Analysis.hs

{-# OPTIONS -fglasgow-exts -fno-implicit-prelude #-}
module Synthesizer.Generic.Analysis where

import qualified Synthesizer.Generic.Signal as SigG
import qualified Synthesizer.Generic.SampledValue as Sample

-- import qualified Synthesizer.Plain.Control as Ctrl

-- import qualified Algebra.Module                as Module
-- import qualified Algebra.Transcendental        as Trans
import qualified Algebra.Algebraic             as Algebraic
-- import qualified Algebra.RealField             as RealField
import qualified Algebra.Field                 as Field
import qualified Algebra.Real                  as Real
import qualified Algebra.Ring                  as Ring
import qualified Algebra.Additive              as Additive

import qualified Algebra.NormedSpace.Maximum   as NormedMax
import qualified Algebra.NormedSpace.Euclidean as NormedEuc
import qualified Algebra.NormedSpace.Sum       as NormedSum

-- import qualified Data.Array as Array

-- import qualified Data.IntMap as IntMap

-- import Algebra.Module((*>))

-- import Data.Array (accumArray)

import qualified Prelude as P
import PreludeBase
import NumericPrelude


{- * Notions of volume -}

{- |
Volume based on Manhattan norm.
-}
volumeMaximum :: (Real.C y, Sample.C y, SigG.C sig) => sig y -> y
volumeMaximum =
   SigG.foldL max zero . rectify
--   maximum . rectify

{- |
Volume based on Energy norm.
-}
volumeEuclidean :: (Algebraic.C y, Sample.C y, SigG.C sig) => sig y -> y
volumeEuclidean =
   Algebraic.sqrt . volumeEuclideanSqr

volumeEuclideanSqr :: (Field.C y, Sample.C y, SigG.C sig) => sig y -> y
volumeEuclideanSqr =
   average . SigG.map sqr

{- |
Volume based on Sum norm.
-}
volumeSum :: (Field.C y, Real.C y, Sample.C y, SigG.C sig) => sig y -> y
volumeSum = average . rectify



{- |
Volume based on Manhattan norm.
-}
volumeVectorMaximum ::
   (NormedMax.C y yv, Ord y, Sample.C y, Sample.C yv, SigG.C sig) =>
   sig yv -> y
volumeVectorMaximum =
   SigG.foldL max zero . SigG.map NormedMax.norm
--   NormedMax.norm
--   maximum . SigG.map NormedMax.norm

{- |
Volume based on Energy norm.
-}
volumeVectorEuclidean ::
   (Algebraic.C y, NormedEuc.C y yv, Sample.C y, Sample.C yv, SigG.C sig) =>
   sig yv -> y
volumeVectorEuclidean =
   Algebraic.sqrt . volumeVectorEuclideanSqr

volumeVectorEuclideanSqr ::
   (Field.C y, NormedEuc.Sqr y yv, Sample.C y, Sample.C yv, SigG.C sig) =>
   sig yv -> y
volumeVectorEuclideanSqr =
   average . SigG.map NormedEuc.normSqr

{- |
Volume based on Sum norm.
-}
volumeVectorSum ::
   (NormedSum.C y yv, Field.C y, Sample.C y, Sample.C yv, SigG.C sig) =>
   sig yv -> y
volumeVectorSum =
   average . SigG.map NormedSum.norm




{- |
Compute minimum and maximum value of the stream the efficient way.
Input list must be non-empty and finite.
-}
bounds :: (Ord y, Sample.C y, SigG.C sig) => sig y -> (y,y)
bounds =
   maybe
      (error "Analysis.bounds: List must contain at least one element.")
      (\(x,xs) ->
          SigG.foldL (\(minX,maxX) y -> (min y minX, max y maxX)) (x,x) xs)
   . SigG.viewL




{- * Miscellaneous -}

{-
histogram:
    length x = sum (histogramDiscrete x)

    units:
    1) histogram (amplify k x) = timestretch k (amplify (1/k) (histogram x))
    2) histogram (timestretch k x) = amplify k (histogram x)
    timestretch: k -> (s -> V) -> (k*s -> V)
    amplify:     k -> (s -> V) -> (s -> k*V)
    histogram:   (a -> b) -> (a^ia*b^ib -> a^ja*b^jb)
    x:           (s -> V)
    1) => (s^ia*(k*V)^ib -> s^ja*(k*V)^jb)
              = (s^ia*V^ib*k -> s^ja*V^jb/k)
       => ib=1, jb=-1
    2) => ((k*s)^ia*V^ib -> (k*s)^ja*V^jb)
              = (s^ia*V^ib -> s^ja*V^jb*k)
       => ia=0, ja=1
    histogram:   (s -> V) -> (V -> s/V)
histogram':
    integral (histogram' x) = integral x
    histogram' (amplify k x) = timestretch k (histogram' x)
    histogram' (timestretch k x) = amplify k (histogram' x)
     -> this does only apply if we slice the area horizontally
        and sum the slice up at each level,
        we must also restrict to the positive values,
        this is not quite the usual histogram
-}

{-
{- |
Input list must be finite.
List is scanned twice, but counting may be faster.
-}
histogramDiscreteArray :: sig Int -> (Int, sig Int)
histogramDiscreteArray [] =
   (error "histogramDiscreteArray: no bounds found", [])
histogramDiscreteArray x =
   let hist =
          accumArray (+) zero
             (bounds x) (attachOne x)
   in  (fst (Array.bounds hist), Array.elems hist)


{- |
Input list must be finite.
If the input signal is empty, the offset is @undefined@.
List is scanned twice, but counting may be faster.
The sum of all histogram values is one less than the length of the signal.
-}
histogramLinearArray :: RealField.C y => sig y -> (Int, sig y)
histogramLinearArray [] =
   (error "histogramLinearArray: no bounds found", [])
histogramLinearArray [x] = (floor x, [])
histogramLinearArray x =
   let (xMin,xMax) = bounds x
       hist =
          accumArray (+) zero
             (floor xMin, floor xMax)
             (meanValues x)
   in  (fst (Array.bounds hist), Array.elems hist)

{- |
Input list must be finite.
If the input signal is empty, the offset is @undefined@.
List is scanned once, counting may be slower.
-}
histogramDiscreteIntMap :: sig Int -> (Int, sig Int)
histogramDiscreteIntMap [] =
   (error "histogramDiscreteIntMap: no bounds found", [])
histogramDiscreteIntMap x =
   let hist = IntMap.fromListWith (+) (attachOne x)
   in  case IntMap.toAscList hist of
          [] -> error "histogramDiscreteIntMap: the list was non-empty before processing ..."
          fAll@((fIndex,fHead):fs) -> (fIndex, fHead :
              concat (zipWith
                 (\(i0,_) (i1,f1) -> replicate (i1-i0-1) zero ++ [f1])
                 fAll fs))

histogramLinearIntMap :: RealField.C y => sig y -> (Int, sig y)
histogramLinearIntMap [] =
   (error "histogramLinearIntMap: no bounds found", [])
histogramLinearIntMap [x] = (floor x, [])
histogramLinearIntMap x =
   let hist = IntMap.fromListWith (+) (meanValues x)
   -- we can rely on the fact that the keys are contiguous
       (startKey:_, elems) = unzip (IntMap.toAscList hist)
   in  (startKey, elems)
   -- This doesn't work, due to a bug in IntMap of GHC-6.4.1
   -- in  (head (IntMap.keys hist), IntMap.elems hist)
-}

{-
The bug in IntMap GHC-6.4.1 is:

*Synthesizer.Plain.Analysis> IntMap.keys $ IntMap.fromList $ [(0,0),(-1,-1::Int)]
[0,-1]
*Synthesizer.Plain.Analysis> IntMap.elems $ IntMap.fromList $ [(0,0),(-1,-1::Int)]
[0,-1]
*Synthesizer.Plain.Analysis> IntMap.assocs $ IntMap.fromList $ [(0,0),(-1,-1::Int)]
[(0,0),(-1,-1)]

The bug has gone in IntMap as shipped with GHC-6.6.
-}

{-
histogramIntMap :: (RealField.C y, Sample.C y, SigG.C sig) =>
   y -> sig y -> (Int, sig Int)
histogramIntMap binsPerUnit =
   histogramDiscreteIntMap . quantize binsPerUnit

quantize :: (RealField.C y, Sample.C y, SigG.C sig) =>
   y -> sig y -> sig Int
quantize binsPerUnit = SigG.map (floor . (binsPerUnit*))

attachOne :: (Sample.C i, SigG.C sig) => sig i -> sig (i,Int)
attachOne = SigG.map (\i -> (i,one))

meanValues ::
   (RealField.C y, Sample.C y, SigG.C sig) => sig y -> [(Int,y)]
meanValues x = concatMap spread (zip x (tail x))

spread ::
   (RealField.C y, Sample.C y, SigG.C sig) => (y,y) -> [(Int,y)]
spread (l0,r0) =
   let (l,r) = if l0<=r0 then (l0,r0) else (r0,l0)
       (li,lf) = splitFraction l
       (ri,rf) = splitFraction r
       k = recip (r-l)
       nodes =
          (li,k*(1-lf)) :
          zip [li+1 ..] (replicate (ri-li-1) k) ++
          (ri, k*rf) :
          []
   in  if li==ri
         then [(li,one)]
         else nodes
-}

{- |
Requires finite length.
This is identical to the arithmetic mean.
-}
directCurrentOffset ::
   (Field.C y, Sample.C y, SigG.C sig) => sig y -> y
directCurrentOffset = average


scalarProduct ::
   (Ring.C y, Sample.C y, SigG.C sig) => sig y -> sig y -> y
scalarProduct xs ys =
   SigG.sum (SigG.zipWith (*) xs ys)

{- |
'directCurrentOffset' must be non-zero.
-}
centroid :: (Field.C y, Sample.C y, SigG.C sig) => sig y -> y
centroid xs =
   scalarProduct (SigG.iterate (one+) zero) xs / SigG.sum xs

{-
centroidAlt :: (Field.C y, Sample.C y, SigG.C sig) => sig y -> y
centroidAlt xs =
   SigG.sum (scanr (+) zero (tail xs)) / sum xs
-}

average :: (Field.C y, Sample.C y, SigG.C sig) => sig y -> y
average x =
   SigG.sum x / fromIntegral (SigG.length x)

rectify :: (Real.C y, Sample.C y, SigG.C sig) => sig y -> sig y
rectify = SigG.map abs

{- |
Detects zeros (sign changes) in a signal.
This can be used as a simple measure of the portion
of high frequencies or noise in the signal.
It ca be used as voiced\/unvoiced detector in a vocoder.

@zeros x !! n@ is @True@ if and only if
@(x !! n >= 0) \/= (x !! (n+1) >= 0)@.
The result will be one value shorter than the input.
-}
zeros :: (Ord y, Ring.C y, Sample.C y, SigG.C sig) =>
   sig y -> sig Bool
zeros =
   SigG.zapWith (/=) . SigG.map (>=zero)



{- |
Detect thresholds with a hysteresis.
-}
flipFlopHysteresis :: (Ord y, Sample.C y, SigG.C sig) =>
   (y,y) -> Bool -> sig y -> sig Bool
flipFlopHysteresis (lower,upper) =
   SigG.scanL
      (\state x ->
          if state
            then not(x<lower)
            else x>upper)

{-
{- |
Almost naive implementation of the chirp transform,
a generalization of the Fourier transform.

More sophisticated algorithms like Rader, Cooley-Tukey, Winograd, Prime-Factor may follow.
-}
chirpTransform :: Ring.C y =>
   y -> sig y -> sig y
chirpTransform z xs =
   let powers = Ctrl.curveMultiscaleNeutral (*) z one
       powerPowers =
          SigG.map (\zn -> Ctrl.curveMultiscaleNeutral (*) zn one) powers
   in  SigG.map (scalarProduct xs) powerPowers
-}