def solveMocoInverse():
# Construct the MocoInverse tool.
inverse = osim.MocoInverse()
# 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))
modelProcessor.append(osim.ModOpAddReserves(1.0))
inverse.setModel(modelProcessor)
# Construct a TableProcessor of the coordinate data and pass it to the
# inverse 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.
inverse.setKinematics(osim.TableProcessor('coordinates.sto'))
# Initial time, final time, and mesh interval.
inverse.set_initial_time(0.81)
inverse.set_final_time(1.79)
inverse.set_mesh_interval(0.02)
# By default, Moco gives an error if the kinematics contains extra columns.
# Here, we tell Moco to allow (and ignore) those extra columns.
inverse.set_kinematics_allow_extra_columns(True)
# Solve the problem and write the solution to a Storage file.
solution = inverse.solve()
solution.getMocoSolution().write(
'example3DWalking_MocoInverse_solution.sto')
# Generate a PDF with plots for the solution trajectory.
model = modelProcessor.process()
report = osim.report.Report(model,
'example3DWalking_MocoInverse_solution.sto',
bilateral=True)
# The PDF is saved to the working directory.
report.generate()
def getWalkingModel():
# Load the 19 DOF, 18 muscle model from file.
model = osim.Model('subject_walk_armless_18musc.osim');
# Add actuators representing the pelvis residual actuators and lumbar
# torques. These actuators are only as strong as they need to be for the
# problem to converge.
addCoordinateActuator(model, 'pelvis_tx', 60)
addCoordinateActuator(model, 'pelvis_ty', 300)
addCoordinateActuator(model, 'pelvis_tz', 35)
addCoordinateActuator(model, 'pelvis_tilt', 60)
addCoordinateActuator(model, 'pelvis_list', 35)
addCoordinateActuator(model, 'pelvis_rotation', 25)
addCoordinateActuator(model, 'lumbar_bending', 40)
addCoordinateActuator(model, 'lumbar_extension', 40)
addCoordinateActuator(model, 'lumbar_rotation', 25)
# We need additional actuators for hip rotation and hip adduction since the
# existing muscle act primarily in the sagittal plane.
addCoordinateActuator(model, 'hip_rotation_r', 100)
addCoordinateActuator(model, 'hip_rotation_l', 100)
addCoordinateActuator(model, 'hip_adduction_r', 100)
addCoordinateActuator(model, 'hip_adduction_l', 100)
# Create a ModelProcessor to make additional modifications to the model.
modelProcessor = osim.ModelProcessor(model)
# Weld the subtalar and toe joints.
jointsToWeld = osim.StdVectorString()
jointsToWeld.append('subtalar_r')
jointsToWeld.append('subtalar_l')
jointsToWeld.append('mtp_r')
jointsToWeld.append('mtp_l')
modelProcessor.append(osim.ModOpReplaceJointsWithWelds(jointsToWeld))
# Apply the ground reaction forces to the model.
modelProcessor.append(osim.ModOpAddExternalLoads('external_loads.xml'))
# Update the muscles: ignore tendon compliance and passive forces, replace
# muscle types with the DeGrooteFregly2016Muscle type, and scale the width
# of the active force length curve.
modelProcessor.append(osim.ModOpIgnoreTendonCompliance())
modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())
modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())
modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))
# Add a set a weak reserves to the sagittal-plane joints in the model.
modelProcessor.append(osim.ModOpAddReserves(1.0))
return modelProcessor
def solveMocoInverseMuscle():
#Construct the MocoInverse tool.
inverse = osim.MocoInverse()
inverse.setName('inverseMuscleTracking')
#Construct a ModelProcessor and set it on the tool.
#Currently the coordinate actuators for the pelvis, along with the reserve
#actuators are fairly generally set, and not weighted at all in cost function.
#These actuators could be more carefully considered to generate an appropriate
#muscle driven simulation. For example, if they max and min control to 1 - the
#pelvis actuators may not produce enough torque.
modelProcessor = osim.ModelProcessor('scaledModelMuscle.osim')
modelProcessor.append(osim.ModOpAddExternalLoads('Jog05_grf.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))
modelProcessor.append(osim.ModOpAddReserves(2))
inverse.setModel(modelProcessor)
#Construct a TableProcessor of the coordinate data and pass it to the
#inverse 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.
inverse.setKinematics(osim.TableProcessor('ikResults_states.sto'))
#Initial time, final time, and mesh interval.
inverse.set_initial_time(osim.Storage('ikResults_states.sto').getFirstTime())
inverse.set_final_time(osim.Storage('ikResults_states.sto').getLastTime())
inverse.set_mesh_interval(0.02)
# By default, Moco gives an error if the kinematics contains extra columns.
# Here, we tell Moco to allow (and ignore) those extra columns.
inverse.set_kinematics_allow_extra_columns(True)
# Solve the problem and write the solution to a Storage file.
solution = inverse.solve()
#Return the solution as an object that we can use
return solution
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)
#Append settings for muscle models
#Set to ignore tendon compliance
modelProcessor.append(osim.ModOpIgnoreTendonCompliance())
#Set to ignore conservative passive fiber forces
modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())
#Set the fiber damping low to limit non-conservative passive forces
#This may also serve to limit the negative muscle forces that can happen
modelProcessor.append(osim.ModOpFiberDampingDGF(1e-02))
####### this parameter could be a factor in the different results coming up
####### along with te active force width (if that didn't change). default is 0.1
####### so this shift may be quite large (the original 1e-05 that is)?
#Scale active force width of muscles
modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))
##### don't think active force width was altered either in original simulations...
#Process and get a variable to call the model
simModel = modelProcessor.process()
#Clean up the printed out model file from the directory
os.remove('simModel.osim')
# %% Simulation set up
#Create the Moco study
study = osim.MocoStudy()
#Initialise the problem
def solveMocoInverseWithEMG():
# This initial block of code is identical to the code above.
inverse = osim.MocoInverse()
modelProcessor = osim.ModelProcessor('subject_walk_armless.osim')
modelProcessor.append(osim.ModOpAddExternalLoads('grf_walk.xml'))
modelProcessor.append(osim.ModOpIgnoreTendonCompliance())
modelProcessor.append(osim.ModOpReplaceMusclesWithDeGrooteFregly2016())
modelProcessor.append(osim.ModOpIgnorePassiveFiberForcesDGF())
modelProcessor.append(osim.ModOpScaleActiveFiberForceCurveWidthDGF(1.5))
modelProcessor.append(osim.ModOpAddReserves(1.0))
inverse.setModel(modelProcessor)
inverse.setKinematics(osim.TableProcessor('coordinates.sto'))
inverse.set_initial_time(0.81)
inverse.set_final_time(1.79)
inverse.set_mesh_interval(0.02)
inverse.set_kinematics_allow_extra_columns(True)
study = inverse.initialize()
problem = study.updProblem()
# Add electromyography tracking.
emgTracking = osim.MocoControlTrackingGoal('emg_tracking')
emgTracking.setWeight(50.0)
# Each column in electromyography.sto is normalized so the maximum value in
# each column is 1.0.
controlsRef = osim.TimeSeriesTable('electromyography.sto')
# Scale the tracked muscle activity based on peak levels from
# "Gait Analysis: Normal and Pathological Function" by
# Perry and Burnfield, 2010 (digitized by Carmichael Ong).
soleus = controlsRef.updDependentColumn('soleus')
gasmed = controlsRef.updDependentColumn('gastrocnemius')
tibant = controlsRef.updDependentColumn('tibialis_anterior')
for t in range(0, controlsRef.getNumRows()):
soleus[t] = 0.77 * soleus[t]
gasmed[t] = 0.87 * gasmed[t]
tibant[t] = 0.37 * tibant[t]
emgTracking.setReference(osim.TableProcessor(controlsRef))
# Associate actuators in the model with columns in electromyography.sto.
emgTracking.setReferenceLabel('/forceset/soleus_r', 'soleus')
emgTracking.setReferenceLabel('/forceset/gasmed_r', 'gastrocnemius')
emgTracking.setReferenceLabel('/forceset/gaslat_r', 'gastrocnemius')
emgTracking.setReferenceLabel('/forceset/tibant_r', 'tibialis_anterior')
problem.addGoal(emgTracking)
# Solve the problem and write the solution to a Storage file.
solution = study.solve()
solution.write('example3DWalking_MocoInverseWithEMG_solution.sto')
# Write the reference data in a way that's easy to compare to the solution.
controlsRef.removeColumn('medial_hamstrings')
controlsRef.removeColumn('biceps_femoris')
controlsRef.removeColumn('vastus_lateralis')
controlsRef.removeColumn('vastus_medius')
controlsRef.removeColumn('rectus_femoris')
controlsRef.removeColumn('gluteus_maximus')
controlsRef.removeColumn('gluteus_medius')
controlsRef.setColumnLabels(
['/forceset/soleus_r', '/forceset/gasmed_r', '/forceset/tibant_r'])
controlsRef.appendColumn('/forceset/gaslat_r', gasmed)
osim.STOFileAdapter.write(controlsRef, 'controls_reference.sto')
# Generate a report comparing MocoInverse solutions without and with EMG
# tracking.
model = modelProcessor.process()
output = 'example3DWalking_MocoInverseWithEMG_report.pdf'
ref_files = [
'example3DWalking_MocoInverseWithEMG_solution.sto',
'controls_reference.sto'
]
report = osim.report.Report(model,
'example3DWalking_MocoInverse_solution.sto',
output=output,
bilateral=True,
ref_files=ref_files)
# The PDF is saved to the working directory.
report.generate()
def muscleDrivenStateTracking():
#Create and name an instance of the MocoTrack tool.
track = osim.MocoTrack()
track.setName('muscleDrivenStateTracking')
#Construct a ModelProcessor and set it on the tool.
#Currently the coordinate actuators for the pelvis, along with the reserve
#actuators are fairly generally set, and not weighted at all in cost function.
#These actuators could be more carefully considered to generate an appropriate
#muscle driven simulation. For example, if they max and min control to 1 - the
#pelvis actuators may not produce enough torque.
modelProcessor = osim.ModelProcessor('scaledModelMuscle.osim')
modelProcessor.append(osim.ModOpAddExternalLoads('Jog05_grf.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))
modelProcessor.append(osim.ModOpAddReserves(2))
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('ikResults_states.sto'))
track.set_states_global_tracking_weight(5)
##### TODO: add more specific weights for different state coordinates like in RRA
#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 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(osim.Storage('ikResults_states.sto').getFirstTime())
track.set_final_time(osim.Storage('ikResults_states.sto').getLastTime())
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'))
effort.setWeight(1)
# # 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 ii in range(forceSet.getSize()):
# forcePath = forceSet.get(ii).getAbsolutePathString()
# if 'pelvis' in str(forcePath):
# effort.setWeightForControl(forcePath, 10)
#Get solver and set the mesh interval
solver = study.initCasADiSolver()
#50 mesh intervals for half gait cycle recommended, keep smaller for now
#19 will produce the same as inverse solution above
# solver.set_num_mesh_intervals(19)
#Set solver parameters
solver.set_optim_constraint_tolerance(1e-3)
solver.set_optim_convergence_tolerance(1e-3)
# Solve and visualize.
solution = study.solve()
#Return the solution as an object that we can use
return solution