A left-strict pair; the base functor for streams of individual elements.

A singleton stream

>>> stdoutLn $ yield "hello"
hello

>>> S.sum $ do {yield 1; yield 2; yield 3}
6 :> ()

>>> let number = lift (putStrLn "Enter a number:") >> lift readLn >>= yield :: Stream (Of Int) IO ()
>>> S.toList $ do {number; number; number}
Enter a number:
1<Enter>
Enter a number:
2<Enter>
Enter a number:
3<Enter>
[1,2,3] :> ()

Stream the elements of a pure, foldable container.

>>> S.print $ each [1..3]
1
2
3

View standard input as a Stream (Of String) m r. By contrast, stdoutLn renders a Stream (Of String) m r to standard output. The names follow Pipes.Prelude

>>> stdoutLn stdinLn
hello<Enter>
hello
world<Enter>
world
^CInterrupted.

>>> stdoutLn $ S.map reverse stdinLn
hello<Enter>
olleh
world<Enter>
dlrow
^CInterrupted.

Read values from stdin, ignoring failed parses.

>>> :set -XTypeApplications
>>> S.sum $ S.take 2 (S.readLn @IO @Int)
10<Enter>
12<Enter>
22 :> ()

>>> S.toList $ S.take 2 (S.readLn @IO @Int)
10<Enter>
1@#$%^&*\<Enter>
12<Enter>
[10,12] :> ()

Read Strings from a Handle using hGetLine

Terminates on end of input

>>> IO.withFile "/usr/share/dict/words" IO.ReadMode $ S.stdoutLn . S.take 3 . S.drop 50000 . S.fromHandle
deflagrator
deflate
deflation

Read the lines of a file, using a function of the type: 'Stream (Of String) IO () -> IO a' to turn the stream into a value of type 'IO a'.

>>> S.writeFile "lines.txt" $ S.take 2 S.stdinLn
hello<Enter>
world<Enter>
>>> S.readFile "lines.txt" S.print
"hello"
"world"

Iterate a pure function from a seed value, streaming the results forever

Iterate a monadic function from a seed value, streaming the results forever

Repeat an element ad inf. .

>>> S.print $ S.take 3 $ S.repeat 1
1
1
1

Repeat a monadic action ad inf., streaming its results.

>>> S.toList $ S.take 2 $ repeatM getLine
one<Enter>
two<Enter>
["one","two"]

Repeat an element several times.

Cycle repeatedly through the layers of a stream, ad inf. This function is functor-general

cycle = forever

>>> rest <- S.print $ S.splitAt 3 $ S.cycle (yield True >> yield False)
True
False
True
>>> S.print $ S.take 3 rest
False
True
False

Repeat an action several times, streaming its results.

>>> S.print $ S.replicateM 2 getCurrentTime
2015-08-18 00:57:36.124508 UTC
2015-08-18 00:57:36.124785 UTC

An infinite stream of enumerable values, starting from a given value. It is the same as S.iterate succ. Because their return type is polymorphic, enumFrom, enumFromThen and iterate are useful with functions like zip and zipWith, which require the zipped streams to have the same return type.

For example, with each [1..] the following bit of connect-and-resume would not compile:

>>> rest <- S.print $ S.zip (S.enumFrom 1) $ S.splitAt 3 $ S.each ['a'..'z']
(1,'a')
(2,'b')
(3,'c')
>>> S.print $ S.take 3 rest
'd'
'e'
'f'

An infinite sequence of enumerable values at a fixed distance, determined by the first and second values. See the discussion of enumFrom

>>> S.print $ S.take 3 $ S.enumFromThen 100 200
100
200
300

Build a Stream by unfolding steps starting from a seed. In particular note that S.unfoldr S.next = id.

The seed can of course be anything, but this is one natural way to consume a pipes Producer. Consider:

>>> S.stdoutLn $ S.take 2 $ S.unfoldr Pipes.next Pipes.stdinLn
hello<Enter>
hello
goodbye<Enter>
goodbye

>>> S.stdoutLn $ S.unfoldr Pipes.next (Pipes.stdinLn >-> Pipes.take 2)
hello<Enter>
hello
goodbye<Enter>
goodbye

>>> S.effects $ S.unfoldr Pipes.next (Pipes.stdinLn >-> Pipes.take 2 >-> Pipes.stdoutLn)
hello<Enter>
hello
goodbye<Enter>
goodbye

Pipes.unfoldr S.next similarly unfolds a Pipes.Producer from a stream.

Write Strings to stdout using putStrLn; terminates on a broken output pipe (The name and implementation are modelled on the Pipes.Prelude stdoutLn).

>>> S.stdoutLn $ S.take 3 $ S.each $ words "one two three four five"
one
two
three

Write Strings to stdout using putStrLn

Unlike stdoutLn, stdoutLn' does not handle a broken output pipe. Thus it can have a polymorphic return value, rather than (), and this kind of "connect and resume" is possible:

>>> rest <- S.stdoutLn' $ S.show $ S.splitAt 3 (each [1..5])
1
2
3
>>> S.toList rest
[4,5] :> ()

Reduce a stream to its return value with a monadic action.

>>> S.mapM_ Prelude.print $ each [1..3]
1
2
3

>>> rest <- S.mapM_ Prelude.print $ S.splitAt 3 $ each [1..10]
1
2
3
>>> S.sum rest
49 :> ()

Print the elements of a stream as they arise.

>>> S.print $ S.take 2 S.stdinLn
hello<Enter>
"hello"
world<Enter>
"world"

Write a succession of strings to a handle as separate lines.

>>> S.toHandle IO.stdout $ each (words "one two three")
one
two
three

Write a series of Strings as lines to a file.

>>> S.writeFile "lines.txt" $ S.take 2 S.stdinLn
hello<Enter>
world<Enter>

>>> S.readFile "lines.txt" S.stdoutLn
hello
world

Reduce a stream, performing its actions but ignoring its elements.

>>> rest <- S.effects $ S.splitAt 2 $ each [1..5]
>>> S.print rest
3
4
5

effects should be understood together with copy and is subject to the rules

S.effects . S.copy       = id
hoist S.effects . S.copy = id

The similar effects and copy operations in Data.ByteString.Streaming obey the same rules.

Remove the elements from a stream of values, retaining the structure of layers.

Where a transformer returns a stream, run the effects of the stream, keeping the return value. This is usually used at the type

drained :: Monad m => Stream (Of a) m (Stream (Of b) m r) -> Stream (Of a) m r
drained = join . fmap (lift . effects)

Here, for example, we split a stream in two places and throw out the middle segment:

>>> rest <- S.print $ S.drained $ S.splitAt 2 $ S.splitAt 5 $ each [1..7]
1
2
>>> S.print rest
6
7

In particular, we can define versions of take and takeWhile which retrieve the return value of the rest of the stream - and which can thus be used with maps:

take' n = S.drained . S.splitAt n
takeWhile' thus = S.drained . S.span thus

Standard map on the elements of a stream.

>>> S.stdoutLn $ S.map reverse $ each (words "alpha beta")
ahpla
ateb

Replace each element of a stream with the result of a monadic action

>>> S.print $ S.mapM readIORef $ S.chain (\ior -> modifyIORef ior (*100)) $ S.mapM newIORef $ each [1..6]
100
200
300
400
500
600

See also chain for a variant of this which ignores the return value of the function and just uses the side effects.

Map layers of one functor to another with a transformation. Compare hoist, which has a similar effect on the monadic parameter.

maps id = id
maps f . maps g = maps (f . g)

Map layers of one functor to another with a transformation. Compare hoist, which has a similar effect on the monadic parameter.

mapsPost id = id
mapsPost f . mapsPost g = mapsPost (f . g)
mapsPost f = maps f

mapsPost is essentially the same as maps, but it imposes a Functor constraint on its target functor rather than its source functor. It should be preferred if fmap is cheaper for the target functor than for the source functor.

Map layers of one functor to another with a transformation involving the base monad.

This function is completely functor-general. It is often useful with the more concrete type

mapped :: (forall x. Stream (Of a) IO x -> IO (Of b x)) -> Stream (Stream (Of a) IO) IO r -> Stream (Of b) IO r

to process groups which have been demarcated in an effectful, IO-based stream by grouping functions like group, split or breaks. Summary functions like fold, foldM, mconcat or toList are often used to define the transformation argument. For example:

>>> S.toList_ $ S.mapped S.toList $ S.split 'c' (S.each "abcde")
["ab","de"]

maps and mapped obey these rules:

maps id              = id
mapped return        = id
maps f . maps g      = maps (f . g)
mapped f . mapped g  = mapped (f <=< g)
maps f . mapped g    = mapped (fmap f . g)
mapped f . maps g    = mapped (f <=< fmap g)

maps is more fundamental than mapped, which is best understood as a convenience for effecting this frequent composition:

mapped phi = decompose . maps (Compose . phi)

A version of mapped that imposes a Functor constraint on the target functor rather than the source functor. This version should be preferred if fmap on the target functor is cheaper.

for replaces each element of a stream with an associated stream. Note that the associated stream may layer any functor.

Replace each element in a stream of individual Haskell values (a Stream (Of a) m r) with an associated functorial step.

for str f  = concats (with str f)
with str f = for str (yields . f)
with str f = maps (\(a:>r) -> r <$ f a) str
with = flip subst
subst = flip with

>>> with (each [1..3]) (yield . Prelude.show) & intercalates (yield "--") & S.stdoutLn
1
--
2
--
3

Replace each element in a stream of individual values with a functorial layer of any sort. subst = flip with and is more convenient in a sequence of compositions that transform a stream.

with = flip subst
for str f = concats $ subst f str
subst f = maps (\(a:>r) -> r <$ f a)
S.concat = concats . subst each

Duplicate the content of stream, so that it can be acted on twice in different ways, but without breaking streaming. Thus, with each [1,2] I might do:

>>> S.print $ each ["one","two"]
"one"
"two"
>>> S.stdoutLn $ each ["one","two"]
one
two

With copy, I can do these simultaneously:

>>> S.print $ S.stdoutLn $ S.copy $ each ["one","two"]
"one"
one
"two"
two

copy should be understood together with effects and is subject to the rules

S.effects . S.copy       = id
hoist S.effects . S.copy = id

The similar operations in Streaming obey the same rules.

Where the actions you are contemplating are each simple folds over the elements, or a selection of elements, then the coupling of the folds is often more straightforwardly effected with Foldl, e.g.

>>> L.purely S.fold (liftA2 (,) L.sum L.product) $ each [1..10]
(55,3628800) :> ()

rather than

>>> S.sum $ S.product . S.copy $ each [1..10]
55 :> (3628800 :> ())

A Control.Foldl fold can be altered to act on a selection of elements by using handles on an appropriate lens. Some such manipulations are simpler and more List-like, using copy:

>>> L.purely S.fold (liftA2 (,) (L.handles (L.filtered odd) L.sum) (L.handles (L.filtered even) L.product)) $ each [1..10]
(25,3840) :> ()

becomes

>>> S.sum $ S.filter odd $ S.product $ S.filter even $ S.copy $ each [1..10]
25 :> (3840 :> ())

or using store

>>> S.sum $ S.filter odd $ S.store (S.product . S.filter even) $ each [1..10]
25 :> (3840 :> ())

But anything that fold of a Stream (Of a) m r into e.g. an m (Of b r) that has a constraint on m that is carried over into Stream f m - e.g. Monad, MonadIO, MonadResource, etc. can be used on the stream. Thus, I can fold over different groupings of the original stream:

>>> (S.toList . mapped S.toList . chunksOf 5) $  (S.toList . mapped S.toList . chunksOf 3) $ S.copy $ each [1..10]
[[1,2,3,4,5],[6,7,8,9,10]] :> ([[1,2,3],[4,5,6],[7,8,9],[10]] :> ())

The procedure can be iterated as one pleases, as one can see from this (otherwise unadvisable!) example:

>>> (S.toList . mapped S.toList . chunksOf 4) $ (S.toList . mapped S.toList . chunksOf 3) $ S.copy $ (S.toList . mapped S.toList . chunksOf 2) $ S.copy $ each [1..12]
[[1,2,3,4],[5,6,7,8],[9,10,11,12]] :> ([[1,2,3],[4,5,6],[7,8,9],[10,11,12]] :> ([[1,2],[3,4],[5,6],[7,8],[9,10],[11,12]] :> ()))

copy can be considered a special case of expand:

  copy = expand $ \p (a :> as) -> a :> p (a :> as)

If Of were an instance of Comonad, then one could write

  copy = expand extend

An alias for copy.

Store the result of any suitable fold over a stream, keeping the stream for further manipulation. store f = f . copy :

>>> S.print $ S.store S.product $ each [1..4]
1
2
3
4
24 :> ()

>>> S.print $ S.store S.sum $ S.store S.product $ each [1..4]
1
2
3
4
10 :> (24 :> ())

Here the sum (10) and the product (24) have been 'stored' for use when finally we have traversed the stream with print . Needless to say, a second pass is excluded conceptually, so the folds that you apply successively with store are performed simultaneously, and in constant memory -- as they would be if, say, you linked them together with Control.Fold:

>>> L.impurely S.foldM (liftA3 (\a b c -> (b, c)) (L.sink Prelude.print) (L.generalize L.sum) (L.generalize L.product)) $ each [1..4]
1
2
3
4
(10,24) :> ()

Fusing folds after the fashion of Control.Foldl will generally be a bit faster than the corresponding succession of uses of store, but by constant factor that will be completely dwarfed when any IO is at issue.

But store / copy is much more powerful, as you can see by reflecting on uses like this:

>>> S.sum $ S.store (S.sum . mapped S.product . chunksOf 2) $ S.store (S.product . mapped S.sum . chunksOf 2) $ each [1..6]
21 :> (44 :> (231 :> ()))

It will be clear that this cannot be reproduced with any combination of lenses, Control.Fold folds, or the like. (See also the discussion of copy.)

It would conceivably be clearer to import a series of specializations of store. It is intended to be used at types like these:

storeM ::  (forall s m . Monad m => Stream (Of a) m s -> m (Of b s))
        -> (Monad n => Stream (Of a) n r -> Stream (Of a) n (Of b r))
storeM = store

storeMIO :: (forall s m . MonadIO m => Stream (Of a) m s -> m (Of b s))
         -> (MonadIO n => Stream (Of a) n r -> Stream (Of a) n (Of b r)
storeMIO = store

It is clear from these types that we are just using the general instances:

instance (Functor f, Monad m)   => Monad (Stream f m)
instance (Functor f, MonadIO m) => MonadIO (Stream f m)

We thus can't be touching the elements of the stream, or the final return value. It is the same with other constraints that Stream (Of a) inherits from the underlying monad, like MonadResource. Thus I can independently filter and write to one file, but nub and write to another, or interact with a database and a logfile and the like:

>>> (S.writeFile "hello2.txt" . S.nubOrd) $ store (S.writeFile "hello.txt" . S.filter (/= "world")) $ each ["hello", "world", "goodbye", "world"]
>>> :! cat hello.txt
hello
goodbye
>>> :! cat hello2.txt
hello
world
goodbye

Apply an action to all values, re-yielding each. The return value (y) of the function is ignored.

>>> S.product $ S.chain Prelude.print $ S.each [1..5]
1
2
3
4
5
120 :> ()

See also mapM for a variant of this which uses the return value of the function to transorm the values in the stream.

Like the sequence but streaming. The result type is a stream of a's, but is not accumulated; the effects of the elements of the original stream are interleaved in the resulting stream. Compare:

sequence :: Monad m =>       [m a]           -> m [a]
sequence :: Monad m => Stream (Of (m a)) m r -> Stream (Of a) m r

This obeys the rule

Remove repeated elements from a Stream. nubOrd of course accumulates a Set of elements that have already been seen and should thus be used with care.

>>> S.toList_ $ S.nubOrd $ S.take 5 S.readLn :: IO [Int]
1<Enter>
2<Enter>
3<Enter>
1<Enter>
2<Enter>
[1,2,3]

Use nubOrdOn to have a custom ordering function for your elements.

More efficient versions of above when working with Ints that use IntSet.

Skip elements of a stream that fail a predicate

Skip elements of a stream that fail a monadic test

Map monadically over a stream, producing a new stream only containing the Just values.

Interpolate a delay of n seconds between yields.

Intersperse given value between each element of the stream.

>>> S.print $ S.intersperse 0 $ each [1,2,3]
1
0
2
0
3

End a stream after n elements; the original return value is thus lost. splitAt preserves this information. Note that, like splitAt, this function is functor-general, so that, for example, you can take not just a number of items from a stream of elements, but a number of substreams and the like.

>>> S.toList $ S.take 3 $ each "with"
"wit" :> ()

>>> S.readFile "stream.hs" (S.stdoutLn . S.take 3)
import Streaming
import qualified Streaming.Prelude as S
import Streaming.Prelude (each, next, yield)

End stream when an element fails a condition; the original return value is lost. By contrast span preserves this information, and is generally more desirable.

S.takeWhile thus = void . S.span thus

To preserve the information - but thus also force the rest of the stream to be developed - write

S.drained . S.span thus

as dropWhile thus is

S.effects . S.span thus

Like takeWhile, but takes a monadic predicate.

Ignore the first n elements of a stream, but carry out the actions

>>> S.toList $ S.drop 2 $ S.replicateM 5 getLine
a<Enter>
b<Enter>
c<Enter>
d<Enter>
e<Enter>
["c","d","e"] :> ()

Because it retains the final return value, drop n is a suitable argument for maps:

>>> S.toList $ concats $ maps (S.drop 4) $ chunksOf 5 $ each [1..20]
[5,10,15,20] :> ()

Ignore elements of a stream until a test succeeds, retaining the rest.

>>> S.print $ S.dropWhile ((< 5) . length) S.stdinLn
one<Enter>
two<Enter>
three<Enter>
"three"
four<Enter>
"four"
^CInterrupted.

Make a stream of foldable containers into a stream of their separate elements. This is just

concat str = for str each

>>> S.print $ S.concat (each ["xy","z"])
'x'
'y'
'z'

Note that it also has the effect of catMaybes, rights map snd and such-like operations.

>>> S.print $ S.concat $ S.each [Just 1, Nothing, Just 2]
1
2
>>> S.print $  S.concat $ S.each [Right 1, Left "Error!", Right 2]
1
2
>>> S.print $ S.concat $ S.each [('A',1), ('B',2)]
1
2

Strict left scan, streaming, e.g. successive partial results. The seed is yielded first, before any action of finding the next element is performed.

>>> S.print $ S.scan (++) "" id $ each (words "a b c d")
""
"a"
"ab"
"abc"
"abcd"

scan is fitted for use with Control.Foldl, thus:

>>> S.print $ L.purely S.scan L.list $ each [3..5]
[]
[3]
[3,4]
[3,4,5]

Strict left scan, accepting a monadic function. It can be used with FoldMs from Control.Foldl using impurely. Here we yield a succession of vectors each recording

>>> let v = L.impurely scanM L.vectorM $ each [1..4::Int] :: Stream (Of (Vector Int)) IO ()
>>> S.print v
[]
[1]
[1,2]
[1,2,3]
[1,2,3,4]

Label each element in a stream with a value accumulated according to a fold.

>>> S.print $ S.scanned (*) 1 id $ S.each [100,200,300]
(100,100)
(200,20000)
(300,6000000)

>>> S.print $ L.purely S.scanned L.product $ S.each [100,200,300]
(100,100)
(200,20000)
(300,6000000)

Make a stream of strings into a stream of parsed values, skipping bad cases

>>> S.sum_ $ S.read $ S.takeWhile (/= "total") S.stdinLn :: IO Int
1000<Enter>
2000<Enter>
total<Enter>
3000

The natural cons for a Stream (Of a).

cons a stream = yield a >> stream

Useful for interoperation:

Data.Text.foldr S.cons (return ()) :: Text -> Stream (Of Char) m ()
Lazy.foldrChunks S.cons (return ()) :: Lazy.ByteString -> Stream (Of Strict.ByteString) m ()

and so on.

slidingWindow accumulates the first n elements of a stream, update thereafter to form a sliding window of length n. It follows the behavior of the slidingWindow function in conduit-combinators.

>>> S.print $ S.slidingWindow 4 $ S.each "123456"
fromList "1234"
fromList "2345"
fromList "3456"

slidingWindowMin finds the minimum in every sliding window of n elements of a stream. If within a window there are multiple elements that are the least, it prefers the first occurrence (if you prefer to have the last occurrence, use the max version and flip your comparator). It satisfies:

slidingWindowMin n s = map minimum (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls compare O(m) times overall where m is the total number of elements in the stream.

slidingWindowMinBy finds the minimum in every sliding window of n elements of a stream according to the given comparison function (which should define a total ordering). See notes above about elements that are equal. It satisfies:

slidingWindowMinBy f n s = map (minimumBy f) (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls the comparison function O(m) times overall where m is the total number of elements in the stream.

slidingWindowMinOn finds the minimum in every sliding window of n elements of a stream according to the given projection function. See notes above about elements that are equal. It satisfies:

slidingWindowMinOn f n s = map (minimumOn (comparing f)) (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls compare on the projected value O(m) times overall where m is the total number of elements in the stream, and it calls the projection function exactly m times.

slidingWindowMax finds the maximum in every sliding window of n elements of a stream. If within a window there are multiple elements that are the largest, it prefers the last occurrence (if you prefer to have the first occurrence, use the min version and flip your comparator). It satisfies:

slidingWindowMax n s = map maximum (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls compare O(m) times overall where m is the total number of elements in the stream.

slidingWindowMaxBy finds the maximum in every sliding window of n elements of a stream according to the given comparison function (which should define a total ordering). See notes above about elements that are equal. It satisfies:

slidingWindowMaxBy f n s = map (maximumBy f) (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls the comparison function O(m) times overall where m is the total number of elements in the stream.

slidingWindowMaxOn finds the maximum in every sliding window of n elements of a stream according to the given projection function. See notes above about elements that are equal. It satisfies:

slidingWindowMaxOn f n s = map (maximumOn (comparing f)) (slidingWindow n s)

Except that it is far more efficient, especially when the window size is large: it calls compare on the projected value O(m) times overall where m is the total number of elements in the stream, and it calls the projection function exactly m times.

Before evaluating the monadic action returning the next step in the Stream, wrapEffect extracts the value in a monadic computation m a and passes it to a computation a -> m y.

The standard way of inspecting the first item in a stream of elements, if the stream is still 'running'. The Right case contains a Haskell pair, where the more general inspect would return a left-strict pair. There is no reason to prefer inspect since, if the Right case is exposed, the first element in the pair will have been evaluated to whnf.

next    :: Monad m => Stream (Of a) m r -> m (Either r    (a, Stream (Of a) m r))
inspect :: Monad m => Stream (Of a) m r -> m (Either r (Of a (Stream (Of a) m r)))

Interoperate with pipes producers thus:

Pipes.unfoldr Stream.next :: Stream (Of a) m r -> Producer a m r
Stream.unfoldr Pipes.next :: Producer a m r -> Stream (Of a) m r

Similarly:

IOStreams.unfoldM (fmap (either (const Nothing) Just) . next) :: Stream (Of a) IO b -> IO (InputStream a)
Conduit.unfoldM   (fmap (either (const Nothing) Just) . next) :: Stream (Of a) m r -> Source a m r

But see uncons, which is better fitted to these unfoldMs

Inspect the first item in a stream of elements, without a return value. uncons provides convenient exit into another streaming type:

IOStreams.unfoldM uncons :: Stream (Of a) IO b -> IO (InputStream a)
Conduit.unfoldM uncons   :: Stream (Of a) m r -> Conduit.Source m a

Split a succession of layers after some number, returning a streaming or effectful pair. This function is the same as the splitsAt exported by the Streaming module, but since this module is imported qualified, it can usurp a Prelude name. It specializes to:

 splitAt :: (Monad m) => Int -> Stream (Of a) m r -> Stream (Of a) m (Stream (Of a) m r)

Split a stream of elements wherever a given element arises. The action is like that of words.

>>> S.stdoutLn $ mapped S.toList $ S.split ' ' $ each "hello world  "
hello
world

Break during periods where the predicate is not satisfied, grouping the periods when it is.

>>> S.print $ mapped S.toList $ S.breaks not $ S.each [False,True,True,False,True,True,False]
[True,True]
[True,True]
>>> S.print $ mapped S.toList $ S.breaks id $ S.each [False,True,True,False,True,True,False]
[False]
[False]
[False]

Break a sequence upon meeting element falls under a predicate, keeping it and the rest of the stream as the return value.

>>> rest <- S.print $ S.break even $ each [1,1,2,3]
1
1
>>> S.print rest
2
3

Yield elements, using a fold to maintain state, until the accumulated value satifies the supplied predicate. The fold will then be short-circuited and the element that breaks it will be put after the break. This function is easiest to use with purely

>>> rest <- each [1..10] & L.purely S.breakWhen L.sum (>10) & S.print
1
2
3
4
>>> S.print rest
5
6
7
8
9
10

Stream elements until one fails the condition, return the rest.

Group successive equal items together

>>> S.toList $ mapped S.toList $ S.group $ each "baaaaad"
["b","aaaaa","d"] :> ()

>>> S.toList $ concats $ maps (S.drained . S.splitAt 1) $ S.group $ each "baaaaaaad"
"bad" :> ()

Group elements of a stream in accordance with the supplied comparison.

>>> S.print $ mapped S.toList $ S.groupBy (>=) $ each [1,2,3,1,2,3,4,3,2,4,5,6,7,6,5]
[1]
[2]
[3,1,2,3]
[4,3,2,4]
[5]
[6]
[7,6,5]

Swap the order of functors in a sum of functors.

>>> S.toList $ S.print $ separate $ maps S.switch $ maps (S.distinguish (=='a')) $ S.each "banana"
'a'
'a'
'a'
"bnn" :> ()
>>> S.toList $ S.print $ separate $ maps (S.distinguish (=='a')) $ S.each "banana"
'b'
'n'
'n'
"aaa" :> ()

Given a stream on a sum of functors, make it a stream on the left functor, with the streaming on the other functor as the governing monad. This is useful for acting on one or the other functor with a fold, leaving the other material for another treatment. It generalizes partitionEithers, but actually streams properly.

>>> let odd_even = S.maps (S.distinguish even) $ S.each [1..10::Int]
>>> :t separate odd_even
separate odd_even
  :: Monad m => Stream (Of Int) (Stream (Of Int) m) ()

Now, for example, it is convenient to fold on the left and right values separately:

>>> S.toList $ S.toList $ separate odd_even
[2,4,6,8,10] :> ([1,3,5,7,9] :> ())

Or we can write them to separate files or whatever:

>>> S.writeFile "even.txt" . S.show $ S.writeFile "odd.txt" . S.show $ S.separate odd_even
>>> :! cat even.txt
2
4
6
8
10
>>> :! cat odd.txt
1
3
5
7
9

Of course, in the special case of Stream (Of a) m r, we can achieve the above effects more simply by using copy

>>> S.toList . S.filter even $ S.toList . S.filter odd $ S.copy $ each [1..10::Int]
[2,4,6,8,10] :> ([1,3,5,7,9] :> ())

But separate and unseparate are functor-general.

Strict fold of a Stream of elements that preserves the return value. The third parameter will often be id where a fold is written by hand:

>>> S.fold (+) 0 id $ each [1..10]
55 :> ()

>>> S.fold (*) 1 id $ S.fold (+) 0 id $ S.copy $ each [1..10]
3628800 :> (55 :> ())

It can be used to replace a standard Haskell type with one more suited to writing a strict accumulation function. It is also crucial to the Applicative instance for Control.Foldl.Fold We can apply such a fold purely

Control.Foldl.purely S.fold :: Monad m => Fold a b -> Stream (Of a) m r -> m (Of b r)

Thus, specializing a bit:

L.purely S.fold L.sum :: Stream (Of Int) Int r -> m (Of Int r)
mapped (L.purely S.fold L.sum) :: Stream (Stream (Of Int)) IO r -> Stream (Of Int) IO r

Here we use the Applicative instance for Control.Foldl.Fold to stream three-item segments of a stream together with their sums and products.

>>> S.print $ mapped (L.purely S.fold (liftA3 (,,) L.list L.product L.sum)) $ chunksOf 3 $ each [1..10]
([1,2,3],6,6)
([4,5,6],120,15)
([7,8,9],504,24)
([10],10,10)

Strict fold of a Stream of elements, preserving only the result of the fold, not the return value of the stream. The third parameter will often be id where a fold is written by hand:

>>> S.fold_ (+) 0 id $ each [1..10]
55

It can be used to replace a standard Haskell type with one more suited to writing a strict accumulation function. It is also crucial to the Applicative instance for Control.Foldl.Fold

Control.Foldl.purely fold :: Monad m => Fold a b -> Stream (Of a) m () -> m b

Strict, monadic fold of the elements of a Stream (Of a)

Control.Foldl.impurely foldM' :: Monad m => FoldM a b -> Stream (Of a) m r -> m (b, r)

Thus to accumulate the elements of a stream as a vector, together with a random element we might write:

>>> L.impurely S.foldM (liftA2 (,) L.vectorM L.random) $ each [1..10::Int] :: IO (Of (Vector Int, Maybe Int) ())
([1,2,3,4,5,6,7,8,9,10],Just 9) :> ()

Strict, monadic fold of the elements of a Stream (Of a)

Control.Foldl.impurely foldM :: Monad m => FoldM a b -> Stream (Of a) m () -> m b

Map each element of the stream to a monoid, and take the monoidal sum of the results.

>>> S.foldMap Sum $ S.take 2 (S.stdinLn)
1<Enter>
2<Enter>
3<Enter>
Sum {getSum = 6} :> ()

Fold a Stream of numbers into their sum with the return value

 mapped S.sum :: Stream (Stream (Of Int)) m r -> Stream (Of Int) m r

>>> S.sum $ each [1..10]
55 :> ()

>>> (n :> rest)  <- S.sum $ S.splitAt 3 $ each [1..10]
>>> System.IO.print n
6
>>> (m :> rest') <- S.sum $ S.splitAt 3 rest
>>> System.IO.print m
15
>>> S.print rest'
7
8
9
10

Fold a Stream of numbers into their sum

Fold a Stream of numbers into their product with the return value

 mapped product :: Stream (Stream (Of Int)) m r -> Stream (Of Int) m r

Fold a Stream of numbers into their product

Exhaust a stream remembering only whether a was an element.

Exhaust a stream deciding whether a was an element.

Run a stream, keeping its length and its return value.

>>> S.print $ mapped S.length $ chunksOf 3 $ S.each [1..10]
3
3
3
1

Run a stream, remembering only its length:

>>> runIdentity $ S.length_ (S.each [1..10] :: Stream (Of Int) Identity ())
10

Convert an effectful Stream into a list alongside the return value

 mapped toList :: Stream (Stream (Of a) m) m r -> Stream (Of [a]) m r

Like toList_, toList breaks streaming; unlike toList_ it preserves the return value and thus is frequently useful with e.g. mapped

>>> S.print $ mapped S.toList $ chunksOf 3 $ each [1..9]
[1,2,3]
[4,5,6]
[7,8,9]

>>> S.print $ mapped S.toList $ chunksOf 2 $ S.replicateM 4 getLine
s<Enter>
t<Enter>
["s","t"]
u<Enter>
v<Enter>
["u","v"]

Convert an effectful Stream (Of a) into a list of as

Note: Needless to say, this function does not stream properly. It is basically the same as Prelude mapM which, like replicateM, sequence and similar operations on traversable containers is a leading cause of space leaks.

Fold streamed items into their monoidal sum

>>> S.mconcat $ S.take 2 $ S.map (Data.Monoid.Last . Just) S.stdinLn
first<Enter>
last<Enter>
Last {getLast = Just "last"} :> ()

A natural right fold for consuming a stream of elements. See also the more general iterT in the Streaming module and the still more general destroy

A natural right fold for consuming a stream of elements. See also the more general iterTM in the Streaming module and the still more general destroy

foldrT (\a p -> Streaming.yield a >> p) = id
foldrT (\a p -> Pipes.yield a     >> p) :: Monad m => Stream (Of a) m r -> Producer a m r
foldrT (\a p -> Conduit.yield a   >> p) :: Monad m => Stream (Of a) m r -> Conduit a m r

Zip two Streams

Zip two Streams using the provided combining function

Zip three Streams together

Zip three Streams with a combining function

The type

Data.List.unzip     :: [(a,b)] -> ([a],[b])

might lead us to expect

Streaming.unzip :: Stream (Of (a,b)) m r -> Stream (Of a) m (Stream (Of b) m r)

which would not stream, since it would have to accumulate the second stream (of bs). Of course, Data.List unzip doesn't stream either.

This unzip does stream, though of course you can spoil this by using e.g. toList:

>>> let xs = Prelude.map (\x -> (x, Prelude.show x)) [1..5 :: Int]

>>> S.toList $ S.toList $ S.unzip (S.each xs)
["1","2","3","4","5"] :> ([1,2,3,4,5] :> ())

>>> Prelude.unzip xs
([1,2,3,4,5],["1","2","3","4","5"])

Note the difference of order in the results. It may be of some use to think why. The first application of toList was applied to a stream of integers:

>>> :t S.unzip $ S.each xs
S.unzip $ S.each xs :: Monad m => Stream (Of Int) (Stream (Of String) m) ()

Like any fold, toList takes no notice of the monad of effects.

toList :: Monad m => Stream (Of a) m r -> m (Of [a] r)

In the case at hand (since I am in ghci) m = Stream (Of String) IO. So when I apply toList, I exhaust that stream of integers, folding it into a list:

>>> :t S.toList $ S.unzip $ S.each xs
S.toList $ S.unzip $ S.each xs
  :: Monad m => Stream (Of String) m (Of [Int] ())

When I apply toList to this, I reduce everything to an ordinary action in IO, and return a list of strings:

>>> S.toList $ S.toList $ S.unzip (S.each xs)
["1","2","3","4","5"] :> ([1,2,3,4,5] :> ())

unzip can be considered a special case of either unzips or expand:

  unzip = unzips . maps (\((a,b) :> x) -> Compose (a :> b :> x))
  unzip = expand $ \p ((a,b) :> abs) -> b :> p (a :> abs)

Separate left and right values in distinct streams. (separate is a more powerful, functor-general, equivalent using Sum in place of Either). So, for example, to permit unlimited user input of Ints on condition of only two errors, we might write:

>>> S.toList $ S.print $ S.take 2 $ partitionEithers $ S.map readEither $ S.stdinLn  :: IO (Of [Int] ())
1<Enter>
2<Enter>
qqqqqqqqqq<Enter>
"Prelude.read: no parse"
3<Enter>
rrrrrrrrrr<Enter>
"Prelude.read: no parse"
[1,2,3] :> ()

partitionEithers = separate . maps S.eitherToSum
lefts  = hoist S.effects . partitionEithers
rights = S.effects . partitionEithers
rights = S.concat

filter p = hoist effects (partition p)

Merge two streams of elements ordered with their Ord instance.

The return values of both streams are returned.

>>> S.print $ merge (each [1,3,5]) (each [2,4])
1
2
3
4
5
((), ())

Merge two streams, ordering them by applying the given function to each element before comparing.

The return values of both streams are returned.

Merge two streams, ordering the elements using the given comparison function.

The return values of both streams are returned.

The catMaybes function takes a Stream of Maybes and returns a Stream of all of the Just values. concat has the same behavior, but is more general; it works for any foldable container type.

The mapMaybe function is a version of map which can throw out elements. In particular, the functional argument returns something of type Maybe b. If this is Nothing, no element is added on to the result Stream. If it is Just b, then b is included in the result Stream.

Note that lazily, strictly, fst', and mapOf are all so-called natural transformations on the primitive Of a functor. If we write

 type f ~~> g = forall x . f x -> g x

then we can restate some types as follows:

 mapOf            :: (a -> b) -> Of a ~~> Of b   -- Bifunctor first
 lazily           ::             Of a ~~> (,) a
 Identity . fst'  ::             Of a ~~> Identity a

Manipulation of a Stream f m r by mapping often turns on recognizing natural transformations of f. Thus maps is far more general the the map of the Streaming.Prelude, which can be defined thus:

 S.map :: (a -> b) -> Stream (Of a) m r -> Stream (Of b) m r
 S.map f = maps (mapOf f)

i.e.

 S.map f = maps (\(a :> x) -> (f a :> x))

This rests on recognizing that mapOf is a natural transformation; note though that it results in such a transformation as well:

 S.map :: (a -> b) -> Stream (Of a) m ~~> Stream (Of b) m

Thus we can maps it in turn.

Convert a standard Haskell pair into a left-strict pair

fst' and snd' extract the first and second element of a pair

>>> S.fst' (1:>"hi")
1
>>> S.snd' (1:>"hi")
"hi"

They are contained in the _first and _second lenses, if any lens library is in scope

>>> import Lens.Micro
>>> (1:>"hi") ^. S._first
1
>>> (1:>"hi") ^. S._second
"hi"

Map a function over the first element of an Of pair

>>> S.mapOf even (1:>"hi")
False :> "hi"

mapOf is just first from the Bifunctor instance

>>> first even (1:>"hi")
False :> "hi"

and is contained in the _first lens

>>> import Lens.Micro
>>> over S._first even (1:>"hi")
False :> "hi"

A lens into the first element of a left-strict pair

A lens into the second element of a left-strict pair

Read an IORef (Maybe a) or a similar device until it reads Nothing. reread provides convenient exit from the io-streams library

reread readIORef    :: IORef (Maybe a) -> Stream (Of a) IO ()
reread Streams.read :: System.IO.Streams.InputStream a -> Stream (Of a) IO ()