B d@sdZddlZddlZddlZddlZddlmZddlm Z m Z m Z m Z m Z mZddlmZmZmZmZmZddlmZmZmZmZdd d d d gZeeeeed ZGdddeZGdddeZGdd d eZGdd d eZGdd d eZ Gdd d eZ!dS)a{ Childen of :class:`dynesty.sampler` used to proposing new live points. Includes: UnitCubeSampler: Uses the unit cube to bound the set of live points (i.e. no bound). SingleEllipsoidSampler: Uses a single ellipsoid to bound the set of live points. MultiEllipsoidSampler: Uses multiple ellipsoids to bound the set of live points. RadFriendsSampler: Uses an N-sphere of fixed radius centered on each live point to bound the set of live points. SupFriendsSampler: Uses an N-cube of fixed length centered on each live point to bound the set of live points. N)Sampler)UnitCube EllipsoidMultiEllipsoid RadFriends SupFriends rand_choice) sample_unif sample_rwalk sample_slice sample_rslice sample_hslice) unitcheckget_enlarge_bootstrap save_samplerrestore_samplerUnitCubeSamplerSingleEllipsoidSamplerMultiEllipsoidSamplerRadFriendsSamplerSupFriendsSampler)unifrwalkslicerslicehslicecszeZdZdZdfdd ZddZd d Zdd d ZdddZdddZ dddZ dddZ ddZ e d ddZZS)! SuperSamplerz` This is a class that provides common functionality to all the implemented samplers NrFcstj|||||||| | | | ||d |j|j|j|j|j|jd|_t|rZ|td<d}|j||j|_|t||_ |_ |j |j |j |j |j|jd|_d|_| pi|_|jd|_|j||j|_t||jd|jd\|_|_|jd|_||_|jd d|_|jd d|_|jd d |_td |jdd|_|jdd|_t dtd|j|j|_ddd|_!|jdd|_"|jdd|_#|jdd|_$ddd|_%dddd|_&dS)N)ncdimblob logvol_init)rrrrrz user-definedz user-definedg?Z update_funcenlarge bootstrapcite nonboundedgrad compute_jacFwalksfaccg?r)nacceptnrejectslicesfmoveg?max_moved) ncontractnexpand)nmovenreflectr2)'super__init__ propose_unif propose_liveZ_PROPOSEcallable _SAMPLINGgetZ propose_pointsamplingZ evolve_point update_unif update_rwalk update_slice update_hslice update_userZ_UPDATEscalekwargs custom_updateZupdate_proposalrr!r"r#methodr$r%r&maxr(r*min rwalk_historyr-r/r0 slice_historyhslice_history)self loglikelihoodprior_transformnpdim live_pointsrFupdate_interval first_updaterstate queue_sizepooluse_poolrDrrr ) __class__c/work/yifan.wang/ringdown/master-ringdown-env/lib/python3.7/site-packages/dynesty/nestedsamplers.pyr7:sd   "  zSuperSampler.__init__cGsdS)NrX)rLargsrXrXrYr8szSuperSampler.propose_unifcGsdS)NrX)rLrZrXrXrYr9szSuperSampler.propose_liveTcCsdS)zFiller function.NrX)rLrupdaterXrXrYr>szSuperSampler.update_unifcCs|d|_|j}|d|d7<|d|d7<|s@dS|d|d}}d|||}|jt||j|j|j9_d|d<d|d<dS) aUpdate the random walk proposal scale based on the current number of accepted/rejected steps. For rwalk the scale is important because it determines the speed of diffusion of points. I.e. if scale is too large, the proposal efficiency will be very low so it's likely that we'll only do one random walk step at the time, thus producing very correlated chain. The keyword update determines if we are just accumulating the number of steps or actually adjusting the scale rCr+acceptr,rejectNg?r)rCrImathexpr*r)rLrr[histr\r]r*rXrXrYr?s  &zSuperSampler.update_rwalkcCs|j}|d|d7<|d|d7<|dr@d|jd<|sHdSt|dd|d}}|d||}t|d d }|j||_d |d<d |d<dS) aUpdate the slice proposal scale based on the relative size of the slices compared to our initial guess. For slice sampling the scale is only 'advisory' in the sense that the right scale will just speed up sampling as we'll have to expand or contract less. It won't affect the quality of the samples much. The keyword update determines if we are just accumulating the number of steps or actually adjusting the scale r3r2Zexpansion_warning_setTZslice_doublingNrg@g?r'r)rJrDrGnpZcliprC)rLrr[r`r3r2ZmultrXrXrYr@s   zSuperSampler.update_slicec Cs|j}|d|d7<|d|d7<|d|dd7<|sNdS|d|d}}|d}d||||d}t|jd|j}|jt||j|9_d|d<d|d<d|d<dS)zUpdate the Hamiltonian slice proposal scale based on the relative amount of time spent moving vs reflecting. The keyword update determines if we are just accumulating the number of steps or actually adjusting the scale r4r5r2rNg?r')rKr<rGr/rCr^r_) rLrr[r`r4r5r2r/ZnormrXrXrYrAszSuperSampler.update_hslicecCs"t|jr|j||j|d|_dS)z;Update the scale based on the user-defined update function.)r[N)r:rErC)rLrr[rXrXrYrBs zSuperSampler.update_usercCst||dS)z Save the state of the dynamic sampler in a file Parameters ---------- fname: string Filename of the save file. N)r)rLfnamerXrXrYsaves zSuperSampler.savecCs t||dS)a Restore the dynamic sampler from a file. It is assumed that the file was created using .save() method of DynamicNestedSampler or as a result of checkpointing during run_nested() Parameters ---------- fname: string Filename of the save file. pool: object(optional) The multiprocessing pool-like object that supports map() calls that will be used in the restored object. )rU)r)rbrUrXrXrYrestoreszSuperSampler.restore)NrFr)T)T)T)T)T)N)__name__ __module__ __qualname____doc__r7r8r9r>r?r@rArBrc staticmethodrd __classcell__rXrX)rWrYr4sK  #   rcsBeZdZdZd fdd ZedfddZd d Zd d ZZ S)raD Samples conditioned on the unit N-cube (i.e. with no bounds). Parameters ---------- loglikelihood : function Function returning ln(likelihood) given parameters as a 1-d `~numpy` array of length `ndim`. prior_transform : function Function transforming a sample from the a unit cube to the parameter space of interest according to the prior. npdim : int Number of parameters accepted by `prior_transform`. live_points : list of 3 `~numpy.ndarray` each with shape (nlive, ndim) Initial set of "live" points. Contains `live_u`, the coordinates on the unit cube, `live_v`, the transformed variables, and `live_logl`, the associated loglikelihoods. method : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`, `'hslice'`}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. update_interval : int Only update the bounding distribution every `update_interval`-th likelihood call. first_update : dict A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user. rstate : `~numpy.random.Generator` `~numpy.random.Generator` instance. queue_size: int Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members. pool: pool Use this pool of workers to execute operations in parallel. use_pool : dict, optional A dictionary containing flags indicating where the provided `pool` should be used to execute operations in parallel. kwargs : dict, optional A dictionary of additional parameters. NrFcsDtj||||||||| | | | ||| p(idt|j|_d|_dS)N)rrr rDnone)r6r7rrunitcubebounding)rLrMrNrOrPrFrQrRrSrTrUrVrDrrr )rWrXrYr7Ss"  zUnitCubeSampler.__init__cCs t|jS)zUpdate the unit cube bound.)copydeepcopyrl)rLsubsetrXrXrYr[xszUnitCubeSampler.updatecGsP|jj|jd}t|j}|j|jkrHt||jj|j|jdg}||fS)zNPropose a new live point by sampling *uniformly* within the unit cube.)rS)size) rlsamplerSraidentityrOr concatenaterandom)rLrZuaxrXrXrYr8}s   zUnitCubeSampler.propose_unifcGsRt|dkr|j|d}n|j|j}|j|ddf}t|j}||fS)zReturn a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.rN) lenrSchoiceintegersnlivelive_urarsrO)rLrZirvrwrXrXrYr9s   zUnitCubeSampler.propose_live)NrFr) rerfrgrhr7rr[r8r9rjrXrX)rWrYrs5 csBeZdZdZd fdd ZedfddZd d Zd d ZZ S)raZ Samples conditioned on a single ellipsoid used to bound the set of live points. Parameters ---------- loglikelihood : function Function returning ln(likelihood) given parameters as a 1-d `~numpy` array of length `ndim`. prior_transform : function Function transforming a sample from the a unit cube to the parameter space of interest according to the prior. npdim : int Number of parameters accepted by `prior_transform`. live_points : list of 3 `~numpy.ndarray` each with shape (nlive, ndim) Initial set of "live" points. Contains `live_u`, the coordinates on the unit cube, `live_v`, the transformed variables, and `live_logl`, the associated loglikelihoods. method : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`, `'hslice'`}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. update_interval : int Only update the bounding distribution every `update_interval`-th likelihood call. first_update : dict A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user. rstate : `~numpy.random.Generator` `~numpy.random.Generator` instance. queue_size: int Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members. pool: pool Use this pool of workers to execute operations in parallel. use_pool : dict, optional A dictionary containing flags indicating where the provided `pool` should be used to execute operations in parallel. kwargs : dict, optional A dictionary of additional parameters. NFrcsbtj||||||||| | | || || p(idtt|jdt|j|jd|_d|_dS)N)rrr rDg?Zsingle) r6r7rrazerosrrsellrm)rLrMrNrOrPrFrQrRrSrTrUrVrDrr r)rWrXrYr7s& zSingleEllipsoidSampler.__init__cCsl|jr|j}nd}|jj|j|d|jf|j|j|d|jdkr`|j |jj t |jt |jS)zKUpdate the bounding ellipsoid using the current set of live points.N)rSr"rUg?)use_pool_updaterUrr[r|rrSr"r!scale_to_logvolZlogvolralogrnro)rLrprUrXrXrYr[s zSingleEllipsoidSampler.updatec Gsd}|j|jkr,|jdk r,|jd|j}n|j}d}x(|jj|jd}|d7}t||r8Pq8W||krtt dt dWdQRX|j|jkrt ||jj |j|jdg}||jjfS) zNPropose a new live point by sampling *uniformly* within the ellipsoid.i'Nr)rSroncez+Ellipsoid sampling is extremely inefficient)rq)rrOr$rrrrSrwarningscatch_warningsfilterwarningswarnrartruaxes)rLrZthreshold_warningnonbniterrvrXrXrYr8 s$    z#SingleEllipsoidSampler.propose_unifcGszt|dkr|j|d}n|j|j}|j|ddf}|jdkrR|jj}n |jdkrf|jj }n t |j }||fS)zReturn a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.rN)rrr) rxrSryrzr{r|r=rrpaxesrarsr)rLrZr}rvrwrXrXrYr9)s      z#SingleEllipsoidSampler.propose_live)NFrr) rerfrgrhr7rr[r8r9rjrXrX)rWrYrs6csBeZdZdZd fdd ZedfddZd d Zd d ZZ S)ra Samples conditioned on the union of multiple (possibly overlapping) ellipsoids used to bound the set of live points. Parameters ---------- loglikelihood : function Function returning ln(likelihood) given parameters as a 1-d `~numpy` array of length `ndim`. prior_transform : function Function transforming a sample from the a unit cube to the parameter space of interest according to the prior. npdim : int Number of parameters accepted by `prior_transform`. live_points : list of 3 `~numpy.ndarray` each with shape (nlive, ndim) Initial set of "live" points. Contains `live_u`, the coordinates on the unit cube, `live_v`, the transformed variables, and `live_logl`, the associated loglikelihoods. method : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`, `'hslice'`}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. update_interval : int Only update the bounding distribution every `update_interval`-th likelihood call. first_update : dict A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user. rstate : `~numpy.random.Generator` `~numpy.random.Generator` instance. queue_size: int Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members. pool: pool Use this pool of workers to execute operations in parallel. use_pool : dict, optional A dictionary containing flags indicating where the provided `pool` should be used to execute operations in parallel. kwargs : dict, optional A dictionary of additional parameters. NFrcshtj||||||||| | | || || p(idtt|jdgt|j|jdgd|_d|_dS)N)rrr rDg?r~)ZctrsZcovsmulti) r6r7rrarrrsmellrm)rLrMrNrOrPrFrQrRrSrTrUrVrDrr r)rWrXrYr7vs& zMultiEllipsoidSampler.__init__cCsl|jr|j}nd}|jj|j|d|jf|j|j|d|jdkr`|j |jj t |jt |jS)zLUpdate the bounding ellipsoids using the current set of live points.N)rSr"rUg?)rrUrr[r|rrSr"r!rlogvolsrarrnro)rLrprUrXrXrYr[s zMultiEllipsoidSampler.updatec Gsd}|j|jkr,|jdk r,|jd|j}n|j}d}x,|jj|jd\}}|d7}t||r8Pq8W||krtt dt dWdQRX|j|jkrt ||jj |j|jdg}||jj|jfS) zXPropose a new live point by sampling *uniformly* within the union of ellipsoids.i'Nr)rSrrz+Ellipsoid sampling is extremely inefficient)rq)rrOr$rrrrSrrrrrrartruellsr)rLrZrrrrvidxrXrXrYr8s$    z"MultiEllipsoidSampler.propose_unifcGst|dkr|j|d}n|j|j}|j|ddf}|d|j}|j|s~|j t j dd|j|s~t d|j dkrt |jj|jj}t||j}|j dkr|jj|j}q|jj|j}n t |j}||fS)zReturn a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.rNT)forcezUpdate of the ellipsoid failed)rrrr)rxrSryrzr{r|rrcontainsZupdate_bound_if_neededrainf RuntimeErrorr=r_rZ logvol_totr rrrrsrO)rLrZr}rvZu_fitZprobsZell_idxrwrXrXrYr9s"       z"MultiEllipsoidSampler.propose_live)NFrr) rerfrgrhr7rr[r8r9rjrXrX)rWrYr>s6csBeZdZdZd fdd ZedfddZd d Zd d ZZ S)ra{ Samples conditioned on the union of (possibly overlapping) N-spheres centered on the current set of live points. Parameters ---------- loglikelihood : function Function returning ln(likelihood) given parameters as a 1-d `~numpy` array of length `ndim`. prior_transform : function Function transforming a sample from the a unit cube to the parameter space of interest according to the prior. npdim : int Number of parameters accepted by `prior_transform`. live_points : list of 3 `~numpy.ndarray` each with shape (nlive, ndim) Initial set of "live" points. Contains `live_u`, the coordinates on the unit cube, `live_v`, the transformed variables, and `live_logl`, the associated loglikelihoods. method : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`, `'hslice'`}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. update_interval : int Only update the bounding distribution every `update_interval`-th likelihood call. first_update : dict A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user. rstate : `~numpy.random.Generator` `~numpy.random.Generator` instance. queue_size: int Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members. pool: pool Use this pool of workers to execute operations in parallel. use_pool : dict, optional A dictionary containing flags indicating where the provided `pool` should be used to execute operations in parallel. kwargs : dict, optional A dictionary of additional parameters. NFrcsDtj||||||||| | | ||| | p(idt|j|_d|_dS)N)rr rrDZballs)r6r7rr radfriendsrm)rLrMrNrOrPrFrQrRrSrTrUrVrDrr r)rWrXrYr72s"  zRadFriendsSampler.__init__cCsl|jr|j}nd}|jj|j|d|jf|j|j|d|jdkr`|j |jj t |jt |jS)z?Update the N-sphere radii using the current set of live points.N)rSr"rUg?)rrUrr[r|rrSr"r!rZ logvol_ballrarrnro)rLrprUrXrXrYr[Ws  zRadFriendsSampler.updatecGsxX|jj|jddd|jf|jdd\}}t||jr|dksT|jd|krPqW|jj}t ||jj|j |jdg}||fS)zrPropose a new live point by sampling *uniformly* within the union of N-spheres defined by our live points.NT)rSreturn_qrg?)rq) rrrr|rrSrr$rurrartrO)rLrZrvqrwrXrXrYr8ks  zRadFriendsSampler.propose_unifcGsRt|dkr"|d}|j|}n|j|j}|j|ddf}|jj}||fS)zPropose a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.rN)rxrSryrzr{r|rr)rLrZrpr}rvrwrXrXrYr9s zRadFriendsSampler.propose_live)NFrr) rerfrgrhr7rr[r8r9rjrXrX)rWrYrs6csBeZdZdZd fdd ZedfddZd d Zd d ZZ S)ray Samples conditioned on the union of (possibly overlapping) N-cubes centered on the current set of live points. Parameters ---------- loglikelihood : function Function returning ln(likelihood) given parameters as a 1-d `~numpy` array of length `ndim`. prior_transform : function Function transforming a sample from the a unit cube to the parameter space of interest according to the prior. npdim : int Number of parameters accepted by `prior_transform`. live_points : list of 3 `~numpy.ndarray` each with shape (nlive, ndim) Initial set of "live" points. Contains `live_u`, the coordinates on the unit cube, `live_v`, the transformed variables, and `live_logl`, the associated loglikelihoods. method : {`'unif'`, `'rwalk'`, `'slice'`, `'rslice'`, `'hslice'`}, optional Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. update_interval : int Only update the bounding distribution every `update_interval`-th likelihood call. first_update : dict A dictionary containing parameters governing when the sampler should first update the bounding distribution from the unit cube to the one specified by the user. rstate : `~numpy.random.Generator` `~numpy.random.Generator` instance. queue_size: int Carry out likelihood evaluations in parallel by queueing up new live point proposals using (at most) this many threads/members. pool: pool Use this pool of workers to execute operations in parallel. use_pool : dict, optional A dictionary containing flags indicating where the provided `pool` should be used to execute operations in parallel. kwargs : dict, optional A dictionary of additional parameters. NFrcsDtj||||||||| | | || || p(idt|j|_d|_dS)N)rrr rDZcubes)r6r7rr supfriendsrm)rLrMrNrOrPrFrQrRrSrTrUrVrDrr r)rWrXrYr7s"  zSupFriendsSampler.__init__cCsl|jr|j}nd}|jj|j|d|jf|j|j|d|jdkr`|j |jj t |jt |jS)zLUpdate the N-cube side-lengths using the current set of live points.N)rSr"rUg?)rrUrr[r|rrSr"r!rZ logvol_cuberarrnro)rLrprUrXrXrYr[s  zSupFriendsSampler.updatecGsxX|jj|jddd|jf|jdd\}}t||jr|dksT|jd|krPqW|jj}|j |jkrt ||jj|j |jdg}||fS)zuPropose a new live point by sampling *uniformly* within the collection of N-cubes defined by our live points.NT)rSrrg?)rq) rrrr|rrSrr$rurrOrart)rLrZrvrrwrXrXrYr8s   zSupFriendsSampler.propose_unifcGsNt|dkr|j|d}n|j|j}|j|ddf}|jj}||fS)zReturn a live point/axes to be used by other sampling methods. If args is not empty, it contains the subset of indices of points to sample from.rN)rxrSryrzr{r|rr)rLrZr}rvrwrXrXrYr9s  zSupFriendsSampler.propose_live)NFrr) rerfrgrhr7rr[r8r9rjrXrX)rWrYrs6)"rhr^rnrnumpyraZsamplerrrmrrrrrr r=r r r r rutilsrrrr__all__r;rrrrrrrXrXrXrYs2  i|'=