def loadStateCommandPairsByStartCoords(foldername):
'''
Get all the data from a set of trajectories, sorted by the starting xy coordinates
Output : dictionary of data whose keys are y then x coordinates
'''
arm = Arm()
dataOut = {}
# j = 0
for el in os.listdir(foldername):
# j = j+1
# if j>4500 or rd.random()<0.5:
data = np.loadtxt(foldername + el)
coordHand = arm.mgdEndEffector(np.array([[data[0, 10]], [data[0,
11]]]))
x, y = str(coordHand[0][0]), str(coordHand[1][0])
if not y in dataOut.keys():
dataOut[y] = {}
if not x in dataOut[y].keys():
dataOut[y][x] = []
traj = []
for i in range(data.shape[0]):
currentState = (data[i][8], data[i][9], data[i][10], data[i][11],
data[i][12], data[i][13])
noisyActiv = ([
data[i][14], data[i][15], data[i][16], data[i][17],
data[i][18], data[i][19]
])
pair = (currentState, noisyActiv)
traj.append(pair)
dataOut[y][x].append(traj)
return dataOut
def __init__(self, rs, sizeOfTarget, saveTraj, thetaFile):
'''
Initializes the parameters used to run the functions below
Inputs:
-arm, armModel, class object
-rs, readSetup, class object
-sizeOfTarget, size of the target, float
-Ukf, unscented kalman filter, class object
-saveTraj, Boolean: true = Data are saved, false = data are not saved
'''
self.arm = Arm()
self.arm.setDT(rs.dt)
self.controller = initRBFNController(rs,thetaFile)
#load the controller, i.e. the vector of parameters theta
theta = self.controller.loadTheta(thetaFile+".theta")
#put theta to a one dimension numpy array, ie row vector form
#theta = matrixToVector(theta)
self.rs = rs
self.sizeOfTarget = sizeOfTarget
#6 is the dimension of the state for the filter, 4 is the dimension of the observation for the filter, 25 is the delay used
self.stateEstimator = StateEstimator(rs.inputDim,rs.outputDim, rs.delayUKF, self.arm)
self.saveTraj = saveTraj
def UnitTestArmModel():
'''
Tests the next state
'''
rs = ReadSetupFile()
arm = Arm()
arm.setDT(rs.dt)
state, estimState, command, noisycommand, nextEstimState, nextState = {}, {}, {}, {}, {}, {}
for el in os.listdir(BrentTrajectoriesFolder):
state[el], estimState[el], command[el], noisycommand[el], nextEstimState[el], nextState[el] = [], [], [], [], [], []
data = np.loadtxt(BrentTrajectoriesFolder + el)
for i in range(data.shape[0]):
estimState[el].append(np.array([data[i][4], data[i][5], data[i][6], data[i][7]]))
state[el].append(np.array([data[i][8], data[i][9], data[i][10], data[i][11]]))
noisycommand[el].append(np.array([data[i][12], data[i][13], data[i][14], data[i][15], data[i][16], data[i][17]]))
command[el].append(np.array([data[i][18], data[i][19], data[i][20], data[i][21], data[i][22], data[i][23]]))
nextEstimState[el].append(np.array([data[i][24], data[i][25], data[i][26], data[i][27]]))
nextState[el].append(np.array([data[i][28], data[i][29], data[i][30], data[i][31]]))
for el in os.listdir(BrentTrajectoriesFolder):
for i in range(len(state[el])):
if rd.random()<0.06:
outNextStateNoisy = arm.computeNextState(noisycommand[el][i],state[el][i])
outNextState = arm.computeNextState(command[el][i],state[el][i])
print("U :", command[el][i])
print("UNoisy :", noisycommand[el][i])
print("---------------------------------------------------------")
print("Real :", nextState[el][i])
print("ArmN :", outNextStateNoisy)
print("Arm :", outNextState)
print("---------------------------------------------------------")
def plotVelocityProfile(what, foldername="None"):
rs = ReadSetupFile()
arm = Arm()
plt.figure(1, figsize=(16, 9))
if what == "CMAES":
for i in range(4):
ax = plt.subplot2grid((2, 2), (i / 2, i % 2))
name = rs.CMAESpath + str(
rs.sizeOfTarget[i]) + "/" + foldername + "/Log/"
makeVelocityData(rs, arm, name, ax)
ax.set_xlabel("time (s)")
ax.set_ylabel("Instantaneous velocity (m/s)")
ax.set_title(
str("Velocity profiles for target " + str(rs.sizeOfTarget[i])))
else:
if what == "Brent":
name = BrentTrajectoriesFolder
else:
name = rs.RBFNpath + foldername + "/Log/"
makeVelocityData(rs, arm, name, plt)
plt.xlabel("time (s)")
plt.ylabel("Instantaneous velocity (m/s)")
plt.title("Velocity profiles for " + what)
plt.savefig("ImageBank/" + what + '_velocity_profiles' + foldername +
'.png',
bbox_inches='tight')
plt.show(block=True)
def getInitPos(foldername):
'''
Get all the initial positions from a set of trajectories, in xy coordinates
Output : dictionary of initial position of all trajectories
'''
arm = Arm()
xy = {}
for el in os.listdir(foldername):
data = np.loadtxt(foldername + el)
coordHand = arm.mgdEndEffector(np.array([[data[0, 10]], [data[0,
11]]]))
#if coordHand[1]<0.58:
xy[el] = (coordHand[0], coordHand[1])
return xy
def getXYEstimError(foldername):
'''
Returns the error estimations in the trajectories from the given foldername
Outputs: -errors: dictionary keys = filenames, values = array of data
'''
arm = Arm()
errors = {}
for el in os.listdir(foldername):
errors[el] = []
data = np.loadtxt(foldername + el)
for i in range(data.shape[0]):
statePos = (data[i][10], data[i][11])
estimStatePos = (data[i][6], data[i][7])
errors[el].append(arm.estimErrorReduced(statePos, estimStatePos))
return errors
def getEstimatedXYHandData(foldername):
'''
Put all the states of trajectories generated by the Brent controller into a dictionary
Outputs: -state: dictionary keys = filenames, values = array of data
'''
arm = Arm()
xyEstim = {}
for el in os.listdir(foldername):
xyEstim[el] = []
data = np.loadtxt(foldername + el)
for i in range(data.shape[0]):
coordHand = arm.mgdEndEffector(
np.array([[data[i][6]], [data[i][7]]]))
xyEstim[el].append((coordHand[0], coordHand[1]))
return xyEstim
def getXYElbowData(foldername):
'''
Put all the states of trajectories generated by the Brent controller into a dictionary
Outputs: -state: dictionary keys = filenames, values = array of data
'''
arm = Arm()
xy = {}
for el in os.listdir(foldername):
xy[el] = []
data = np.loadtxt(foldername + el)
for i in range(data.shape[0]):
coordElbow, coordHand = arm.mgdFull(
np.array([[data[i][10]], [data[i][11]]]))
xy[el].append((coordElbow[0], coordElbow[1]))
return xy
def getXYEstimErrorOfSpeed(foldername):
'''
Returns the error estimations in the trajectories as a function of speed from the given foldername
Outputs: -state: dictionary keys = filenames, values = array of data
'''
arm = Arm()
errors = {}
for el in os.listdir(foldername):
errors[el] = []
data = np.loadtxt(foldername + el)
for i in range(data.shape[0]):
speed = arm.cartesianSpeed(
(data[i][8], data[i][9], data[i][10], data[i][11]))
statePos = (data[i][10], data[i][11])
estimStatePos = (data[i][6], data[i][7])
error = arm.estimErrorReduced(statePos, estimStatePos)
errors[el].append((speed, error))
return errors
def plotManipulability2():
rs = ReadSetupFile()
fig = plt.figure(1, figsize=(16, 9))
arm = Arm()
q1 = np.linspace(-0.6, 2.6, 100, True)
q2 = np.linspace(-0.2, 3, 100, True)
target = [rs.XTarget, rs.YTarget]
pos = []
for i in range(len(q1)):
for j in range(len(q2)):
config = np.array([q1[i], q2[j]])
coordHa = arm.mgdEndEffector(config)
pos.append(coordHa)
x, y, cost = [], [], []
for el in pos:
x.append(el[0])
y.append(el[1])
config = arm.mgi(el[0], el[1])
manip = arm.manipulability(config, target)
cost.append(manip)
xi = np.linspace(-0.7, 0.8, 100)
yi = np.linspace(-0.5, 0.8, 100)
zi = griddata(x, y, cost, xi, yi)
#t1 = plt.scatter(x, y, c=cost, marker=u'o', s=5, cmap=cm.get_cmap('RdYlBu'))
#CS = plt.contourf(xi, xi, zi, 15, cmap=cm.get_cmap('RdYlBu'))
t1 = plt.scatter(x, y, c=cost, s=5, cmap=cm.get_cmap('RdYlBu'))
CS = plt.contourf(xi, yi, zi, 15, cmap=cm.get_cmap('RdYlBu'))
fig.colorbar(t1, shrink=0.5, aspect=5)
plt.scatter(rs.XTarget, rs.YTarget, c="g", marker=u'*', s=200)
#plt.plot([-0.3,0.3], [rs.YTarget, rs.YTarget], c = 'g')
plt.xlabel("X (m)")
plt.ylabel("Y (m)")
plt.title(str("Manipulability map"))
plt.savefig("ImageBank/manipulability2.png", bbox_inches='tight')
plt.show(block=True)
def plotExperimentSetup():
rs = ReadSetupFile()
fig = plt.figure(1, figsize=(16, 9))
arm = Arm()
q1 = np.linspace(-0.6, 2.6, 100, True)
q2 = np.linspace(-0.2, 3, 100, True)
posIni = np.loadtxt(pathDataFolder + rs.experimentFilePosIni)
xi, yi = [], []
xb, yb = [0], [0]
t = 0
for el in posIni:
if el[1] == np.min(posIni, axis=0)[1] and t == 0:
t += 1
a, b = arm.mgi(el[0], el[1])
a1, b1 = arm.mgdFull(np.array([[a], [b]]))
xb.append(a1[0])
xb.append(b1[0])
yb.append(a1[1])
yb.append(b1[1])
xi.append(el[0])
yi.append(el[1])
pos = []
for i in range(len(q1)):
for j in range(len(q2)):
coordHa = arm.mgdEndEffector(np.array([[q1[i]], [q2[j]]]))
pos.append(coordHa)
x, y = [], []
for el in pos:
x.append(el[0])
y.append(el[1])
plt.scatter(x, y)
plt.scatter(xi, yi, c='r')
plt.scatter(0, 0.6175, c="r", marker=u'*', s=200)
plt.plot(xb, yb, c='r')
plt.plot([-0.3, 0.3], [0.6175, 0.6175], c='g')
plt.savefig("ImageBank/setup.png", bbox_inches='tight')
plt.show(block=True)
