Bonsai_extra_effectsmodule Bonsai := Bonsai.Contmodule Effect := Bonsai.Effectval exactly_once : unit Effect.t Bonsai.t -> Bonsai.graph @ local -> unitAs its name implies, exactly_once runs the event passed in via Bonsai.t exactly once.
val exactly_once_with_value :
?sexp_of_model:('a -> Core.Sexp.t) ->
?equal:('a -> 'a -> bool) ->
'a Effect.t Bonsai.t ->
Bonsai.graph @ local ->
'a option Bonsai.tAs its name implies, exactly_once runs the event passed in via Bonsai.t exactly once. The return value is stored and returned. None is returned while the effect is executing.
val bonk : Bonsai.graph @ local -> (unit Effect.t -> unit Effect.t) Bonsai.tbonk returns a function which takes an effect and transforms it into the same effect, but it will be enqueued onto the Bonsai action queue instead of running immediately. This can be useful in niche situations where you have a batch of effects that would otherwise interleave and have bad performance behavior.
val chain_incr_effects :
'a Bonsai.t ->
Bonsai.graph @ local ->
(('a -> unit Ui_effect.t) list -> unit Ui_effect.t) Bonsai.tchain_incr_effects input effects allows you to sequentially schedule effects that depend on a common 'a Bonsai.t, while ensuring that no effect will receive a stale input. This function short-circuits if the input becomes inactive while actions are still being applied.
This is particularly useful for modeling a set of interacting state machines. The outputs of each computation can be collected into a single Bonsai.t, which is then provided to each state machine through an injected action. This util allows model recomputations made in the `i`th state machine to be immediately visible to the apply_action logic of the `i+1`th state machine.
In contrast, just resolving a value with let%arr and scheduling multiple dependent effects with `Effect.Many` will provide all state machines with the state of the world before *any* of them recalculated state.