def ellipsoid_points_random(a=1, b=1, c=1, n=1000):
pts = np.zeros((n, 3))
i = 0
CC = a * b * c * 1000
while i < n:
theta = arccos(urand(-1, 1))
phi = urand(0, 2 * np.pi)
y = urand(0, 1)
r = R(a, b, c, theta, phi)
pp = p(a, b, c, theta, phi)
if CC * y <= pp:
pts[i, :] = [
r * sin(theta) * np.cos(phi), r * sin(theta) * sin(phi),
r * cos(theta)
]
i += 1
# print('%.f/%.f'%(i,n))
x = np.column_stack(pts)
tris = ch(x.T).simplices
tris, normals, centroids = surface_normals(x.T, tris)
return x.T, tris, normals, centroids
def tournament_selection(self, k=0.75):
pOne, pTwo = rchoice(self.population), rchoice(self.population)
# modified to avoid using __cmp__ method of chromosome
if urand() < k:
return pOne if pOne > pTwo else pTwo
else:
return pOne if pOne < pTwo else pTwo
def pareto_tournament_selection(self, k=0.75):
'''Psuedo-Pareto tournament selection for multi-objective offspring. Offspring that are
better in a majority (2/3) of the fitness terms win the tournaments.'''
pOne, pTwo = rchoice(self.population), rchoice(self.population)
f1 = MATH.array([pOne.fitness,pOne.parsimony,pOne.finitewts])
f2 = MATH.array([pTwo.fitness,pTwo.parsimony,pTwo.finitewts])
if urand() < k:
return pOne if MATH.sign((f1-f2)).sum() > 0 else pTwo
else:
return pOne if MATH.sign((f1-f2)).sum() < 0 else pTwo
def mate(self, parentOne, parentTwo):
"""Accepts two parents and returns ONE offspring; if the offspring is unchanged from one of
the parents, the fitness values are not re-evaluated, just copied. Only one category of
mutation is performed on a single offspring."""
# one parent will form the basis for the new offspring; the other is just there for
# potential crossover (by default the mother will become the offspring)
mother, father = parentOne.copy(), parentTwo.copy()
# fitEval will be set to True if any mutations occur that make the offspring different from
# its copied parent; this way we avoid unnecessary fitness evaluations
fitEval = False
# only one category of mutation is allowed per mating event
r = urand()
if r < self.pC: # parental crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
fNode = rchoice(father.tree.getNodes())
CGAGenerator.single_crossover(mNode,fNode)
elif r < self.pC + self.pHC: # headless chicken crossover
fitEval = True
mNode = rchoice(mother.tree.getNodes())
rNode = rchoice(self.initialize_tree().getNodes())
CGAGenerator.single_crossover(mNode,rNode)
elif r < self.pC + self.pHC + self.pM: # point mutation (uses pM/node for mutation prob.)
fitEval = True
for n in mother.tree.getNodes():
if urand() < self.pM:
CGAGenerator.point_mutate(mother.tree, n)
fitEval = True
elif r < self.pC + self.pHC + self.pM + self.pP: # pruning - guaranteed to do one pruning operation
fitEval = True
mNode = rchoice(mother.tree.getNodes())
CGAGenerator.prune(mother.tree,mNode)
elif r < self.pC + self.pHC + self.pM + self.pP + self.pG: # growth - guaranteed to do one growth op.
fitEval = True
mTerm = rchoice(mother.tree.getTermini())
CGAGenerator.grow(mother.tree,mTerm)
else: # offspring will just be a copy
pass
if fitEval:
mother.fitness,mother.parsimony,mother.finitewts = self.evaluate_fitness(mother.tree)
return mother
def _get_ngmix_obs(self, gal_img, psf_img, psf_pars=[0.0, 0.0, -0.01, 0.01, 0.15, 1.0], fit_psf=True):
"""
"""
eps=0.01
lm_pars={'maxfev': 2000,
'xtol': 5.0e-5,
'ftol': 5.0e-5}
img_shape = gal_img.shape
# Jacobian
gal_jacob=ngmix.DiagonalJacobian(scale=self._pixel_scale, x=int((img_shape[0]-1)/2.), y=int((img_shape[1]-1)/2.))
psf_jacob=ngmix.DiagonalJacobian(scale=self._pixel_scale, x=int((img_shape[0]-1)/2.), y=int((img_shape[1]-1)/2.))
# PSF fittitng
psf_noise = np.sqrt(np.sum(psf_img**2)) / 500
psf_weight = np.ones_like(psf_img) / psf_noise**2
psf_obs=ngmix.Observation(psf_img, weight=psf_weight, jacobian=psf_jacob)
if fit_psf:
pfitter=ngmix.fitting.LMSimple(psf_obs,'gauss',lm_pars=lm_pars)
guess=np.array(psf_pars)
guess[0] += urand(low=-eps,high=eps)
guess[1] += urand(low=-eps,high=eps)
guess[2] += urand(low=-eps, high=eps)
guess[3] += urand(low=-eps, high=eps)
guess[4] *= (1.0 + urand(low=-eps, high=eps))
guess[5] *= (1.0 + urand(low=-eps, high=eps))
pfitter.go(guess)
# print(np.abs((pfitter.get_result()['g']-np.array([-0.01, 0.01]))/np.array([-0.01, 0.01])))
psf_gmix_fit = pfitter.get_gmix()
psf_obs.set_gmix(psf_gmix_fit)
# Gal fitting
# Get noise level direclty from the image
sigma_noise = self.mad(gal_img)
noise = np.random.normal(size=img_shape) * sigma_noise
weight = np.ones_like(gal_img) * 1/sigma_noise**2.
obs = ngmix.Observation(gal_img, weight=weight, noise=noise, jacobian=gal_jacob, psf=psf_obs)
return obs
def Tau_scatter():
r = urand(0., 1.)
tau_sca = -np.log(r)
return tau_sca
def Tau_ffs(tau_path):
r = urand(0., 1.)
tau_sca = -np.log(1. - (r) * (1. - np.exp(-tau_path)))
return tau_sca
def HG_scatter(g):
r = (1. - urand(low=(0.), high=(1.)))
return 1.0 / (2.0 * g) * ((1.0 + g**2.) - ((1 - g**2.) /
(1 - g + 2 * g * r))**2.)
observer[1] = 0.0
observer[2] = scipy.cos(obs_incl)
Errorcount = 0
No_interact = 0
#run loop until wanted amount of photon packets simulated
while (packet_count < n_packet):
#set origin of path back at star
O[:] = Star[:]
#set packet weight
W_packet = 1.0
#generate packet direction
phot_phi = 2. * np.pi * urand(low=0., high=1.)
phot_theta = urand(low=cos_theta_min, high=cos_theta_max)
direction[0] = np.sqrt(1. - phot_theta**2.) * scipy.cos(phot_phi)
direction[1] = np.sqrt(1. - phot_theta**2.) * scipy.sin(phot_phi)
direction[2] = phot_theta
#if emitter is exactly on same z-coord as vertex, see if beam goes
#to cells above or below, in case of purely radial beam flip coin,
#raytrace detects edge intersects
ind_cell[:] = 0
for i in range(0, n_z + 1):
if (O[2] == grid_vertex[0, i, 0, 2]):
if (phot_theta > 0.0):
ind_cell[1] = i
