content: --
  -- This module is a Haskell port of Tyler Neely's historian Rust
  -- [crate](https://github.com/spacejam/historian), which in turn seems to be
  -- derived from his [loghisto](https://github.com/spacejam/loghisto) Go
  -- library. Neely's code bases both use the following license.
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  module Spex.Experiment.Histogram (module Spex.Experiment.Histogram) where
  
  import Control.Exception
  import Data.Vector (Vector)
  import Data.Vector qualified as Vector
  import Data.Word
  import Text.Printf
  
  ------------------------------------------------------------------------
  
  precision :: Double
  precision = 100.0
  
  buckets :: Int
  buckets = 2 ^ 16
  
  data Histogram = Histogram
    { values :: Vector Word64
    , sum :: Word64
    , count :: Word64
    }
  
  newHistogram :: Histogram
  newHistogram =
    Histogram {values = Vector.replicate buckets 0, sum = 0, count = 0}
  
  {- | The value @v@ must be positive. For values larger or equal to @1@ the
  compression loss is less than @1%@.
  -}
  measure :: (RealFrac a) => a -> Histogram -> Histogram
  measure value histogram =
    let
      index = fromEnum (compress value)
      oldValue = histogram.values Vector.! index
    in
      Histogram
        { values = histogram.values Vector.// [(index, oldValue + 1)]
        , sum = histogram.sum + round value
        , count = histogram.count + 1
        }
  
  compress :: (RealFrac a) => a -> Word16
  compress v =
    let d, d' :: Double
        d = realToFrac v
  
        d' = precision * log (1.0 + abs d) + 0.5
    in  assert
          (d' <= realToFrac (maxBound :: Word16))
          (fromIntegral (fromEnum d'))
  
  decompress :: Word16 -> Double
  decompress w = exp (realToFrac w / precision) - 1
  
  percentile :: Double -> Histogram -> Maybe Double
  percentile p histogram
    | p > 100.0 = error "percentile: percentiles cannot be over 100"
    | otherwise =
        if histogram.count == 0
          then Nothing
          else
            let target :: Double
                target =
                  let d = realToFrac histogram.count * (p / 100.0)
                  in  if d == 0.0 then 1.0 else d
            in  go target histogram.values
    where
      go :: Double -> Vector Word64 -> Maybe Double
      go target = go' 0 0.0
        where
          go' :: Word16 -> Double -> Vector Word64 -> Maybe Double
          go' idx acc xs = case Vector.uncons xs of
            Nothing -> Nothing
            Just (v, vs) ->
              let sum' = realToFrac v + acc
              in  if sum' >= target
                    then Just (decompress idx)
                    else go' (succ idx) sum' vs
  
  {- | @stats@ returns `Nothing` if no @measure@s have been made, otherwise it
  returns `Just` of a list of the minimum, median, the 90-, 99-, 99.9- and
  99.99-th percentile, the maximum, total count, and the sum of all @measure@s.
  -}
  stats :: Histogram -> Maybe [Double]
  stats histogram =
    if histogram.count == 0
      then Nothing
      else
        let mps = map (\p -> percentile p histogram) [0, 50, 90, 99, 99.9, 99.99, 100]
            mps' =
              mps
                ++ [Just (int2Double histogram.count), Just (int2Double histogram.sum)]
        in  Just (map (maybe (read "NaN") id) mps')
    where
      int2Double = fromIntegral
  
  prettyPrintHistogram :: String -> Histogram -> IO ()
  prettyPrintHistogram name histogram = do
    putStr name
    putStr ": "
    case stats histogram of
      Nothing -> putStrLn "NaN"
      Just s -> do
        putStrLn ""
        printf
          (concat (replicate 9 "%-10s"))
          ("min" :: String)
          ("med" :: String)
          ("90" :: String)
          ("99" :: String)
          ("99.9" :: String)
          ("99.99" :: String)
          ("max" :: String)
          ("count" :: String)
          ("sum" :: String)
        putStrLn ""
        mapM_ (printf "%-10.2f") s
        putStrLn ""
  
  apply :: Int -> (a -> a) -> a -> a
  apply 0 _f x = x
  apply n f x = apply (n - 1) f (f x)
  
  bench :: IO ()
  bench =
    let
      h = newHistogram
      h' = apply 5000 (measure 100) h
    in
      prettyPrintHistogram "bench" h'
  