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Dunai

Build Status Version on Hackage

This repository implements a generalized version of reactive programming, on top of which other variants like Yampa, Classic FRP and Reactive Values can be implemented.

Installation

$ cabal update
$ cabal install --lib dunai

Dependencies

Dunai currently supports GHC versions 7.6.3 to 8.10.4.

Examples

Open a GHCi session and import the main Dunai module. If all goes well, we should see no error messages:

$ ghci
ghci> import Data.MonadicStreamFunction

An MSF is a time-varying transformation applied to a series of inputs as they come along, one by one.

Use the primitive arr :: (a -> b) -> MSF m a b to turn any pure function into an MSF that applies the given function to every input. The function embed :: MSF m a b -> [a] -> m [b] runs an MSF with a series of inputs, collecting the outputs:

ghci> embed (arr (+1)) [1,2,3,4,5]
[2,3,4,5,6]

MSFs can have side effects; hence the m that accompanies the type MSF in the signatures of arr and embed. The function arrM turns a monadic function of type a -> m b into an MSF that will constantly apply the function to each input.

For example, the function print takes a value and prints it to the terminal (a side effect in the IO monad), producing an empty () output. Elevating or lifting print into an MSF will turn it into a processor that prints each input passed to it:

ghci> :type print
print :: Show a => a -> IO ()
ghci> :type arrM print
arrM print :: Show a => MSF IO a ()

If we now run that MSF with five inputs, all are printed to the terminal:

ghci> embed (arrM print) [1,2,3,4,5]
1
2
3
4
5
[(), (), (), (), ()]

As we can see, after all side effects, embed collects all the outputs, which GHCi shows at the end.

When we only care about the side effects and not the output list, we can discard it with Control.Monad.void. (Dunai provides an auxiliary function embed_ for the same purpose.)

ghci> import Control.Monad (void)
ghci> void $ embed (arrM print) [1,2,3,4,5]
1
2
3
4
5

MSFs can be piped into one another with the functions (>>>) or (.), so that the output of one MSF is fed as input to another MSF at each point:

ghci> void $ embed (arr (+1) >>> arrM print) [1,2,3,4,5]
2
3
4
5
6

A monadic computation without arguments can be lifted into an MSF with the function constM:

ghci> :type getLine
getLine :: IO String
ghci> :type constM getLine
constM getLine :: MSF IO a String

This MSF will get a line of text from the terminal every time it is called, which we can pipe into an MSF that will print it back.

ghci> void $ embed (constM getLine >>> arrM putStrLn) [(), ()]
What the user types, the computer repeats.
What the user types, the computer repeats.
Once again, the computer repeats.
Once again, the computer repeats.

Notice how we did not care about the values in the input list to embed: the only thing that matters is how many elements it has, which determines how many times embed will run the MSF.

Simulations can run indefinitely with the function reactimate :: MSF m () () -> m (), which is useful when the input to the MSFs being executed is being produced by another MSFs, like in the case above with constM getLine producing inputs consumed by arrM putStrLn:

ghci> reactimate (constM getLine >>> arr reverse >>> arrM putStrLn)
Hello
olleH
Haskell is awesome
emosewa si lleksaH
^C

Dunai has a very extensive API and supports many programming styles. MSFs are applicatives, so we can transform them using applicative style, and they are categories, so they can be piped into one another with Control.Category.(.). For example, the line above can also be written as:

ghci> reactimate (arrM putStrLn . (reverse <$> constM getLine))

which is equivalent to:

ghci> reactimate (arrM putStrLn . fmap reverse . constM getLine)

Other writing styles (e.g., arrow notation) are also supported. This versatility makes it possible for you to use the notation you feel most comfortable with.

MSFs are immensely expressive. With MSFs, you can implement stream programming, functional reactive programming (both classic and arrowized), reactive programming, and reactive values, among many others. The real power of MSFs comes from the ability to carry out temporal transformations (e.g., delays), to apply different transformations at different points in time, and to work with different monads. See the documentation below to understand how capable they are.

Further references

Reading

The best introduction to the fundamentals of Monadic Stream Functions is:

The following papers are also related to MSFs:

Video

Games

  • The Bearriver Arcade. Fun arcade games made using bearriver.
  • Haskanoid. Haskell breakout game implemented using the Functional Reactive Programming library Yampa (compatible with Dunai/Bearriver).

Structure and internals

This project is split in three parts:

  • Dunai: a reactive library that combines monads and arrows.
  • BearRiver: Yampa implemented on top of Dunai.
  • Examples: ballbounce
    • sample applications that work both on traditional Yampa and BearRiver.

We need to add examples of apps written in classic FRP, reactive values, etc. The game haskanoid works both with Yampa and with Bearriver/dunai.

Performance

Performance is ok, simpler games will be playable without further optimisations. This uses unaccelerated SDL 1.2. The speed is comparable to Yampa's.

2016-05-09 15:29:41 dash@dash-desktop:~/Projects/PhD/Yampa/yampa-clocks-dunai$ ./.cabal-sandbox/bin/haskanoid

Performance report :: Time per frame: 13.88ms, FPS: 72.04610951008645, Total running time: 1447
Performance report :: Time per frame: 16.46ms, FPS: 60.75334143377886, Total running time: 3093
Performance report :: Time per frame: 17.48ms, FPS: 57.20823798627002, Total running time: 4841
Performance report :: Time per frame: 19.56ms, FPS: 51.12474437627812, Total running time: 6797
Performance report :: Time per frame: 19.96ms, FPS: 50.100200400801604, Total running time: 8793
Performance report :: Time per frame: 19.44ms, FPS: 51.440329218106996, Total running time: 10737

It runs almost in constant memory, with about 50% more memory consumption than with Yampa (200k for Yampa and 300K for dunai/bearriver). There is very minor leaking, probably we can fix that with seq.

We have obtained different figures tracking different modules. In the paper, we provided figures for the whole game, but we need to run newer reliable benchmarks including every module and only things that live in FRP.Yampa, FRP.BearRiver and Data.MonadicStreamFunction.

You can try it with:

git clone https://github.com/ivanperez-keera/haskanoid.git
cd haskanoid/
cabal install -f-wiimote -f-kinect -fbearriver

Related Projects

ivanperez-keera/Yampa

turion/rhine

Contributions

We follow: http://nvie.com/posts/a-successful-git-branching-model/

Feel free to open new issues. We are looking for:

  • Unexplored ways of using MSFs.
  • Other games or applications that use MSFs (including but not limited to Yampa games).
  • Fixes. The syntax and behaviour are still experimental. If something breaks/sounds strange, please open an issue.

About the name

Dunai (aka. Danube, or Дунай) is one of the main rivers in Europe, originating in Germany and touching Austria, Slovakia, Hungary, Croatia, Serbia, Romania, Bulgaria, Moldova and Ukraine.

Other FRP libraries, like Yampa, are named after rivers. Dunai has been chosen due to the authors' relation with some of the countries it passes through, and knowing that this library has helped unite otherwise very different people from different backgrounds.