Imports

Find minimum and maximum elements i.e. (min, max).

Like length, except with a more general Num return value

Definition:

>>> genericLength = fmap getSum $ Scanl.foldMap (Sum . const  1)
>>> genericLength = Scanl.mkScanl (\n _ -> n + 1) 0

Pre-release

Determine the maximum element in a stream using the supplied comparison function.

Computes the minimum element with respect to the given comparison function

Determine the length of the input stream.

Definition:

>>> length = Scanl.genericLength
>>> length = fmap getSum $ Scanl.foldMap (Sum . const  1)

Include only those elements that pass a predicate.

>>> Stream.toList $ Stream.scanl (Scanl.filter (> 5) Scanl.sum) $ Stream.fromList [1..10]
[0,0,0,0,0,0,6,13,21,30,40]

>>> filter p = Scanl.postscanlMaybe (Scanl.filtering p)
>>> filter p = Scanl.filterM (return . p)
>>> filter p = Scanl.mapMaybe (\x -> if p x then Just x else Nothing)

Make a scan that yields the supplied value on any input.

Pre-release

Scans the input stream building a list.

Warning! working on large lists accumulated as buffers in memory could be very inefficient, consider using Streamly.Data.Array instead.

>>> toList = Scanl.mkScanr (:) []

Modify a scan to receive a Maybe input, the Just values are unwrapped and sent to the original scan, Nothing values are discarded.

>>> catMaybes = Scanl.mapMaybe id
>>> catMaybes = Scanl.filter isJust . Scanl.lmap fromJust

Determine the maximum element in a stream.

Definitions:

>>> maximum = Scanl.maximumBy compare
>>> maximum = Scanl.mkScanl1 max

Same as the following but without a default maximum. The Max Monoid uses the minBound as the default maximum:

>>> maximum = fmap Data.Semigroup.getMax $ Scanl.foldMap Data.Semigroup.Max

Determine the minimum element in a stream using the supplied comparison function.

Definitions:

>>> minimum = Scanl.minimumBy compare
>>> minimum = Scanl.mkScanl1 min

Same as the following but without a default minimum. The Min Monoid uses the maxBound as the default maximum:

>>> maximum = fmap Data.Semigroup.getMin $ Scanl.foldMap Data.Semigroup.Min

Take at most n input elements and scan them using the supplied scan. A negative count is treated as 0.

>>> Stream.toList $ Stream.scanl (Scanl.take 2 Scanl.toList) $ Stream.fromList [1..10]
[[],[1],[1,2]]

Like filter but with a monadic predicate.

>>> f p x = p x >>= \r -> return $ if r then Just x else Nothing
>>> filterM p = Scanl.mapMaybeM (f p)

Represents the result of the step of a Fold. Partial returns an intermediate state of the fold, the fold step can be called again with the state or the driver can use extract on the state to get the result out. Done returns the final result and the fold cannot be driven further.

Pre-release

lmapM f scan maps the monadic function f on the input of the scan.

Map a monadic function on the output of a scan.

Map a monadic function over the result b in Step s b.

Internal

Scans its input building a pure stream.

>>> toStreamK = fmap StreamK.reverse Scanl.toStreamKRev

Internal

Change the underlying monad of a scan. Also known as hoist.

Pre-release

The type Scanl m a b represents a consumer of an input stream of values of type a and returning a final value of type b in Monad m. The constructor of a scan is Scanl step initial extract final.

The scan uses an internal state of type s. The initial value of the state s is created by initial. This function is called once and only once before the scan starts consuming input. Any resource allocation can be done in this function.

The step function is called on each input, it consumes an input and returns the next intermediate state (see Step) or the final result b if the scan terminates.

The extract function is used by the scan driver to map the current state s of the scan to the scan result. Thus extract can be called multiple times.

Before a scan terminates, final is called once and only once (unless the scan terminated in initial itself). Any resources allocated by initial can be released in final. In scan that do not require any cleanup extract and final are typically the same.

When implementing scan combinators, care should be taken to cleanup any state of the argument folds held by the fold by calling the respective final at all exit points of the scan. Also, final should not be called more than once. Note that if a scan terminates by Done constructor, there is no state to cleanup.

NOTE: The constructor is not yet released, smart constructors are provided to create scans.

Take the input, stop when the predicate succeeds taking the succeeding element as well.

Example:

>>> input = Stream.fromList "hello\nthere\n"
>>> line = Scanl.takeEndBy (== '\n') Scanl.toList
>>> Stream.toList $ Stream.scanl line input
["","h","he","hel","hell","hello","hello\n"]

Like takeEndBy but drops the element on which the predicate succeeds.

Example:

>>> input = Stream.fromList "hello\nthere\n"
>>> line = Scanl.takeEndBy_ (== '\n') Scanl.toList
>>> Stream.toList $ Stream.scanl line input
["","h","he","hel","hell","hello","hello"]

Lift a Maybe returning function to a scan.

teeWith k f1 f2 distributes its input to both f1 and f2 until any one of them terminates. The outputs of the two scans are combined using the function k.

Definition:

>>> teeWith k f1 f2 = fmap (uncurry k) (Scanl.tee f1 f2)

Example:

>>> avg = Scanl.teeWith (/) Scanl.sum (fmap fromIntegral Scanl.length)
>>> Stream.toList $ Stream.postscanl avg $ Stream.fromList [1.0..10.0]
[1.0,1.5,2.0,2.5,3.0,3.5,4.0,4.5,5.0,5.5]

Note that nested applications of teeWith do not fuse.

Pre-release

lmap f scan maps the function f on the input of the scan.

Definition:

>>> lmap = Scanl.lmapM return

Example:

>>> sumSquared = Scanl.lmap (\x -> x * x) Scanl.sum
>>> Stream.toList $ Stream.scanl sumSquared (Stream.enumerateFromTo 1 10)
[0,1,5,14,30,55,91,140,204,285,385]

A scan that drains all its input, running the effects and discarding the results.

>>> drain = Scanl.drainMapM (const (return ()))
>>> drain = Scanl.mkScanl (\_ _ -> ()) ()

Returns the latest element of the input stream, if any.

>>> latest = Scanl.mkScanl1 (\_ x -> x)
>>> latest = fmap getLast $ Scanl.foldMap (Last . Just)

Discard Rights and unwrap Lefts in an Either stream.

Pre-release

Discard Lefts and unwrap Rights in an Either stream.

Pre-release

Remove the either wrapper and flatten both lefts and as well as rights in the output stream.

Definition:

>>> catEithers = Scanl.lmap (either id id)

Pre-release

Postscan the input of a Scanl to change it in a stateful manner using another Scanl.

This is basically an append operation.

Pre-release

Make a scan from a left fold style pure step function and initial value of the accumulator.

If your Scanl returns only Partial (i.e. never returns a Done) then you can use mkScanl* constructors.

Make a scan from a left fold style monadic step function and initial value of the accumulator.

Make a strict left scan, for non-empty streams, using first element as the starting value. Returns Nothing if the stream is empty.

Pre-release

Like mkScanl1 but with a monadic step function.

Pre-release

Make a scan using a right fold style step function and a terminal value. It performs a strict right fold via a left fold using function composition. Note that a strict right fold can only be useful for constructing strict structures in memory. For reductions this will be very inefficient.

Definitions:

>>> mkScanr f z = fmap (flip appEndo z) $ Scanl.foldMap (Endo . f)
>>> mkScanr f z = fmap ($ z) $ Scanl.mkScanl (\g x -> g . f x) id

Example:

>>> Stream.toList $ Stream.scanl (Scanl.mkScanr (:) []) $ Stream.enumerateFromTo 1 5
[[],[1],[1,2],[1,2,3],[1,2,3,4],[1,2,3,4,5]]

Scan using a Maybe returning scan, filter out Nothing values.

>>> postscanlMaybe p f = Scanl.postscanl p (Scanl.catMaybes f)

Pre-release

If Partial then map the state, if Done then call the next step.

Make a scan from a consumer.

Internal

Buffers the input stream to a pure stream in the reverse order of the input.

This is more efficient than toStreamK. toStreamK has exactly the same performance as reversing the stream after toStreamKRev.

Pre-release

A scan for filtering elements based on a predicate.

Adapt a pure scan to any monad.

>>> generalizeInner = Scanl.morphInner (return . runIdentity)

Pre-release

Find minimum and maximum element using the provided comparison function.

Make a terminating scan using a pure step function, a pure initial state and a pure state extraction function.

Pre-release

Make a terminating scan with an effectful step function and initial state, and a state extraction function.

>>> mkScantM = Scanl.Scanl

We can just use Scanl but it is provided for completeness.

Pre-release

Like mkScanr but with a monadic step function.

Example:

>>> toList = Scanl.mkScanrM (\a xs -> return $ a : xs) (return [])

Pre-release

Make a scan that runs the supplied effect once and then yields the result on any input.

Pre-release

Find the minimum element in a rolling window.

See the performance related comments in windowRange.

If you want to compute the minimum of the entire stream minimum is much faster.

Time: \(\mathcal{O}(n*w)\) where \(w\) is the window size.

The maximum element in a rolling window.

See the performance related comments in windowRange.

If you want to compute the maximum of the entire stream maximum would be much faster.

Time: \(\mathcal{O}(n*w)\) where \(w\) is the window size.

Determine the maximum and minimum in a rolling window.

This implementation traverses the entire window buffer to compute the range whenever we demand it. It performs better than the dequeue based implementation in streamly-statistics package when the window size is small (< 30).

If you want to compute the range of the entire stream range would be much faster.

Space: \(\mathcal{O}(n)\) where n is the window size.

Time: \(\mathcal{O}(n*w)\) where \(w\) is the window size.

Represents incremental input for a scan. Insert means a new element is being added to the collection, Replace means an old value in the collection is being replaced with a new value.

Convert an incremental scan to a cumulative scan using the entire input stream as a single window.

>>> cumulativeScan = Scanl.lmap Scanl.Insert

incrScan collector is an incremental sliding window scan that does not require all the intermediate elements in each step of the scan computation. This maintains n elements in the window, when a new element comes it slides out the oldest element. The new element along with the old element are supplied to the collector scan.

Like incrScan but also provides the ring array to the scan. The ring array reflects the state of the ring after inserting the incoming element.

IMPORTANT NOTE: The ring is mutable, therefore, references to it should not be stored and used later, the state would have changed by then. If you need to store it then copy it to an array or another ring and store it.

Apply a pure function on the latest and the oldest element of the window.

>>> incrRollingMap f = Scanl.incrRollingMapM (\x y -> return $ f x y)

Apply an effectful function on the entering and the exiting element of the window. The first argument of the mapped function is the exiting element and the second argument is the entering element.

The number of elements in the rolling window.

This is the \(0\)th power sum.

>>> incrCount = Scanl.incrPowerSum 0

Sum of all the elements in a rolling window:

\(S = \sum_{i=1}^n x_{i}\)

This is the first power sum.

>>> incrSum = Scanl.incrPowerSum 1

Uses Kahan-Babuska-Neumaier style summation for numerical stability of floating precision arithmetic.

Space: \(\mathcal{O}(1)\)

Time: \(\mathcal{O}(n)\)

The sum of all the elements in a rolling window. The input elements are required to be integral numbers.

This was written in the hope that it would be a tiny bit faster than incrSum for Integral values. But turns out that incrSum is 2% faster than this even for integral values!

Internal

Sum of the \(k\)th power of all the elements in a rolling window:

\(S_k = \sum_{i=1}^n x_{i}^k\)

>>> incrPowerSum k = Scanl.lmap (fmap (^ k)) Scanl.incrSum

Space: \(\mathcal{O}(1)\)

Time: \(\mathcal{O}(n)\)

Like incrPowerSum but powers can be negative or fractional. This is slower than incrPowerSum for positive intergal powers.

>>> incrPowerSumFrac p = Scanl.lmap (fmap (** p)) Scanl.incrSum

Arithmetic mean of elements in a sliding window:

\(\mu = \frac{\sum_{i=1}^n x_{i}}{n}\)

This is also known as the Simple Moving Average (SMA) when used in the sliding window and Cumulative Moving Avergae (CMA) when used on the entire stream.

>>> incrMean = Scanl.teeWith (/) Scanl.incrSum Scanl.incrCount

Space: \(\mathcal{O}(1)\)

Time: \(\mathcal{O}(n)\)

mapMaybe f scan maps a Maybe returning function f on the input of the scan, filters out Nothing elements, and return the values extracted from Just.

>>> mapMaybe f = Scanl.lmap f . Scanl.catMaybes
>>> mapMaybe f = Scanl.mapMaybeM (return . f)

>>> f x = if even x then Just x else Nothing
>>> scn = Scanl.mapMaybe f Scanl.toList
>>> Stream.toList $ Stream.scanl scn (Stream.enumerateFromTo 1 10)
[[],[],[2],[2],[2,4],[2,4],[2,4,6],[2,4,6],[2,4,6,8],[2,4,6,8],[2,4,6,8,10]]

Send the elements of tuples in a stream of tuples through two different scans.

                          |-------Scanl m a x--------|
---------stream of (a,b)--|                          |----m (x,y)
                          |-------Scanl m b y--------|

Definition:

>>> unzip = Scanl.unzipWith id

This is the consumer side dual of the producer side zip operation.

Change the predicate function of a Scanl from a -> b to accept an additional state input (s, a) -> b. Convenient to filter with an addiitonal index or time input.

>>> filterWithIndex = Scanl.with Scanl.indexed Scanl.filter

filterWithAbsTime = with timestamped filter
filterWithRelTime = with timeIndexed filter

Pre-release

Definition:

>>> foldMap f = Scanl.lmap f Scanl.mconcat

Make a scan from a pure function that scans the output of the function using mappend and mempty.

>>> sum = Scanl.foldMap Data.Monoid.Sum
>>> Stream.toList $ Stream.scanl sum $ Stream.enumerateFromTo 1 3
[Sum {getSum = 0},Sum {getSum = 1},Sum {getSum = 3},Sum {getSum = 6}]

Monoid concat. Scan an input stream consisting of monoidal elements using mappend and mempty.

Definition:

>>> mconcat = Scanl.sconcat mempty

>>> monoids = fmap Data.Monoid.Sum $ Stream.enumerateFromTo 1 3
>>> Stream.toList $ Stream.scanl Scanl.mconcat monoids
[Sum {getSum = 0},Sum {getSum = 1},Sum {getSum = 3},Sum {getSum = 6}]

Semigroup concat. Append the elements of an input stream to a provided starting value.

Definition:

>>> sconcat = Scanl.mkScanl (<>)

>>> semigroups = fmap Data.Monoid.Sum $ Stream.enumerateFromTo 1 3
>>> Stream.toList $ Stream.scanl (Scanl.sconcat 3) semigroups
[Sum {getSum = 3},Sum {getSum = 4},Sum {getSum = 6},Sum {getSum = 9}]

Determine the sum of all elements of a stream of numbers. Returns additive identity (0) when the stream is empty. Note that this is not numerically stable for floating point numbers.

>>> sum = Scanl.cumulativeScan Scanl.incrSum

Same as following but numerically stable:

>>> sum = Scanl.mkScanl (+) 0
>>> sum = fmap Data.Monoid.getSum $ Scanl.foldMap Data.Monoid.Sum

Determine the product of all elements of a stream of numbers. Returns multiplicative identity (1) when the stream is empty. The scan terminates when it encounters (0) in its input.

Same as the following but terminates on multiplication by 0:

>>> product = fmap Data.Monoid.getProduct $ Scanl.foldMap Data.Monoid.Product

Scan the input of a Scanl to change it in a stateful manner using another Scanl. The scan stops as soon as any of the scans terminates.

This is basically an append operation.

Pre-release

Returns the index of the latest element if the element matches the given value.

Definition:

>>> elemIndices a = Scanl.findIndices (== a)

Returns the index of the latest element if the element satisfies the given predicate.

Returns the latest element omitting the first occurrence that satisfies the given equality predicate.

Example:

>>> input = Stream.fromList [1,3,3,5]
>>> Stream.toList $ Stream.postscanlMaybe (Scanl.deleteBy (==) 3) input
[1,3,5]

Compose two scans such that the combined scan accepts a stream of Either and routes the Left values to the first scan and Right values to the second scan.

Definition:

>>> partition = Scanl.partitionBy id

Terminates with Nothing as soon as it finds an element different than the previous one, returns the element if the entire input consists of the same element.

Split elements in the input stream into two parts using a pure splitter function, direct each part to a different scan and zip the results.

Definitions:

>>> unzipWith f = Scanl.unzipWithM (return . f)
>>> unzipWith f fld1 fld2 = Scanl.lmap f (Scanl.unzip fld1 fld2)

This scan terminates as soon as any of the input scans terminate.

Pre-release

A scan that buffers its input to a pure stream.

Warning! working on large streams accumulated as buffers in memory could be very inefficient, consider using Streamly.Data.Array instead.

>>> toStream = fmap Stream.fromList Scanl.toList

Pre-release

Scan the input of a Scanl to change it in a stateful manner using another Scanl. The scan restarts with a fresh state if it terminates.

Pre-release

Pair each element of a scan input with its index, starting from index 0.

>>> indexed = Scanl.postscanlMaybe Scanl.indexing

sampleFromthen offset stride samples the element at offset index and then every element at strides of stride.

Distribute one copy of the stream to each scan and zip the results.

                |-------Scanl m a b--------|
---stream m a---|                          |---m (b,c)
                |-------Scanl m a c--------|

Definition:

>>> tee = Scanl.teeWith (,)

Example:

>>> t = Scanl.tee Scanl.sum Scanl.length
>>> Stream.toList $ Stream.scanl t (Stream.enumerateFromTo 1.0 10.0)
[(0.0,0),(1.0,1),(3.0,2),(6.0,3),(10.0,4),(15.0,5),(21.0,6),(28.0,7),(36.0,8),(45.0,9),(55.0,10)]

Definition:

>>> foldMapM f = Scanl.lmapM f Scanl.mconcat

Make a scan from a monadic function that scans the output of the function using mappend and mempty.

>>> sum = Scanl.foldMapM (return . Data.Monoid.Sum)
>>> Stream.toList $ Stream.scanl sum $ Stream.enumerateFromTo 1 3
[Sum {getSum = 0},Sum {getSum = 1},Sum {getSum = 3},Sum {getSum = 6}]

Definitions:

>>> drainMapM f = Scanl.lmapM f Scanl.drain
>>> drainMapM f = Scanl.foldMapM (void . f)

Drain all input after passing it through a monadic function. This is the dual of mapM_ on stream producers.

Compute a numerically stable arithmetic mean of all elements in the input stream.

Compute an Int sized polynomial rolling hash of a stream.

>>> rollingHash = Scanl.rollingHashWithSalt Scanl.defaultSalt

Compute an Int sized polynomial rolling hash

H = salt * k ^ n + c1 * k ^ (n - 1) + c2 * k ^ (n - 2) + ... + cn * k ^ 0

Where c1, c2, cn are the elements in the input stream and k is a constant.

This hash is often used in Rabin-Karp string search algorithm.

See https://en.wikipedia.org/wiki/Rolling_hash

Buffers the input stream to a list in the reverse order of the input.

Definition:

>>> toListRev = Scanl.mkScanl (flip (:)) []

Warning! working on large lists accumulated as buffers in memory could be very inefficient, consider using Streamly.Array instead.

Get the top n elements using the supplied comparison function.

To get bottom n elements instead:

>>> bottomBy cmp = Scanl.topBy (flip cmp)

Example:

>>> stream = Stream.fromList [2::Int,7,9,3,1,5,6,11,17]
>>> Stream.toList (Stream.scanl (Scanl.topBy compare 3) stream) >>= mapM MutArray.toList
[[],[17],[17,11],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9]]

Pre-release

Return the latest unique element using the supplied comparison function. Returns Nothing if the current element is same as the last element otherwise returns Just.

Example, strip duplicate path separators:

>>> input = Stream.fromList "//a//b"
>>> f x y = x == '/' && y == '/'
>>> Stream.toList $ Stream.postscanlMaybe (Scanl.uniqBy f) input
"/a/b"

Space: O(1)

Pre-release

Distribute one copy of the stream to each scan and collect the results in a container.

                |-------Scanl m a b--------|
---stream m a---|                          |---m [b]
                |-------Scanl m a b--------|
                |                          |
                           ...

>>> Stream.toList $ Stream.scanl (Scanl.distribute [Scanl.sum, Scanl.length]) (Stream.enumerateFromTo 1 5)
[[0,0],[1,1],[3,2],[6,3],[10,4],[15,5]]

>>> distribute = Prelude.foldr (Scanl.teeWith (:)) (Scanl.const [])

This is the consumer side dual of the producer side sequence operation.

Stops as soon as any of the scans stop.

>>> mapMaybeM f = Scanl.lmapM f . Scanl.catMaybes

Unfold and flatten the input stream of a scan.

Stream.scanl (unfoldMany u f) == Stream.scanl f . Stream.unfoldMany u

Pre-release

Compute an Int sized polynomial rolling hash of the first n elements of a stream.

>>> rollingHashFirstN n = Scanl.take n Scanl.rollingHash

Pre-release

Buffers the input stream to a pure stream in the reverse order of the input.

>>> toStreamRev = fmap Stream.fromList Scanl.toListRev

Warning! working on large streams accumulated as buffers in memory could be very inefficient, consider using Streamly.Data.Array instead.

Pre-release

Scan the input stream to top n elements.

Definition:

>>> top = Scanl.topBy compare

>>> stream = Stream.fromList [2::Int,7,9,3,1,5,6,11,17]
>>> Stream.toList (Stream.scanl (Scanl.top 3) stream) >>= mapM MutArray.toList
[[],[17],[17,11],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9],[17,11,9]]

Pre-release

Get the bottom most n elements using the supplied comparison function.

Scan the input stream to bottom n elements.

Definition:

>>> bottom = Scanl.bottomBy compare

>>> stream = Stream.fromList [2::Int,7,9,3,1,5,6,11,17]
>>> Stream.toList (Stream.scanl (Scanl.bottom 3) stream) >>= mapM MutArray.toList
[[],[1],[1,2],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3],[1,2,3]]

Pre-release

>>> rollingMap f = Scanl.rollingMapM (\x y -> return $ f x y)

Apply a function on every two successive elements of a stream. The first argument of the map function is the previous element and the second argument is the current element. When processing the very first element in the stream, the previous element is Nothing.

Pre-release

See uniqBy.

Definition:

>>> uniq = Scanl.uniqBy (==)

Emit only repeated elements, once.

Unimplemented

A scan that drains the first n elements of its input, running the effects and discarding the results.

Definition:

>>> drainN n = Scanl.take n Scanl.drain

Pre-release

>>> takingEndBy p = Scanl.takingEndByM (return . p)

>>> takingEndBy_ p = Scanl.takingEndByM_ (return . p)

>>> droppingWhile p = Scanl.droppingWhileM (return . p)

Strip all leading and trailing occurrences of an element passing a predicate and make all other consecutive occurrences uniq.

> prune p = Stream.dropWhileAround p $ Stream.uniqBy (x y -> p x && p y)

> Stream.prune isSpace (Stream.fromList "  hello      world!   ")
"hello world!"

Space: O(1)

Unimplemented

Attach a Pipe on the input of a Scanl.

Pre-release

Zip a stream with the input of a scan using the supplied function.

Unimplemented

Zip a stream with the input of a scan.

>>> zip = Scanl.zipStreamWithM (curry return)

Unimplemented

Like unzipWith but with a monadic splitter function.

Definition:

>>> unzipWithM k f1 f2 = Scanl.lmapM k (Scanl.unzip f1 f2)

Pre-release

Partition the input over two scans using an Either partitioning predicate.

                                    |-------Scanl b x--------|
-----stream m a --> (Either b c)----|                        |----(x,y)
                                    |-------Scanl c y--------|

Example, send input to either scan randomly:

>>> :set -package random
>>> import System.Random (randomIO)
>>> randomly a = randomIO >>= \x -> return $ if x then Left a else Right a
>>> f = Scanl.partitionByM randomly Scanl.length Scanl.length
>>> Stream.toList $ Stream.scanl f (Stream.enumerateFromTo 1 10)
...

Example, send input to the two scans in a proportion of 2:1:

>>> :set -fno-warn-unrecognised-warning-flags
>>> :set -fno-warn-x-partial
>>> :{
proportionately m n = do
 ref <- newIORef $ cycle $ concat [replicate m Left, replicate n Right]
 return $ \a -> do
     r <- readIORef ref
     writeIORef ref $ tail r
     return $ Prelude.head r a
:}

>>> :{
main = do
 g <- proportionately 2 1
 let f = Scanl.partitionByM g Scanl.length Scanl.length
 r <- Stream.toList $ Stream.scanl f (Stream.enumerateFromTo (1 :: Int) 10)
 print r
:}

>>> main
...

This is the consumer side dual of the producer side mergeBy operation.

Terminates as soon as any of the scans terminate.

Pre-release

Same as partitionByM but with a pure partition function.

Example, count even and odd numbers in a stream:

>>> :{
 let f = Scanl.partitionBy (\n -> if even n then Left n else Right n)
                     (fmap (("Even " ++) . show) Scanl.length)
                     (fmap (("Odd "  ++) . show) Scanl.length)
  in Stream.toList $ Stream.postscanl f (Stream.enumerateFromTo 1 10)
:}
["Odd 1","Even 1","Odd 2","Even 2","Odd 3","Even 3","Odd 4","Even 4","Odd 5","Even 5"]

Pre-release

Pair each element of a scan input with its index, starting from index 0.

>>> indexing = Scanl.indexingWith 0 (+ 1)

>>> indexingRev n = Scanl.indexingWith n (subtract 1)

A default salt used in the implementation of rollingHash.

Returns Just for the first occurrence of an element, returns Nothing for any other occurrences.

Example:

>>> stream = Stream.fromList [1::Int,1,2,3,4,4,5,1,5,7]
>>> Stream.toList $ Stream.postscanlMaybe Scanl.nub stream
[1,2,3,4,5,7]

Pre-release

Like nub but specialized to a stream of Int, for better performance.

Pre-release

Count non-duplicate elements in the stream.

Definition:

>>> countDistinct = fmap Set.size Scanl.toSet
>>> countDistinct = Scanl.postscanl Scanl.nub $ Scanl.catMaybes $ Scanl.length

The memory used is proportional to the number of distinct elements in the stream, to guard against using too much memory use it as a scan and terminate if the count reaches more than a threshold.

Space: \(\mathcal{O}(n)\)

Pre-release

Like countDistinct but specialized to a stream of Int, for better performance.

Definition:

>>> countDistinctInt = fmap IntSet.size Scanl.toIntSet
>>> countDistinctInt = Scanl.postscanl Scanl.nubInt $ Scanl.catMaybes $ Scanl.length

Pre-release

Scan the input adding it to a set.

Definition:

>>> toSet = Scanl.mkScanl (flip Set.insert) Set.empty

Scan the input adding it to an int set. For integer inputs this performs better than toSet.

Definition:

>>> toIntSet = Scanl.mkScanl (flip IntSet.insert) IntSet.empty

Scans the values for each key using the supplied scan.

Once the scan for a key terminates, any future values of the key are ignored.

Equivalent to the following except that the scan is not restarted:

>>> classify f fld = Scanl.demux f (const fld)

Same as classify except that it uses mutable IORef cells in the Map, providing better performance.

Equivalent to the following except that the scan is not restarted:

>>> classifyIO f fld = Scanl.demuxIO f (const fld)

demux getKey getScan: In a key value stream, scan values corresponding to each key using a key specific scan. getScan is invoked to generate a key specific scan when a key is encountered for the first time in the stream. If a scan does not exist corresponding to the key then Nothing is returned otherwise the result of the scan is returned.

If a scan terminates, another instance of the scan is started upon receiving an input with that key, getScan is invoked again whenever the key is encountered again.

This can be used to scan a stream, splitting it based on different keys.

Since the scan generator function is monadic we can add scans dynamically. For example, we can maintain a Map of keys to scans in an IORef and lookup the scan from that corresponding to a key. This Map can be changed dynamically, scans for new keys can be added or scans for old keys can be deleted or modified.

Compare with classify, the scan in classify is a static scan.

Pre-release

This is specialized version of demux that uses mutable IO cells as scan accumulators for better performance.

This is the most general of all demux, classify operations.

The first component of the output tuple is a key-value Map of in-progress scans. The scan returns the scan result as the second component of the output tuple.

See demux for documentation.

This is specialized version of demuxGeneric that uses mutable IO cells as scan accumulators for better performance.

Keep in mind that the values in the returned Map may be changed by the ongoing scan if you are using those concurrently in another thread.

Be aware that the values in the intermediate Maps would be mutable.