Randomly refine (or coarsen) an Element in each dimension. More...

#include <Random.hpp>

Public Types
using	options = tmpl::list< ProbabilityWeights >

using	compute_tags_for_observation_box = tmpl::list<>

using	argument_tags = tmpl::list<>

Public Member Functions
	Random (std::unordered_map< amr::Flag, size_t > probability_weights)

std::string	observation_name () override

template<size_t Dim, typename Metavariables >
auto	operator() (Parallel::GlobalCache< Metavariables > &, const ElementId< Dim > &element_id) const

void	pup (PUP::er &p) override

Public Member Functions inherited from amr::Criterion
	Criterion (CkMigrateMessage *msg)

	WRAPPED_PUPable_abstract (Criterion)

virtual std::string	observation_name ()=0

template<typename ComputeTagsList , typename DataBoxType , typename Metavariables >
auto	evaluate (const ObservationBox< ComputeTagsList, DataBoxType > &box, Parallel::GlobalCache< Metavariables > &cache, const ElementId< Metavariables::volume_dim > &element_id) const
	Evaluates the AMR criteria by selecting the appropriate derived class and forwarding its `argument_tags` from the ObservationBox (along with the GlobalCache and ArrayIndex) to the call operator of the derived class. More...

Static Public Attributes
static constexpr Options::String	help

Detailed Description

Randomly refine (or coarsen) an Element in each dimension.

You can specify a probability for each possible amr::Flag. It is evaluated in each dimension separately. Details:

Probabilities are specified as integer weights. The probability for an amr::Flag is its weight over the sum of all weights.
Flags with weight zero do not need to be specified.
If all weights are zero, amr::Flag::DoNothing is always chosen.

Member Function Documentation

std::string amr::Criteria::Random::observation_name ( )

inlineoverridevirtual

constexpr Options::String amr::Criteria::Random::help

staticconstexpr

Initial value:

= {

"Randomly refine (or coarsen) the grid"}

The documentation for this class was generated from the following file: