd_model import BiorbdModel +from ..utils import _var_mapping +from ..utils import bounds_from_ranges from ...limits.path_conditions import Bounds from ...misc.mapping import BiMapping, BiMappingList -from ..utils import _var_mapping, bounds_from_ranges -from ...optimization.solution.solution_data import SolutionMerge -from ..utils import bounds_from_ranges -from .biorbd_model import BiorbdModel class MultiBiorbdModel: @@ -738,51 +731,19 @@ def partitioned_forward_dynamics(self, q_u, qdot_u, tau, external_forces=None, f raise NotImplementedError("partitioned_forward_dynamics is not implemented yet for MultiBiorbdModel") @staticmethod - def animate(solution: Any, show_now: bool = True, tracked_markers: list = None, **kwargs: Any) -> None | list: - try: - import bioviz - except ModuleNotFoundError: - raise RuntimeError("bioviz must be install to animate the model") - - check_version(bioviz, "2.3.0", "2.4.0") - - states = solution.stepwise_states(to_merge=SolutionMerge.NODES) - if not isinstance(states, (list, tuple)): - states = [states] - - if tracked_markers is None: - tracked_markers = [None] * len(states) - - all_bioviz = [] - for idx_phase, data in enumerate(states): - # This calls each of the function that modify the internal dynamic model based on the parameters - nlp = solution.ocp.nlp[idx_phase] - - if isinstance(nlp.model, MultiBiorbdModel): - if nlp.model.nb_models > 1: - raise NotImplementedError( - f"Animation is only implemented for MultiBiorbdModel with 1 model." - f" There are {nlp.model.nb_models} models in the phase {idx_phase}." - ) - else: - model = nlp.model.models[0] - - biorbd_model: BiorbdModel = model - - all_bioviz.append(bioviz.Viz(biorbd_model.path, **kwargs)) - all_bioviz[-1].load_movement(solution.ocp.nlp[idx_phase].variable_mappings["q"].to_second.map(data["q"])) - - if tracked_markers[idx_phase] is not None: - all_bioviz[-1].load_experimental_markers(tracked_markers[idx_phase]) - - if show_now: - b_is_visible = [True] * len(all_bioviz) - while sum(b_is_visible): - for i, b in enumerate(all_bioviz): - if b.vtk_window.is_active: - b.update() - else: - b_is_visible[i] = False - return None - else: - return all_bioviz + def animate( + ocp, + solution, + show_now: bool = True, + show_tracked_markers: bool = False, + viewer: str = "pyorerun", + n_frames: int = 0, + **kwargs, + ): + from .viewer_bioviz import animate_with_bioviz_for_loop + from .viewer_pyorerun import animate_with_pyorerun + + if viewer == "bioviz": + return animate_with_bioviz_for_loop(ocp, solution, show_now, show_tracked_markers, n_frames, **kwargs) + if viewer == "pyorerun": + return animate_with_pyorerun(ocp, solution, show_now, show_tracked_markers, **kwargs) diff --git a/bioptim/models/biorbd/viewer_bioviz.py b/bioptim/models/biorbd/viewer_bioviz.py new file mode 100644 index 000000000..8cbf1d91a --- /dev/null +++ b/bioptim/models/biorbd/viewer_bioviz.py @@ -0,0 +1,161 @@ +import numpy as np +from typing import Any + +from .biorbd_model import BiorbdModel +from .viewer_utils import _prepare_tracked_markers_for_animation +from ...limits.objective_functions import ObjectiveFcn +from ...misc.enums import Node +from ...misc.utils import check_version + + +def animate_with_bioviz_for_loop( + ocp: "OptimalControlProgram", + solution: "Solution", + show_now: bool = True, + show_tracked_markers: bool = True, + n_frames: int = None, + **kwargs, +): + """ + Animate the solution(s) using bioviz + + Parameters + ---------- + ocp: OptimalControlProgram + The optimal control program + solution: Solution| List[Solution] + The solution(s) to animate + show_now: bool + If the animation should be shown immediately + show_tracked_markers: bool + If the tracked markers should be shown + n_frames: int + The number of frames to interpolate + kwargs + Any other parameters to pass to bioviz + """ + n_frames = count_in_track_markers(ocp.nlp, n_frames) + data_to_animate = interpolate_data(solution, ocp, n_frames) + tracked_markers = get_tracked_markers(show_tracked_markers, ocp, n_frames) + + all_bioviz = [] + for i, data in enumerate(data_to_animate): + all_bioviz.append( + animate_with_bioviz( + ocp, + solution=data, + show_now=show_now, + tracked_markers=tracked_markers, + **kwargs, + ) + ) + + return all_bioviz + + +def animate_with_bioviz( + ocp, + solution: "SolutionData", + show_now: bool = True, + tracked_markers: list[np.ndarray] = None, + **kwargs: Any, +) -> None | list: + try: + import bioviz + except ModuleNotFoundError: + raise RuntimeError("bioviz must be install to animate the model") + + check_version(bioviz, "2.0.0", "2.4.0") + + if "q_roots" in solution and "q_joints" in solution: + states = np.vstack((solution["q_roots"], solution["q_joints"])) + else: + states = solution["q"] + + if not isinstance(states, (list, tuple)): + states = [states] + + if tracked_markers is None: + tracked_markers = [None] * len(states) + + all_bioviz = [] + for idx_phase, data in enumerate(states): + if not isinstance(ocp.nlp[idx_phase].model, BiorbdModel): + raise NotImplementedError("Animation is only implemented for biorbd models") + + # This calls each of the function that modify the internal dynamic model based on the parameters + nlp = ocp.nlp[idx_phase] + + # noinspection PyTypeChecker + biorbd_model: BiorbdModel = nlp.model + + all_bioviz.append(bioviz.Viz(biorbd_model.path, **kwargs)) + all_bioviz[-1].load_movement(ocp.nlp[idx_phase].variable_mappings["q"].to_second.map(solution["q"])) + + if "q_roots" in solution and "q_joints" in solution: + # TODO: Fix the mapping for this case + raise NotImplementedError("Mapping is not implemented for this case") + q = data + else: + q = ocp.nlp[idx_phase].variable_mappings["q"].to_second.map(data) + all_bioviz[-1].load_movement(q) + + if tracked_markers[idx_phase] is not None: + all_bioviz[-1].load_experimental_markers(tracked_markers[idx_phase]) + + return play_bioviz_animation(all_bioviz) if show_now else all_bioviz + + +def play_bioviz_animation(all_bioviz: list) -> None: + """Play the animation of the list of bioviz objects""" + b_is_visible = [True] * len(all_bioviz) + while sum(b_is_visible): + for i, b in enumerate(all_bioviz): + if b.vtk_window.is_active: + b.update() + else: + b_is_visible[i] = False + return None + + +def count_in_track_markers(nlps, n_frames) -> int: + """Ipuch, Legacy Code: I'm very not sure what it does if anyone knows, fix the name of the function""" + for idx_phase in range(len(nlps)): + for objective in nlps[idx_phase].J: + if objective.target is not None: + if objective.type in ( + ObjectiveFcn.Mayer.TRACK_MARKERS, + ObjectiveFcn.Lagrange.TRACK_MARKERS, + ) and objective.node[0] in (Node.ALL, Node.ALL_SHOOTING): + n_frames += objective.target.shape[2] + break + return n_frames + + +def interpolate_data(solution, ocp, n_frames) -> list: + """Interpolate the data to be animated""" + data_to_animate = [] + if n_frames == 0: + try: + data_to_animate = [solution.interpolate(sum([nlp.ns for nlp in ocp.nlp]) + 1)] + except ValueError: + data_to_animate = solution.interpolate([nlp.ns for nlp in ocp.nlp]) + elif n_frames > 0: + data_to_animate = solution.interpolate(n_frames) + if not isinstance(data_to_animate, list): + data_to_animate = [data_to_animate] + + return data_to_animate + + +def get_tracked_markers(show_tracked_markers, ocp, n_frames): + """Get the tracked markers if needed""" + if show_tracked_markers and len(ocp.nlp) == 1: + return _prepare_tracked_markers_for_animation(ocp.nlp, n_shooting=n_frames) + elif show_tracked_markers and len(ocp.nlp) > 1: + raise NotImplementedError( + "Tracking markers is not implemented for multiple phases. " + "Set show_tracked_markers to False such that sol.animate(show_tracked_markers=False)." + ) + else: + return [None for _ in range(len(ocp.nlp))] diff --git a/bioptim/models/biorbd/viewer_pyorerun.py b/bioptim/models/biorbd/viewer_pyorerun.py new file mode 100644 index 000000000..00d03bd0d --- /dev/null +++ b/bioptim/models/biorbd/viewer_pyorerun.py @@ -0,0 +1,127 @@ +import biorbd_casadi as biorbd +import numpy as np +import pyorerun +from typing import Any + +from .biorbd_model import BiorbdModel +from .multi_biorbd_model import MultiBiorbdModel +from ...optimization.solution.solution_data import SolutionMerge + + +def animate_with_pyorerun( + ocp, + solution, + show_now: bool, + show_tracked_markers: bool, + **kwargs: Any, +) -> None: + try: + import pyorerun + except ModuleNotFoundError: + raise RuntimeError("pyorerun must be install to animate the model") + + data_to_animate, models, tracked_markers = prepare_pyorerun_animation(ocp, solution, show_now, show_tracked_markers) + launch_rerun(data_to_animate, show_now, tracked_markers, models, **kwargs) + + +def prepare_pyorerun_animation(ocp, solution, show_now=True, show_tracked_markers=True) -> tuple[list, list, None]: + """Extract data from the solution to isolate the data for each phase and each model""" + n_phases = ocp.n_phases + data_to_animate = solution.decision_states(to_merge=SolutionMerge.NODES) + if n_phases == 1: + data_to_animate = [data_to_animate] + + data_to_animate = set_time(solution, n_phases, data_to_animate) + + models = [] + for i, nlp in enumerate(ocp.nlp): + if isinstance(nlp.model, MultiBiorbdModel): + data_to_animate, models = set_data_for_multibiorbd_model(nlp, data_to_animate, models, i) + else: + models += [nlp.model.model] + + if show_tracked_markers: + raise NotImplementedError( + "Tracking markers is not implemented for pyorerun. " + "Set show_tracked_markers to False such that sol.animate(show_tracked_markers=False)." + ) + # TODO : Implement tracking markers for pyorerun + # with the _prepare_tracked_markers_for_animation in viewer_utils.py + else: + tracked_markers = None + + return data_to_animate, models, tracked_markers + + +def set_time(solution, n_phases, data_to_animate): + """Set the time in the data_to_animate, handles multiple phases""" + time = ( + [np.concatenate(solution.decision_time()).squeeze()] + if n_phases == 1 + else [np.concatenate(t).squeeze() for t in solution.decision_time()] + ) + + for i in range(n_phases): + data_to_animate[i]["time"] = time[i] + + return data_to_animate + + +def set_data_for_multibiorbd_model(nlp, data_to_animate: list, models: list, i: int): + """Duplicate the data for each model in the MultiBiorbdModel and models""" + models += [model for model in nlp.model.models] + temp_data_animate = [data_to_animate[i].copy() for _ in range(nlp.model.nb_models)] + for j, model in enumerate(nlp.model.models): + for key in data_to_animate[i].keys(): + if key == "time": + continue + index = nlp.model.variable_index(key, j) + temp_data_animate[j][key] = temp_data_animate[j][key][index, :] + + data_to_animate[i] = temp_data_animate[0] + data_to_animate += temp_data_animate[1:] + + return data_to_animate, models + + +def launch_rerun( + solution: "SolutionData", + show_now: bool = True, + tracked_markers: list[np.ndarray] = None, + models: BiorbdModel | list[BiorbdModel] = None, + **kwargs: Any, +): + if not isinstance(solution, (list, tuple)): + solution = [solution] + + if tracked_markers is None: + tracked_markers = [None] * len(solution) + prerun = pyorerun.MultiPhaseRerun() + + for idx_phase, (data, model, tm) in enumerate(zip(solution, models, tracked_markers)): + + if "q_roots" in data and "q_joints" in data: + try: + data["q"] = np.vstack((data["q_roots"], data["q_joints"])) + except: + raise NotImplementedError( + "Found q_roots and q_joints in the solution. This is not supported yet with animation in pyorerun" + ) + + prerun.add_phase(t_span=data["time"], phase=idx_phase) + + if not isinstance(model, biorbd.Model): + raise NotImplementedError( + f"Animation is only implemented for biorbd models. Got {model.__class__.__name__}" + ) + + biorbd_model = pyorerun.BiorbdModel.from_biorbd_object(model) + + prerun.add_animated_model( + biorbd_model, + data["q"], + tracked_markers=tm if tm is not None else None, + phase=idx_phase, + ) + + prerun.rerun(notebook=not show_now) diff --git a/bioptim/models/biorbd/viewer_utils.py b/bioptim/models/biorbd/viewer_utils.py new file mode 100644 index 000000000..067ae73e8 --- /dev/null +++ b/bioptim/models/biorbd/viewer_utils.py @@ -0,0 +1,61 @@ +import numpy as np +from scipy.interpolate import interp1d + +from ...limits.objective_functions import ObjectiveFcn +from ...misc.enums import Node + + +def _prepare_tracked_markers_for_animation( + nlps: list["NonLinearProgram", ...], n_shooting: int = None +) -> list[np.ndarray, ...]: + """Prepare the markers which are tracked to the animation""" + + all_tracked_markers = [] + + for phase, nlp in enumerate(nlps): + n_frames = sum(nlp.ns) + 1 if n_shooting is None else n_shooting + 1 + + n_states_nodes = nlp.n_states_nodes + + tracked_markers = None + for objective in nlp.J: + if objective.target is not None: + if objective.type in ( + ObjectiveFcn.Mayer.TRACK_MARKERS, + ObjectiveFcn.Lagrange.TRACK_MARKERS, + ) and objective.node[0] in (Node.ALL, Node.ALL_SHOOTING): + tracked_markers = np.full((3, nlp.model.nb_markers, n_states_nodes), np.nan) + + for i in range(len(objective.rows)): + tracked_markers[objective.rows[i], objective.cols, :] = objective.target[i, :, :] + + missing_row = np.where(np.isnan(tracked_markers))[0] + if missing_row.size > 0: + tracked_markers[missing_row, :, :] = 0 + + # interpolation + if n_frames > 0 and tracked_markers is not None: + x = np.linspace(0, n_states_nodes - 1, n_states_nodes) + xnew = np.linspace(0, n_states_nodes - 1, n_frames) + f = interp1d(x, tracked_markers, kind="cubic") + tracked_markers = f(xnew) + + all_tracked_markers.append(tracked_markers) + + return all_tracked_markers + + +def _check_models_comes_from_same_super_class(all_nlp: list["NonLinearProgram", ...]): + """Check that all the models comes from the same super class""" + for i, nlp in enumerate(all_nlp): + model_super_classes = nlp.model.__class__.mro()[:-1] # remove object class + nlps = all_nlp.copy() + del nlps[i] + for j, sub_nlp in enumerate(nlps): + if not any([isinstance(sub_nlp.model, super_class) for super_class in model_super_classes]): + raise RuntimeError( + f"The animation is only available for compatible models. " + f"Here, the model of phase {i} is of type {nlp.model.__class__.__name__} and the model of " + f"phase {j + 1 if i < j else j} is of type {sub_nlp.model.__class__.__name__} and " + f"they don't share the same super class." + ) diff --git a/bioptim/optimization/solution/solution.py b/bioptim/optimization/solution/solution.py index ab1c90f69..9a65dbdd6 100644 --- a/bioptim/optimization/solution/solution.py +++ b/bioptim/optimization/solution/solution.py @@ -1,17 +1,14 @@ -from copy import deepcopy -from typing import Any - import numpy as np from casadi import vertcat, DM, Function +from copy import deepcopy from matplotlib import pyplot as plt from scipy import interpolate as sci_interp -from scipy.interpolate import interp1d +from typing import Any from .solution_data import SolutionData, SolutionMerge, TimeAlignment, TimeResolution from ..optimization_vector import OptimizationVectorHelper from ...dynamics.ode_solver import OdeSolver from ...interfaces.solve_ivp_interface import solve_ivp_interface -from ...limits.objective_functions import ObjectiveFcn from ...limits.path_conditions import InitialGuess, InitialGuessList from ...limits.penalty_helpers import PenaltyHelpers from ...misc.enums import ( @@ -1225,6 +1222,7 @@ def animate( shooting_type: Shooting = None, show_now: bool = True, show_tracked_markers: bool = False, + viewer: str = "bioviz", **kwargs: Any, ) -> None | list: """ @@ -1241,6 +1239,8 @@ def animate( If the bioviz exec() function should be called automatically. This is blocking method show_tracked_markers: bool If the tracked markers should be displayed + viewer: str + The viewer to use. Currently, bioviz or pyorerun kwargs: Any Any parameters to pass to bioviz @@ -1249,108 +1249,22 @@ def animate( A list of bioviz structures (one for each phase). So one can call exec() by hand """ + from ...models.biorbd.viewer_utils import _check_models_comes_from_same_super_class + if shooting_type: self.integrate(shooting_type=shooting_type) - for idx_phase in range(len(self.ocp.nlp)): - for objective in self.ocp.nlp[idx_phase].J: - if objective.target is not None: - if objective.type in ( - ObjectiveFcn.Mayer.TRACK_MARKERS, - ObjectiveFcn.Lagrange.TRACK_MARKERS, - ) and objective.node[0] in (Node.ALL, Node.ALL_SHOOTING): - n_frames += objective.target.shape[2] - break - - data_to_animate = [] - if n_frames == 0: - try: - data_to_animate = [self.interpolate(sum([nlp.ns for nlp in self.ocp.nlp]) + 1)] - except ValueError: - data_to_animate = self.interpolate([nlp.ns for nlp in self.ocp.nlp]) - elif n_frames > 0: - data_to_animate = self.interpolate(n_frames) - if not isinstance(data_to_animate, list): - data_to_animate = [data_to_animate] - - if show_tracked_markers and len(self.ocp.nlp) == 1: - tracked_markers = self._prepare_tracked_markers_for_animation(n_shooting=n_frames) - elif show_tracked_markers and len(self.ocp.nlp) > 1: - raise NotImplementedError( - "Tracking markers is not implemented for multiple phases. " - "Set show_tracked_markers to False such that sol.animate(show_tracked_markers=False)." - ) - else: - tracked_markers = [None for _ in range(len(self.ocp.nlp))] - - # assuming that all the models or the same type. - self._check_models_comes_from_same_super_class() - - all_bioviz = [] - for i, data in enumerate(data_to_animate): - all_bioviz.append( - self.ocp.nlp[i].model.animate( - self.ocp, - solution=data, - show_now=show_now, - tracked_markers=tracked_markers, - **kwargs, - ) - ) - - return all_bioviz - - def _check_models_comes_from_same_super_class(self): - """Check that all the models comes from the same super class""" - for i, nlp in enumerate(self.ocp.nlp): - model_super_classes = nlp.model.__class__.mro()[:-1] # remove object class - nlps = self.ocp.nlp.copy() - del nlps[i] - for j, sub_nlp in enumerate(nlps): - if not any([isinstance(sub_nlp.model, super_class) for super_class in model_super_classes]): - raise RuntimeError( - f"The animation is only available for compatible models. " - f"Here, the model of phase {i} is of type {nlp.model.__class__.__name__} and the model of " - f"phase {j + 1 if i < j else j} is of type {sub_nlp.model.__class__.__name__} and " - f"they don't share the same super class." - ) - - def _prepare_tracked_markers_for_animation(self, n_shooting: int = None) -> list: - """Prepare the markers which are tracked to the animation""" - - all_tracked_markers = [] - - for phase, nlp in enumerate(self.ocp.nlp): - n_frames = sum(nlp.ns) + 1 if n_shooting is None else n_shooting + 1 - - n_states_nodes = self.ocp.nlp[phase].n_states_nodes - - tracked_markers = None - for objective in nlp.J: - if objective.target is not None: - if objective.type in ( - ObjectiveFcn.Mayer.TRACK_MARKERS, - ObjectiveFcn.Lagrange.TRACK_MARKERS, - ) and objective.node[0] in (Node.ALL, Node.ALL_SHOOTING): - tracked_markers = np.full((3, nlp.model.nb_markers, n_states_nodes), np.nan) + _check_models_comes_from_same_super_class(self.ocp.nlp) - for i in range(len(objective.rows)): - tracked_markers[objective.rows[i], objective.cols, :] = objective.target[i, :, :] - - missing_row = np.where(np.isnan(tracked_markers))[0] - if missing_row.size > 0: - tracked_markers[missing_row, :, :] = 0 - - # interpolation - if n_frames > 0 and tracked_markers is not None: - x = np.linspace(0, n_states_nodes - 1, n_states_nodes) - xnew = np.linspace(0, n_states_nodes - 1, n_frames) - f = interp1d(x, tracked_markers, kind="cubic") - tracked_markers = f(xnew) - - all_tracked_markers.append(tracked_markers) - - return all_tracked_markers + type(self.ocp.nlp[0].model).animate( + ocp=self.ocp, + solution=self, + show_now=show_now, + show_tracked_markers=show_tracked_markers, + viewer=viewer, + n_frames=n_frames, + **kwargs, + ) @staticmethod def _dispatch_params(params): diff --git a/tests/shard3/test_global_torque_driven_ocp.py b/tests/shard3/test_global_torque_driven_ocp.py index 52d23af0b..94b8e1f47 100644 --- a/tests/shard3/test_global_torque_driven_ocp.py +++ b/tests/shard3/test_global_torque_driven_ocp.py @@ -2,10 +2,11 @@ Test for file IO """ -import os -import pytest import numpy as np import numpy.testing as npt +import os +import pytest + from bioptim import ( OdeSolver, ConstraintList, @@ -17,6 +18,7 @@ PhaseDynamics, SolutionMerge, ) +from bioptim.models.biorbd.viewer_utils import _prepare_tracked_markers_for_animation from tests.utils import TestUtils @@ -417,7 +419,7 @@ def test_track_marker_2D_pendulum(ode_solver, defects_type, phase_dynamics): TestUtils.simulate(sol) # testing that preparing tracked markers for animation properly works - tracked_markers = sol._prepare_tracked_markers_for_animation(n_shooting) + tracked_markers = _prepare_tracked_markers_for_animation(sol.ocp.nlp, n_shooting) npt.assert_equal(tracked_markers[0].shape, (3, 2, n_shooting + 1)) npt.assert_equal(tracked_markers[0][0, :, :], np.zeros((2, n_shooting + 1))) npt.assert_almost_equal(tracked_markers[0][1:, :, 0], np.array([[0.82873751, 0.5612772], [0.22793516, 0.24205527]])) diff --git a/tests/shard4/test_simulate.py b/tests/shard4/test_simulate.py index fc3acfd6e..1f736d367 100644 --- a/tests/shard4/test_simulate.py +++ b/tests/shard4/test_simulate.py @@ -1,11 +1,13 @@ -import warnings -import os from sys import platform -import pytest import numpy as np import numpy.testing as npt +import os +import pytest +import warnings + from bioptim import Shooting, OdeSolver, SolutionIntegrator, Solver, PhaseDynamics, SolutionMerge, ControlType +from bioptim.models.biorbd.viewer_utils import _check_models_comes_from_same_super_class @pytest.mark.parametrize("scaled", [True, False]) @@ -648,7 +650,7 @@ def test_check_models_comes_from_same_super_class(): solver.set_print_level(0) sol = ocp.solve(solver) - sol._check_models_comes_from_same_super_class() + _check_models_comes_from_same_super_class(sol.ocp.nlp) class FakeModel: def __init__(self): @@ -662,7 +664,7 @@ def __init__(self): "Here, the model of phase 0 is of type FakeModel " "and the model of phase 0 is of type BiorbdModel and they don't share the same super class.", ): - sol._check_models_comes_from_same_super_class() + _check_models_comes_from_same_super_class(sol.ocp.nlp) @pytest.mark.parametrize("phase_dynamics", [PhaseDynamics.SHARED_DURING_THE_PHASE, PhaseDynamics.ONE_PER_NODE])