B d@sJdZddlZdgZGdddZddZdd Zd d ZGd ddZdS) z The wrapper around multiprocessing pool that can be helpful with dynesty since it avoids some overhead that one would get with standard pool NPoolc@seZdZdZdS) FunctionCachezU Singleton class to cache the functions and optional arguments between calls N)__name__ __module__ __qualname____doc__rrY/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/dynesty/pool.pyrsrcCs(|t_|t_|t_|t_|t_|t_dS)zi Initialized function used to initialize the singleton object inside each worker of the pool N)rloglikeprior_transform logl_args logl_kwargs ptform_args ptform_kwargs)r r r r rrrrr initializers rcCstj|ftjtjS)z" Likelihood function call )rr r r )xrrr loglike_cache"srcCstj|ftjtjS)z Prior transform call )rr rr)rrrr prior_transform_cache*src@sNeZdZdZdddZddZddZd d Zed d Z d dZ ddZ dS)ra9 The multiprocessing pool wrapper class It is intended to be used as a context manager for dynesty sampler only. Parameters ---------- njobs: int The number of multiprocessing jobs/processes loglikelihood: function ln(likelihood) function prior_transform: function Function transforming from a unit cube to the parameter space of interest according to the prior logl_args: tuple(optional) The optional arguments to be added to the likelihood function call logl_kwargs: tuple(optional) The optional keywords to be added to the likelihood function call ptform_args: tuple(optional) The optional arguments to be added to the prior transform function call ptform_kwargs: tuple(optional) The optional keywords to be added to the prior transform function call Attributes ---------- loglikelihood: function ln(likelihood) function prior_transform: function Function transforming from a unit cube to the parameter space of interest according to the prior Examples -------- To use the dynest pool you have to use it with the context manager:: with dynesty.pool.Pool(16, like, prior_transform) as pool: dns = DynamicNestedSampler(pool.like, pool.prior_transform, ndim, pool =pool) Also note that you have to provide the .like/.prior_transform attributes from the pool object to the Nested samper rather than your original functions! NcCs@||_||_||_||_||_||_||_t|_t |_ d|_ dS)N) r r rrnjobs loglike_0prior_transform_0rr rr pool)selfrr r r r rrrrr __init__bsz Pool.__init__cCsJ|j|j|jpd|jpi|jp d|jp(if}t|jt ||_ t ||S)z# Activate the pool r) rrr r rrmprrrr)rZinitargsrrr __enter__us zPool.__enter__cCs|j||S)z| Apply the function F to the list x Parameters ========== F: function x: iterable )rmap)rFrrrr rs zPool.mapcCsJy|jWn YnXyt`t`t`t`t`t`Wn YnXdS)N) r terminaterr r r r rr)rexc_typeexc_valexc_tbrrr __exit__s z Pool.__exit__cCs|jS)z< Return the number of processes in the pool )r)rrrr sizesz Pool.sizecCs|jdS)N)rclose)rrrr r$sz Pool.closecCs|jdS)N)rjoin)rrrr r%sz Pool.join)NNNN) rrrrrrrr"propertyr#r$r%rrrr r2s.    ) rmultiprocessingr__all__rrrrrrrrr s