LogLike = LogLike1 + LogLike2 - CoordTransTerm
print "calculate likelihood: {0} s".format(time.time()-tstart) , LogLike
#print 'EFAC:', np.abs(p0)
#print 'ECORR:', np.abs(p2)
#print 'RNAMP: %s, RNIDX: %s' % (np.exp(p3[0]), p3[1])
print 'plist:', plist
return LogLike
LogLike = lambda x:loglikelihood(x) * -1.
from testmcmc import SliceSampleMC as mcmc
from pylab import *
res, xmax, pmax = mcmc(LogLike, plist, m=1000, n =10000, ss=0.1)
#oldres = np.load('whitenoise.npy')
#res = np.vstack((oldres, res))
np.save('whitenoise', res)
plist = xmax
#print loglikelihood(plist)
#plist = fmin(loglikelihood, plist)
#plist = fmin_powell(loglikelihood, plist)
sys.exit(0)
p0 = plist[:np0]
p1 = plist[np0:np1]
p2 = plist[np1:np2]
#p3 = plist[np2:np3]
for i,p in enumerate(T2EFAC):
md.__dict__[p] = np.abs(p0[i])
#print 'phi', np.log(phi)
#print 'f', np.log10(f)
return LogLike
#print np.max(plist**4)
#plist[16] = -16
#print np.max(plist**4)
#print loglikelihood(plist)
#sys.exit(0)
LogLike = lambda x:loglikelihood(x) * -1.
from testmcmc import SliceSampleMC as mcmc
#from pylab import *
oldres = np.load('rednoise.npy')
res, xmax, pmax = mcmc(LogLike, plist, m=100, n = 10000, ss=0.1,progressbar=True)
#sys.exit(0)
res = np.array(res)
res = np.vstack((oldres, res))
np.save('rednoise', res)
plist = xmax
#sys.exit(0)
#plist = fmin(loglikelihood, plist)
#plist = fmin_powell(loglikelihood, plist)
p0 = plist[:np0]
p1 = plist[np0:np1]
p2 = plist[np1:np2]
p3 = plist[np2:np3]
for i,p in enumerate(T2EFAC):
md.__dict__[p] = np.abs(p0[i])
for i,p in enumerate(T2EQUAD):
#print 'phi', np.log(phi)
#print 'f', np.log10(f)
return LogLike
#print np.max(plist**4)
#plist[16] = -16
#print np.max(plist**4)
#print loglikelihood(plist)
#sys.exit(0)
LogLike = lambda x: loglikelihood(x) * -1.
from testmcmc import SliceSampleMC as mcmc
#from pylab import *
res, xmax, pmax = mcmc(LogLike, plist, m=100, n=1000, ss=0.1, progressbar=True)
#oldres = np.load('rednoise.npy')
#res = np.vstack((oldres, res))
res = np.array(res)
np.save('rednoise', res)
plist = xmax
#sys.exit(0)
#plist = fmin(loglikelihood, plist)
#plist = fmin_powell(loglikelihood, plist)
p0 = plist[:np0]
p1 = plist[np0:np1]
p2 = plist[np1:np2]
p3 = plist[np2:np3]
for i, p in enumerate(T2EFAC):
md.__dict__[p] = np.abs(p0[i])
for i, p in enumerate(T2EQUAD):
