I have a function that pattern matches on its arguments to produce a computation in StateT () Maybe (). This computation can fail when run, in which case I want the current pattern match branch to fail, so to speak.
I highly doubt it's possible to have something like
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f (Just n1) (Just n2) = do
m <- compute (n1 + n2)
guard (m == 42)
f (Just n) _ = do
m <- compute n
guard (m == 42)
f _ (Just n) = do
m <- compute n
guard (m == 42)
behave in the way I want it to: When the first computation fails due to the guard or somewhere in compute, I want f to try the next pattern.
Obviously the above can't work, because StateT (as any other monad might) involves an additional parameter when expanded, so I probably can't formulate this as simple pattern guards.
The following does what I want, but it's ugly:
f' :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f' a b = asum (map (\f -> f a b) [f1, f2, f3])
where
f1 a b = do
Just n1 <- pure a
Just n2 <- pure b
m <- compute (n1 + n2)
guard (m == 42)
f2 a _ = do
Just n <- pure a
m <- compute n
guard (m == 42)
f3 _ b = do
Just n <- pure b
m <- compute n
guard (m == 42)
A call like execStateT (f (Just 42) (Just 1)) () would fail for f but return Just () for f', because it matches f2.
How do I get the behavior of f' while having elegant pattern matching with as little auxiliary definitions as possible like in f? Are there other, more elegant ways to formulate this?
Complete runnable example:
#! /usr/bin/env stack
-- stack --resolver=lts-11.1 script
import Control.Monad.Trans.State
import Control.Applicative
import Control.Monad
import Data.Foldable
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f (Just n1) (Just n2) = do
m <- compute (n1 + n2)
guard (m == 42)
f (Just n) _ = do
m <- compute n
guard (m == 42)
f _ (Just n) = do
m <- compute n
guard (m == 42)
f' :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f' a b = asum (map (\f -> f a b) [f1, f2, f3])
where
f1 a b = do
Just n1 <- pure a
Just n2 <- pure b
m <- compute (n1 + n2)
guard (m == 42)
f2 a _ = do
Just n <- pure a
m <- compute n
guard (m == 42)
f3 _ b = do
Just n <- pure b
m <- compute n
guard (m == 42)
main = do
print $ execStateT (f (Just 42) (Just 1)) () -- Nothing
print $ execStateT (f' (Just 42) (Just 1)) () -- Just (), because `f2` succeeded
Edit: I elicited quite some clever answers with this question so far, thanks! Unfortunately, they mostly suffer from overfitting to the particular code example I've given. In reality, I need something like this for unifying two expressions (let-bindings, to be precise), where I want to try unifying the RHS of two simultaneous lets if possible and fall through to the cases where I handle let bindings one side at a time by floating them. So, actually there's no clever structure on Maybe arguments to exploit and I'm not computeing on Int actually.
The answers so far might benefit others beyond the enlightenment they brought me though, so thanks!
Edit 2: Here's some compiling example code with probably bogus semantics:
module Unify (unify) where
import Control.Applicative
import Control.Monad.Trans.State.Strict
data Expr
= Var String -- meta, free an bound vars
| Let String Expr Expr
-- ... more cases
-- no Eq instance, fwiw
-- | If the two terms unify, return the most general unifier, e.g.
-- a substitution (`Map`) of meta variables for terms as association
-- list.
unify :: [String] -> Expr -> Expr -> Maybe [(String, Expr)]
unify metaVars l r = execStateT (go [] [] l r) [] -- threads the current substitution as state
where
go locals floats (Var x) (Var y)
| x == y = return ()
go locals floats (Var x) (Var y)
| lookup x locals == Just y = return ()
go locals floats (Var x) e
| x `elem` metaVars = tryAddSubstitution locals floats x e
go locals floats e (Var y)
| y `elem` metaVars = tryAddSubstitution locals floats y e
-- case in point:
go locals floats (Let x lrhs lbody) (Let y rrhs rbody) = do
go locals floats lrhs rrhs -- try this one, fail current pattern branch if rhss don't unify
-- if we get past the last statement, commit to this branch, no matter
-- the next statement fails or not
go ((x,y):locals) floats lbody rbody
-- try to float the let binding. terms mentioning a floated var might still
-- unify with a meta var
go locals floats (Let x rhs body) e = do
go locals (Left (x,rhs):floats) body e
go locals floats e (Let y rhs body) = do
go locals (Right (y,rhs):floats) body e
go _ _ _ _ = empty
tryAddSubstitution = undefined -- magic
When I need something like this, I just use asum with the blocks inlined. Here I also condensed the multiple patterns Just n1 <- pure a; Just n2 <- pure b into one, (Just n1, Just n2) <- pure (a, b).
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f a b = asum
[ do
(Just n1, Just n2) <- pure (a, b)
m <- compute (n1 + n2)
guard (m == 42)
, do
Just n <- pure a
m <- compute n
guard (m == 42)
, do
Just n <- pure b
m <- compute n
guard (m == 42)
]
You can also use chains of <|>, if you prefer:
f :: Maybe Int -> Maybe Int -> StateT () Maybe ()
f a b
= do
(Just n1, Just n2) <- pure (a, b)
m <- compute (n1 + n2)
guard (m == 42)
<|> do
Just n <- pure a
m <- compute n
guard (m == 42)
<|> do
Just n <- pure b
m <- compute n
guard (m == 42)
This is about as minimal as you can get for this kind of “fallthrough”.
If you were using Maybe alone, you would be able to do this with pattern guards:
import Control.Monad
import Control.Applicative
ensure :: Alternative f => (a -> Bool) -> a -> f a
ensure p a = a <$ guard (p a)
compute :: Int -> Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> Maybe Int
f (Just m) (Just n)
| Just x <- ensure (== 42) =<< compute (m + n)
= return x
f (Just m) _
| Just x <- ensure (== 42) =<< compute m
= return x
f _ (Just n)
| Just x <- ensure (== 42) =<< compute n
= return x
f _ _ = empty
(ensure is a general purpose combinator. Cf. Lift to Maybe using a predicate)
As you have StateT on the top, though, you would have to supply a state in order to pattern match on Maybe, which would foul up everything. That being so, you are probably better off with something in the vein of your "ugly" solution. Here is a whimsical attempt at improving its looks:
import Control.Monad
import Control.Applicative
import Control.Monad.State
import Control.Monad.Trans
import Data.Foldable
ensure :: Alternative f => (a -> Bool) -> a -> f a
ensure p a = a <$ guard (p a)
compute :: Int -> StateT () Maybe Int
compute = return
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f a b = asum (map (\c -> f' (c a b)) [liftA2 (+), const, flip const])
where
f' = ensure (== 42) <=< compute <=< lift
While this is an answer specific to the snippet I've given, the refactorings only apply limited to the code I was facing.
Perhaps it's not that far-fetched of an idea to extract the skeleton of the asum expression above to a more general combinator:
-- A better name would be welcome.
selector :: Alternative f => (a -> a -> a) -> (a -> f b) -> a -> a -> f b
selector g k x y = asum (fmap (\sel -> k (sel x y)) [g, const, flip const])
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f = selector (liftA2 (+)) (ensure (== 42) <=< compute <=< lift)
Though it is perhaps a bit awkward of a combinator, selector does show the approach is more general than it might appear at first: the only significant restriction is that k has to produce results in some Alternative context.
P.S.: While writing selector with (<|>) instead of asum is arguably more tasteful...
selector g k x y = k (g x y) <|> k x <|> k y
... the asum version straightforwardly generalises to an arbitrary number of pseudo-patterns:
selector :: Alternative f => [a -> a -> a] -> (a -> f b) -> a -> a -> f b
selector gs k x y = asum (fmap (\g -> k (g x y)) gs)
It looks like you could get rid of the whole pattern match by relying on the fact that Int forms a Monoid with addition and 0 as the identity element, and that Maybe a forms a Monoid if a does. Then your function becomes:
f :: Maybe Int -> Maybe Int -> StateT () Maybe Int
f a b = pure $ a <> b >>= compute >>= pure . mfilter (== 42)
You could generalise by passing the predicate as an argument:
f :: Monoid a => (a -> Bool) -> Maybe a -> Maybe a -> StateT () Maybe a
f p a b = pure $ a <> b >>= compute >>= pure . mfilter p
The only thing is that compute is now taking a Maybe Int as input, but that is just a matter of calling traverse inside that function with whatever computation you need to do.
Edit: Taking into account your last edit, I find that if you spread your pattern matches into separate computations that may fail, then you can just write
f a b = f1 a b <|> f2 a b <|> f3 a b
where f1 (Just a) (Just b) = compute (a + b) >>= check
f1 _ _ = empty
f2 (Just a) _ = compute a >>= check
f2 _ _ = empty
f3 _ (Just b) = compute b >>= check
f3 _ _ = empty
check x = guard (x == 42)
Related
How can I get a maximum element of an effectful container where computing attribute to compare against also triggers an effect?
There has to be more readable way of doing things like:
latest dir = Turtle.fold (z (ls dir)) Fold.maximum
z :: MonadIO m => m Turtle.FilePath -> m (UTCTime, Turtle.FilePath)
z mx = do
x <- mx
d <- datefile x
return (d, x)
I used overloaded version rather than non-overloaded maximumBy but the latter seems better suite for ad-hoc attribute selection.
How can I be more methodic in solving similar problems?
So I know nothing about Turtle; no idea whether this fits well with the rest of the Turtle ecosystem. But since you convinced me in the comments that maximumByM is worth writing by hand, here's how I would do it:
maximumOnM :: (Monad m, Ord b) => (a -> m b) -> [a] -> m a
maximumOnM cmp [x] = return x -- skip the effects if there's no need for comparison
maximumOnM cmp (x:xs) = cmp x >>= \b -> go x b xs where
go x b [] = return x
go x b (x':xs) = do
b' <- cmp x'
if b < b' then go x' b' xs else go x b xs
I generally prefer the *On versions of things -- which take a function that maps to an Orderable element -- to the *By versions -- which take a function that does the comparison directly. A maximumByM would be similar but have a type like Monad m => (a -> a -> m Ordering) -> [a] -> m a, but this would likely force you to redo effects for each a, and I'm guessing it's not what you want. I find *On more often matches with the thing I want to do and the performance characteristics I want.
Since you're already familiar with Fold, you might want to get to know FoldM, which is similar.
data FoldM m a b =
-- FoldM step initial extract
forall x . FoldM (x -> a -> m x) (m x) (x -> m b)
You can write:
maximumOnM ::
(Ord b, Monad m)
=> (a -> m b) -> FoldM m a (Maybe a)
maximumOnM f = FoldM combine (pure Nothing) (fmap snd)
where
combine Nothing a = do
f_a <- f a
pure (Just (f_a, a))
combine o#(Just (f_old, old)) new = do
f_new <- f new
if f_new > f_old
then pure $ Just (f_new, new)
else pure o
Now you can use Foldl.foldM to run the fold on a list (or other Foldable container). Like Fold, FoldM has an Applicative instance, so you can combine multiple effectful folds into one that interleaves the effects of each of them and combines their results.
It's possible to run effects on foldables using reducers package.
I'm not sure if it's correct, but it leverages existing combinators and instances (except for Bounded (Maybe a)).
import Data.Semigroup.Applicative (Ap(..))
import Data.Semigroup.Reducer (foldReduce)
import Data.Semigroup (Max(..))
import System.IO (withFile, hFileSize, IOMode(..))
-- | maxLength
--
-- >>> getMax $ maxLength ["abc","a","hello",""]
-- 5
maxLength :: [String] -> (Max Int)
maxLength = foldReduce . map (length)
-- | maxLengthIO
--
-- Note, this runs IO...
--
-- >>> (getAp $ maxLengthIO ["package.yaml", "src/Lib.hs"]) >>= return . getMax
-- Just 1212
--
-- >>> (getAp $ maxLengthIO []) >>= return . getMax
-- Nothing
maxLengthIO :: [String] -> Ap IO (Max (Maybe Integer))
maxLengthIO xs = foldReduce (map (fmap Just . f) xs) where
f :: String -> IO Integer
f s = withFile s ReadMode hFileSize
instance Ord a => Bounded (Maybe a) where
maxBound = Nothing
minBound = Nothing
I have a recursive function f that takes two parameters x and y. The function is uniquely determined by the first parameter; the second one merely makes things easier.
I now want to memoise that function w.r.t. it's first parameter while ignoring the second one. (I.e. f is evaluated at most one for every value of x)
What is the easiest way to do that? At the moment, I simply define an array containing all values recursively, but that is a somewhat ad-hoc solution. I would prefer some kind of memoisation combinator that I can just throw at my function.
EDIT: to clarify, the function f takes a pair of integers and a list. The first integer is some parameter value, the second one denotes the index of an element in some global list xs to consume.
To avoid indexing the list, I pass the partially consumed list to f as well, but obviously, the invariant is that if the first parameter is (m, n), the second one will always be drop n xs, so the result is uniquely determined by the first parameter.
Just using a memoisation combinator on the partially applied function will not work, since that will leave an unevaluated thunk \xs -> … lying around. I could probably wrap the two parameters in a datatype whose Eq instance ignores the second value (and similarly for other instances), but that seems like a very ad-hoc solution. Is there not an easier way?
EDIT2: The concrete function I want to memoise:
g :: [(Int, Int)] -> Int -> Int
g xs n = f 0 n
where f :: Int -> Int -> Int
f _ 0 = 0
f m n
| m == length xs = 0
| w > n = f (m + 1) n
| otherwise = maximum [f (m + 1) n, v + f (m + 1) (n - w)]
where (w, v) = xs !! m
To avoid the expensive indexing operation, I instead pass the partially-consumed list to f as well:
g' :: [(Int, Int)] -> Int -> Int
g' xs n = f xs 0 n
where f :: [(Int, Int)] -> Int -> Int -> Int
f [] _ _ = 0
f _ _ 0 = 0
f ((w,v) : xs) m n
| w > n = f xs (m + 1) n
| otherwise = maximum [f xs (m + 1) n, v + f xs (m + 1) (n - w)]
Memoisation of f w.r.t. the list parameter is, of course, unnecessary, since the list does not (morally) influence the result. I would therefore like the memoisation to simply ignore the list parameter.
Your function is unnecessarily complicated. You don't need the index m at all:
foo :: [(Int, Int)] -> Int -> Int
foo [] _ = 0
foo _ 0 = 0
foo ((w,v):xs) n
| w > n = foo xs n
| otherwise = foo xs n `max` foo xs (n - w) + v
Now if you want to memoize foo then both the arguments must be considered (as it should be).
We'll use the monadic memoization mixin method to memoize foo:
First, we create an uncurried version of foo (because we want to memoize both arguments):
foo' :: ([(Int, Int)], Int) -> Int
foo' ([], _) = 0
foo' (_, 0) = 0
foo' ((w,v):xs, n)
| w > n = foo' (xs, n)
| otherwise = foo' (xs, n) `max` foo' (xs, n - w) + v
Next, we monadify the function foo' (because we want to thread a memo table in the function):
foo' :: Monad m => ([(Int, Int)], Int) -> m Int
foo' ([], _) = return 0
foo' (_, 0) = return 0
foo' ((w,v):xs, n)
| w > n = foo' (xs, n)
| otherwise = do
a <- foo' (xs, n)
b <- foo' (xs, n - w)
return (a `max` b + v)
Then, we open the self-reference in foo' (because we want to call the memoized function):
type Endo a = a -> a
foo' :: Monad m => Endo (([(Int, Int)], Int) -> Int)
foo' _ ([], _) = return 0
foo' _ (_, 0) = return 0
foo' self ((w,v):xs, n)
| w > n = foo' (xs, n)
| otherwise = do
a <- self (xs, n)
b <- self (xs, n - w)
return (a `max` b + v)
We'll use the following memoization mixin to memoize our function foo':
type Dict a b m = (a -> m (Maybe b), a -> b -> m ())
memo :: Monad m => Dict a b m -> Endo (a -> m b)
memo (check, store) super a = do
b <- check a
case b of
Just b -> return b
Nothing -> do
b <- super a
store a b
return b
Our dictionary (memo table) will use the State monad and a Map data structure:
import Prelude hiding (lookup)
import Control.Monad.State
import Data.Map.Strict
mapDict :: Ord a => Dict a b (State (Map a b))
mapDict = (check, store) where
check a = gets (lookup a)
store a b = modify (insert a b)
Finally, we combine everything to create a memoized function memoFoo:
import Data.Function (fix)
type MapMemoized a b = a -> State (Map a b) b
memoFoo :: MapMemoized ([(Int, Int)], Int) Int
memoFoo = fix (memo mapDict . foo')
We can recover the original function foo as follows:
foo :: [(Int, Int)] -> Int -> Int
foo xs n = evalState (memoFoo (xs, n)) empty
Hope that helps.
I have trouble in understanding the following fmap instance.Can someone explain me what fmap do(in this case) and how it can be used? Or write it less obfuscated?
Thanks!
newtype Parser a = P { getParser :: String -> Maybe (a, String) }
instance Functor Parser where
fmap f (P p) = P $ \s -> fmap (applyToFirst f) $ p s
{-|
Applies a function to the first component of a pair.
-}
applyToFirst :: (a -> b) -> (a, c) -> (b, c)
applyToFirst f (x, y) = (f x, y)
What does it do?
It transforms a parser X into parser Y, where Y does the following: runs parser X and applies function f to first element of parsing result pair.
How to use it?
p1 :: Parser String
p1 = P (\s -> Just ("foo", "bar"))
p2 :: Parser String
p2 = fmap (\s -> s ++ s) p1
Now (getParser p2) "whatever" equals Just ("foofoo", "bar").
Could it be less obfuscated?
It is not obfuscated actually. Haskell takes time to get used to.
Learn You a Haskell presents the addStuff function:
import Control.Monad.Instances
addStuff :: Int -> Int
addStuff = do
a <- (*2) -- binds (*2) to a
b <- (+10) -- binds (+10) to b
return (a+b) -- return has type sig: 'Monad m => a -> m a'
Are the types of a, b, and return (a+b) all Int -> Int? I think so, but I'm not sure how bind-ing plays a role.
I tried to implement it using >>=, but I'm not sure how to complete it (hence ...).
addStuff' :: Int -> Int
addStuff' = (*2) >>= (+10) >>= ...
Please give me a hint to complete it, as well as edit my understanding of the do notation version.
As I understand, the ... needs to include a type of Int -> Int. In the do version, I could use a and b, but I'm not sure how to add them with the >>= version.
When working with the reader monad (a.k.a. the function monad), you have the type a -> b, which can be rewritten as (->) a b. The actual monad instance here is
instance Monad ((->) r) where
return x = const x
f >>= g = \r -> g (f r) r
Notice that during >>=, the type is
(>>=) :: ((->) r a) -> (a -> ((->) r b)) -> ((->) r b)
Which can be rewritten as
(>>=) :: (r -> a) -> (a -> (r -> b)) -> (r -> b)
Or even
(>>=) :: (r -> a) -> (a -> r -> b) -> (r -> b)
So as you can see, what >>= does is take a single input, apply that to f, and then apply that result to g to produce a new function r -> b. So for your example, you could use:
addStuff' :: Int -> Int
addStuff' = (*2) >>= (+)
And so addStuff' 10 == 30, since it performs the computation (10 * 2) + (10). Note how 10 is fed both to (*2) and (+), and the result of (10*2) is fed to (+) as well. It might make things a little more clear to see it as
test :: Int -> (Int, Int, Int)
test = do
x <- (*2)
y <- (*3)
z <- (*5)
return (x, y, z)
And it's result would be
> test 1
(2, 3, 5)
> test 10
(20, 30, 50)
What this essentially is doing is taking the argument to test "before" it's been applied, feeding it to each of the functions on the right hand side of the <-s, and then combining that result in the return.
So how can you write these without do notation? You could do something like
test :: Int -> (Int, Int, Int)
test =
(\r -> r * 2) >>= (\x ->
(\r -> r * 3) >>= (\y ->
(\r -> r * 5) >>= (\z ->
return (x, y, z))))
Which, admittedly, is not very readable, even with formatting, but the gist is basically that r gets fed to each intermediate function, which produces a result, and a couple nested lambda expressions later you return all three of those results in a tuple.
With a bit of simplification, you could also make each of those nested lambdas into two arguments lambdas:
test =
(\r -> r * 2) >>=
(\x r -> r * 3) >>=
(\y r -> r * 5) >>=
(\z r -> const (x, y, z) r)
I've also replaced the last \z -> return (x, y, z) with its equivalent \z -> const (x, y, z) => \z r -> const (x, y, z) r, just so they all have the same form.
As a rough rule if you want to manually desugar do-notation, first erase the do at the top and flip the bind arrow (<-) on the left-hand-side to a (>>=) on the right-hand-side with the variable on the left as a lambda variable on the right. So:
addStuff :: Int -> Int
addStuff = do
a <- (*2)
... rest ...
Becomes:
addStuff :: Int -> Int
addStuff =
(*2) >>= (\a ->
... rest ...
)
This is recursive, so the next term in the do-notation then becomes nested in the lambda of the desugared term above it, all the way down to the last expression which is just the body of the nested lambda expression.
The desugaring is quite mechanical, it's defined by the following rewrites, where ; denotes a newline.
do { a <- f ; m } ≡ f >>= \a -> do { m }
do { f ; m } ≡ f >> do { m }
do { m } ≡ m
Both a and b are of type Int while return (a+b) has type Int -> Int which is the last term in the do-notation so it has to be identical to the toplevel signature. Using -XScopedTypeVariables we can manually annotate the subterms:
{-# LANGUAGE ScopedTypeVariables #-}
import Control.Monad.Instances
addStuff :: Int -> Int
addStuff = do
(a :: Int) <- (*2)
(b :: Int) <- (+10)
(return (a+b)) :: Int -> Int
Thanks to bheklilr.
I wrote my own code.
addStuff :: Int -> Int
addStuff = (\r -> r * 2) >>= (\x ->
(\r -> r + 10) >>= (\y ->
return (x + y)))
Are there a possibility to stop a recursive algorithm when it throws some exception provided by us, save it's state, ask user something and then continue the recursion from the saved place?
I changed the question.
I read a file system recursively and keep data in a tree. Suddenly I face with a hidden directory. Can I stop calculations and ask now user should I place information about the directory in my tree and then continue calculations?
About working with IO:
obtainTree :: ByteString -> Tree
...
main = print $ obtainTree partition
as I understand to work with IO inside the algorithm we have to use function like this:
obtainTree :: ByteString -> IO Tree
but can we avoid it?
Sure you can do it. You can always set things up so that you capture the remaining computation as a continuation, which can be resumed externally.
Here's one way to do something like this:
-- intended to be put in a module that only exports the following list:
-- (Resumable, Prompted, prompt, runResumable, extract, resume)
import Control.Applicative
newtype Resumable e r a = R { runResumable :: Either (Prompted e r a) a }
data Prompted e r a = P e (r -> Resumable e r a)
suspend :: e -> (r -> Resumable e r a) -> Resumable e r a
suspend e = R . Left . P e
instance Functor (Resumable e r) where
fmap f (R (Right x)) = pure $ f x
fmap f (R (Left (P e g))) = suspend e $ \x -> f <$> g x
instance Applicative (Resumable e r) where
pure = R . Right
(R (Right f)) <*> (R (Right x)) = pure $ f x
(R (Left (P e f))) <*> x = suspend e $ \y -> f y <*> x
f <*> (R (Left (P e g))) = suspend e $ \y -> f <*> g y
instance Monad (Resumable e r) where
return = pure
(R (Right x)) >>= f = f x
(R (Left (P e f))) >>= g = suspend e $ \x -> f x >>= g
prompt :: e -> Resumable e r r
prompt e = suspend e pure
extract :: Prompted e r a -> e
extract (P e _) = e
resume :: Prompted e r a -> r -> Either (Prompted e r a) a
resume (P _ f) e = runResumable $ f e
This lets you divide up your logic into an internal piece that runs inside Resumable and an external piece that handles the results of the internal part's prompting using whatever method it likes.
Here's a simple example of using this:
askAboutNegatives :: [Int] -> Resumable Int Bool [Int]
askAboutNegatives [] = return []
askAboutNegatives (x:xs) = do
keep <- if x < 0 then prompt x else return True
rest <- askAboutNegatives xs
return $ if keep then x:rest else rest
main :: IO ()
main = do
let ls = [1, -4, 2, -7, 3]
loopIfNeeded (Right r) = return r
loopIfNeeded (Left p) = do
putStrLn $ "Would you like to keep " ++ show (extract p)
i <- getLine
loopIfNeeded $ resume p (i == "y")
asked <- loopIfNeeded $ runResumable (askAboutNegatives ls)
print asked
As a way of making this use case simpler, the module containing Resumable can be augmented to also export this function:
runResumableWithM :: Monad m => (e -> m r) -> Resumable e r a -> m a
runResumableWithM f x = case runResumable x of
Right y -> return y
Left (P e g) -> do
r <- f e
runResumableWithM f $ g r
Which would allow rewriting main from that example as the somewhat simpler:
main :: IO ()
main = do
let ls = [1, -4, 2, -7, 3]
ask x = do
putStrLn $ "Would you like to keep " ++ show x
i <- getLine
return $ i == "y"
asked <- runResumableWithM ask (askAboutNegatives ls)
print asked
The one real issue with this approach is that every prompt must have the same type. Otherwise, it handles the problem nicely, using continuations to capture the rest of the computation implicitly when needed.
First thing first, a pure code cannot go to IO or we can say a pure function needs to become impure if it tries to use some impure function (i.e trying to use IO). In case you are wondering why this so, think about this: If the pure function ask the impure function about some data to complete its own processing then it looses "Referential transparency" because now the pure function can return different result for same input due to the involved impure (IO) call, hence it is no more pure.
Based on the above information, your solution will be as simple as making use of higher order function to ask the user about the information. Something like:
parseFileSystem :: FileSystem -> (Directory -> IO Tree) -> IO Tree
Here the (Directory -> IO Tree) is the function that will ask user about the required information and return a Tree data based on it.