Randomly refine (or coarsen) an Element in each dimension.
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#include <Random.hpp>
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using | options = tmpl::list< ProbabilityWeights > |
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using | compute_tags_for_observation_box = tmpl::list<> |
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using | argument_tags = tmpl::list<> |
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| Random (std::unordered_map< amr::Flag, size_t > probability_weights) |
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std::string | observation_name () override |
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template<size_t Dim, typename Metavariables > |
auto | operator() (Parallel::GlobalCache< Metavariables > &, const ElementId< Dim > &element_id) const |
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void | pup (PUP::er &p) override |
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| Criterion (CkMigrateMessage *msg) |
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| WRAPPED_PUPable_abstract (Criterion) |
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virtual std::string | observation_name ()=0 |
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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...
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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.
◆ observation_name()
std::string amr::Criteria::Random::observation_name |
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inlineoverridevirtual |
◆ help
Initial value:= {
"Randomly refine (or coarsen) the grid"}
The documentation for this class was generated from the following file:
- src/ParallelAlgorithms/Amr/Criteria/Random.hpp