def lnprior(pars):
"""
Return probability of parameter values according to prior knowledge.
Parameter limits should be done here through uniform prior ditributions
"""
logprob = (naima.uniform_prior(pars[0], 0., np.inf) +
naima.uniform_prior(pars[1], -1, 5))
return logprob
def lnprior(pars):
"""
Return probability of parameter values according to prior knowledge.
Parameter limits should be done here through uniform prior ditributions
"""
logprob = (naima.uniform_prior(pars[0], 0., np.inf) +
naima.uniform_prior(pars[1], -1, 5))
return logprob
def lnprior(pars):
"""
Return probability of parameter values according to prior knowledge.
Parameter limits should be done here through uniform prior ditributions
"""
# Limit norm and B to be positive
logprob = naima.uniform_prior(pars[0], 0., np.inf) \
+ naima.uniform_prior(pars[1], -1, 5) \
+ naima.uniform_prior(pars[3], 0, np.inf)
return logprob
def lnprior(pars):
"""
Return probability of parameter values according to prior knowledge.
Parameter limits should be done here through uniform prior ditributions
"""
# Limit norm and B to be positive
logprob = naima.uniform_prior(pars[0], 0., np.inf) \
+ naima.uniform_prior(pars[1], -1, 5) \
+ naima.uniform_prior(pars[3], 0, np.inf)
return logprob
def prior_func(self, pars):
'''
The prior function that encodes any previous knowledge
we have about the parameter space constraints.
Good choice of prior function is necessary for the fit
to converge correctly. Parameter space can be best
constrained from previous observations if any.
Parameters:
------------
pars: list_like
list of free parameters of the model
Returns:
---------
Uniform prior (in this case) distribution of the parameters.
'''
# The order of the command line args is very imp
if self.intrinsic:
prior = naima.uniform_prior(pars[0], 1.7, 1.9) \
+ naima.normal_prior(pars[1], 1e15, 1e16) \
+ naima.uniform_prior(pars[2], 0.01, 2.1) \
+ naima.uniform_prior(pars[3], 1.5e5, 2.5e5)
else:
prior = naima.uniform_prior(pars[0], 1.7, 1.9) \
+ naima.normal_prior(pars[1], 1e15, 1e16) \
+ naima.uniform_prior(pars[2], 0.01, 2.1) \
+ naima.uniform_prior(pars[3], 1.5e6, 2.5e9) \
+ naima.uniform_prior(pars[4], 0, 20) \
+ naima.uniform_prior(pars[5], 5, 20) \
return prior
def lut_prior(pars):
lnprior = (uniform_prior(pars[0], log_ampl[0], log_ampl[-1]) +
uniform_prior(pars[1], alpha[0], alpha[-1]) +
uniform_prior(pars[2], beta[0], beta[-1]) +
uniform_prior(pars[3], log_e_max[0], log_e_max[-1]) +
uniform_prior(pars[4], log_e_min[0], log_e_min[-1]) +
uniform_prior(pars[5], B[0], B[-1]))
return lnprior
def prior_func(self, pars):
'''
The prior function that encodes any previous knowledge
we have about the parameter space constraints.
Good choice of prior function is necessary for the fit
to converge correctly. Parameter space can be best
constrained from previous observations if any.
Parameters:
------------
pars: list_like
list of free parameters of the model
Returns:
---------
Uniform prior (in this case) distribution of the parameters.
'''
#The order of the command line args is very imp
if self.intrinsic:
prior = naima.uniform_prior(pars[0], 1.8, 2.45) \
+naima.normal_prior(pars[1], 7e15, 8e7) \
+ naima.uniform_prior(pars[2], 0.1, 2.1) \
#if self.intrinsic:
# prior = naima.normal_prior(pars[0], 2.3, 0.002) \
# + naima.normal_prior(pars[1], 7e15, 6.2e7) \
# + naima.uniform_prior(pars[2], 0.1, 2.1) \
# #+ naima.normal_prior(pars[3], 2.2e5, 0.9e2) \
else:
prior = naima.uniform_prior(pars[0], 1.8, 2.5) \
+ naima.uniform_prior(pars[1], 7e15, 8e15) \
+ naima.uniform_prior(pars[2], 0.9, 2.1) \
+ naima.uniform_prior(pars[3], 1.5e5, 2.5e5) \
+ naima.uniform_prior(pars[4], 1, 45) \
+ naima.uniform_prior(pars[5], 1, 80)
return prior
def ECPL_lnprior(pars):
logprob = naima.uniform_prior(pars[0], 0.0, np.inf) + naima.uniform_prior(
pars[1], -1, 5
)
return logprob
def PionDecay_ECPL_lnprior(pars):
logprob = naima.uniform_prior(pars[1], -1, 5)
return logprob
def IC_We_lnprior(pars):
logprob = naima.uniform_prior(pars[1], -1, 5)
return logprob
def lnprior(pars):
# Limit amplitude to positive domain
logprob = naima.uniform_prior(pars[0], 0.0, np.inf)
return logprob
def LP_lnprior(pars):
logprob = naima.uniform_prior(pars[0], 0., np.inf) \
+ naima.uniform_prior(pars[1], -1, 5)
return logprob
def IC_We_lnprior(pars):
logprob = naima.uniform_prior(pars[1], -1, 5)
return logprob
def PionDecay_ECPL_lnprior(pars):
logprob = naima.uniform_prior(pars[1], -1, 5)
return logprob
def LP_lnprior(pars):
logprob = naima.uniform_prior(pars[0], 0., np.inf) \
+ naima.uniform_prior(pars[1], -1, 5)
return logprob
def ECPL_lnprior(pars):
logprob = naima.uniform_prior(pars[0], 0.0, np.inf) + naima.uniform_prior(
pars[1], -1, 5)
return logprob
def lnprior(pars):
# Limit amplitude to positive domain
logprob = naima.uniform_prior(pars[0], 0.0, np.inf)
return logprob
