Synthesizer.LLVM.Parameterized.Signal

Documentation

data T p a

Constructors

forall state packed size ioContext startParamTuple startParamValue startParamPacked startParamSize nextParamTuple nextParamValue nextParamPacked nextParamSize . (Storable startParamTuple, Storable nextParamTuple, MakeValueTuple startParamTuple startParamValue, MakeValueTuple nextParamTuple nextParamValue, Memory startParamValue startParamPacked, Memory nextParamValue nextParamPacked, IsSized startParamPacked startParamSize, IsSized nextParamPacked nextParamSize, Memory state packed, IsSized packed size) => Cons (forall r c. Phi c => nextParamValue -> state -> T r c (a, state)) (forall r. startParamValue -> CodeGenFunction r state) (p -> IO (ioContext, (nextParamTuple, startParamTuple))) (ioContext -> IO ())

Instances

Functor (T p)

simple :: (Storable startParamTuple, Storable nextParamTuple, MakeValueTuple startParamTuple startParamValue, MakeValueTuple nextParamTuple nextParamValue, Memory startParamValue startParamPacked, Memory nextParamValue nextParamPacked, IsSized startParamPacked startParamSize, IsSized nextParamPacked nextParamSize, Memory state packed, IsSized packed size) => (forall r c. Phi c => nextParamValue -> state -> T r c (al, state)) -> (forall r. startParamValue -> CodeGenFunction r state) -> T p nextParamTuple -> T p startParamTuple -> T p al

map :: (Storable ph, MakeValueTuple ph pl, Memory pl pp, IsSized pp ps) => (forall r. pl -> a -> CodeGenFunction r b) -> T p ph -> T p a -> T p b

mapSimple :: (forall r. a -> CodeGenFunction r b) -> T p a -> T p b

iterate :: (Storable ph, MakeValueTuple ph pl, Memory pl pp, IsSized pp ps, Storable a, MakeValueTuple a al, Memory al packed, IsSized packed s) => (forall r. pl -> al -> CodeGenFunction r al) -> T p ph -> T p a -> T p al

($#) :: (T p a -> b) -> a -> b

mapAccum :: (Storable pnh, MakeValueTuple pnh pnl, Memory pnl pnp, IsSized pnp pns, Storable psh, MakeValueTuple psh psl, Memory psl psp, IsSized psp pss, Memory s struct, IsSized struct sa) => (forall r. pnl -> a -> s -> CodeGenFunction r (b, s)) -> (forall r. psl -> CodeGenFunction r s) -> T p pnh -> T p psh -> T p a -> T p b

zipWith :: (Storable ph, MakeValueTuple ph pl, Memory pl pp, IsSized pp ps) => (forall r. pl -> a -> b -> CodeGenFunction r c) -> T p ph -> T p a -> T p b -> T p c

zipWithSimple :: (forall r. a -> b -> CodeGenFunction r c) -> T p a -> T p b -> T p c

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

timeline edit

tail :: T p a -> T p a

tail empty generates the empty signal.

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

append :: Phi a => T p a -> T p a -> T p a

Appending many signals is inefficient, since in cascadingly appended signals the parts are counted in an unary way. Concatenating infinitely many signals is impossible. If you want to concatenate a lot of signals, please render them to lazy storable vectors first.

signal modifiers

interpolateConstant :: (Memory a struct, IsSized struct size, C b, IsFloating b, CmpRet b Bool, Storable b, MakeValueTuple b (Value b), IsConst b, IsFirstClass b, IsSized b sb) => T p b -> T p a -> T p a

Stretch signal in time by a certain factor.

This can be used for doing expensive computations of filter parameters at a lower rate. Alternatively, we could provide an adaptive map that recomputes output values only if the input value changes, or if the input value differs from the last processed one by a certain amount.

mix :: IsArithmetic a => T p (Value a) -> T p (Value a) -> T p (Value a)

mixStereo :: IsArithmetic a => T p (T (Value a)) -> T p (T (Value a)) -> T p (T (Value a))

envelope :: IsArithmetic a => T p (Value a) -> T p (Value a) -> T p (Value a)

envelopeStereo :: IsArithmetic a => T p (Value a) -> T p (T (Value a)) -> T p (T (Value a))

amplify :: (IsArithmetic a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a size) => T p a -> T p (Value a) -> T p (Value a)

amplifyStereo :: (IsArithmetic a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a size) => T p a -> T p (T (Value a)) -> T p (T (Value a))

signal generators

constant :: (Storable a, MakeValueTuple a al, Memory al packed, IsSized packed s) => T p a -> T p al

exponentialCore :: (Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p a -> T p (Value a)

exponential2 :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p a -> T p (Value a)

exponentialBoundedCore :: (Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, Real a, IsConst a) => T p a -> T p a -> T p a -> T p (Value a)

exponentialBounded2 :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, Real a, IsConst a) => T p a -> T p a -> T p a -> T p (Value a)

Exponential curve that remains at the bound value if it would fall below otherwise. This way you can avoid extremal values, e.g. denormalized ones. The initial value and the bound value must be positive.

osciCore :: (Storable t, MakeValueTuple t (Value t), IsFirstClass t, IsSized t size, Fraction t, IsConst t) => T p t -> T p t -> T p (Value t)

osci :: (Storable t, MakeValueTuple t (Value t), Storable c, MakeValueTuple c cl, IsFirstClass t, IsSized t size, Memory cl cp, IsSized cp cs, Fraction t, IsConst t) => (forall r. cl -> Value t -> CodeGenFunction r y) -> T p c -> T p t -> T p t -> T p y

osciSimple :: (Storable t, MakeValueTuple t (Value t), IsFirstClass t, IsSized t size, Fraction t, IsConst t) => (forall r. Value t -> CodeGenFunction r y) -> T p t -> T p t -> T p y

osciSaw :: (C a0, IsConst a0, Replicate a0 a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a size, Fraction a, IsPrimitive a, IsConst a) => T p a -> T p a -> T p (Value a)

rampCore :: (Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p a -> T p (Value a)

parabolaCore :: (Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p a -> T p a -> T p (Value a)

rampSlope :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeInInf :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeOutInf :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

rampInf :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeIn :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeOut :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeInMap :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

parabolaFadeOutMap :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

ramp :: (C a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a s, IsArithmetic a, IsConst a) => T p a -> T p (Value a)

noise :: (C a, IsFloating a, IsConst a, NumberOfElements D1 a, IsSized a ps, MakeValueTuple a (Value a), Storable a) => T p Word32 -> T p a -> T p (Value a)

noise seed rate

The rate parameter is for adjusting the amplitude such that it is uniform across different sample rates and after frequency filters. The rate is the ratio of the current sample rate to the default sample rate, where the variance of the samples would be one. If you want that at sample rate 22050 the variance is 1, then in order to get a consistent volume at sample rate 44100 you have to set rate = 2.

I use the variance as quantity and not the amplitude, because the amplitude makes only sense for uniformly distributed samples. However, frequency filters transform the probabilistic density of the samples towards the normal distribution according to the central limit theorem.

noiseCoreAlt :: T p Word32 -> T p (Value Word32)

noiseCore :: T p Word32 -> T p (Value Word32)

conversion from and to storable vectors

fromStorableVector :: (Storable a, MakeValueTuple a value, Memory value struct) => T p (Vector a) -> T p value

fromStorableVectorLazy :: (Storable a, MakeValueTuple a value, Memory value struct) => T p (Vector a) -> T p value

piecewiseConstant :: (Storable a, MakeValueTuple a value, Memory value struct, IsSized struct size) => T p (T Int a) -> T p value

asTypeOfEventListElement :: Ptr a -> T p (T Int a) -> Ptr a

lazySize :: T p LazySize -> T p ()

Turns a lazy chunky size into a signal generator with unit element type. The signal length is the only information that the generator provides. Using zipWith you can use this signal as a lazy take.

derefFillPtr :: Importer (Ptr param -> Word32 -> Ptr a -> IO Word32)

run :: (Storable a, MakeValueTuple a value, Memory value struct) => T p value -> IO (Int -> p -> Vector a)

render :: (Storable a, MakeValueTuple a value, Memory value struct) => T p value -> Int -> p -> Vector a

This is not really a function, see renderChunky.

derefChunkPtr :: Importer (Ptr nextParamStruct -> Ptr stateStruct -> Word32 -> Ptr struct -> IO Word32)

compileChunky :: (Memory value struct, Memory state stateStruct, IsSized stateStruct stateSize, Memory startParamValue startParamStruct, Memory nextParamValue nextParamStruct, IsSized startParamStruct startParamSize, IsSized nextParamStruct nextParamSize) => (forall r. nextParamValue -> state -> T r (Value Bool, state) (value, state)) -> (forall r. startParamValue -> CodeGenFunction r state) -> IO (FunPtr (Ptr startParamStruct -> IO (Ptr stateStruct)), FunPtr (Ptr stateStruct -> IO ()), FunPtr (Ptr nextParamStruct -> Ptr stateStruct -> Word32 -> Ptr struct -> IO Word32))

runChunkyPattern :: (Storable a, MakeValueTuple a value, Memory value struct) => T p value -> IO (LazySize -> p -> Vector a)

Renders a signal generator to a chunky storable vector with given pattern. If the pattern is shorter than the generated signal this means that the signal is shortened.

runChunky :: (Storable a, MakeValueTuple a value, Memory value struct) => T p value -> IO (ChunkSize -> p -> Vector a)

renderChunky :: (Storable a, MakeValueTuple a value, Memory value struct) => ChunkSize -> T p value -> p -> Vector a

This looks like a function, but it is not a function since it depends on LLVM being initialized with LLVM.initializeNativeTarget before. It is also problematic since you cannot control when and how often the underlying LLVM code is compiled. The compilation cannot be observed, thus it is referential transparent. But this influences performance considerably and I assume that you use this package exclusively for performance reasons.