def test_AdvectivePropagation(self):
ConstMagVec = crpropa.Vector3d(0.)
BField = crpropa.UniformMagneticField(ConstMagVec)
ConstAdvVec = crpropa.Vector3d(0., 1e6, 0.)
AdvField = crpropa.UniformAdvectionField(ConstAdvVec)
precision = 1e-4
minStep = 1*pc
maxStep = 10*kpc
epsilon = 0.
Dif = crpropa.DiffusionSDE(BField, AdvField, precision, minStep, maxStep, epsilon)
c = crpropa.Candidate()
c.current.setId(crpropa.nucleusId(1,1))
c.current.setEnergy(10*TeV)
c.current.setDirection(crpropa.Vector3d(1,0,0))
# check for position
Dif.process(c)
pos = c.current.getPosition()
self.assertEqual(pos.x, 0.)
self.assertAlmostEqual(pos.y, minStep/c_light*1e6)
self.assertEqual(pos.z, 0.)
# Step size is increased to maxStep
self.assertAlmostEqual(c.getNextStep()/minStep, 5.) #AlmostEqual due to rounding error
def test_NeutralPropagation(self):
c = crpropa.Candidate()
c.current.setId(crpropa.nucleusId(1,0))
c.current.setEnergy(10*TeV)
c.current.setDirection(crpropa.Vector3d(1,0,0))
# check for position
self.Dif.process(c)
pos = c.current.getPosition()
self.assertAlmostEqual(pos.x/pc, 1.) #AlmostEqual due to rounding error
self.assertEqual(pos.y, 0.)
self.assertEqual(pos.z, 0.)
# Step size is increased to maxStep
self.assertAlmostEqual(c.getNextStep()/self.maxStep, 1.) #AlmostEqual due to rounding error
def initial_directions(M, source_l, source_b, weights, d, NN, energies=None):
lons0 = []
lats0 = []
for isource in range(len(source_l)):
l = source_l[isource] * np.pi / 180.0 - (2.0 * np.pi)
b = Lat2CoLat(source_b[isource] * np.pi / 180.0)
#M.addParticles(Id, E, l, b, E0**-1)
print(isource, weights[isource], l, b)
meanDir = crpropa.Vector3d(-1, 0, 0)
meanDir.setRThetaPhi(-1, b, l)
kappa = 50.0
ncrs = int((weights[isource] / total) * NN)
#if ncrs>0: ncrs = 500
#Emin = 10.0
energies = powerlaw.Power_Law(xmin=10.0, xmax=100.0,
parameters=[2.2]).generate_random(ncrs)
#print (energies)
for i in xrange(ncrs):
particleId = crpropa.nucleusId(1, 1)
energy = (energies[i]) * crpropa.EeV
energy = 10.0 * crpropa.EeV
galCenter = crpropa.Vector3d(-1, 0, 0)
theta_k = 0.8 * (100.0 * crpropa.EeV / energy) * np.sqrt(
d[isource] / 10.0)
kappa = (81.0 / theta_k)**2
#print (kappa)
momentumVector = crpropa.Random.instance().randFisherVector(
meanDir, kappa)
M.addParticle(particleId, energy, momentumVector)
lons0.append(momentumVector.getPhi())
lats0.append(momentumVector.getTheta())
lons0 = np.array(lons0) - np.pi
lats0 = -CoLat2Lat(np.array(lats0))
return (lons0, lats0)
def test_DiffusionEnergy100PeV(self):
x, y, z = [], [], []
E = 10 * PeV
D = self.Dif.getScale()*6.1e24*(E/(4*GeV))**self.Dif.getAlpha()
L_max = 50 * kpc
std_exp = np.sqrt(2*D*L_max/c_light)
mean_exp = 0.
N = 10**4
maxTra = crpropa.MaximumTrajectoryLength(L_max)
for i in range(N):
c = crpropa.Candidate()
c.current.setId(crpropa.nucleusId(1,1))
c.current.setEnergy(E)
while c.getTrajectoryLength() < L_max:
maxTra.process(c)
self.Dif.process(c)
x.append(c.current.getPosition().x)
y.append(c.current.getPosition().y)
z.append(c.current.getPosition().z)
A2 = Anderson(z)['testStatistic']
self.assertLess(A2, 2.274, msg=self.msg1)
meanX, meanY, meanZ = np.mean(x), np.mean(y), np.mean(z)
stdZ = np.std(z)
# no diffusion in perpendicular direction
self.assertAlmostEqual(meanX, 0.)
self.assertAlmostEqual(meanY, 0.)
# diffusion in parallel direction
# compare the mean and std of the z-positions with expected values
# z_mean = 0. (no advection)
# z_ std = (2*D_parallel*t)^0.5
# Take 4 sigma errors due to limited number of candidates into account
stdOfMeans = std_exp/np.sqrt(N)
stdOfStds = std_exp/np.sqrt(N)/np.sqrt(2.)
self.assertLess(abs((meanZ-mean_exp)/4./stdOfMeans), 1., msg=self.msg1)
self.assertLess(abs((stdZ-std_exp)/4./stdOfStds), 1., msg=self.msg1)
# You could also phrase: vertical incident angles are more frequent due to the
# larger visible size of the area of the surface element than flat angles.
# In[2]:
get_ipython().magic(u'matplotlib inline')
import matplotlib.pyplot as plt
# source setup
source = crpropa.Source()
# inward=True for inwards directed emission and False for outwards directed emission
center, radius, inward = crpropa.Vector3d(
0, 0, 0) * crpropa.kpc, 20 * crpropa.kpc, True
source.add(crpropa.SourceLambertDistributionOnSphere(center, radius, inward))
source.add(crpropa.SourceParticleType(-crpropa.nucleusId(1, 1)))
source.add(crpropa.SourceEnergy(100 * crpropa.EeV))
sim.run(source, n)
print("Number of hits: %i" % len(output))
lons = []
for c in output:
v = c.current.getDirection()
lons.append(v.getPhi())
plt.hist(np.array(lons), bins=30, color='k', histtype='step')
plt.xlabel('lon', fontsize=20)
plt.ylabel('counts', fontsize=20)
plt.savefig('lon_distribution_lamberts.png', bbox_inches='tight')
plt.close()
crdata = np.genfromtxt("crpropa_output.txt")
id = crdata[:, 0]
E = crdata[:, 3] * crpropa.EeV
E0 = crdata[:, 4] * crpropa.EeV
px = crdata[:, 11]
py = crdata[:, 12]
pz = crdata[:, 13]
galactic_longitude = np.arctan2(-1. * py, -1. * px)
galactic_latitude = np.pi / 2 - np.arccos(-pz / (px * px + py * py + pz * pz))
M.addParticles(id, E, galactic_longitude, galactic_latitude, E0**-1)
# Alternatively, data can be added manually.
# This provides freedom to adapt to customized weights used in the simulation
for i in xrange(1000):
particleId = crpropa.nucleusId(1, 1)
energy = 10 * crpropa.EeV
galCenter = crpropa.Vector3d(-1, 0, 0)
momentumVector = crpropa.Random.instance().randFisherVector(galCenter, 200)
M.addParticle(particleId, energy, momentumVector)
# ## Plot Maps
#
# The probability maps for the individual particles can be accessed directly and plotted with healpy.
#
# In[2]:
get_ipython().magic('matplotlib inline')
import healpy
import sys import healpy as hp a1, b1 = (12.6 - 18.25) / 2.75, (19.5 - 18.25) / 2.75 a2, b2 = (0. - 0.2) / 0.12, (1 - 0.2) / 0.12 a3, b3 = (0 - 10.97) / 3.80, (25. - 10.97) / 3.8 a4, b4 = (0 - 2.84) / 1.3, (8 - 2.84) / 1.3 E = float(sys.argv[1]) lon = float(sys.argv[2]) lat = float(sys.argv[3]) s1 = int(sys.argv[4]) s2 = int(sys.argv[5]) s3 = int(sys.argv[6]) pid = -crp.nucleusId(1, 1) sun = crp.Vector3d(-8.5, 0, 0) * crp.kpc E = E * crp.EeV nhat = hp.dir2vec(lon, lat, lonlat=True) direc = crp.Vector3d() direc.setXYZ(nhat[0], nhat[1], nhat[2]) ps = crp.ParticleState(pid, E, sun, direc) cr = crp.Candidate(ps) sim = crp.ModuleList() sim.add(crp.Redshift()) sim.add(crp.PhotoPionProduction(crp.CMB)) sim.add(crp.PhotoPionProduction(crp.IRB)) sim.add(crp.PhotoDisintegration(crp.CMB)) sim.add(crp.PhotoDisintegration(crp.IRB))
Id = [int(x) for x in crdata[:,0]]
E = crdata[:,3] * crpropa.EeV
E0 = crdata[:,4] * crpropa.EeV
px = crdata[:,11]
py = crdata[:,12]
pz = crdata[:,13]
galactic_longitude = np.arctan2(-1. * py, -1. *px)
galactic_latitude = np.pi / 2 - np.arccos( -pz / (px*px + py*py+ pz*pz) )
M.addParticles(Id, E, galactic_longitude, galactic_latitude, E0**-1)
# Alternatively, data can be added manually.
# This provides freedom to adapt to customized weights used in the simulation
for i in xrange(1000):
particleId = crpropa.nucleusId(1,1)
energy = 10 * crpropa.EeV
galCenter = crpropa.Vector3d(-1,0,0)
momentumVector = crpropa.Random.instance().randFisherVector(galCenter, 200)
M.addParticle(particleId, energy, momentumVector)
# ## Plot Maps
#
# The probability maps for the individual particles can be accessed directly and plotted with healpy.
#
# In[3]:
get_ipython().magic(u'matplotlib inline')
# crpropa backtracking
# set up GMF
B = crp.JF12Field()
B.randomStriated(seed)
B.randomTurbulent(seed)
# set up simulation
sim = crp.ModuleList()
sim.add(crp.PropagationCK(B, 1e-3, 0.1 * crp.parsec, 100 * crp.parsec))
obs = crp.Observer()
obs.add(crp.ObserverLargeSphere(crp.Vector3d(0), gal_size * crp.kpc))
sim.add(obs)
position = crp.Vector3d(-8.5, 0, 0) * crp.kpc # start at the earth
pid = crp.nucleusId(1, 1) # proton
pid *= -1 # antiparticle, for backtracking
if read_in_galaxy_map:
galaxy_map = np.genfromtxt("maps/galaxy_map.txt")
else:
galaxy_map = np.zeros(hp.nside2npix(nside))
for i in xrange(len(events)):
if i % 10 == 0: print i, len(events)
event = events[i]
if event["is_Auger"]:
mean_energy = event["E"] * crp.EeV
omega = CR.omega_one_exp(event["dec"], True, ff)
sigma = Auger_angular_sigma
else:
mean_energy = event["E"] * crp.EeV / E_scale
def test_FullTransport(self):
x, y, z = [], [], []
E = 10 * TeV
D = self.DifAdv.getScale()*6.1e24*(E/(4*GeV))**self.DifAdv.getAlpha()
L_max = 50 * kpc
epsilon = 0.1
advSpeed = 1e6
std_exp_x = np.sqrt(2*epsilon*D*L_max/c_light)
std_exp_y = np.sqrt(2*epsilon*D*L_max/c_light)
std_exp_z = np.sqrt(2*D*L_max/c_light)
mean_exp_x = advSpeed * L_max / c_light
mean_exp_y = 0.
mean_exp_z = 0.
N = 10**4
maxTra = crpropa.MaximumTrajectoryLength(L_max)
ConstMagVec = crpropa.Vector3d(0*nG,0*nG,1*nG)
BField = crpropa.UniformMagneticField(ConstMagVec)
ConstAdvVec = crpropa.Vector3d(advSpeed, 0., 0.)
AdvField = crpropa.UniformAdvectionField(ConstAdvVec)
precision = 1e-4
minStep = 1*pc
maxStep = 10*kpc
DifAdv = crpropa.DiffusionSDE(BField, AdvField, precision, minStep, maxStep, epsilon)
for i in range(N):
c = crpropa.Candidate()
c.current.setId(crpropa.nucleusId(1,1))
c.current.setEnergy(E)
while c.getTrajectoryLength() < L_max:
maxTra.process(c)
DifAdv.process(c)
x.append(c.current.getPosition().x)
y.append(c.current.getPosition().y)
z.append(c.current.getPosition().z)
# test for normality
A2 = Anderson(x)['testStatistic']
self.assertLess(A2, 2.274, msg=self.msg1)
A2 = Anderson(y)['testStatistic']
self.assertLess(A2, 2.274, msg=self.msg1)
A2 = Anderson(z)['testStatistic']
self.assertLess(A2, 2.274, msg=self.msg1)
meanX, meanY, meanZ = np.mean(x), np.mean(y), np.mean(z)
stdX, stdY, stdZ = np.std(x), np.std(y), np.std(z)
# diffusion in parallel direction
# compare the mean and std of the z-positions with expected values
# z_mean = 0. (no advection)
# z_ std = (2*D_parallel*t)^0.5
# Take 4 sigma errors due to limited number of candidates into account
stdOfMeans_x = std_exp_x/np.sqrt(N)
stdOfStds_x = std_exp_x/np.sqrt(N)/np.sqrt(2.)
self.assertLess(abs((meanX-mean_exp_x)/4./stdOfMeans_x), 1., msg=self.msg1)
self.assertLess(abs((stdX-std_exp_x)/4./stdOfStds_x), 1., msg=self.msg1)
stdOfMeans_y = std_exp_y/np.sqrt(N)
stdOfStds_y = std_exp_y/np.sqrt(N)/np.sqrt(2.)
self.assertLess(abs((meanY-mean_exp_y)/4./stdOfMeans_y), 1., msg=self.msg1)
self.assertLess(abs((stdY-std_exp_y)/4./stdOfStds_y), 1., msg=self.msg1)
stdOfMeans_z = std_exp_z/np.sqrt(N)
stdOfStds_z = std_exp_z/np.sqrt(N)/np.sqrt(2.)
self.assertLess(abs((meanZ-mean_exp_z)/4./stdOfMeans_z), 1., msg=self.msg1)
self.assertLess(abs((stdZ-std_exp_z)/4./stdOfStds_z), 1., msg=self.msg1)
