Synthesizer.State.Signal

ToDo: Better name for the module is certainly Synthesizer.Generator.Signal

Documentation

data T a

Cf. StreamFusion Data.Stream

Constructors

Cons !(StateT s Maybe a) !s

Instances

Monad T

Methods

(>>=) :: T a -> (a -> T b) -> T b

(>>) :: T a -> T b -> T b

return :: a -> T a

fail :: String -> T a

Functor T

Methods

fmap :: (a -> b) -> T a -> T b

(<$) :: a -> T b -> T a

Applicative T

Methods

pure :: a -> T a

(<*>) :: T (a -> b) -> T a -> T b

liftA2 :: (a -> b -> c) -> T a -> T b -> T c

(*>) :: T a -> T b -> T b

(<*) :: T a -> T b -> T a

Foldable T

Methods

fold :: Monoid m => T m -> m

foldMap :: Monoid m => (a -> m) -> T a -> m

foldr :: (a -> b -> b) -> b -> T a -> b

foldr' :: (a -> b -> b) -> b -> T a -> b

foldl :: (b -> a -> b) -> b -> T a -> b

foldl' :: (b -> a -> b) -> b -> T a -> b

foldr1 :: (a -> a -> a) -> T a -> a

foldl1 :: (a -> a -> a) -> T a -> a

toList :: T a -> [a]

null :: T a -> Bool

length :: T a -> Int

elem :: Eq a => a -> T a -> Bool

maximum :: Ord a => T a -> a

minimum :: Ord a => T a -> a

sum :: Num a => T a -> a

product :: Num a => T a -> a

C T

Methods

format :: Show x => Int -> T x -> ShowS

Write0 T

Methods

fromList :: Storage (T y) => LazySize -> [y] -> T y

repeat :: Storage (T y) => LazySize -> y -> T y

replicate :: Storage (T y) => LazySize -> Int -> y -> T y

iterate :: Storage (T y) => LazySize -> (y -> y) -> y -> T y

iterateAssociative :: Storage (T y) => LazySize -> (y -> y -> y) -> y -> T y

unfoldR :: Storage (T y) => LazySize -> (s -> Maybe (y, s)) -> s -> T y

Transform0 T

Methods

cons :: Storage (T y) => y -> T y -> T y

takeWhile :: Storage (T y) => (y -> Bool) -> T y -> T y

dropWhile :: Storage (T y) => (y -> Bool) -> T y -> T y

span :: Storage (T y) => (y -> Bool) -> T y -> (T y, T y)

viewL :: Storage (T y) => T y -> Maybe (y, T y)

viewR :: Storage (T y) => T y -> Maybe (T y, y)

zipWithAppend :: Storage (T y) => (y -> y -> y) -> T y -> T y -> T y

map :: (Storage (T y0), Storage (T y1)) => (y0 -> y1) -> T y0 -> T y1

scanL :: (Storage (T y0), Storage (T y1)) => (y1 -> y0 -> y1) -> y1 -> T y0 -> T y1

crochetL :: (Storage (T y0), Storage (T y1)) => (y0 -> s -> Maybe (y1, s)) -> s -> T y0 -> T y1

Read0 T

Methods

toList :: Storage (T y) => T y -> [y]

toState :: Storage (T y) => T y -> T y

foldL :: Storage (T y) => (s -> y -> s) -> s -> T y -> s

foldR :: Storage (T y) => (y -> s -> s) -> s -> T y -> s

index :: Storage (T y) => T y -> Int -> y

Write T y

Transform T y

Read T y

Write T y

Methods

unfoldRN :: T -> (s -> Maybe (y, s)) -> s -> T y

C y yv => C y (T yv)

Methods

(*>) :: y -> T yv -> T yv

Eq y => Eq (T y)

Methods

(==) :: T y -> T y -> Bool

(/=) :: T y -> T y -> Bool

Show y => Show (T y)

Methods

showsPrec :: Int -> T y -> ShowS

show :: T y -> String

showList :: [T y] -> ShowS

Semigroup (T y)

Methods

(<>) :: T y -> T y -> T y

sconcat :: NonEmpty (T y) -> T y

stimes :: Integral b => b -> T y -> T y

Monoid (T y)

Methods

mempty :: T y

mappend :: T y -> T y -> T y

mconcat :: [T y] -> T y

C y => C (T y)

Methods

zero :: T y

(+) :: T y -> T y -> T y

(-) :: T y -> T y -> T y

negate :: T y -> T y

Transform (T y)

Methods

take :: Int -> T y -> T y

drop :: Int -> T y -> T y

dropMarginRem :: Int -> Int -> T y -> (Int, T y)

splitAt :: Int -> T y -> (T y, T y)

reverse :: T y -> T y

NFData y => NormalForm (T y)

Methods

evaluateHead :: T y -> ()

Read (T y)

Methods

null :: T y -> Bool

length :: T y -> Int

Storage (T y)

Associated Types

data Constraints (T y) :: *

Methods

constraints :: T y -> Constraints (T y)

Transform (T y)

Methods

take :: T -> T y -> T y

drop :: T -> T y -> T y

splitAt :: T -> T y -> (T y, T y)

Read (T y)

Methods

length :: T y -> T

type ProcessOf T

type ProcessOf T = T

data Constraints (T y)

data Constraints (T y) = StateConstraints

runViewL :: T y -> (forall s. (s -> Maybe (y, s)) -> s -> x) -> x

It is a common pattern to use switchL or viewL in a loop in order to traverse a signal. However this needs repeated packing and unpacking of the viewL function and the state. It seems that GHC is not clever enough to detect, that the view function does not change. With runViewL you can unpack a stream once and use an efficient viewL in the loop.

runSwitchL :: T y -> (forall s. (forall z. z -> (y -> s -> z) -> s -> z) -> s -> x) -> x

generate :: (acc -> Maybe (y, acc)) -> acc -> T y

unfoldR :: (acc -> Maybe (y, acc)) -> acc -> T y

generateInfinite :: (acc -> (y, acc)) -> acc -> T y

fromList :: [y] -> T y

toList :: T y -> [y]

fromStorableSignal :: Storable a => T a -> T a

fromStrictStorableSignal :: Storable a => Vector a -> T a

toStorableSignal :: Storable a => ChunkSize -> T a -> T a

toStrictStorableSignal :: Storable a => Int -> T a -> Vector a

toStorableSignalVary :: Storable a => LazySize -> T a -> T a

fromPiecewiseConstant :: (C time, Integral time) => T time a -> T a

iterate :: (a -> a) -> a -> T a

iterateAssociative :: (a -> a -> a) -> a -> T a

repeat :: a -> T a

crochetL :: (x -> acc -> Maybe (y, acc)) -> acc -> T x -> T y

scanL :: (acc -> x -> acc) -> acc -> T x -> T acc

scanLClip :: (acc -> x -> acc) -> acc -> T x -> T acc

input and output have equal length, that's better for fusion

map :: (a -> b) -> T a -> T b

unzip :: T (a, b) -> (T a, T b)

This function will recompute the input lists and is thus probably not what you want. If you want to avoid recomputation please consider Causal.Process.

unzip3 :: T (a, b, c) -> (T a, T b, T c)

delay1 :: a -> T a -> T a

This is a fusion friendly implementation of delay. However, in order to be a crochetL the output has the same length as the input, that is, the last element is removed - at least for finite input.

delay :: y -> Int -> T y -> T y

take :: Int -> T a -> T a

takeWhile :: (a -> Bool) -> T a -> T a

replicate :: Int -> a -> T a

functions consuming multiple lists

zipWith :: (a -> b -> c) -> T a -> T b -> T c

zipWithStorable :: (Storable b, Storable c) => (a -> b -> c) -> T a -> T b -> T c

zipWith3 :: (a -> b -> c -> d) -> T a -> T b -> T c -> T d

zipWith4 :: (a -> b -> c -> d -> e) -> T a -> T b -> T c -> T d -> T e

zip :: T a -> T b -> T (a, b)

zip3 :: T a -> T b -> T c -> T (a, b, c)

zip4 :: T a -> T b -> T c -> T d -> T (a, b, c, d)

functions based on foldL

foldL' :: (x -> acc -> acc) -> acc -> T x -> acc

foldL :: (acc -> x -> acc) -> acc -> T x -> acc

foldL1 :: (x -> x -> x) -> T x -> x

length :: T a -> Int

equal :: Eq a => T a -> T a -> Bool

functions based on foldR

foldR :: (x -> acc -> acc) -> acc -> T x -> acc

Other functions

null :: T a -> Bool

empty :: T a

singleton :: a -> T a

cons :: a -> T a -> T a

This is expensive and should not be used to construct lists iteratively!

viewL :: T a -> Maybe (a, T a)

viewR :: Storable a => T a -> Maybe (T a, a)

viewRSize :: Storable a => ChunkSize -> T a -> Maybe (T a, a)

switchL :: b -> (a -> T a -> b) -> T a -> b

switchR :: Storable a => b -> (T a -> a -> b) -> T a -> b

extendConstant :: T a -> T a

This implementation requires that the input generator has to check repeatedly whether it is finished.

drop :: Int -> T a -> T a

dropMarginRem :: Int -> Int -> T a -> (Int, T a)

This implementation expects that looking ahead is cheap.

dropMargin :: Int -> Int -> T a -> T a

dropMatch :: T b -> T a -> T a

index :: Int -> T a -> a

splitAt :: Storable a => Int -> T a -> (T a, T a)

splitAtSize :: Storable a => ChunkSize -> Int -> T a -> (T a, T a)

dropWhile :: (a -> Bool) -> T a -> T a

span :: Storable a => (a -> Bool) -> T a -> (T a, T a)

spanSize :: Storable a => ChunkSize -> (a -> Bool) -> T a -> (T a, T a)

cycle :: T a -> T a

mix :: C a => T a -> T a -> T a

sub :: C a => T a -> T a -> T a

neg :: C a => T a -> T a

append :: T a -> T a -> T a infixr 5

appendStored :: Storable a => T a -> T a -> T a

appendStoredSize :: Storable a => ChunkSize -> T a -> T a -> T a

concat :: [T a] -> T a

certainly inefficient because of frequent list deconstruction

concatStored :: Storable a => [T a] -> T a

concatStoredSize :: Storable a => ChunkSize -> [T a] -> T a

liftA2 :: (a -> b -> c) -> T a -> T b -> T c

reverse :: T a -> T a

reverseStored :: Storable a => T a -> T a

reverseStoredSize :: Storable a => ChunkSize -> T a -> T a

sum :: C a => T a -> a

maximum :: Ord a => T a -> a

init :: T y -> T y

sliceVert :: Int -> T y -> [T y]

zapWith :: (a -> a -> b) -> T a -> T b

Deprecated: use mapAdjacent

zapWithAlt :: (a -> a -> b) -> T a -> T b

Deprecated: use mapAdjacent

mapAdjacent :: (a -> a -> b) -> T a -> T b

modifyStatic :: Simple s ctrl a b -> ctrl -> T a -> T b

modifyModulated :: Simple s ctrl a b -> T ctrl -> T a -> T b

Here the control may vary over the time.

linearComb :: C t y => T t -> T y -> y

mapTails :: (T y0 -> y1) -> T y0 -> T y1

zipWithTails :: (y0 -> T y1 -> y2) -> T y0 -> T y1 -> T y2

only non-empty suffixes are processed

zipWithTails1 :: (y0 -> T y1 -> y2) -> T y0 -> T y1 -> T y2

in contrast to zipWithTails it also generates the empty suffix (once)

zipWithTailsInf :: (y0 -> T y1 -> y2) -> T y0 -> T y1 -> T y2

in contrast to zipWithTails it appends infinitely many empty suffixes

zipWithAppend :: (y -> y -> y) -> T y -> T y -> T y

zipStep :: (s -> Maybe (a, s)) -> (t -> Maybe (a, t)) -> (a -> a -> a) -> (s, t) -> Maybe (a, (s, t))

delayLoop

Arguments

:: (T y -> T y)

processor that shall be run in a feedback loop

-> T y

prefix of the output, its length determines the delay

-> T y

delayLoopOverlap

Arguments

:: C y

=> Int

-> (T y -> T y)

processor that shall be run in a feedback loop

-> T y

input

-> T y

output has the same length as the input

sequence_ :: Monad m => T (m a) -> m ()

mapM_ :: Monad m => (a -> m ()) -> T a -> m ()

fold :: Monoid m => T m -> m

Counterpart to mconcat.

monoidConcat :: Monoid m => T m -> m

Deprecated: Use foldMap instead.

foldMap :: Monoid m => (a -> m) -> T a -> m

monoidConcatMap :: Monoid m => (a -> m) -> T a -> m

Deprecated: Use foldMap instead.

catMaybes :: T (Maybe a) -> T a

flattenPairs :: T (a, a) -> T a

interleave :: T y -> T y -> T y

interleaveAlt :: T y -> T y -> T y