Hoogle Search
Within LTS Haskell 24.65 (ghc-9.10.3)
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safe-coloured-text Text.Colour.Code No documentation available.
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serialise Codec.Serialise.Decoding No documentation available.
isValid :: UTF8Encoding -> [Word8] -> Boolserialise Codec.Serialise.Internal.GeneralisedUTF8 Is the given byte sequence valid under the given encoding?
flatTermIdentity :: (Serialise a, Eq a) => a -> Boolserialise Codec.Serialise.Properties Ensure that serializing and deserializing a value with the FlatTerm form results in the original value being returned.
hasValidFlatTerm :: Serialise a => a -> Boolserialise Codec.Serialise.Properties Ensure that serializing a value into a FlatTerm gives us a valid FlatTerm back.
serialiseIdentity :: (Serialise a, Eq a) => a -> Boolserialise Codec.Serialise.Properties Ensure that serializing and deserializing some value results in the original value being returned.
runIdentity :: Identity a -> astreaming Streaming No documentation available.
void :: Functor f => f a -> f ()streaming Streaming void value discards or ignores the result of evaluation, such as the return value of an IO action.
Examples
Replace the contents of a Maybe Int with unit:>>> void Nothing Nothing
>>> void (Just 3) Just ()
Replace the contents of an Either Int Int with unit, resulting in an Either Int ():>>> void (Left 8675309) Left 8675309
>>> void (Right 8675309) Right ()
Replace every element of a list with unit:>>> void [1,2,3] [(),(),()]
Replace the second element of a pair with unit:>>> void (1,2) (1,())
Discard the result of an IO action:>>> mapM print [1,2] 1 2 [(),()]
>>> void $ mapM print [1,2] 1 2
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streaming Streaming.Prelude 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"
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streaming Streaming.Prelude 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.