/
mass_estimator.py
173 lines (152 loc) · 5.54 KB
/
mass_estimator.py
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from stellarpop.estimator import Estimator
class MassEstimator(Estimator):
"""
An object used to determine estimates of stellar masses. This inherits
from the base class NestedSampler, although this functionality is not
necessary for simple MCMC chains.
"""
def __init__(self,priors,data,model,constraints=[]):
self.data = data
self.model = model
self.priors = priors
self.names = priors.keys()
self.constraints = constraints
if 'redshift' not in self.names:
self.format = 'old'
else:
self.format = 'new'
def fastMCMC(self,niter,nburn,nthin=1):
from Sampler import SimpleSample as sample
from scipy import interpolate
import pymc,numpy,time
import ndinterp
models = self.model.models
data = self.data
filters = data.keys()
# Remove redshift dict
for key in models.keys():
if type(models[key])==type({}):
z = models[key].keys()[0]
models[key] = models[key][z]
pars = [self.priors[key] for key in self.names]
ax = {}
doExp = []
cube2par = []
i = 0
for key in self.model.axes_names:
ax[key] = i
i += 1
i = 0
for key in self.names:
if key[0]=='X':
continue
if key.find('log')==0:
pntkey = key.split('log')[1]
#self.priors[key].value = numpy.log10(best[ax[pntkey]])
doExp.append(True)
else:
pntkey = key
doExp.append(False)
#self.priors[key].value = best[ax[pntkey]]
cube2par.append(ax[pntkey])
doExp = numpy.array(doExp)==True
par2cube = numpy.argsort(cube2par)
# add stellar mass parameters
pars.append(pymc.Uniform('log_Mlens',9.,12.))
pars.append(pymc.Uniform('log_Msrc',9.,12.))
M = numpy.empty(len(filters))
D = numpy.empty(len(filters))
V = numpy.empty(len(filters))
for i in range(D.size):
f = filters[i]
D[i] = data[f]['mag']
V[i] = data[f]['sigma']**2
@pymc.deterministic
def mass_and_logp(value=0.,pars=pars):
logp = 0
p = numpy.array(pars[:-2])
p[doExp] = 10**p[doExp]
p = numpy.atleast_2d(p[par2cube])
mlens, msrc = pars[-2],pars[-1]
for i in range(M.size):
filt = filters[i]
M[i] = models[filt].eval(p)
if M[i]==0:
return [-1.,-1e300]
if len(D[i])==1:
# sdss magnitude
ml,ms = M[i][0] - 2.5*mlens, M[i][1] - 2.5*Msrc
f = 10**(-0.4*ml) + 10**(-0.4*ms)
f = -2.5*np.log10(f)
logp += -0.5*(f-D[i])**2./V[i]
elif len(D[i]) ==2:
# HST/Keck magnitude
logp += -0.5*(M[i][0] - 2.5*mlens - D[i][0])**2./V[i][0] - 0.5*(M[i][1] - 2.5*msrc - D[i][1])**2./V[i][1]
return logp
@pymc.observed
def loglikelihood(value=0.,lp=mass_and_logp):
return lp
cov = []
for key in self.names:
if key=='age':
cov.append(0.5)
elif key=='logage':
cov.append(0.03)
elif key=='tau':
cov.append(0.1)
elif key=='logtau':
cov.append(0.03)
elif key=='tau_V':
cov.append(self.priors[key]['prior'].value/20.)
elif key=='logtau_V':
cov.append(0.1)
elif key=='tauV':
cov.append(self.priors[key]['prior'].value/20.)
elif key=='logtauV':
cov.append(0.1)
elif key=='Z':
cov.append(self.priors[key]['prior'].value/20.)
elif key=='logZ':
cov.append(0.03)
elif key=='redshift':
P = self.priors['redshift']
if type(P)==type(pymc.Normal('t',0.,1)):
cov.append(P.parents['tau']**-0.5)
elif type(P)==type(pymc.Uniform('t',0.,1.)):
cov.append((P.parents['upper']-P.parents['lower'])/10.)
else:
cov.append(P.parents['cov'])
#cov.append(0.1)
cov += [0.5,0.5] # masses
cov = numpy.array(cov)
costs = self.constraints+[loglikelihood]
from SampleOpt import Sampler,AMAOpt
S = AMAOpt(pars,costs,[mass_and_logp],cov=cov)
S.sample(nburn/4)
S = Sampler(pars,costs,[mass_and_logp])
S.setCov(cov)
S.sample(nburn/4)
S = Sampler(pars,costs,[mass_and_logp])
S.setCov(cov)
S.sample(nburn/2)
logps,trace,dets = S.result()
cov = numpy.cov(trace[nburn/4:].T)
S = AMAOpt(pars,costs,[mass_and_logp],cov=cov/4.)
S.sample(nburn/2)
logps,trace,dets = S.result()
S = Sampler(pars,costs,[mass_and_logp])
S.setCov(cov)
S.sample(nburn/2)
logps,trace,dets = S.result()
cov = numpy.cov(trace[nburn/4:].T)
S = Sampler(pars,costs,[mass_and_logp])
S.setCov(cov)
S.sample(niter)
logps,trace,dets = S.result()
logL = dets['mass_and_logp'].T
o = {'logP':logps,'logL':logL}
cnt = 0
for key in self.names:
o[key] = trace[:,cnt].copy()
cnt += 1
return o