Likelihood¶
Instances of Likelihood
are called by worker sampling
processes for evaluation of a likelihood function.

class
xpsi.Likelihood.
Likelihood
(star, signals, num_energies=128, fast_rel_num_energies=0.25, threads=1, llzero=1e+90, externally_updated=False, prior=None)[source]¶ Bases:
xpsi.ParameterSubspace.ParameterSubspace
A container for all objects related to likelihood evaluation.
A collective for objects pertaining to a statistical analysis of Xray signals. These objects include Xray data (sub)sets, the model instruments used to acquire these data sets, and the model star and model backgrounds.
Parameters:  star (obj) – An instance of
Star
. This instance is the model star.  signals (list) –
Either:
 num_energies (float) – Number of energies to compute specific photon flux signals at for likelihood evaluation. These energies will be distributed linearly in logarithmic space within the union of waveband coverages achieved by some set of instruments. Gaps in waveband coverage will be skipped.
 fast_rel_num_energies (float) – Fraction of the normal number of energies to use in fast mode.
 threads (int) – The number of
OpenMP
threads to spawn for integration. The default number of threads used by lowlevel integration routines is1
. The number can be increased if the parallelisation paradigm on a cluster is changed such that, e.g., per node there is one thread per CPU, instead of oneOpenMPI
process. It is recommended thatthreads
is1
; more threads are useful when performing integrals at high resolution, but not necessarily when integrating many times as when sampling, because the MPI parallelisation paradigm is invoked.  llzero (float) – The minimum loglikelihood setting for MultiNest. Points whose loglikelihood is lower than this value are ignored, which is useful for defining complicated priors.
 externally_updated (bool) – Update the parameter values upon call to the likelihood object?
If so, then pass
False
. A parameter vector then needs to be passed to the likelihood object. Otherwise, safely assume that the new parameter values are set externally, e.g., in a prior object when inverse sampling the prior for nested sampling.

__call__
(p=None, reinitialise=False, force=False)[source]¶ Evaluate the logarithm of the joint likelihood over all pulsations.
Parameters:  p (list) – Parameter vector if parameters not updated externally.
 reinitialise (optional[bool]) – Call
self.reinitialise()
?  force (optional[bool]) – Force complete reevaluation even if some parameters are unchanged. To faciliate this, all parameter caches are cleared.
Returns: The logarithm of the likelihood.

__str__
()¶ Get a summary of the parameters constituting the subspace.

check
(hypercube_points, loglikelihood_call_vals, rtol_loglike, atol_loglike=0.0, logprior_call_vals=None, rtol_logprior=None, atol_logprior=None, physical_points=None)[source]¶ Perform checks on the likelihood evaluator and the prior density.
Can be called from
nested()
to execute a check before automatically commencing a sampling process.Parameters:  hypercube_points (ndarray[n,m]) – A set of
n
points in the unit hypercube, wherem
is dimensionality (self.num_params
) of the sampling space – i.e., of the hypercube. If you want to pass the physical points instead, just passNone
here.  physical_points (optional(ndarray[n,m])) – A set of
n
points in the physical parameter space, wherem
is dimensionality (self.num_params
) of the sampling space. Thehypercube_points
, if notNone
, will be ignored.
 hypercube_points (ndarray[n,m]) – A set of

less_than_llzero
¶ Get a number less than the minimum loglikelihood threshold.

llzero
¶ Get the minimum loglikelihood setting passed to MultiNest.

random_near_llzero
¶ Get the minimum loglikelihood scaled randomly by up to an order of magnitude.

synthesise
(p, reinitialise=False, force=False, **kwargs)[source]¶ Synthesise pulsation data.
Parameters:  p (list) – Parameter vector.
 reinitialise (optional[bool]) – Call
self.reinitialise()
?  force (optional[bool]) – Force complete reevaluation even if some parameters are unchanged.
 kwargs (dict) – Keyword arguments propagated to custom signal synthesis methods. Examples of such arguments include exposure times or required total count numbers (see example notebooks0.

threads
¶ Get the number of threads spawned for integration.
 star (obj) – An instance of