def runToy2D():
np.random.seed(0)
random.seed(0)
for i in range(20):
print ' '*random.randint(0,70) + '*'
# priors:
aP = prior.uniform(-5,5) # will have 2D gaussian toy likelihood
bP = prior.uniform(-5,5)
g.priors = [aP,bP]
# parameters ('name', x_init, isFrozen):
a = particle.param('a',aP.sample(),False)
b = particle.param('b',bP.sample(),False)
g.initParams = [a,b]
for i,p in enumerate(g.initParams): # important!
p.setPrior(i)
# which indexes are thawed:
for i,p in enumerate(g.initParams):
if(not p.isFrozen):
g.thawedIdxs.append(i)
# choose likelihood:
g.likelihood = likelihood.toyGauss()
# mass vector for parameters:
g.masses = np.ones(len(g.initParams))
# create the initial particle set:
particles = []
for i in range(g.nParticles):
particles.append(particle.particle(g.initParams))
particles[i].assignPriorSample()
# set the inference running:
fname = '/Users/jmrv/Documents/school/mit/research/software/nest/samples/toy2d.txt'
nested.sample(particles,fname)
pos = posterior.posterior(fname)
plotname = '/Users/jmrv/Documents/school/mit/research/software/nest/plots/toy2d.pdf'
pos.plotMarginals(plotname)
def runToy1D():
np.random.seed(1)
random.seed(1)
for i in range(20):
print ' '*random.randint(0,70) + '*'
# priors:
aP = prior.uniform(-5,5) # single parameter. will have gaussian toy likelihood
g.priors = [aP]
# parameters ('name', x_init, isFrozen):
a = particle.param('a',aP.sample(),False)
g.initParams = [a]
for i,p in enumerate(g.initParams): # important!
p.setPrior(i)
# which indexes are thawed:
for i,p in enumerate(g.initParams):
if(not p.isFrozen):
g.thawedIdxs.append(i)
# choose likelihood:
g.likelihood = likelihood.toy1D()
# mass vector for parameters:
g.masses = np.ones(len(g.initParams))*0.05
# create the initial particle set:
particles = []
for i in range(g.nParticles):
particles.append(particle.particle(g.initParams,g.initStep))
particles[i].assignPriorSample()
# set the inference running:
nested.sample(particles)
def runSc():
np.random.seed(0)
random.seed(0)
for i in range(20):
print ' '*random.randint(0,70) + '*'
# priors:
normP = prior.uniform(1e-2,1,isLog=True) # powerlaw norm
alphaP = prior.uniform(2,4) # powerlaw power
nHP = prior.uniform(1.0,3.0) # nH (absorption)
scAreaP = prior.uniform(1e-6, 1e-3, isLog=True) # Sc line area
area1P = prior.uniform(1e-6, 1e-3, isLog=True) # nuisance line 1
center1P = prior.uniform(3.5, 3.7) #
sigma1P = prior.uniform(0.001, 0.010) # natural width
area2P = prior.uniform(1e-5, 1e-4, isLog=True) # nuisance line 2
center2P = prior.uniform(3.75, 4.0) #
sigma2P = prior.uniform(0.001, 0.010) #
g.priors = [ normP, alphaP, \
nHP, \
scAreaP, \
#area1P,center1P,sigma1P, \
area2P,center2P,sigma2P ]
# parameters ('name', x_init, isFrozen):
norm = particle.param('norm',5e-2,False)
alpha = particle.param('alpha',2.9,False)
nH = particle.param('nH',2.0,True)
scarea = particle.param('Sc area',1e-5,False)
area1 = particle.param('area1',1e-5,False)
center1 = particle.param('center1',3.6,False)
sigma1 = particle.param('sigma1',0.005,False)
area2 = particle.param('area2',1.8e-5,False)
center2 = particle.param('center2',3.87,False)
sigma2 = particle.param('sigma2',0.005,False)
g.initParams = [ norm,alpha, \
nH, \
scarea, \
#area1,center1,sigma1, \
area2,center2,sigma2]
for i,p in enumerate(g.initParams):
p.setPrior(i)
# which indexes are thawed:
for i,p in enumerate(g.initParams):
if(not p.isFrozen):
g.thawedIdxs.append(i)
# mass vector for parameters:
g.masses = np.ones(len(g.initParams))*0.1
# data and likelihood:
g.likelihood = likelihood.ScLike( g.datadir+'column.warf',
g.datadir+'column.wrmf',
g.datadir+'column.pi',
g.modeldir+'phabs1e22.txt',
[3.4,5])
# create the initial particle set:
particles = []
for i in range(g.nParticles):
particles.append(particle.particle(g.initParams))
particles[i].assignPriorSample()
# set the inference running:
fname = g.sampleDir+'samples.txt'
nested.sample(particles,fname)
pos = posterior.posterior(fname)
plotname = g.plotDir+'sctest.pdf'
pos.plotMarginals(plotname)
