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guesses.py
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guesses.py
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'''
This script contains classes to set initial guesses to the optimization
variables. Two classes are implemented: one for a hot-start and one for
a cold-start.
'''
# %% Import packages
import pandas as pd
import numpy as np
import scipy.interpolate as interpolate
from scipy.interpolate import interp1d
# %% This class sets a cold-start guess for the optimization variables.
# All variables are constant except for the pelvis moving forward at a
# constant speed.
class coldStart:
def __init__(self, N, d, joints, muscles, targetSpeed):
self.N = N
self.d = d
self.joints = joints
self.targetSpeed = targetSpeed
self.muscles = muscles
def getGuessFinalTime(self, gaitCycleSimulation):
allSpeeds = np.linspace(0.73, 2.73, 21)
if gaitCycleSimulation == 'half':
allFinalTimes = np.linspace(0.70, 0.35, 21)
elif gaitCycleSimulation == 'full':
allFinalTimes = np.linspace(0.70*2, 0.35*2, 21)
idxTargetSpeed = np.argmax(allSpeeds >= self.targetSpeed)
self.guessFinalTime = allFinalTimes[idxTargetSpeed]
return self.guessFinalTime
# Mesh points.
def getGuessPosition(self, scaling):
g = [0] * (self.N + 1)
g_pelvis_tx = np.linspace(0, self.guessFinalTime * self.targetSpeed,
self.N)
g_pelvis_tx = np.append(g_pelvis_tx, g_pelvis_tx[-1] +
(g_pelvis_tx[-1] - g_pelvis_tx[-2]))
g_pelvis_ty = [0.9385] * (self.N + 1)
self.guessPosition = pd.DataFrame()
for count, joint in enumerate(self.joints):
if joint == 'pelvis_tx':
self.guessPosition.insert(count, joint,
g_pelvis_tx / scaling.iloc[0][joint])
elif joint == 'pelvis_ty':
self.guessPosition.insert(count, joint,
g_pelvis_ty / scaling.iloc[0][joint])
else:
self.guessPosition.insert(count, joint,
g / scaling.iloc[0][joint])
return self.guessPosition
def getGuessVelocity(self, scaling):
g = [0] * (self.N + 1)
g_pelvis_tx = [self.targetSpeed] * (self.N + 1)
self.guessVelocity = pd.DataFrame()
for count, joint in enumerate(self.joints):
if joint == 'pelvis_tx':
self.guessVelocity.insert(count, joint,
g_pelvis_tx / scaling.iloc[0][joint])
else:
self.guessVelocity.insert(count, joint,
g / scaling.iloc[0][joint])
return self.guessVelocity
def getGuessAcceleration(self, scaling):
g = [0] * self.N
self.guessAcceleration = pd.DataFrame()
for count, joint in enumerate(self.joints):
self.guessAcceleration.insert(count, joint,
g / scaling.iloc[0][joint])
return self.guessAcceleration
def getGuessActivation(self, scaling):
g = [0.1] * (self.N + 1)
self.guessActivation = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessActivation.insert(count, muscle,
g / scaling.iloc[0][muscle])
return self.guessActivation
def getGuessActivationDerivative(self, scaling):
g = [0.01] * self.N
guessActivationDerivative = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
guessActivationDerivative.insert(count, muscle,
g / scaling.iloc[0][muscle])
return guessActivationDerivative
def getGuessForce(self, scaling):
g = [0.1] * (self.N + 1)
self.guessForce = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessForce.insert(count, muscle, g / scaling.iloc[0][muscle])
return self.guessForce
def getGuessForceDerivative(self, scaling):
g = [0.01] * self.N
self.guessForceDerivative = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessForceDerivative.insert(count, muscle,
g / scaling.iloc[0][muscle])
return self.guessForceDerivative
def getGuessTorqueActuatorActivation(self, torqueActuatorJoints):
g = [0.1] * (self.N + 1)
self.guessTorqueActuatorActivation = pd.DataFrame()
for count, torqueActuatorJoint in enumerate(torqueActuatorJoints):
self.guessTorqueActuatorActivation.insert(
count, torqueActuatorJoint, g)
return self.guessTorqueActuatorActivation
def getGuessTorqueActuatorExcitation(self, torqueActuatorJoints):
g = [0.1] * self.N
guessTorqueActuatorExcitation = pd.DataFrame()
for count, torqueActuatorJoint in enumerate(torqueActuatorJoints):
guessTorqueActuatorExcitation.insert(count, torqueActuatorJoint, g)
return guessTorqueActuatorExcitation
# Collocation points.
def getGuessActivationCol(self):
guessActivationCol = pd.DataFrame(columns=self.muscles)
for k in range(self.N):
for c in range(self.d):
guessActivationCol = guessActivationCol.append(
self.guessActivation.iloc[k], ignore_index=True)
return guessActivationCol
def getGuessForceCol(self):
guessForceCol = pd.DataFrame(columns=self.muscles)
for k in range(self.N):
for c in range(self.d):
guessForceCol = guessForceCol.append(
self.guessForce.iloc[k], ignore_index=True)
return guessForceCol
def getGuessForceDerivativeCol(self):
guessForceDerivativeCol = pd.DataFrame(columns=self.muscles)
for k in range(self.N):
for c in range(self.d):
guessForceDerivativeCol = guessForceDerivativeCol.append(
self.guessForceDerivative.iloc[k], ignore_index=True)
return guessForceDerivativeCol
def getGuessTorqueActuatorActivationCol(self, torqueActuatorJoints):
guessTorqueActuatorActivationCol = (
pd.DataFrame(columns=torqueActuatorJoints))
for k in range(self.N):
for c in range(self.d):
guessTorqueActuatorActivationCol = (
guessTorqueActuatorActivationCol.append(
self.guessTorqueActuatorActivation.iloc[k],
ignore_index=True))
return guessTorqueActuatorActivationCol
def getGuessPositionCol(self):
guessPositionCol = pd.DataFrame(columns=self.joints)
for k in range(self.N):
for c in range(self.d):
guessPositionCol = guessPositionCol.append(
self.guessPosition.iloc[k], ignore_index=True)
return guessPositionCol
def getGuessVelocityCol(self):
guessVelocityCol = pd.DataFrame(columns=self.joints)
for k in range(self.N):
for c in range(self.d):
guessVelocityCol = guessVelocityCol.append(
self.guessVelocity.iloc[k], ignore_index=True)
return guessVelocityCol
def getGuessAccelerationCol(self):
guessAccelerationCol = pd.DataFrame(columns=self.joints)
for k in range(self.N):
for c in range(self.d):
guessAccelerationCol = guessAccelerationCol.append(
self.guessAcceleration.iloc[k], ignore_index=True)
return guessAccelerationCol
# %% This class sets a hot-start guess for the optimization variables.
# Joints positions, velocities, and accelerations are based on experimental
# data. Muscle variables are set to constant values.
class hotStart:
def __init__(self, Qs, N, d, joints, muscles, targetSpeed,
periodicQsJointsA, periodicQdotsJointsA,
periodicOppositeJoints=[]):
self.Qs = Qs
self.N = N
self.d = d
self.joints = joints
self.targetSpeed = targetSpeed
self.muscles = muscles
self.periodicQsJointsA = periodicQsJointsA
self.periodicOppositeJoints = periodicOppositeJoints
self.periodicQdotsJointsA = periodicQdotsJointsA
def splineQs(self):
self.Qs_spline = self.Qs.copy()
self.Qdots_spline = self.Qs.copy()
self.Qdotdots_spline = self.Qs.copy()
for joint in self.joints:
spline = interpolate.InterpolatedUnivariateSpline(self.Qs['time'],
self.Qs[joint],
k=3)
self.Qs_spline[joint] = spline(self.Qs['time'])
splineD1 = spline.derivative(n=1)
self.Qdots_spline[joint] = splineD1(self.Qs['time'])
splineD2 = spline.derivative(n=2)
self.Qdotdots_spline[joint] = splineD2(self.Qs['time'])
def interpQs(self):
self.splineQs()
tOut = np.linspace(self.Qs['time'].iloc[0],
self.Qs['time'].iloc[-1],
self.N + 1)
self.Qs_spline_interp = pd.DataFrame()
self.Qdots_spline_interp = pd.DataFrame()
self.Qdotdots_spline_interp = pd.DataFrame()
for count, joint in enumerate(self.joints):
set_interp = interp1d(self.Qs['time'], self.Qs_spline[joint])
self.Qs_spline_interp.insert(count, joint, set_interp(tOut))
set_interp = interp1d(self.Qs['time'], self.Qdots_spline[joint])
self.Qdots_spline_interp.insert(count, joint, set_interp(tOut))
set_interp = interp1d(self.Qs['time'], self.Qdotdots_spline[joint])
self.Qdotdots_spline_interp.insert(count, joint, set_interp(tOut))
def getGuessFinalTime(self, gaitCycleSimulation='half'):
allSpeeds = np.linspace(0.73, 2.73, 21)
if gaitCycleSimulation == 'half':
allFinalTimes = np.linspace(0.70, 0.35, 21)
elif gaitCycleSimulation == 'full':
allFinalTimes = np.linspace(0.70*2, 0.35*2, 21)
idxTargetSpeed = np.argmax(allSpeeds >= self.targetSpeed)
self.guessFinalTime = allFinalTimes[idxTargetSpeed]
return self.guessFinalTime
# Mesh points.
def getGuessPosition(self, scaling, adjustInitialStatePelvis_tx=True):
self.interpQs()
self.guessPosition = pd.DataFrame()
for count, joint in enumerate(self.joints):
self.guessPosition.insert(count, joint,
self.Qs_spline_interp[joint] /
scaling.iloc[0][joint])
if adjustInitialStatePelvis_tx:
self.guessPosition['pelvis_tx'] -= (
self.guessPosition.iloc[0]['pelvis_tx'])
return self.guessPosition
def getGuessVelocity(self, scaling):
self.interpQs()
self.guessVelocity = pd.DataFrame()
for count, joint in enumerate(self.joints):
self.guessVelocity.insert(count, joint,
self.Qdots_spline_interp[joint] /
scaling.iloc[0][joint])
return self.guessVelocity
def getGuessAcceleration(self, scaling, nullGuessAcceleration=False):
self.interpQs()
self.guessAcceleration = pd.DataFrame()
g = [0] * self.N
for count, joint in enumerate(self.joints):
if nullGuessAcceleration:
self.guessAcceleration.insert(count, joint,
g / scaling.iloc[0][joint])
else:
self.guessAcceleration.insert(
count, joint, self.Qdotdots_spline_interp[joint] /
scaling.iloc[0][joint])
return self.guessAcceleration
def getGuessActivation(self, scaling):
g = [0.1] * (self.N + 1)
self.guessActivation = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessActivation.insert(count, muscle,
g / scaling.iloc[0][muscle])
return self.guessActivation
def getGuessActivationDerivative(self, scaling):
g = [0.01] * self.N
guessActivationDerivative = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
guessActivationDerivative.insert(count, muscle,
g / scaling.iloc[0][muscle])
return guessActivationDerivative
def getGuessForce(self, scaling):
g = [0.1] * (self.N + 1)
self.guessForce = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessForce.insert(count, muscle, g / scaling.iloc[0][muscle])
return self.guessForce
def getGuessForceDerivative(self, scaling):
g = [0.01] * self.N
self.guessForceDerivative = pd.DataFrame()
for count, muscle in enumerate(self.muscles):
self.guessForceDerivative.insert(count, muscle,
g / scaling.iloc[0][muscle])
return self.guessForceDerivative
def getGuessTorqueActuatorActivation(self, torqueActuatorJoints):
g = [0.1] * (self.N + 1)
self.guessTorqueActuatorActivation = pd.DataFrame()
for count, torqueActuatorJoint in enumerate(torqueActuatorJoints):
self.guessTorqueActuatorActivation.insert(
count, torqueActuatorJoint, g)
return self.guessTorqueActuatorActivation
def getGuessTorqueActuatorExcitation(self, torqueActuatorJoints):
g = [0.1] * self.N
guessTorqueActuatorExcitation = pd.DataFrame()
for count, torqueActuatorJoint in enumerate(torqueActuatorJoints):
guessTorqueActuatorExcitation.insert(
count, torqueActuatorJoint, g)
return guessTorqueActuatorExcitation
# Collocation points.
def getGuessActivationCol(self):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessActivation.iloc[k])
guessActivationCol = pd.DataFrame.from_records(temp)
return guessActivationCol
def getGuessForceCol(self):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessForce.iloc[k])
guessForceCol = pd.DataFrame.from_records(temp)
return guessForceCol
def getGuessForceDerivativeCol(self):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessForceDerivative.iloc[k])
guessForceDerivativeCol = pd.DataFrame.from_records(temp)
return guessForceDerivativeCol
def getGuessTorqueActuatorActivationCol(self, torqueActuatorJoints):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessTorqueActuatorActivation.iloc[k])
guessTorqueActuatorActivationCol = pd.DataFrame.from_records(temp)
return guessTorqueActuatorActivationCol
def getGuessPositionCol(self):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessPosition.iloc[k])
guessPositionCol = pd.DataFrame.from_records(temp)
return guessPositionCol
def getGuessVelocityCol(self):
temp = []
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessVelocity.iloc[k])
guessVelocityCol = pd.DataFrame.from_records(temp)
return guessVelocityCol
def getGuessAccelerationCol(self):
temp = []
guessAccelerationCol = pd.DataFrame(columns=self.joints)
for k in range(self.N):
for c in range(self.d):
temp.append(self.guessAcceleration.iloc[k])
guessAccelerationCol = pd.DataFrame.from_records(temp)
return guessAccelerationCol