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Within LTS Haskell 24.34 (ghc-9.10.3)
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addConsumes :: [MediaType] -> Swagger -> Swaggerservant-swagger Servant.Swagger.Internal Add accepted content types to every operation in the spec.
windowPowerSum :: forall (m :: Type -> Type) a . (Monad m, Num a) => Int -> Fold m (a, Maybe a) astreamly-core Streamly.Internal.Data.Fold Sum of the <math>th power of all the elements in a rolling window: <math>
>>> powerSum k = lmap (^ k) sum
Space: <math> Time: <math>-
streamly-core Streamly.Internal.Data.Fold Like powerSum but powers can be negative or fractional. This is slower than powerSum for positive intergal powers.
>>> powerSumFrac p = lmap (** p) sum
windowSum :: forall (m :: Type -> Type) a . (Monad m, Num a) => Fold m (a, Maybe a) astreamly-core Streamly.Internal.Data.Fold Sum of all the elements in a rolling window: <math> This is the first power sum.
>>> sum = powerSum 1
Uses Kahan-Babuska-Neumaier style summation for numerical stability of floating precision arithmetic. Space: <math> Time: <math>windowSumInt :: forall (m :: Type -> Type) a . (Monad m, Integral a) => Fold m (a, Maybe a) astreamly-core Streamly.Internal.Data.Fold The sum of all the elements in a rolling window. The input elements are required to be intergal numbers. This was written in the hope that it would be a tiny bit faster than sum for Integral values. But turns out that sum is 2% faster than this even for intergal values! Internal
Consume :: s1 -> PipeState s1 s2streamly-core Streamly.Internal.Data.Pipe No documentation available.
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streamly-core Streamly.Internal.Data.SVar.Type No documentation available.
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streamly-core Streamly.Internal.Data.SVar.Type No documentation available.
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streamly-core Streamly.Internal.Unicode.Stream Pre-release
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streamly-core Streamly.Internal.Unicode.Stream No documentation available.