burst-detection (empty) → 1.0
raw patch · 7 files changed
+577/−0 lines, 7 filesdep +basedep +burst-detectiondep +criterionsetup-changed
Dependencies added: base, burst-detection, criterion, deepseq
Files
- LICENSE +340/−0
- README.md +14/−0
- Setup.hs +2/−0
- benchmark/Main.hs +33/−0
- burst-detection.cabal +47/−0
- src/Math/Bursts.hs +132/−0
- tests/Main.hs +9/−0
+ LICENSE view
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+ README.md view
@@ -0,0 +1,14 @@+Introduction+============++This project is a Haskell implementation of the bursts detection+algorithm proposed by Zhang [1].++This project concains C and Haskell generic implementations. For much+more optimized one that can run on FPGA you can refer to+http://parsci.com/+ +References+==========++[1] Zhang, X. (2006). Fast Algorithms for Burst Detection. Thesis. New York University.
+ Setup.hs view
@@ -0,0 +1,2 @@+import Distribution.Simple+main = defaultMain
+ benchmark/Main.hs view
@@ -0,0 +1,33 @@+{-# LANGUAGE BangPatterns #-}+-- |+-- Copyright : (C) 2013 Parallel Scientific Labs, LLC.+-- License : GPLv2+--+-- Burst prototype based on the work of Jonathan Jouty.+--+module Main+ where++import Math.Bursts++import Criterion.Main+import Criterion.Measurement+import Control.Monad ( void )+import Control.Exception ( evaluate )+import Text.Printf+++main = do+ defaultMain + [ bgroup "burst" + [ bench "depth 18" $ nf go 18+ ]+ ]+ let samples = 20+ d <- time_ $ void . evaluate $ go samples+ printf "Samples per second: %14.4f\n" $ (fromIntegral samples)/d+ printf "Nanoseconds per sample: %.4f\n" $ d/(fromIntegral samples)*1e9+ where+ go n = sbtBurstAnalysisMany ([0..n]) w+ w :: Window Int+ w = initialWindow 4096 10 100 defaultAggregate
+ burst-detection.cabal view
@@ -0,0 +1,47 @@+name: burst-detection+version: 1.0+synopsis: Burst detection algorithms+description: Library implements burst detection algorithms that follows+ .+ Zhang, X. (2006). Fast Algorithms for Burst Detection. Thesis. New York University.++homepage: http://parsci.com/+license: GPL-2+license-file: LICENSE+author: serguey.zefirov@parsci.com+maintainer: alexander.vershilov@parsci.com+copyright: (c) 2013 Parallel Scientific Labs, LLC+category: Math+build-type: Simple+extra-source-files: README.md+cabal-version: >=1.10++source-repository head+ type: git+ location: git://github.com/ps-labs/burst-detection.git++library+ exposed-modules: Math.Bursts+ build-depends: base >=4.6 && <4.7,+ deepseq >=1.3 && <1.4+ ghc-options: -O2+ hs-source-dirs: src+ default-language: Haskell2010++test-suite simple-tests+ build-depends: base >=4.6 && <4.7,+ burst-detection+ type: exitcode-stdio-1.0+ main-is: Main.hs+ hs-source-dirs: tests+ default-language: Haskell2010++benchmark c-bench+ build-depends: base >= 4.6 && <4.7,+ criterion >= 0.8 && <0.9,+ burst-detection+ type: exitcode-stdio-1.0+ ghc-options: -O2+ main-is: Main.hs+ hs-source-dirs: benchmark+ default-language: Haskell2010
+ src/Math/Bursts.hs view
@@ -0,0 +1,132 @@+-- |+-- Copyright : (C) 2013 Parallel Scientific Labs, LLC.+-- License : GPLv2++{-# LANGUAGE BangPatterns #-}++module Math.Bursts+ ( -- * Data Structures+ Range(..)+ , Window+ , initialWindow+ , defaultAggregate+ -- * Analysis+ , sbtBurstAnalysis+ , sbtBurstAnalysisMany+ ) where++import Control.Monad+import Control.DeepSeq+import Data.Bits+import Data.List (foldl')++-- |Representation of ranges.+data Range = Range+ { rangeStart, rangeEnd, rangeMagn :: !Int+ }+ deriving (Eq, Ord, Show)++instance NFData Range where+ rnf (Range a b c) = a `seq` b `seq` c `seq` ()++-- |Join two ranges that are close to each other.+-- The first range is one with smaller rangeStart. The second range is one with biggest rangeEnd.+joinCloseRanges :: Range -> Range -> Range+joinCloseRanges (Range newStart _ m1) (Range _ newEnd m2) = Range newStart newEnd (max m1 m2)++-- |The window structure.+data Window a = Window+ { windowSamples :: [a] -- ^ Samples+ , windowAverage :: a -- ^ Average to compute threshold.+ , windowThreeSigma1000 :: a+ -- ^ Three sigma (n*sigma, actually), multiplied by 1000. Again, it is needed to compute a threshold.+ , windowThresholdAggreg :: a -> a -> (a, a) -- ^ Threshold aggregation function.+ }++instance NFData a => NFData (Window a) where+ rnf (Window a b c _) = a `seq` b `seq` c `seq` ()++-- |Initial empty window.+initialWindow :: Num a => Int -> a -> a -> (a -> a -> (a,a))-> Window a+initialWindow len avg ts1000 aggreg+ | (len .&. (len-1)) /= 0 = error $ "length "++show len++" is not power of two."+ | otherwise = Window (replicate len 0) avg ts1000 aggreg++defaultAggregate :: (Integral a, Num a) => a -> a -> (a,a)+defaultAggregate average threeSigma1000 =+ (average*2, div (threeSigma1000*1414+500) 1000)++-- |Shifted Binary Tree burst analysis.+-- Returns Maybe Range. Just range indicates a burst detected.+-- Also returns a new window structure.+sbtBurstAnalysis :: (Ord a, Num a, Integral a, Show a) => a -> Window a -> (Window a, Maybe Range)+sbtBurstAnalysis sample window@(Window samples initialAvg initialThreeSigma1000 aggreg) = (newWindow, foundRange)+ where+ !foundRange = analyze 1 (2*initialAvg) (nextThreeSigma initialThreeSigma1000) samplesWithRanges+ newSamples = sample : init samples+ !newWindow = window { windowSamples = newSamples }+ samplesRanges = map (\i -> Range i i 1) [0..]+ nextThreeSigma ts1000 = div (ts1000*1404+500) 1000+ computeThreshold avg ts1000 = avg + div (ts1000 + 500) 1000+ initialThreshold = computeThreshold initialAvg initialThreeSigma1000+ samplesWithRanges = zipWith (\s r -> (False, s >= initialThreshold, s, r)) newSamples samplesRanges+ extractRange mbRange [] = fmap snd mbRange+ extractRange Nothing ((any, burst, _, r) : brss)+ | burst = extractRange (Just (True, r)) brss+ | any = extractRange (Just (False, r)) brss+ | otherwise = extractRange Nothing brss+ extractRange (Just (prevB, prevR)) ((any, burst, _, r) : brss)+ | prevB && not burst = Just prevR+ | prevB && burst = extractRange (Just (True, joinCloseRanges prevR r)) brss+ | burst = extractRange (Just (True, r)) brss+ | otherwise = Just prevR+ analyze magn average threeSigma1000 samplesWithRanges+ | length samplesWithRanges >= 4 = -- trace ("averaged samples "++show averagedSamples) $ trace ("samplesWithRanges "++show samplesWithRanges) $+ analyze (2*magn) nextAvg nextThreeSigma1000 averaged+ | otherwise = -- trace ("final samplesWithRanges "++show samplesWithRanges) $+ extractRange Nothing samplesWithRanges+ where+ (nextAvg, nextThreeSigma1000) = aggreg average threeSigma1000+ thisThreshold = computeThreshold average threeSigma1000+ avg ((any1, b1,s1,r1):bsrs@((any2, b2,s2,r2):_)) =+ (any1 || any2 || b1 || b2, thisB, z, avgR)+ : avg bsrs+ where+ avgR+ | b1 && b2 && thisB = (\r -> r { rangeMagn = magn}) $ joinCloseRanges r1 r2+ | b1 = r1+ | b2 = r2+ | any1 = r1+ | any2 = r2+ | otherwise = joinCloseRanges r1 r2+ thisB = z >= thisThreshold+ z = s1 + s2+ avg _ = []+ combineRanges False ((any1,b1,s1,r1):bsrs@((any2,b2,s2,r2):_))+ = headSample : combineRanges True bsrs+ where+ headSample+ | b1 && b2 = (True, True, s1, joinCloseRanges r1 r2)+ | b1 = (True, True, s1, r1)+ | b2 = (True, True, s1, r2)+ | otherwise = (any1 || any2, False, s1, if any2 then r2 else r1)+ combineRanges True ((any1,b1,s1,r1):((any2,b2,s2,r2):bsrs))+ = combineRanges False (headSample : bsrs)+ where+ headSample+ | b1 && b2 = (True, True, s2, joinCloseRanges r1 r2)+ | b1 = (True, True, s2, r1)+ | b2 = (True, True, s2, r2)+ | otherwise = (any1 || any2, False, s2, if any2 then r2 else r1)+ combineRanges _ rs = rs+ averagedSamples = avg samplesWithRanges+ averaged = combineRanges False averagedSamples+{-# INLINABLE sbtBurstAnalysis #-}+{-# SPECIALIZE sbtBurstAnalysis :: Int -> Window Int -> (Window Int, Maybe Range) #-}++sbtBurstAnalysisMany :: (Ord a, Num a, Integral a, Show a) => [a] -> Window a -> (Window a, Maybe Range)+sbtBurstAnalysisMany samples window = foldl' analyzeSample (window, Nothing) samples+ where+ analyzeSample (!window, _) sample = sbtBurstAnalysis sample window+{-# INLINABLE sbtBurstAnalysisMany #-}+{-# SPECIALIZE sbtBurstAnalysisMany :: [Int] -> Window Int -> (Window Int, Maybe Range) #-}
+ tests/Main.hs view
@@ -0,0 +1,9 @@+module Main+ where++import Math.Bursts++main = undefined++t1 len x xcount zcount = sbtBurstAnalysisMany (replicate xcount x ++ replicate zcount 0) (initialWindow len 100 20000 defaultAggregate)+t2 len xs zcount = sbtBurstAnalysisMany (xs ++ replicate zcount 0) (initialWindow len 100 20000 defaultAggregate)