My specific problem is like this:
Given an Event t [a] and an Event t () (let's say it's a tick event), I want to produce an Event t a, that is, an event that is giving me consecutive items from input list for every occurence of tick event.
Reflex has following helper:
zipListWithEvent :: (Reflex t, MonadHold t m, MonadFix m) => (a -> b -> c) -> [a] -> Event t b -> m (Event t c)
which is doing exactly what I want, but does not take an event as an input, but just a list. Given that I have an Event t [a], I thought I could produce an event containing event and just switch, but the problem is that zipListWithEven operates in monadic context, therefore I can get:
Event t (m (Event t a))
which is something that switch primitive does not accept.
Now, maybe I'm approaching it in wrong way, so here's my general problem. Given an event that's producing list of coordinates and tick event, I want to produce an event that I can "use" to move an object along the coordinates. So each time tick fires, the position is updated. And each time I update the coordinates list, it begins to produce positions from that new list.
I'm not entirely sure if I understand the semantics of your desired functions correctly, but in the reactive-banana library, I would solve the problem like this:
trickle :: MonadMoment m => Event [a] -> Event () -> Event a
trickle eadd etick = do
bitems <- accumB [] $ unions -- 1
[ flip (++) <$> eadd -- 2
, drop 1 <$ etick -- 3
]
return $ head <$> filterE (not . null) (bitems <# etick) -- 4
The code works as follows:
The Behavior bitems records the current lists of items.
Items are added when eadd happens, ...
... and one item is removed when etick happens.
The result is an event that happens whenever etick happens, and that contains the first element of the (previously) current list whenever that list is nonempty.
This solution does not seem to require any fancy or intricate reasoning.
Naming the parts:
coords :: Event t [Coord]
ticks :: Event t ()
If we want to remember the most recent Coord until the next firing of ticks, then we necessarily have to be in the some monad Reflex m. This is the monad that allow the transient Event to be persisted.
The core thing you'd like to remember is a stack of Coord. Let's try this:
data Stack a = CS {
cs_lastPop :: Maybe a
, cs_stack :: [a]
} deriving (Show)
stack0 = CS Nothing []
pop :: Stack a -> Stack a
pop (CS _ [] ) = CS Nothing []
pop (CS _ (x:xs)) = CS (Just x) xs
reset :: [a] -> Stack a -> Stack a
reset cs (CS l _) = CS l cs
Nothing reactive there yet, two functions that tweak the Stack Coord in the way you mention in your question.
The reflex code to drive this would build a Dynamic t (Stack Coord), by specifying its initial state and all the things that modify it:
coordStack <- foldDyn ($) stack0 (leftmost [
reset <$> coords
, pop <$ ticks
])
The leftmost here takes a list of Stack Coord -> Stack Coord functions, which are applied in turn to stack0 by foldDyn ($) (as long as coords and ticks never occur in same frame).
Driving all this in main:
main :: IO ()
main = mainWidget $ do
t0 <- liftIO getCurrentTime
-- Some make up 'coords' data, pretending (Coord ~ Char)
coordTimes <- tickLossy 2.5 t0
coords <- zipListWithEvent (\c _ -> c) ["greg","TOAST"] coordTimes
ticks <- tickLossy 1 t0
coordStack <- foldDyn ($) stack0 (leftmost [
reset <$> coords
, pop <$ ticks
])
display coordStack
I've made a type which is supposed to emulate a "stream". This is basically a list without memory.
data Stream a = forall s. Stream (s -> Maybe (a, s)) s
Basically a stream has two elements. A state s, and a function that takes the state, and returns an element of type a and the new state.
I want to be able to perform operations on streams, so I've imported Data.Foldable and defined streams on it as such:
import Data.Foldable
instance Foldable Stream where
foldr k z (Stream sf s) = go (sf s)
where
go Nothing = z
go (Just (e, ns)) = e `k` go (sf ns)
To test the speed of my stream, I've defined the following function:
mysum = foldl' (+) 0
And now we can compare the speed of ordinary lists and my stream type:
x1 = [1..n]
x2 = Stream (\s -> if (s == n + 1) then Nothing else Just (s, s + 1)) 1
--main = print $ mysum x1
--main = print $ mysum x2
My streams are about half the speed of lists (full code here).
Furthermore, here's a best case situation, without a list or a stream:
bestcase :: Int
bestcase = go 1 0 where
go i c = if i == n then c + i else go (i+1) (c+i)
This is a lot faster than both the list and stream versions.
So I've got two questions:
How to I get my stream version to be at least as fast as a list.
How to I get my stream version to be close to the speed of bestcase.
As it stands the foldl' you are getting from Foldable is defined in terms of the foldr you gave it. The default implementation is the brilliant and surprisingly good
foldl' :: (b -> a -> b) -> b -> t a -> b
foldl' f z0 xs = foldr f' id xs z0
where f' x k z = k $! f z x
But foldl' is the specialty of your type; fortunately the Foldable class includes foldl' as a method, so you can just add this to your instance.
foldl' op acc0 (Stream sf s0) = loop s0 acc0
where
loop !s !acc = case sf s of
Nothing -> acc
Just (a,s') -> loop s' (op acc a)
For me this seems to give about the same time as bestcase
Note that this is a standard case where we need a strictness annotation on the accumulator. You might look in the vector package's treatment of a similar type https://hackage.haskell.org/package/vector-0.10.12.2/docs/src/Data-Vector-Fusion-Stream.html for some ideas; or in the hidden 'fusion' modules of the text library https://github.com/bos/text/blob/master/Data/Text/Internal/Fusion .
The Idea
Hello! I'm trying to implement in Haskell an image processing library based on dataflow ideology. I've got a problem connected to how I want to handle the flow of control.
The main idea is to introduce a time. The time is a Float, which could be accessed anywhere in the code (you can think of it like about State monad, but a little funnier). The funny thing about it, is that we can use timeShift operation on results, affecting the time corresponding operations would see.
An example would be best to explain this situation. Lets use following dataflow diagram:
-- timeShift(*2) --
-- / \
-- readImage -- addImages -> out
-- \ /
-- blur ----------
and its pseudocode (which deos not typecheck - its not important if we use do or let notation here, the idea should be clear):
test = do
f <- frame
a <- readImage $ "test" + show f + ".jpg"
aBlur <- blur a
a' <- a.timeShift(*2)
out <- addImage aBlur a'
main = print =<< runStateT test 5
The 5 is the time we want to run the test function with. The timeShift function affects all the operations on the left of it (in the dataflow diagram) - in this case the function readImage would be run twice - for both branches - the lower one would use frame 5 and the upper one frame 5*2 = 10.
The problem
I'm providing here a very simple implementation, that works great, but has some caveats I want to solve. The problem is, that I want to keep the order of all IO operations. Look at the bottom for example, which will clarify what I mean.
Sample implementation
Below is a sample implementation of the algorithm and a code, which constructs following dataflow graph:
-- A --- blur --- timeShift(*2) --
-- \
-- addImages -> out
-- /
-- B --- blur --------------------
the code:
import Control.Monad.State
-- for simplicity, lets assume an Image is just a String
type Image = String
imagesStr = ["a0","b1","c2","d3","e4","f5","g6","h7","i8","j9","k10","l11","m12","n13","o14","p15","q16","r17","s18","t19","u20","v21","w22","x23","y24","z25"]
images = "abcdefghjiklmnoprstuwxyz"
--------------------------------
-- Ordinary Image processing functions
blurImg' :: Image -> Image
blurImg' img = "(blur " ++ img ++ ")"
addImage' :: Image -> Image -> Image
addImage' img1 img2 = "(add " ++ img1 ++ " " ++ img2 ++ ")"
--------------------------------
-- Functions processing Images in States
readImage1 :: StateT Int IO Image
readImage1 = do
t <- get
liftIO . putStrLn $ "[1] reading image with time: " ++ show t
return $ imagesStr !! t
readImage2 :: StateT Int IO Image
readImage2 = do
t <- get
liftIO . putStrLn $ "[2] reading image with time: " ++ show t
return $ imagesStr !! t
blurImg :: StateT Int IO Image -> StateT Int IO Image
blurImg img = do
i <- img
liftIO $ putStrLn "blurring"
return $ blurImg' i
addImage :: StateT Int IO Image -> StateT Int IO Image -> StateT Int IO Image
addImage img1 img2 = do
i1 <- img1
i2 <- img2
liftIO $ putStrLn "adding images"
return $ addImage' i1 i2
timeShift :: StateT Int IO Image -> (Int -> Int) -> StateT Int IO Image
timeShift img f = do
t <- get
put (f t)
i <- img
put t
return i
test = out where
i1 = readImage1
j1 = readImage2
i2 = blurImg i1
j2 = blurImg j1
i3 = timeShift i2 (*2)
out = addImage i3 j2
main = do
print =<< runStateT test 5
print "end"
The output is:
[1] reading image with time: 10
blurring
[2] reading image with time: 5
blurring
adding images
("(add (blur k10) (blur f5))",5)
"end"
and should be:
[1] reading image with time: 10
[2] reading image with time: 5
blurring
blurring
adding images
("(add (blur k10) (blur f5))",5)
"end"
Please note that the correct output is ("(add (blur k10) (blur f5))",5) - which means, that we added image k10 to f5 - from respectively 10th and 5th frame.
Further requirements
I'm looking for a solution, which would allow users to write simple code (like in test function - it could of course be in a Monad), but I do not want them to handle the time-shifting logic by hand.
Conclusions
The only difference is the order of IO actions execution. I would love to preserve the order of the IO actions just like they are written in the test function. I was trying to implement the idea using Countinuations, Arrows and some funny states, but without success.
Dataflow and functional reactive programming libraries in Haskell are usually written in terms of Applicative or Arrow. These are abstractions for computations that are less general than Monads - the Applicative and Arrow typeclasses do not expose a way for the structure of computations to depend on the results of other computations. As a result, libraries exposing only these typeclasses can reason about the structure of computations in the library independently of performing those computations. We will solve your problem in terms of the Applicative typeclass
class Functor f => Applicative f where
-- | Lift a value.
pure :: a -> f a
-- | Sequential application.
(<*>) :: f (a -> b) -> f a -> f b
Applicative allows a library user to make new computations with pure, operate on existing computations with fmap (from Functor) and compose computations together with <*>, using the result of one computation as an input for another. It does not allow a library user to make a computation that makes another computation and then use the result of that computation directly; there's no way a user can write join :: f (f a) -> f a. This restriction will keep our library from running into the problem I described in my other answer.
Transformers, free, and the ApT transformer
Your example problem is quite involved, so we are going to pull out a bunch of high level Haskell tricks, and make a few new ones of our own. The first two tricks we are going to pull out are transformers and free data types. Transformers are types that take types with a kind like that of Functors, Applicatives or Monads and produce new types with the same kind.
Transformers typically look like the following Double example. Double can take any Functor or Applicative or Monad and make a version of it that always holds two values instead of one
newtype Double f a = Double {runDouble :: f (a, a)}
Free data types are transformers that do two things. First, given some simpler property of the underlying type the gain new exciting properties for the transformed type. The Free Monad provides a Monad given any Functor, and the free Applicative, Ap, makes an Applicative out of any Functor. The other thing "free" types do is they "free" the implementation of the interpreter as much as possible. Here are the types for the free Applicative, Ap, the free Monad, Free, and the free monad transfomer, FreeT. The free monad transformer provides a monad transformer for "free" given a Functor
-- Free Applicative
data Ap f a where
Pure :: a -> Ap f a
Ap :: f a -> Ap f (a -> b) -> Ap f b
-- Base functor of the free monad transformer
data FreeF f a b
= Pure a
| Free (f b)
-- Free monad transformer
newtype FreeT f m a = FreeT {runFreeT :: m (FreeF f a (FreeT f m a)}
-- The free monad is the free monad transformer applied to the Identity monad
type Free f = FreeT f Identity
Here's a sketch of our goal - we want to provide an Applicative interface for combining computations, which, at the bottom, allows Monadic computations. We want to "free" the interpreter as much as possible so that it can hopefully reorder computations. To do this, we will be combining both the free Applicative and the free monad transformer.
We want an Applicative interface, and the easiest one to make is the one we can get for "free", which aligns nicely with out goal of "freeing the interpeter" as much as possible. This suggests our type is going to look like
Ap f a
for some Functor f and any a. We'd like the underlying computation to be over some Monad, and Monads are functors, but we'd like to "free" the interpreter as much as posssible. We'll grab the free monad transformer as the underlying functor for Ap, giving us
Ap (FreeT f m) a
for some Functor f, some Monad m, and any a. We know the Monad m is probably going to be IO, but we'll leave our code as generic as possible. We just need to provide the Functor for FreeT. All Applicatives are Functors, so Ap itself could be used for f, we'd write something like
type ApT m a = Ap (FreeT (ApT m) m) a
This gives the compiler fits, so instead we'll mover the Ap inside and define
newtype ApT m a = ApT {unApT :: FreeT (Ap (ApT m)) m a}
We'll derive some instances for this and discuss its real motivation after an interlude.
Interlude
To run all of this code, you'll need the following. The Map and Control.Concurrent are only needed for sharing computations, more on that much later.
{-# LANGUAGE GADTs #-}
{-# LANGUAGE RankNTypes #-}
{-# LANGUAGE GeneralizedNewtypeDeriving #-}
module Main where
import Control.Monad.Trans.Class
import Control.Monad.IO.Class
import Control.Monad.Trans.Reader
import Control.Applicative
import Control.Applicative.Free hiding (Pure)
import qualified Control.Applicative.Free as Ap (Ap(Pure))
import Control.Monad.Trans.Free
import qualified Data.Map as Map
import Control.Concurrent
Stuffing it
I mislead you in the previous section, and pretended to discover ApT from resoning about the problem. I actually discovered ApT by trying anything and everything to try to stuff Monadic computations into an Applicative and be able to control their order when it came out. For a long time, I was trying to solve how to implement mapApM (below) in order to write flipImage (my replacement for your blur). Here's the ApT Monad transformer in all its glory. It's intended to be used as the Functor for an Ap, and, by using Ap as its own Functor for FreeT, can magically stuff values into an Applicative that shouldn't seem possible.
newtype ApT m a = ApT {unApT :: FreeT (Ap (ApT m)) m a}
deriving (Functor, Applicative, Monad, MonadIO)
It could derive even more instances from FreeT, these are just the ones we need. It can't derive MonadTrans, but we can do that ourselves:
instance MonadTrans ApT where
lift = ApT . lift
runApT :: ApT m a -> m (FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a))
runApT = runFreeT . unApT
The real beauty of ApT is we can write some seemingly impossible code like
stuffM :: (Functor m, Monad m) => m (ApT m a) -> ApT m a
stuffMAp :: (Functor m, Monad m) => m (ApT m a) -> Ap (ApT m) a
The m on the outside disappeares, even into Ap that's merely Applicative.
This works because of the following cycle of functions, each of which can stuff the output from the function above it into the input of the function below it. The first function starts with an ApT m a, and the last one ends with one. (These definitions aren't part of the program)
liftAp' :: ApT m a ->
Ap (ApT m) a
liftAp' = liftAp
fmapReturn :: (Monad m) =>
Ap (ApT m) a ->
Ap (ApT m) (FreeT (Ap (ApT m)) m a)
fmapReturn = fmap return
free' :: Ap (ApT m) (FreeT (Ap (ApT m)) m a) ->
FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a)
free' = Free
pure' :: a ->
FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a)
pure' = Pure
return' :: (Monad m) =>
FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a) ->
m (FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a))
return' = return
freeT :: m (FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a)) ->
FreeT (Ap (ApT m)) m a
freeT = FreeT
apT :: FreeT (Ap (ApT m)) m a ->
ApT m a
apT = ApT
This lets us write
-- Get rid of an Ap by stuffing it into an ApT.
stuffAp :: (Monad m) => Ap (ApT m) a -> ApT m a
stuffAp = ApT . FreeT . return . Free . fmap return
-- Stuff ApT into Free
stuffApTFree :: (Monad m) => ApT m a -> FreeF (Ap (ApT m)) a (FreeT (Ap (ApT m)) m a)
stuffApTFree = Free . fmap return . liftAp
-- Get rid of an m by stuffing it into an ApT
stuffM :: (Functor m, Monad m) => m (ApT m a) -> ApT m a
stuffM = ApT . FreeT . fmap stuffApTFree
-- Get rid of an m by stuffing it into an Ap
stuffMAp :: (Functor m, Monad m) => m (ApT m a) -> Ap (ApT m) a
stuffMAp = liftAp . stuffM
And some utility functions for working on a transformer stack
mapFreeT :: (Functor f, Functor m, Monad m) => (m a -> m b) -> FreeT f m a -> FreeT f m b
mapFreeT f fa = do
a <- fa
FreeT . fmap Pure . f . return $ a
mapApT :: (Functor m, Monad m) => (m a -> m b) -> ApT m a -> ApT m b
mapApT f = ApT . mapFreeT f . unApT
mapApM :: (Functor m, Monad m) => (m a -> m b) -> Ap (ApT m) a -> Ap (ApT m) b
mapApM f = liftAp . mapApT f . stuffAp
We'd like to start writing our example image processors, but first we need to take another diversion to address a hard requirement.
A hard requirement - input sharing
Your first example shows
-- timeShift(*2) --
-- / \
-- readImage -- addImages -> out
-- \ /
-- blur ----------
implying that the result of readImage should be shared between blur and timeShift(*2). I take this to mean that the results of readImage should only be computed once for each time.
Applicative isn't powerful enough to capture this. We'll make a new typeclass to represent computations whose output can be divided into multiple identical streams.
-- The class of things where input can be shared and divided among multiple parts
class Applicative f => Divisible f where
(<\>) :: (f a -> f b) -> f a -> f b
We'll make a transformer that adds this capability to existing Applicatives
-- A transformer that adds input sharing
data LetT f a where
NoLet :: f a -> LetT f a
Let :: LetT f b -> (LetT f b -> LetT f a) -> LetT f a
And provide some utility functions and instances for it
-- A transformer that adds input sharing
data LetT f a where
NoLet :: f a -> LetT f a
Let :: LetT f b -> (LetT f b -> LetT f a) -> LetT f a
liftLetT :: f a -> LetT f a
liftLetT = NoLet
mapLetT :: (f a -> f b) -> LetT f a -> LetT f b
mapLetT f = go
where
go (NoLet a) = NoLet (f a)
go (Let b g) = Let b (go . g)
instance (Applicative f) => Functor (LetT f) where
fmap f = mapLetT (fmap f)
-- I haven't checked that these obey the Applicative laws.
instance (Applicative f) => Applicative (LetT f) where
pure = NoLet . pure
NoLet f <*> a = mapLetT (f <*>) a
Let c h <*> a = Let c ((<*> a) . h)
instance (Applicative f) => Divisible (LetT f) where
(<\>) = flip Let
Image processors
With all of our transformers in place, we can start writing our image processors. At the bottom of our stack we have our ApT from an earlier section
Ap (ApT IO)
The computations need to be able to read the time from the environment, so we'll add a ReaderT for that
ReaderT Int (Ap (ApT IO))
Finally, we'd like to be able to share computations, so we'll add out LetT transformer on top, giving the entire type IP for our image processors
type Image = String
type IP = LetT (ReaderT Int (Ap (ApT IO)))
We'll read images from IO. getLine makes fun interactive examples.
readImage :: Int -> IP Image
readImage n = liftLetT $ ReaderT (\t -> liftAp . liftIO $ do
putStrLn $ "[" ++ show n ++ "] reading image for time: " ++ show t
--getLine
return $ "|image [" ++ show n ++ "] for time: " ++ show t ++ "|"
)
We can shift the time of inputs
timeShift :: (Int -> Int) -> IP a -> IP a
timeShift f = mapLetT shift
where
shift (ReaderT g) = ReaderT (g . f)
Add multiple images together
addImages :: Applicative f => [f Image] -> f Image
addImages = foldl (liftA2 (++)) (pure [])
And flip images pretending to use some library that's stuck in IO. I couldn't figure out how to blur a string...
inIO :: (IO a -> IO b) -> IP a -> IP b
inIO = mapLetT . mapReaderT . mapApM
flipImage :: IP [a] -> IP [a]
flipImage = inIO flip'
where
flip' ma = do
a <- ma
putStrLn "flipping"
return . reverse $ a
Interpreting LetT
Our LetT for sharing results is at the top of our transformer stack. We'll need to interpret it to get at the computations underneath it. To interpret LetT we will need a way to share results in IO, which memoize provides, and an interpeter that removes the LetT transformer from the top of the stack.
To share computations we need to store them somewhere, this memoizes an IO computation in IO, making sure it happens only once even across multiple threads.
memoize :: (Ord k) => (k -> IO a) -> IO (k -> IO a)
memoize definition = do
cache <- newMVar Map.empty
let populateCache k map = do
case Map.lookup k map of
Just a -> return (map, a)
Nothing -> do
a <- definition k
return (Map.insert k a map, a)
let fromCache k = do
map <- readMVar cache
case Map.lookup k map of
Just a -> return a
Nothing -> modifyMVar cache (populateCache k)
return fromCache
In order to interpret a Let, we need an evaluator for the underlying ApT IO to incorporate into the definitions for the Let bindings. Since the result of computations depends on the environment read from the ReaderT, we will incorporate dealing with the ReaderT into this step. A more sophisticated approach would use transformer classes, but transformer classes for Applicative is a topic for a different question.
compileIP :: (forall x. ApT IO x -> IO x) -> IP a -> IO (Int -> ApT IO a)
compileIP eval (NoLet (ReaderT f)) = return (stuffAp . f)
compileIP eval (Let b lf) = do
cb <- compileIP eval b
mb <- memoize (eval . cb)
compileIP eval . lf . NoLet $ ReaderT (liftAp . lift . mb)
Interpreting ApT
Our interpreter uses the following State to avoid needing to peek inside AsT, FreeT, and FreeF all the time.
data State m a where
InPure :: a -> State m a
InAp :: State m b -> State m (b -> State m a) -> State m a
InM :: m a -> State m a
instance Functor m => Functor (State m) where
fmap f (InPure a) = InPure (f a)
fmap f (InAp b sa) = InAp b (fmap (fmap (fmap f)) sa)
fmap f (InM m) = InM (fmap f m)
Interpereting Ap is harder than it looks. The goal is to take data that's in Ap.Pure and put it in InPure and data that's in Ap and put it in InAp. interpretAp actually needs to call itself with a larger type each time it goes into a deeper Ap; the function keeps picking up another argument. The first argument t provides a way to simplify these otherwise exploding types.
interpretAp :: (Functor m) => (a -> State m b) -> Ap m a -> State m b
interpretAp t (Ap.Pure a) = t a
interpretAp t (Ap mb ap) = InAp sb sf
where
sb = InM mb
sf = interpretAp (InPure . (t .)) $ ap
interperetApT gets data out of ApT, FreeT, and FreeF and into State m
interpretApT :: (Functor m, Monad m) => ApT m a -> m (State (ApT m) a)
interpretApT = (fmap inAp) . runApT
where
inAp (Pure a) = InPure a
inAp (Free ap) = interpretAp (InM . ApT) $ ap
With these simple interpreting pieces we can make strategies for interpreting results. Each strategy is a function from the interpreter's State to a new State, with possible side effect happening on the way. The order the strategy chooses to execute side effects in determines the order of the side effects. We'll make two example strategies.
The first strategy performs only one step on everything that's ready to be computed, and combines results when they are ready. This is probably the strategy that you want.
stepFB :: (Functor m, Monad m) => State (ApT m) a -> m (State (ApT m) a)
stepFB (InM ma) = interpretApT ma
stepFB (InPure a) = return (InPure a)
stepFB (InAp b f) = do
sf <- stepFB f
sb <- stepFB b
case (sf, sb) of
(InPure f, InPure b) -> return (f b)
otherwise -> return (InAp sb sf)
This other strategy performs all the calculations as soon as it knows about them. It performs them all in a single pass.
allFB :: (Functor m, Monad m) => State (ApT m) a -> m (State (ApT m) a)
allFB (InM ma) = interpretApT ma
allFB (InPure a) = return (InPure a)
allFB (InAp b f) = do
sf <- allFB f
sb <- allFB b
case (sf, sb) of
(InPure f, InPure b) -> return (f b)
otherwise -> allFB (InAp sb sf)
Many, many other strategies are possible.
We can evaluate a strategy by running it until it produces a single result.
untilPure :: (Monad m) => ((State f a) -> m (State f a)) -> State f a -> m a
untilPure s = go
where
go state =
case state of
(InPure a) -> return a
otherwise -> s state >>= go
Executing the intepreter
To execute the interpreter, we need some example data. Here are a few interesting examples.
example1 = (\i -> addImages [timeShift (*2) i, flipImage i]) <\> readImage 1
example1' = (\i -> addImages [timeShift (*2) i, flipImage i, flipImage . timeShift (*2) $ i]) <\> readImage 1
example1'' = (\i -> readImage 2) <\> readImage 1
example2 = addImages [timeShift (*2) . flipImage $ readImage 1, flipImage $ readImage 2]
The LetT interpreter needs to know what evaluator to use for bound values, so we'll define our evaluator only once. A single interpretApT kicks off the evaluation by finding the initial State of the interpreter.
evaluator :: ApT IO x -> IO x
evaluator = (>>= untilPure stepFB) . interpretApT
We'll compile example2, which is essentially your example, and run it for time 5.
main = do
f <- compileIP evaluator example2
a <- evaluator . f $ 5
print a
Which produces almost the desired result, with all reads happening before any flips.
[2] reading image for time: 5
[1] reading image for time: 10
flipping
flipping
"|01 :emit rof ]1[ egami||5 :emit rof ]2[ egami|"
A Monad can not reorder the component steps that make up img1 and img2 in
addImage :: (Monad m) => m [i] -> m [i] -> m [i]
addImage img1 img2 = do
i1 <- img1
i2 <- img2
return $ i1 ++ i2
if there exists any m [i] whose result depends on a side effect. Any MonadIO m has an m [i] whose result depends on a side effect, therefore you cannot reorder the component steps of img1 and img2.
The above desugars to
addImage :: (Monad m) => m [i] -> m [i] -> m [i]
addImage img1 img2 =
img1 >>=
(\i1 ->
img2 >>=
(\i2 ->
return (i1 ++ i2)
)
)
Let's focus on the first >>= (remembering that (>>=) :: forall a b. m a -> (a -> m b) -> m b). Specialized for our type, this is (>>=) :: m [i] -> ([i] -> m [i]) -> m [i]. If we are going to implement it, we'd have to write something like
(img1 :: m [i]) >>= (f :: [i] -> m [i]) = ...
In order to do anything with f, we need to pass it an [i]. The only correct [i] we have is stuck inside img1 :: m [i]. We need the result of img1 to do anything with f. There are now two possibilities. We either can or can not determine the result of img1 without executing its side effects. We will examine both cases, starting with when we can not.
can not
When we can not determine the result of img1 without executing its side effects, we have only one choice - we must execute img1 and all of its side effects. We now have an [i], but all of img1s side effects have already been executed. There's no way we can execute any of the side effects from img2 before some of the side effects of img1 because the side effects of img1 have already happened.
can
If we can determine the result of img1 without executing its side effects, we're in luck. We find the result of img1 and pass that to f, getting a new m [i] holding the result we want. We can now examine the side effects of both img1 and the new m [i] and reorder them (although there's a huge caveat here about the associative law for >>=).
the problem at hand
As this applies to our case, for any MonadIO, there exists the following, whose result can not be determined without executing its side effects, placing us firmly in the can not case where we can not re-order side effects.
counterExample :: (MonadIO m) => m String
counterExample = liftIO getLine
There are also many other counter examples, such as anything like readImage1 or readImage2 that must actually read the image from IO.
Most Haskell FRP frameworks like AFRP, Yampa and Reactive-banana make a difference between continuous time-varying functions and discrete ones. Usually they call them behaviors and events.
One exception is Netwire, which uses an inhibition monoid to model events. What are pros and cons of such an approach?
In particular, I'm interested in application of FRP to robot controlling. For example, this paper http://haskell.cs.yale.edu/?post_type=publication&p=182 show a way to encode a task and HSM abstractions in FRP using events. Can this be directly translated to Netwire?
The advantage of events as potentially inhibited signals is that it allows you to encode most even complicated reactive formulas very concisely. Imagine a switch that displays "yes" when pressed and "no" otherwise:
"yes" . switchPressed <|> "no"
The idea is that switchPressed acts like the identity wire if its corresponding event occurs and inhibits otherwise. That's where <|> comes in. If the first wire inhibits, it tries the second. Here is a hypothetical robot arm controlled by two buttons (left and right):
robotArm = integral_ 0 . direction
direction =
((-1) . leftPressed <|> 0) +
(1 . rightPressed <|> 0)
While the robot arm is hypothetical, this code is not. It's really the way you would write this in Netwire.
After some trials I've implemented the behavior I needed. Basically, You write a custom inhibitor type which catches the concept of events you need. In my case it was
data Inhibitor = Done | Timeout | Interrupt deriving Show
Done means normal finishing and the rest constructors signal some kind of an error.
After it, you write any custom combinators you need. In my case I needed a way to stop computations and signal a error further:
timeout deadline w | deadline <= 0 = inhibit Timeout
| otherwise = mkGen $ \dt a -> do
res <- stepWire w dt a
case res of
(Right o, w') -> return (Right o, timeout (deadline - dt) w')
(Left e, _) -> return (Left e, inhibit e)
This is a variant of switchBy which allows you to change the wire once. Note, it passes the inhibition signal of a new wire:
switchOn new w0 =
mkGen $ \dt x' ->
let select w' = do
(mx, w) <- stepWire w' dt x'
case mx of
Left ex -> stepWire (new ex) dt x'
Right x -> return (Right x, switchOn new w)
in select w0
And this is a variant of (-->) which catches the idea of interrupting the task chain.
infixr 1 ~>
w1 ~> w2 = switchOn ( \e -> case e of
Done -> w2
_ -> inhibit e
) w1
This is my attempt at a FIFO queue:
type Queue a = [a] -> [a]
empty :: Queue a
empty = id
remove :: Int -> Queue a -> ([a], Queue a)
remove n queue = (take n (queue []), (\x -> drop n (queue x)));
add :: [a] -> Queue a -> Queue a
add elems queue = (\x -> queue (elems ++ x))
empty creates an empty queue, remove takes the first n elements of the queue and returns the rest of the queue as the second element of the tuple, and add adds the list elems to the queue.
Will this add/remove 1 element in O(1) time and n elements in O(n) time?
What you have implemented effectively amounts to difference lists. (See: dlist.)
Difference lists allow for cheap appends, but unfortunately your removal will take linear time. It becomes more clear if we rewrite your code slightly:
type Queue a = [a] -> [a]
empty :: Queue a
empty = id
toList :: Queue a -> [a]
toList q = q []
fromList :: [a] -> Queue a
fromList = (++)
remove :: Int -> Queue a -> ([a], Queue a)
remove n q = (xs, fromList ys)
where
(xs, ys) = splitAt n (toList q)
add :: [a] -> Queue a -> Queue a
add xs q = (++ xs) . q
Note that I have made the conversion to and from lists a bit more explicit than it was in your code. You clearly see that the core of your removal code gets bracketed between toList and fromList.
Well, sidestepping your question somewhat, the classic purely functional implementation of a FIFO queue is as a pair of lists, one for the "front" and one for the "back." You enqueue elements by adding them as the head of the back list, and dequeue by taking the head of the front list; if the front list is empty, you "rotate" the queue by reversing the back list and swapping that with the empty front list. In code:
import Control.Monad
import Data.List
import Data.Maybe
data FIFO a = FIFO [a] [a]
deriving Show
empty :: FIFO a
empty = FIFO [] []
isEmpty :: FIFO a -> Bool
isEmpty (FIFO [] []) = True
isEmpty _ = False
enqueue :: a -> FIFO a -> FIFO a
enqueue x (FIFO front back) = FIFO front (x:back)
-- | Remove the head off the queue. My type's different from yours
-- because I use Maybe to handle the case where somebody tries to
-- dequeue off an empty FIFO.
dequeue :: FIFO a -> Maybe (a, FIFO a)
dequeue queue = case queue of
FIFO [] [] -> Nothing
FIFO (x:f) b -> Just (x, FIFO f b)
otherwise -> dequeue (rotate queue)
where rotate (FIFO [] back) = FIFO (reverse back) []
-- | Elements exit the queue in the order they appear in the list.
fromList :: [a] -> FIFO a
fromList xs = FIFO xs []
-- | Elements appear in the result list in the order they exit the queue.
toList :: FIFO a -> [a]
toList = unfoldr dequeue
That's the classic implementation. Now your operations can be written in terms of that:
-- | Enqueue multiple elements. Elements exit the queue in the order
-- they appear in xs.
add :: [a] -> FIFO a -> FIFO a
add xs q = foldl' (flip enqueue) q xs
To write remove in terms of dequeue, you need to handle all of those intermediate FIFOs from the (a, FIFO a) result of dequeue. One way to do that is to use the State monad:
import Control.Monad.State
-- | Remove n elements from the queue. My result type is different
-- from yours, again, because I handle the empty FIFO case. If you
-- try to remove too many elements, you get a bunch of Nothings at
-- the end of your list.
remove :: Int -> FIFO a -> ([Maybe a], FIFO a)
remove n q = runState (removeM n) q
-- | State monad action to dequeue n elements from the state queue.
removeM :: Int -> State (FIFO a) [Maybe a]
removeM n = replicateM n dequeueM
-- | State monad action to dequeue an element from the state queue.
dequeueM :: State (FIFO a) (Maybe a)
dequeueM = do q <- get
case dequeue q of
Just (x, q') -> put q' >> return (Just x)
Nothing -> return Nothing
I was looking for a FIFO queue that's faster than taking a list and reversing it. Stefan's add isn't performant (O(n)), so here's what's worked for me after benchmarking:
add :: a -> Queue a -> Queue a
add x f = f . (x:)