1.0.0/html_user/exampleMocoTrack_8py-example.html

# -------------------------------------------------------------------------- #

# OpenSim Moco: exampleMocoTrack.py                                          #

# -------------------------------------------------------------------------- #

# Copyright (c) 2019 Stanford University and the Authors                     #

#                                                                            #

# Author(s): Nicholas Bianco                                                 #

#                                                                            #

# Licensed under the Apache License, Version 2.0 (the "License") you may     #

# not use this file except in compliance with the License. You may obtain a  #

# copy of the License at http://www.apache.org/licenses/LICENSE-2.0          #

#                                                                            #

# Unless required by applicable law or agreed to in writing, software        #

# distributed under the License is distributed on an "AS IS" BASIS,          #

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.   #

# See the License for the specific language governing permissions and        #

# limitations under the License.                                             #

# -------------------------------------------------------------------------- #


# This example features two different tracking problems solved using the

# MocoTrack tool.

#  - The first problem demonstrates the basic usage of the tool interface

#    to solve a torque-driven marker tracking problem.

#  - The second problem shows how to customize a muscle-driven state tracking

#    problem using more advanced features of the tool interface.

#

# See the README.txt next to this file for more information.


import os

import opensim as osim


def torqueDrivenMarkerTracking():


    # Create and name an instance of the MocoTrack tool.

    track = osim.MocoTrack()

    track.setName("torque_driven_marker_tracking")


    # Construct a ModelProcessor and add it to the tool. ModelProcessors

    # accept a base model and allow you to easily modify the model by appending

    # ModelOperators. Operations are performed in the order that they are

    # appended to the model.

    # Create the base Model by passing in the model file.

    modelProcessor = osim.ModelProcessor("subject_walk_armless.osim")

    # Add ground reaction external loads in lieu of a ground-contact model.

    modelProcessor.append(osim.ModOpAddExternalLoads("grf_walk.xml"))

    # Remove all the muscles in the model's ForceSet.

    modelProcessor.append(osim.ModOpRemoveMuscles())

    # Add CoordinateActuators to the model degrees-of-freedom. This ignores the

    # pelvis coordinates which already have residual CoordinateActuators.

    modelProcessor.append(osim.ModOpAddReserves(250))

    track.setModel(modelProcessor)


    # Use this convenience function to set the MocoTrack markers reference

    # directly from a TRC file. By default, the markers data is filtered at

    # 6 Hz and if in millimeters, converted to meters.

    track.setMarkersReferenceFromTRC("marker_trajectories.trc")


    # There is marker data in the 'marker_trajectories.trc' associated with

    # model markers that no longer exists (i.e. markers on the arms). Set this

    # flag to avoid an exception from being thrown.

    track.set_allow_unused_references(True)


    # Increase the global marker tracking weight, which is the weight

    # associated with the internal MocoMarkerTrackingCost term.

    track.set_markers_global_tracking_weight(10)


    # Increase the tracking weights for individual markers in the data set

    # placed on bony landmarks compared to markers located on soft tissue.

    markerWeights = osim.MocoWeightSet()

    markerWeights.cloneAndAppend(osim.MocoWeight("R.ASIS", 20))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.ASIS", 20))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.PSIS", 20))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.PSIS", 20))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.Knee", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.Ankle", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.Heel", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.MT5", 5))

    markerWeights.cloneAndAppend(osim.MocoWeight("R.Toe", 2))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.Knee", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.Ankle", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.Heel", 10))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.MT5", 5))

    markerWeights.cloneAndAppend(osim.MocoWeight("L.Toe", 2))

    track.set_markers_weight_set(markerWeights)


    # Initial time, final time, and mesh interval. The number of mesh points

    # used to discretize the problem is computed internally using these values.

    track.set_initial_time(0.81)

    track.set_final_time(1.65)

    track.set_mesh_interval(0.05)


    # Solve! Use track.solve() to skip visualizing.

    solution = track.solveAndVisualize()


def muscleDrivenStateTracking():


    # Create and name an instance of the MocoTrack tool.

    track = osim.MocoTrack()

    track.setName("muscle_driven_state_tracking")


    # Construct a ModelProcessor and set it on the tool. The default

    # muscles in the model are replaced with optimization-friendly

    # DeGrooteFregly2016Muscles, and adjustments are made to the default muscle

    # parameters.

    modelProcessor = osim.ModelProcessor("subject_walk_armless.osim")

    modelProcessor.append(osim.ModOpAddExternalLoads("grf_walk.xml"))

    modelProcessor.append(osim.ModOpIgnoreTendonCompliance())

    modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())

    # Only valid for DeGrooteFregly2016Muscles.

    modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())

    # Only valid for DeGrooteFregly2016Muscles.

    modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))

    track.setModel(modelProcessor)


    # Construct a TableProcessor of the coordinate data and pass it to the

    # tracking tool. TableProcessors can be used in the same way as

    # ModelProcessors by appending TableOperators to modify the base table.

    # A TableProcessor with no operators, as we have here, simply returns the

    # base table.

    track.setStatesReference(osim.TableProcessor("coordinates.sto"))

    track.set_states_global_tracking_weight(10)


    # This setting allows extra data columns contained in the states

    # reference that don't correspond to model coordinates.

    track.set_allow_unused_references(True)


    # Since there is only coordinate position data in the states references,

    # this setting is enabled to fill in the missing coordinate speed data using

    # the derivative of splined position data.

    track.set_track_reference_position_derivatives(True)


    # Initial time, final time, and mesh interval.

    track.set_initial_time(0.81)

    track.set_final_time(1.65)

    track.set_mesh_interval(0.08)


    # Instead of calling solve(), call initialize() to receive a pre-configured

    # MocoStudy object based on the settings above. Use this to customize the

    # problem beyond the MocoTrack interface.

    study = track.initialize()


    # Get a reference to the MocoControlCost that is added to every MocoTrack

    # problem by default.

    problem = study.updProblem()

    effort = osim.MocoControlGoal.safeDownCast(problem.updGoal("control_effort"))


    # Put a large weight on the pelvis CoordinateActuators, which act as the

    # residual, or 'hand-of-god', forces which we would like to keep as small

    # as possible.

    model = modelProcessor.process()

    model.initSystem()

    forceSet = model.getForceSet()

    for i in range(forceSet.getSize()):

        forcePath = forceSet.get(i).getAbsolutePathString()

        if 'pelvis' in str(forcePath):

            effort.setWeightForControl(forcePath, 10)


    # Solve and visualize.

    solution = study.solve()

    study.visualize(solution)


# Solve the torque-driven marker tracking problem.

# This problem takes a few minutes to solve.

torqueDrivenMarkerTracking()


# Solve the muscle-driven state tracking problem.

# This problem could take an hour or more to solve, depending on the number of

# processor cores available for parallelization. With 12 cores, it takes around

# 25 minutes.

muscleDrivenStateTracking()