celerite.modeling
This module provides some infrastructure that makes it easy to implement abstract "models" to be used within the celerite framework. Many of the methods are probably more generally applicable but the implementation constraints can be simplified since we're just concerned about supporting the needs of celerite.
The basic premise is that a Model is an object that has an ordered set of named parameters. These parameters are assumed to be continuous but they can have bounds. There is also the concept of an "active set" of parameters that are being varied in a fit procedure. The other parameters are "frozen" to a particular value. Frozen parameters can be "thawed" to be returned to the active set.
There isn't a formal requirement for the "value" interface that a Model subclass should implement but in some cases, a model will be expected to implement a get_value method that returns the "value" of the model (this can mean many different things but we'll motivate this with an example below) for the current setting of the parameters.
Since these models will be used in the context of Bayesian parameter estimation each model also implements a Model.log_prior method that computes the log of the prior probability of the current setting of the model parameters.
The full interface is described in detail below and the tutorials demonstrate the basic usage of the protocol.
celerite.modeling.Model
celerite.modeling.ModelSet
celerite.modeling.ConstantModel