def loglike(x):
start = time.time()
# parameter values
theta = x[0]
phi = x[1]
freq = 10 ** x[2]
mc = 10 ** x[3]
dist = 10 ** x[4]
psi = x[5]
inc = x[6]
phase = x[7]
pdist = x[8:]
# generate list of waveforms for all puslars
s = PALutils.createResidualsFast(
psr, theta, phi, mc, dist, freq, phase, psi, inc, pdist=pdist, evolve=False, phase_approx=True
)
loglike = 0
for ct, p in enumerate(psr):
diff = p.res - s[ct]
loglike += -0.5 * (logdetTerm[ct] + np.dot(diff, np.dot(p.invCov, diff)))
if np.isnan(loglike):
print "NaN log-likelihood. Not good..."
return -np.inf
else:
return loglike
def loglike(x):
start = time.time()
# parameter values
theta = x[0]
phi = x[1]
freq = 10**x[2]
mc = 10**x[3]
dist = 10**x[4]
psi = x[5]
inc = x[6]
phase = x[7]
pdist = x[8:]
# generate list of waveforms for all puslars
s = PALutils.createResidualsFast(psr, theta, phi, mc, dist, freq, phase, psi, inc,\
pdist=pdist, evolve=False, phase_approx=True)
loglike = 0
for ct, p in enumerate(psr):
diff = p.res - s[ct]
loglike += -0.5 * (logdetTerm[ct] + np.dot(diff, np.dot(p.invCov, diff)))
if np.isnan(loglike):
print 'NaN log-likelihood. Not good...'
return -np.inf
else:
return loglike
