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The Taxonomy of FRP

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Evan Czaplicki

on 17 September 2014

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Transcript of The Taxonomy of FRP

Taxonomy of FRP
Evan Czaplicki
Signal Graphs
n : Signal Int
n = count Mouse.clicks
Mouse.position : Signal (Int,Int)
Prezi / elm-lang.org
m = count Mouse.clicks
n = m
?
Inputs
Nodes
lift : (a -> b) -> Signal a -> Signal b
lift2 : (a -> b -> c) -> Signal a -> Signal b -> Signal c
foldp : (a -> b -> b) -> b -> Signal a -> Signal b
Inputs
Transform
State
Functional Reactive Programming
Signal graphs are dynamic
Inputs can be created at any time
No equational reasoning
flatMap : Signal a -> (a -> Signal b) -> Signal b
Pure
Monadic FRP
n : Signal Int
n = count Mouse.clicks
m = count Mouse.clicks
n = m
bind : Signal a -> (a -> Signal b) -> Signal b
Applicative FRP
Disallow flatMap, bind, join
join : Signal (Signal a) -> Signal a
Signals "start" when the program starts
Memory must grow linearly with time!
Signal graphs are static
All inputs are created at startup
n : Signal Int
n = count Mouse.clicks
m = count Mouse.clicks
n ≠ m
Impure
Monadic FRP
join : Signal (Signal a) -> Signal a
n : Signal Int
n = count Mouse.clicks
m = count Mouse.clicks
n = m
Arrowized FRP
Explicitly model signal nodes!
pure : (a -> b) -> Automaton a b
n : Signal Int
n = count Mouse.clicks
m = count Mouse.clicks
n = m
Flapjax
FrTime
Reactive Extensions
Bacon.js
Elm
Real-Time FRP
Event-Driven FRP
state : b -> (a -> b -> b) -> Automaton a b
Stop modeling
input nodes!
step : a -> Automaton a b -> (Automaton a b, b)
loop : s -> Automaton (a,s) (b,s) -> Automaton a b
Yampa
Netwire
Automaton (in Elm)
in Elm
Signal a = Automaton World a
Dynamic Signals
Static Signals
Dynamic Signal Graphs
Full transcript