def __init__(self, E_nu, nu_dir, e_angle_generator):
self.nuebar = particle(NUEBAR, 0, E_nu, nu_dir)
self.ebar = particle(EBAR, e0_e, 0, np.array([0, 0, 0]))
self.n = particle(NEUTRON, e0_n, 0, np.array([0, 0, 0]))
ROOT.gRandom.SetSeed(0)
self.rng = ROOT.TRandom3()
cos_e_theta = e_angle_generator(E_nu)
self.initialize(cos_e_theta)
def merge(arr, l, m, r):
n1 = m - l + 1
n2 = r - m
# create temp arrays
L = [particle(0, 0, 0, 0, 0, 0)] * (n1)
R = [particle(0, 0, 0, 0, 0, 0)] * (n2)
# Copy data to temp arrays L[] and R[]
for i in xrange(0, n1):
L[i] = arr[l + i]
for j in xrange(0, n2):
R[j] = arr[m + 1 + j]
# Merge the temp arrays back into arr[l..r]
i = 0 # Initial index of first subarray
j = 0 # Initial index of second subarray
k = l # Initial index of merged subarray
while i < n1 and j < n2:
if L[i].wt >= R[j].wt:
arr[k] = L[i]
i += 1
else:
arr[k] = R[j]
j += 1
k += 1
# Copy the remaining elements of L[], if there
# are any
while i < n1:
arr[k] = L[i]
i += 1
k += 1
# Copy the remaining elements of R[], if there
# are any
while j < n2:
arr[k] = R[j]
j += 1
k += 1
def __init__(self, target, pt_number, landmarks, gps_error):
self.target = target
self.landmarks = landmarks
self.pt_number = pt_number
self.gps_error = gps_error
# self.x = 0.0
# self.y = 0.0
self.pts = []
self.residual = []
for i in range(pt_number):
pt = particle(self.target)
pt.set_noise(0.05, 0.05, 2.0)
self.pts.append(pt)
def __init__(self,num,Vmax,iterations,C1,C2,filename):
print "begin Initial parameters"
self.__numParticle=num
self.__Vmax=Vmax
self.__Max_ITERATIONS=iterations
self.__C1=C1
self.__C2=C2
self.__filename=filename
self.__particleNum=[]
self.__gbest=1.0
self.__gbestb=-2.0
for i in xrange(self.__numParticle):
p=particle(0.05*i,0.01*i)
self.__particleNum.append(p)
print "have initial done the num:%s,iterations:%s" %(self.__numParticle,self.__Max_ITERATIONS)
def __init__(self,num,Vmax,iterations,C1,C2,filename):
print "begin Initial parameters"
self.__numParticle=num
self.__Vmax=Vmax
self.__Max_ITERATIONS=iterations
self.__C1=C1
self.__C2=C2
self.__filename=filename
self.__particleNum=[]
self.__gbest=1.0
self.__gbestb=-2.0
for i in xrange(self.__numParticle):
p=particle(0.05*i,0.01*i)
self.__particleNum.append(p)
print "have initial done the num:%s,iterations:%s" %(self.__numParticle,self.__Max_ITERATIONS)
thrd.daemon = True # python will shut down the thread when the code end
thrd.start() # start running the thread
# deciding for manual or A*
tryagain = True
while (tryagain == True):
try:
option = int(input("1 - Manual mode / 2 - A*: "))
except:
continue
# manual mode
if option == 1:
tryagain = False
print("\n\n"+"-"*50+"\n--- Use the numpad to move particle around ---\n"+"-"*50+"\n\n")
p = particle(mp,(mp.start[0][0],mp.start[0][1]))
while(True):
p.update()
nxt = get_key()
val = p.move_particle(nxt)
if nxt == "close" or val == -1:
break
else:
pass
print("End poit reached!\nSelect the maze and press esc to close...\n")
# A*
elif option == 2:
tryagain = False
import particleModelEnsemble as pmEns
def objectBlacklist(obj):
"""
find out which of an ojects attributes cannot be pickled
"""
d = obj.__dict__
black = []
for k in d.keys():
try:
pkl.dumps(d[k])
except:
black+=[k]
return black
PF = plasmaField(fastOn = True)
p = particle()
ep = emittingParticle()
p.inject(PF)
PS = ps.particleSolver(p)
PS.solve()
PE = pmEns.basicParticleEnsemble(pmEns.basicGriddedParticleParameterGenerator, 'pickleTestEns')
PE.generate(n = (2,2))
PE.ensembleSolve()
print 'done!'
L = [ p,ep, PE, PS, gm.gasMixture()]
n = [ 'particle','emitting particle', 'particle ensemble', 'particle solver', 'gas mixture']
print "begin pickling..."
for i in range(len(L)):
try:
def particle_filter(motions, measurements, N=500): # I know it's tempting, but don't change N!
# --------
#
# Make particles
#
#f = open("data.txt", 'w')
p = []
for i in range(N):
r = particle()
r.set_noise(bearing_noise, steering_noise, distance_noise)
p.append(r)
#f.write("%s\t%s\n" %(r.x, r.y))
# --------
#
# Update particles
#
real = particle()
real.set(45,75,pi)
for t in range(len(motions)):
if motions[t] == 0:
real.set(real.x +cos(real.orientation), real.y +sin(real.orientation), real.orientation)
# motion update (prediction)
# moving every particle for every robot motion
p2 = []
#f.write("Next set\n\n")
plot_x = []
plot_y = []
for i in range(N):
p2.append(p[i].move(motions[t]))
plot_x.append(p[i].x)
plot_y.append(p[i].y)
#f.write("%s\t%s\n" %(r.x, r.y))
p = p2
real_x = real.x
real_y = real.y
plt.plot(plot_x, plot_y, '.')
plt.plot(real_x, real_y, 'ro')
plt.plot([10,10],[10,20],'k')
plt.plot([20,20],[0,10],'k')
plt.plot([10,20],[20,20],'k')
plt.plot([0,20],[30,30],'k')
plt.plot([10,10],[40,60],'k')
plt.plot([30,30],[60,70],'k')
plt.plot([0,10],[60,60],'k')
plt.plot([0,10],[70,70],'k')
plt.plot([20,20],[60,70],'k')
plt.plot([30,30],[10,40],'k')
plt.plot([20,30],[40,40],'k')
plt.plot([20,40],[50,50],'k')
plt.plot([20,20],[60,70],'k')
plt.plot([20,50],[70,70],'k')
plt.plot([40,40],[0,10],'k')
plt.plot([40,40],[30,40],'k')
plt.plot([40,40],[50,60],'k')
plt.plot([40,50],[20,20],'k')
plt.plot([50,50],[0,30],'k')
plt.plot([50,50],[50,70],'k')
plt.plot([50,60],[30,30],'k')
plt.plot([50,60],[40,40],'k')
plt.plot([60,60],[0,10],'k')
plt.plot([60,60],[40,50],'k')
plt.plot([60,60],[60,80],'k')
plt.plot([60,70],[10,10],'k')
plt.plot([60,80],[20,20],'k')
plt.plot([60,70],[30,30],'k')
plt.plot([70,80],[40,40],'k')
plt.plot([70,70],[40,50],'k')
plt.plot([70,70],[60,70],'k')
plt.grid(b=1)
plt.axis([0, 80, 0, 80],)
plt.show()
# measurement update
# getting the probability measurement for each particle
w = []
for i in range(N):
w.append(p[i].measurement_prob(measurements[t]))
# resampling (reproducing, sex)
p3 = []
index = int(random.random() * N)
beta = 0.0
mw = max(w)
for i in range(N):
beta += random.random() * 2.0 * mw
while beta > w[index]:
beta -= w[index]
index = (index + 1) % N
p3.append(p[index])
p = p3
return [get_position(p), plot_x, plot_y]
def getParticles(self):
self.particles = []
for p in range(self.tree.Particle_size):
self.particles.append(particle(self.tree, p))
def smearJets(jets):
smearedJets = []
for jet in jets:
smearedJets.append(particle(None, None, jet))
smearedJets.sort(key=lambda jet: jet.pt, reverse=True)
return smearedJets
def particle_filter(motions, measurements, N=500, start_pos = [45, 45, 0]): # I know it's tempting, but don't change N!
# --------
#
# Make particles
#
#f = open("data.txt", 'w')
p = []
for i in range(N):
r = particle()
r.set_noise(bearing_noise, steering_noise, distance_noise)
p.append(r)
#f.write("%s\t%s\n" %(r.x, r.y))
# --------
#
# Update particles
#
#real = particle()
#real.set(start_pos[0], start_pos[1], start_pos[2])
for t in range(len(motions)):
print t
"""
turn_val = 3.25
if motions[t] == 0:
real.set(real.x +cos(real.orientation), real.y +sin(real.orientation), real.orientation)
elif motions[t] == 1:
if real.orientation == 0:
new_x = real.x - turn_val
new_y = real.y - turn_val
elif real.orientation == pi/2:
new_x = real.x + turn_val
new_y = real.y - turn_val
elif real.orientation == pi:
new_x = real.x + turn_val
new_y = real.y + turn_val
elif real.orientation == 3*pi/2:
new_x = real.x - turn_val
new_y = real.y + turn_val
new_o = (real.orientation + (3 * pi / 2)) % (2 * pi)
real.set(new_x, new_y, new_o)
elif motions[t] == 2:
if real.orientation == 0:
new_x = real.x - turn_val
new_y = real.y + turn_val
elif real.orientation == pi/2:
new_x = real.x - turn_val
new_y = real.y - turn_val
elif real.orientation == pi:
new_x = real.x + turn_val
new_y = real.y - turn_val
elif real.orientation == 3*pi/2:
new_x = real.x + turn_val
new_y = real.y + turn_val
new_o = (real.orientation + (pi / 2)) % (2 * pi)
real.set(new_x, new_y, new_o)
elif motions[t] == 3:
if real.orientation == 0:
new_x = real.x - 2 * turn_val
new_y = real.y
elif real.orientation == pi/2:
new_x = real.x
new_y = real.y - 2 * turn_val
elif real.orientation == pi:
new_x = real.x + 2 * turn_val
new_y = real.y
elif real.orientation == 3*pi/2:
new_x = real.x
new_y = real.y + 2 * turn_val
new_o = (real.orientation + (pi)) % (2 * pi)
real.set(new_x, new_y, new_o)
"""
# motion update (prediction)
# moving every particle for every robot motion
p2 = []
#f.write("Next set\n\n")
plot_x = []
plot_y = []
for i in range(N):
p2.append(p[i].move(motions[t]))
plot_x.append(p2[i].x)
plot_y.append(p2[i].y)
#f.write("%s\t%s\n" %(r.x, r.y))
p = p2
#real_x = real.x
#real_y = real.y
## save fig
## Video writer - animations and videos
plt.clf()
plt.plot(plot_x, plot_y, '.')
#plt.plot(real_x, real_y, 'ro')
plt.plot([10,10],[10,20],'k')
plt.plot([20,20],[0,10],'k')
plt.plot([10,20],[20,20],'k')
plt.plot([0,20],[30,30],'k')
plt.plot([10,10],[40,60],'k')
plt.plot([30,30],[60,70],'k')
plt.plot([0,10],[60,60],'k')
plt.plot([0,10],[70,70],'k')
plt.plot([20,20],[60,70],'k')
plt.plot([30,30],[10,40],'k')
plt.plot([20,30],[40,40],'k')
plt.plot([20,40],[50,50],'k')
plt.plot([20,20],[60,70],'k')
plt.plot([20,50],[70,70],'k')
plt.plot([40,40],[0,10],'k')
plt.plot([40,40],[30,40],'k')
plt.plot([40,40],[50,60],'k')
plt.plot([40,50],[20,20],'k')
plt.plot([50,50],[0,30],'k')
plt.plot([50,50],[50,70],'k')
plt.plot([50,60],[30,30],'k')
plt.plot([50,60],[40,40],'k')
plt.plot([60,60],[0,10],'k')
plt.plot([60,60],[40,50],'k')
plt.plot([60,60],[60,80],'k')
plt.plot([60,70],[10,10],'k')
plt.plot([60,80],[20,20],'k')
plt.plot([60,70],[30,30],'k')
plt.plot([70,80],[40,40],'k')
plt.plot([70,70],[40,50],'k')
plt.plot([70,70],[60,70],'k')
plt.grid(b=1)
plt.axis([0, 80, 0, 80],)
filename = str('Testing/Robot_11/Image_%02d' % t) + '.png'
plt.savefig(filename, dpi=100)
print "saving figure", filename
#plt.show()
# measurement update
# getting the probability measurement for each particle
w = []
for i in range(N):
w.append(p[i].measurement_prob(measurements[t]))
# resampling (reproducing, sex)
p3 = []
index = int(random.random() * N)
beta = 0.0
mw = max(w)
for i in range(N):
beta += random.random() * 2.0 * mw
while beta > w[index]:
beta -= w[index]
index = (index + 1) % N
p3.append(p[index])
p = p3
return get_position(p)
