Synthesizer.LLVM.CausalParameterized.Process

Documentation

data T p a b

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 -> a -> state -> T r c (b, state)) (forall r. startParamValue -> CodeGenFunction r state) (p -> IO (ioContext, (nextParamTuple, startParamTuple))) (ioContext -> IO ())

Instances

Arrow (T p)

Category (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 -> a -> state -> T r c (b, state)) -> (forall r. startParamValue -> CodeGenFunction r state) -> T p nextParamTuple -> T p startParamTuple -> T p 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 b

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 b

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

apply :: T p a b -> T p a -> T p b

compose :: T p a b -> T p b c -> T p a c

first :: T p b c -> T p (b, d) (c, d)

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

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

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

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

The first output value is the start value. Thus integrate delays by one sample compared with integrate0.

($<) :: T p (a, b) c -> T p a -> T p b c

applyFst :: T p (a, b) c -> T p a -> T p b c

($>) :: T p (a, b) c -> T p b -> T p a c

applySnd :: T p (a, b) c -> T p b -> T p a c

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

($*#) :: (Storable ah, MakeValueTuple ah a, Memory a am, IsSized am as) => T p a b -> ah -> T p b

provide constant input in a comfortable way

($<#) :: (Storable ah, MakeValueTuple ah a, Memory a am, IsSized am as) => T p (a, b) c -> ah -> T p b c

($>#) :: (Storable bh, MakeValueTuple bh b, Memory b bm, IsSized bm bs) => T p (a, b) c -> bh -> T p a c

mapAccumSimple :: (Memory s struct, IsSized struct sa) => (forall r. a -> s -> CodeGenFunction r (b, s)) -> (forall r. CodeGenFunction r s) -> T p a b

loop :: (Storable ch, MakeValueTuple ch c, Memory c cp, IsSized cp cs) => T p ch -> T p (a, c) (b, c) -> T p a b

Not quite the loop of ArrowLoop because we need a delay of one time step and thus an initialization value.

For a real ArrowLoop.loop, that is a zero-delay loop, we would formally need a MonadFix instance of CodeGenFunction. But this will not become reality, since LLVM is not able to re-order code in a way that allows to access a result before creating the input.

replicateControlled :: Int -> T p (c, x) x -> T p (c, x) x

feedbackControlled :: (Storable ch, MakeValueTuple ch c, Memory c cp, IsSized cp cs) => T p ch -> T p ((ctrl, a), c) b -> T p (ctrl, b) c -> T p (ctrl, a) b

fromModifier :: (Flatten ah al, Flatten bh bl, Flatten ch cl, Flatten sh sl, Memory sl sp, IsSized sp ss) => Simple sh ch ah bh -> T p (cl, al) bl

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

Run a causal process independently on each stereo channel.

stereoFromMonoControlled :: T p (c, a) b -> T p (c, T a) (T b)

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

stereoFromVector :: (IsPrimitive a, IsPrimitive b) => T p (Value (Vector D2 a)) (Value (Vector D2 b)) -> T p (T (Value a)) (T (Value b))

vectorize :: (Access n a va, Access n b vb) => T p a b -> T p va vb

vectorizeSize :: (Access n a va, Access n b vb) => n -> T p a b -> T p va vb

replaceChannel :: (Access n a va, Access n b vb) => Int -> T p a b -> T p va vb -> T p va vb

Given a vector process, replace the i-th output by output that is generated by a scalar process from the i-th input.

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

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

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

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

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

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

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

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

mapExponential :: (C a, IsFloating a, IsConst a, Storable a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a size) => T p a -> T p a -> T p (Value a) (Value a)

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

quantizeLift k f applies the process f to every kth sample and repeats the result k times.

Like SigP.interpolateConstant this function can be used for computation of filter parameters at a lower rate. This can be useful, if you have a frequency control signal at sample rate that shall be used both for an oscillator and a frequency filter.

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

Compute the phases from phase distortions and frequencies.

It's like integrate but with wrap-around performed by fraction. For FM synthesis we need also negative phase distortions, thus we use addToPhase which supports that.

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

shapeModOsci :: (IsFirstClass t, IsSized t size, Fraction t, IsConst t, C t) => (forall r. c -> Value t -> CodeGenFunction r y) -> T p (c, (Value t, Value t)) y

delay :: (Storable a, MakeValueTuple a al, Memory al ap, IsSized ap as) => T p a -> T p Int -> T p al al

Delay time must be non-negative.

The initial value is needed in order to determine the ring buffer element type.

ptrAsTypeOf :: Ptr a -> a -> Ptr a

delay1 :: (Storable a, MakeValueTuple a al, Memory al ap, IsSized ap as) => T p a -> T p al al

Delay by one sample. For very small delay times (say up to 8) it may be more efficient to apply delay1 several times or to use a pipeline, e.g. pipeline (id :: T (Vector D4 Float) (Vector D4 Float)) delays by 4 samples in an efficient way. In principle it would be also possible to use unpack (delay1 (const $ toVector (0,0,0,0))) but unpack causes an additional delay. Thus unpack (id :: T (Vector D4 Float) (Vector D4 Float)) may do, what you want.

comb :: (C a, Storable a, IsArithmetic a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a as) => T p a -> T p Int -> T p (Value a) (Value a)

Delay time must be greater than zero!

combStereo :: (C a, Storable a, IsArithmetic a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a as) => T p a -> T p Int -> T p (T (Value a)) (T (Value a))

reverb :: (C a, Random a, Storable a, IsArithmetic a, MakeValueTuple a (Value a), IsFirstClass a, IsSized a as, RandomGen g) => g -> Int -> (a, a) -> (Int, Int) -> T p (Value a) (Value a)

pipeline :: (Access n a v, Zero v, Memory v vp, IsSized vp s) => T p v v -> T p a a

This allows to compute a chain of equal processes efficiently, if all of these processes can be bundled in one vectorial process. Applications are an allpass cascade or an FM operator cascade.

The function expects that the vectorial input process works like parallel scalar processes. The different pipeline stages may be controlled by different parameters, but the structure of all pipeline stages must be equal. Our function feeds the input of the pipelined process to the zeroth element of the Vector. The result of processing the i-th element (the i-th channel, so to speak) is fed to the (i+1)-th element. The (n-1)-th element of the vectorial process is emitted as output of pipelined process.

The pipeline necessarily introduces a delay of (n-1) values. For simplification we extend this to n values delay. If you need to combine the resulting signal from the pipeline with another signal in a zip-like way, you may delay that signal with pipeline id. The first input values in later stages of the pipeline are initialized with zero. If this is not appropriate for your application, then we may add a more sensible initialization.

linearInterpolation :: (C a, IsArithmetic a, IsConst a) => Value a -> (Value a, Value a) -> CodeGenFunction r (Value a)

frequencyModulationLinear :: (C a, IsFloating a, CmpRet a Bool, Storable a, MakeValueTuple a (Value a), IsConst a, IsFirstClass a, IsSized a sa) => T p (Value a) -> T p (Value a) (Value a)

 frequencyModulationLinear signal

is a causal process mapping from a shrinking factor to the modulated input signal. Similar to interpolateConstant but the factor is reciprocal and controllable and we use linear interpolation. The shrinking factor must be non-negative.

trigger :: (Storable a, MakeValueTuple a al, Select al, Memory al as, IsSized as asize) => T p a -> T p al -> T p (Value Bool) al

trigger fill signal send signal to the output and restart it whenever the Boolean process input is True. Before the first occurrence of True and between instances of the signal the output is filled with the fill value.

Attention: This function will crash if the input generator uses fromStorableVectorLazy, piecewiseConstant or lazySize, since these functions contain mutable references and in-place updates, and thus they cannot read lazy Haskell data multiple times.

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

On each restart the parameters of type b are passed to the signal.

triggerParam :: (MakeValueTuple a al, MakeValueTuple b bl) => Param.T p a -> (Param.T p b -> Sig.T p a) -> T p (Value Bool, bl) al triggerParam fill sig =

runStorable :: (Storable a, MakeValueTuple a valueA, Memory valueA structA, Storable b, MakeValueTuple b valueB, Memory valueB structB) => T p valueA valueB -> IO (p -> Vector a -> Vector b)

applyStorable :: (Storable a, MakeValueTuple a valueA, Memory valueA structA, Storable b, MakeValueTuple b valueB, Memory valueB structB) => T p valueA valueB -> p -> Vector a -> Vector b

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

compileChunky :: (Memory valueA structA, Memory valueB structB, Memory state stateStruct, IsSized stateStruct stateSize, Memory startParamValue startParamStruct, Memory nextParamValue nextParamStruct, IsSized startParamStruct startParamSize, IsSized nextParamStruct nextParamSize) => (forall r. nextParamValue -> valueA -> state -> T r (Value Bool, (Value (Ptr structB), state)) (valueB, 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 structA -> Ptr structB -> IO Word32))

runStorableChunky :: (Storable a, MakeValueTuple a valueA, Memory valueA structA, Storable b, MakeValueTuple b valueB, Memory valueB structB) => T p valueA valueB -> IO (p -> Vector a -> Vector b)

runStorableChunkyCont :: (Storable a, MakeValueTuple a valueA, Memory valueA structA, Storable b, MakeValueTuple b valueB, Memory valueB structB) => T p valueA valueB -> IO ((Vector a -> Vector b) -> p -> Vector a -> Vector b)

This function should be used instead of StorableVector.Lazy.Pattern.splitAt and subsequent append, because it does not have the risk of a memory leak.

applyStorableChunky :: (Storable a, MakeValueTuple a valueA, Memory valueA structA, Storable b, MakeValueTuple b valueB, Memory valueB structB) => T p valueA valueB -> p -> Vector a -> Vector b