def __init__(self,
rs,
sizeOfTarget,
thetafile,
arm="Arm26",
estim="Inv",
actor=None,
logDir="Best",
saveDir=None):
self.rs = rs
self.posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
self.arm = ArmType[arm]()
self.arm.setDT(rs.dt)
if (not self.det):
self.arm.setNoise(rs.noise)
if (rs.costClass == None):
self.trajCost = CostDDPG(rs)
else:
self.trajCost = rs.costClass(rs)
if (len(self.posIni.shape) == 1):
self.posIni = self.posIni.reshape((1, self.posIni.shape[0]))
if estim == "Inv":
self.stateEstimator = StateEstimator(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "Reg":
self.stateEstimator = StateEstimatorRegression(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
elif estim == "Hyb":
self.stateEstimator = StateEstimatorHyb(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "No":
self.stateEstimator = StateEstimatorNoFeedBack(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
else:
raise TypeError("This Estimator do not exist")
if (saveDir == None):
self.foldername = self.rs.OPTIpath + str(self.sizeOfTarget) + "/"
else:
self.foldername = saveDir
self.costStore = []
self.trajTimeStore = []
self.lastCoord = []
self.sizeOfTarget = sizeOfTarget
self.thetafile = thetafile
self.actor = actor
self.saveName = self.foldername + logDir + "/"
self.nbReset = 0
self.cost = 0
self.max = 0
self.progressTab = [0.1, 0.25, 0.5, 0.75, 1.]
self.progress = 1
self.reset()
def __init__(self, rs, sizeOfTarget, thetafile, arm="Arm26", estim="Inv", actor=None, logDir="Best", saveDir=None):
self.rs=rs
self.posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
self.arm=ArmType[arm]()
self.arm.setDT(rs.dt)
if(not self.det):
self.arm.setNoise(rs.noise)
if(rs.costClass==None):
self.trajCost=CostDDPG(rs)
else:
self.trajCost=rs.costClass(rs)
if(len(self.posIni.shape)==1):
self.posIni=self.posIni.reshape((1,self.posIni.shape[0]))
if estim=="Inv" :
self.stateEstimator = StateEstimator(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim=="Reg":
self.stateEstimator = StateEstimatorRegression(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim == "Hyb" :
self.stateEstimator = StateEstimatorHyb(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim =="No" :
self.stateEstimator = StateEstimatorNoFeedBack(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
else :
raise TypeError("This Estimator do not exist")
if(saveDir==None):
self.foldername=self.rs.OPTIpath + str(self.sizeOfTarget) + "/"
else :
self.foldername=saveDir
self.costStore = []
self.trajTimeStore = []
self.lastCoord = []
self.sizeOfTarget = sizeOfTarget
self.thetafile = thetafile
self.actor=actor
self.saveName = self.foldername + logDir + "/"
self.nbReset=0
self.cost=0
self.max=0
self.progressTab=[0.1,0.25,0.5,0.75,1.]
self.progress=1
self.reset()
class DDPGEnv(Env):
print_interval = 20
def __init__(self,
rs,
sizeOfTarget,
thetafile,
arm="Arm26",
estim="Inv",
actor=None,
logDir="Best",
saveDir=None):
self.rs = rs
self.posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
self.arm = ArmType[arm]()
self.arm.setDT(rs.dt)
if (not self.det):
self.arm.setNoise(rs.noise)
if (rs.costClass == None):
self.trajCost = CostDDPG(rs)
else:
self.trajCost = rs.costClass(rs)
if (len(self.posIni.shape) == 1):
self.posIni = self.posIni.reshape((1, self.posIni.shape[0]))
if estim == "Inv":
self.stateEstimator = StateEstimator(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "Reg":
self.stateEstimator = StateEstimatorRegression(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
elif estim == "Hyb":
self.stateEstimator = StateEstimatorHyb(rs.inputDim, rs.outputDim,
rs.delayUKF, self.arm)
elif estim == "No":
self.stateEstimator = StateEstimatorNoFeedBack(
rs.inputDim, rs.outputDim, rs.delayUKF, self.arm)
else:
raise TypeError("This Estimator do not exist")
if (saveDir == None):
self.foldername = self.rs.OPTIpath + str(self.sizeOfTarget) + "/"
else:
self.foldername = saveDir
self.costStore = []
self.trajTimeStore = []
self.lastCoord = []
self.sizeOfTarget = sizeOfTarget
self.thetafile = thetafile
self.actor = actor
self.saveName = self.foldername + logDir + "/"
self.nbReset = 0
self.cost = 0
self.max = 0
self.progressTab = [0.1, 0.25, 0.5, 0.75, 1.]
self.progress = 1
self.reset()
def getActionSize(self):
return self.rs.outputDim
def getStateSize(self):
return self.rs.inputDim
def getActionBounds(self):
return [[1] * self.rs.outputDim, [0] * self.rs.outputDim]
def saveCost(self):
writeArray(self.costStore, self.foldername + "CostCMAES/", "traj",
".cost")
writeArray(self.trajTimeStore, self.foldername + "TrajTime/", "traj",
".time")
writeArray(self.lastCoord, self.foldername + "finalX/", "x", ".last")
def act(self, action):
action = np.array(action[0])
action = action + np.random.normal(0., 0.2, action.shape)
return self.__act__(action)
def __act__(self, action):
if self.rs.det:
realU = muscleFilter(action)
#computation of the arm state
realNextState = self.arm.computeNextState(realU,
self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
estimNextState = realNextState
else:
#realU = getNoisyCommand(U,self.arm.getMusclesParameters().getKnoiseU())
realU = getNoisyCommand(action, self.arm.musclesP.knoiseU)
realU = muscleFilter(realU)
#computation of the arm state
realNextState = self.arm.computeNextState(realU,
self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
action = muscleFilter(action)
estimNextState = self.stateEstimator.getEstimState(
tmpState, action)
#print estimNextState
self.arm.setState(realNextState)
#computation of the cost
#get dotq and q from the state vector
_, q = self.arm.getDotQAndQFromStateVector(tmpState)
self.coordHand = self.arm.mgdEndEffector(q)
costAct = self.trajCost.computeStateTransitionCost(
realU, self.coordHand)
#print ("dotq :",dotq)
#computation of the coordinates to check if the target is reach or not
self.i += 1
self.t += self.rs.dt
self.estimState = estimNextState
if (self.coordHand[1] >= self.rs.YTarget
or self.i >= self.rs.maxSteps):
costAct += self.trajCost.computeFinalReward(
self.arm, self.t, self.coordHand, self.sizeOfTarget)
self.cost += costAct
return [realU], [costAct]
def actAndStore(self, action):
if self.rs.det:
realU = muscleFilter(action)
#computation of the arm state
realNextState = self.arm.computeNextState(realU,
self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
estimNextState = realNextState
else:
#realU = getNoisyCommand(U,self.arm.getMusclesParameters().getKnoiseU())
realU = getNoisyCommand(action, self.arm.musclesP.knoiseU)
realU = muscleFilter(realU)
#computation of the arm state
realNextState = self.arm.computeNextState(realU,
self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
action = muscleFilter(action)
estimNextState = self.stateEstimator.getEstimState(
tmpState, action)
#print estimNextState
self.arm.setState(realNextState)
#computation of the cost
#get dotq and q from the state vector
_, q = self.arm.getDotQAndQFromStateVector(tmpState)
coordElbow, self.coordHand = self.arm.mgdFull(q)
cost = self.trajCost.computeStateTransitionCost(realU, self.coordHand)
stepStore = []
stepStore.append(self.vectarget)
stepStore.append(self.estimState)
stepStore.append(tmpState)
stepStore.append(realU)
stepStore.append(action)
stepStore.append(estimNextState)
stepStore.append(realNextState)
stepStore.append([coordElbow[0], coordElbow[1]])
stepStore.append([self.coordHand[0], self.coordHand[1]])
#print ("before",stepStore)
tmpstore = np.array(stepStore).flatten()
row = [item for sub in tmpstore for item in sub]
#print ("store",row)
self.dataStore.append(row)
self.i += 1
self.t += self.rs.dt
self.estimState = estimNextState
return [realU], [cost]
def state(self):
return [self.arm.getState()]
def reset(self, noise=True):
#print("Episode : "+str(self.nbReset)+ ", Progression :"+str(self.progress))
#self.nbReset+=1
#print(self.cost)
#Discrete begining
"""
i = rd.randint(0,len(self.posIni)-1)
#i=0
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(self.posIni[i][0], self.posIni[i][1])
"""
#Progress
"""
i = ((rd.random()*6*np.pi - 3*np.pi)*self.progressTab[self.progress]-6*np.pi)/12
j= (rd.random()*0.3-0.15)*self.progressTab[self.progress]+0.25
#i=0
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(self.rs.XTarget+ j*np.cos(i), self.rs.YTarget+ j*np.sin(i))
"""
"""
i = - 6*np.pi/12
j=rd.randint(1,4)/10.
#j=0.1
q1, q2 = self.arm.mgi(self.rs.XTarget+ j*np.cos(i), self.rs.YTarget+ j*np.sin(i))
"""
x, y = self.begin()
q1, q2 = self.arm.mgi(x, y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1, q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0., 0.
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
def begin(self):
return self.beginDiscret()
def beginOnePoint(self):
return self.x, self.y
def beginArcProgress(self):
i = (rd.random() * 6 * np.pi - 9 * np.pi) / 12.
j = rd.randint(1, self.progress) / 10.
return self.rs.XTarget + j * np.cos(i), self.rs.YTarget + j * np.sin(i)
def beginDiscret(self):
i = rd.randint(0, len(self.posIni) - 1)
return self.posIni[i][0], self.posIni[i][1]
def isFinished(self):
if (self.coordHand[1] >= self.rs.YTarget
or self.i >= self.rs.maxSteps):
return True
return False
def OneTraj(self, x, y):
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(x, y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1, q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0, 0
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
self.vectarget = [0.0, 0.0, self.rs.XTarget, self.rs.YTarget]
totalCost = 0
while (not self.isFinished()):
action = self.actor.action(self.estimState)
_, cost = self.__act__(action)
totalCost += cost[0]
totalCost += self.trajCost.computeFinalReward(self.arm, self.t,
self.coordHand,
self.sizeOfTarget)
return totalCost, self.t
def saveOneTraj(self, x, y):
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(x, y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1, q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0, 0
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
self.dataStore = []
self.vectarget = [0.0, 0.0, self.rs.XTarget, self.rs.YTarget]
totalCost = 0
while (not self.isFinished()):
action = self.actor.action(self.estimState)
_, cost = self.actAndStore(action)
totalCost += cost[0]
totalCost += self.trajCost.computeFinalReward(self.arm, self.t,
self.coordHand,
self.sizeOfTarget)
filename = findDataFilename(self.saveName + "Log/",
"traj" + str(x) + "-" + str(y), ".log")
np.savetxt(filename, self.dataStore)
#used to ignore dispersion when the target line is not crossed
if self.coordHand[1] >= self.rs.YTarget:
self.lastCoord.append(self.coordHand[0])
return totalCost, self.t
def nTraj(self, (x, y), repeat):
costAll, trajTimeAll = np.zeros(repeat), np.zeros(repeat)
for i in range(repeat):
costAll[i], trajTimeAll[i] = self.OneTraj(x, y)
meanCost = np.mean(costAll)
meanTrajTime = np.mean(trajTimeAll)
return meanCost, meanTrajTime
class DDPGEnv(Env):
print_interval = 20
def __init__(self, rs, sizeOfTarget, thetafile, arm="Arm26", estim="Inv", actor=None, logDir="Best", saveDir=None):
self.rs=rs
self.posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
self.arm=ArmType[arm]()
self.arm.setDT(rs.dt)
if(not self.det):
self.arm.setNoise(rs.noise)
if(rs.costClass==None):
self.trajCost=CostDDPG(rs)
else:
self.trajCost=rs.costClass(rs)
if(len(self.posIni.shape)==1):
self.posIni=self.posIni.reshape((1,self.posIni.shape[0]))
if estim=="Inv" :
self.stateEstimator = StateEstimator(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim=="Reg":
self.stateEstimator = StateEstimatorRegression(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim == "Hyb" :
self.stateEstimator = StateEstimatorHyb(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
elif estim =="No" :
self.stateEstimator = StateEstimatorNoFeedBack(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
else :
raise TypeError("This Estimator do not exist")
if(saveDir==None):
self.foldername=self.rs.OPTIpath + str(self.sizeOfTarget) + "/"
else :
self.foldername=saveDir
self.costStore = []
self.trajTimeStore = []
self.lastCoord = []
self.sizeOfTarget = sizeOfTarget
self.thetafile = thetafile
self.actor=actor
self.saveName = self.foldername + logDir + "/"
self.nbReset=0
self.cost=0
self.max=0
self.progressTab=[0.1,0.25,0.5,0.75,1.]
self.progress=1
self.reset()
def getActionSize(self):
return self.rs.outputDim
def getStateSize(self):
return self.rs.inputDim
def getActionBounds(self):
return [[1]*self.rs.outputDim, [0]*self.rs.outputDim]
def saveCost(self):
writeArray(self.costStore,self.foldername+"CostCMAES/","traj",".cost")
writeArray(self.trajTimeStore, self.foldername+"TrajTime/","traj",".time")
writeArray(self.lastCoord, self.foldername+"finalX/","x",".last")
def act(self, action):
action=np.array(action[0])
action = action + np.random.normal(0.,0.2,action.shape)
return self.__act__(action)
def __act__(self, action):
if self.rs.det:
realU = muscleFilter(action)
#computation of the arm state
realNextState = self.arm.computeNextState(realU, self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
estimNextState = realNextState
else:
#realU = getNoisyCommand(U,self.arm.getMusclesParameters().getKnoiseU())
realU = getNoisyCommand(action,self.arm.musclesP.knoiseU)
realU = muscleFilter(realU)
#computation of the arm state
realNextState = self.arm.computeNextState(realU, self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
action = muscleFilter(action)
estimNextState = self.stateEstimator.getEstimState(tmpState,action)
#print estimNextState
self.arm.setState(realNextState)
#computation of the cost
#get dotq and q from the state vector
_, q = self.arm.getDotQAndQFromStateVector(tmpState)
self.coordHand = self.arm.mgdEndEffector(q)
costAct = self.trajCost.computeStateTransitionCost(realU, self.coordHand)
#print ("dotq :",dotq)
#computation of the coordinates to check if the target is reach or not
self.i+=1
self.t += self.rs.dt
self.estimState = estimNextState
if(self.coordHand[1] >= self.rs.YTarget or self.i >= self.rs.maxSteps):
costAct += self.trajCost.computeFinalReward(self.arm,self.t, self.coordHand, self.sizeOfTarget)
self.cost+=costAct
return [realU], [costAct]
def actAndStore(self, action):
if self.rs.det:
realU = muscleFilter(action)
#computation of the arm state
realNextState = self.arm.computeNextState(realU, self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
estimNextState = realNextState
else:
#realU = getNoisyCommand(U,self.arm.getMusclesParameters().getKnoiseU())
realU = getNoisyCommand(action,self.arm.musclesP.knoiseU)
realU = muscleFilter(realU)
#computation of the arm state
realNextState = self.arm.computeNextState(realU, self.arm.getState())
#computation of the approximated state
tmpState = self.arm.getState()
action = muscleFilter(action)
estimNextState = self.stateEstimator.getEstimState(tmpState,action)
#print estimNextState
self.arm.setState(realNextState)
#computation of the cost
#get dotq and q from the state vector
_, q = self.arm.getDotQAndQFromStateVector(tmpState)
coordElbow, self.coordHand = self.arm.mgdFull(q)
cost = self.trajCost.computeStateTransitionCost(realU, self.coordHand)
stepStore=[]
stepStore.append(self.vectarget)
stepStore.append(self.estimState)
stepStore.append(tmpState)
stepStore.append(realU)
stepStore.append(action)
stepStore.append(estimNextState)
stepStore.append(realNextState)
stepStore.append([coordElbow[0], coordElbow[1]])
stepStore.append([self.coordHand[0], self.coordHand[1]])
#print ("before",stepStore)
tmpstore = np.array(stepStore).flatten()
row = [item for sub in tmpstore for item in sub]
#print ("store",row)
self.dataStore.append(row)
self.i+=1
self.t += self.rs.dt
self.estimState = estimNextState
return [realU], [cost]
def state(self):
return [self.arm.getState()]
def reset(self, noise=True):
#print("Episode : "+str(self.nbReset)+ ", Progression :"+str(self.progress))
#self.nbReset+=1
#print(self.cost)
#Discrete begining
"""
i = rd.randint(0,len(self.posIni)-1)
#i=0
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(self.posIni[i][0], self.posIni[i][1])
"""
#Progress
"""
i = ((rd.random()*6*np.pi - 3*np.pi)*self.progressTab[self.progress]-6*np.pi)/12
j= (rd.random()*0.3-0.15)*self.progressTab[self.progress]+0.25
#i=0
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(self.rs.XTarget+ j*np.cos(i), self.rs.YTarget+ j*np.sin(i))
"""
"""
i = - 6*np.pi/12
j=rd.randint(1,4)/10.
#j=0.1
q1, q2 = self.arm.mgi(self.rs.XTarget+ j*np.cos(i), self.rs.YTarget+ j*np.sin(i))
"""
x, y = self.begin()
q1, q2 = self.arm.mgi(x,y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1,q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0., 0.
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
def begin(self):
return self.beginDiscret()
def beginOnePoint(self):
return self.x, self.y
def beginArcProgress(self):
i = (rd.random()*6*np.pi - 9*np.pi)/12.
j=rd.randint(1,self.progress)/10.
return self.rs.XTarget+ j*np.cos(i), self.rs.YTarget+ j*np.sin(i)
def beginDiscret(self):
i = rd.randint(0,len(self.posIni)-1)
return self.posIni[i][0], self.posIni[i][1]
def isFinished(self):
if(self.coordHand[1] >= self.rs.YTarget or self.i >= self.rs.maxSteps):
return True
return False
def OneTraj(self, x, y):
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(x, y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1,q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0, 0
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
self.vectarget=[0.0, 0.0, self.rs.XTarget, self.rs.YTarget]
totalCost=0
while(not self.isFinished()):
action=self.actor.action(self.estimState)
_,cost = self.__act__(action)
totalCost+= cost[0]
totalCost += self.trajCost.computeFinalReward(self.arm,self.t,self.coordHand,self.sizeOfTarget)
return totalCost, self.t
def saveOneTraj(self, x, y):
#computes the articular position q1, q2 from the initial coordinates (x, y)
q1, q2 = self.arm.mgi(x, y)
#creates the state vector [dotq1, dotq2, q1, q2]
q = createVector(q1,q2)
state = np.array([0., 0., q1, q2])
#print("start state --------------: ",state)
#computes the coordinates of the hand and the elbow from the position vector
self.coordHand = self.arm.mgdEndEffector(q)
#assert(coordHand[0]==x and coordHand[1]==y), "Erreur de MGD" does not work because of rounding effects
#initializes parameters for the trajectory
self.i, self.t, self.cost = 0, 0, 0
self.stateEstimator.initStore(state)
self.arm.setState(state)
self.estimState = state
self.dataStore=[]
self.vectarget=[0.0, 0.0, self.rs.XTarget, self.rs.YTarget]
totalCost=0
while(not self.isFinished()):
action=self.actor.action(self.estimState)
_,cost = self.actAndStore(action)
totalCost+= cost[0]
totalCost += self.trajCost.computeFinalReward(self.arm,self.t,self.coordHand,self.sizeOfTarget)
filename = findDataFilename(self.saveName+"Log/","traj"+str(x)+"-"+str(y),".log")
np.savetxt(filename,self.dataStore)
#used to ignore dispersion when the target line is not crossed
if self.coordHand[1] >= self.rs.YTarget:
self.lastCoord.append(self.coordHand[0])
return totalCost, self.t
def nTraj(self, (x, y), repeat):
costAll, trajTimeAll = np.zeros(repeat), np.zeros(repeat)
for i in range(repeat):
costAll[i], trajTimeAll[i] = self.OneTraj(x, y)
meanCost = np.mean(costAll)
meanTrajTime = np.mean(trajTimeAll)
return meanCost, meanTrajTime
