CONAN._classes.fit_setup.sampling
=================================

.. py:method:: CONAN._classes.fit_setup.sampling(sampler='dynesty', n_cpus=4, n_chains=64, n_steps=2000, n_burn=500, emcee_move='stretch', n_live=300, dyn_dlogz=0.1, force_nlive=False, nested_sampling='static', verbose=True)

   configure sampling

   :param sampler: sampler to use. Default is "dynesty". Options are ["emcee","dynesty"].
   :type sampler: str;
   :param n_cpus: number of cpus to use for parallelization.
   :type n_cpus: int;
   :param n_chains: number of chains/walkers
   :type n_chains: int;
   :param n_steps: length of each chain. the effective total steps becomes n_steps*n_chains.
   :type n_steps: int;
   :param n_burn: number of steps to discard as burn-in
   :type n_burn: int;
   :param emcee_move: sampler algorithm to use in traversing the parameter space. Options are ["demc","snooker",stretch].
                      The default is stretch to use the emcee StretchMove.
   :type emcee_move: str;
   :param n_live: number of live points to use for dynesty sampler. Default is 300.
   :type n_live: int;
   :param dyn_dlogz: stopping criterion for dynesty sampler. Default is 0.1.
   :type dyn_dlogz: float;
   :param force_nlive: force dynesty to use n_live even if less than the required ndim*(ndim+1)//2. Default is False.
   :type force_nlive: bool;
   :param nested_sampling: type of nested sampling to use. Default is "static".
                           Options are ["static","dynamic[pfrac]"] where pfrac is a float from [0, 1] that determine the posterior.evidence fraction.
                           "dynamic[1.0]" performs sampling optimized for 100% posterior evaluation and "dynamic[0.8]" is 80% posterior, 20% evidence.
   :type nested_sampling: str;
   :param verbose: print output. Default is True.
   :type verbose: bool;