def optimize(data, niter, oname=None, first=True):
import pymc, pyfits, numpy
import indexTricks as iT
priors = data['PRIORS']
models = data['MODELS']
pars = data['PARAMS']
image = {}
for key in data['IMG'].keys():
image[key] = data['IMG'][key].copy()
ZP = data['ZP']
filters = [filt for filt in data['FILTERS']]
sigmas = data['SIGMA']
if 'GAIN' in data.keys():
gain = data['GAIN']
doSigma = True
else:
doSigma = False
if 'OVRS' in data.keys():
OVRS = data['OVRS']
else:
OVRS = 1
MASK = data['MASK'].copy()
mask = MASK == 0
mask_r = mask.ravel()
key2index = {}
i = 0
for key in filters:
key2index[key] = i
i += 1
model2index = {}
i = 0
for key in filters:
for model in models[key]:
model2index[model.name] = i
i += 1
imshape = MASK.shape
yc, xc = iT.overSample(imshape, OVRS)
if doSigma == True:
nu = {}
eta = {}
background = {}
counts = {}
sigmask = {}
for key in filters:
nu[key] = pymc.Uniform('nu_%s' % key,
-6,
6,
value=log10(gain[key]))
eta[key] = pymc.Uniform('eta_%s' % key, -4, 5, value=1.)
background[key] = sigmas[key]
sigmask[key] = image[key] > 1.5 * sigmas[key]**0.5
counts[key] = image[key][sigmask[key]].copy()
pars.append(nu[key])
pars.append(eta[key])
def getSigma(n=nu, e=eta, b=background, c=counts, m=mask):
sigma = b.copy()
sigma[m] += ((10**n) * c)**e
return numpy.sqrt(sigma).ravel()
sigmas = []
for key in filters:
parents = {
'n': nu[key],
'e': eta[key],
'b': background[key],
'c': counts[key],
'm': sigmask[key]
}
sigmas.append(
pymc.Deterministic(eval=getSigma,
name='sigma_%s' % key,
parents=parents,
doc='',
trace=False,
verbose=False))
else:
for key in filters:
sigmas[key] = sigmas[key].ravel()
for key in filters:
image[key] = image[key].ravel()
@pymc.deterministic(trace=False)
def logpAndMags(p=pars):
lp = 0.
mags = []
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
lp += linearmodelSB(p,
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
OVRS=OVRS)
mags += [model.Mag(ZP[key]) for model in models[key]]
return lp, mags
@pymc.deterministic
def lp(lpAM=logpAndMags):
return lpAM[0]
@pymc.deterministic
def Mags(lpAM=logpAndMags):
return lpAM[1]
@pymc.observed
def logpCost(value=0., logP=lp):
return logP
costs = [logpCost]
if priors is not None:
@pymc.observed
def colorPrior(value=0., M=Mags):
lp = 0.
for p in priors:
color = M[model2index[p[0]]] - M[model2index[p[1]]]
lp += p[2](color)
return lp
costs.append(colorPrior)
def resid(p):
model = numpy.empty(0)
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
model = numpy.append(
model,
linearmodelSB(p,
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
levMar=True,
OVRS=OVRS))
return model
print "Optimizing", niter
from SampleOpt import AMAOpt as Opt, levMar as levMar
default = numpy.empty(0)
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
default = numpy.append(default, simage)
# levMar(pars,resid,default)
cov = None
if 'COV' in data.keys():
cov = data['COV']
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.sample(niter / 10)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 4.
O.sample(niter / 4)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 10.
O.sample(niter / 4)
O = Opt(pars, costs, [lp, Mags], cov=cov)
O.set_minprop(len(pars) * 2)
O.cov = O.cov / 10.
O.sample(niter)
logp, trace, result = O.result()
mags = numpy.array(result['Mags'])
for key in model2index.keys():
result[key] = mags[:, model2index[key]].copy()
del result['Mags']
output = {}
for key in filters:
indx = key2index[key]
if doSigma == True:
sigma = sigmas[indx].value
else:
sigma = sigmas[key]
simage = (image[key] / sigma)[mask_r]
m = linearmodelSB([p.value for p in pars],
simage,
sigma[mask_r],
mask,
models[key],
xc,
yc,
noResid=True,
OVRS=OVRS)
output[key] = m
return output, (logp, trace, result)
