-- The model demonstrates using the arrays
-- as described in model Linear Array from Berkeley Madonna.
--
-- It defines only one helper function generateArray.
-- If the model used vectors (Data.Vector) then there would be
-- no need in this helper function.
{-# LANGUAGE RecursiveDo #-}
import Data.Array
import Control.Monad
import Control.Monad.Trans
import qualified Data.Vector as V
import Simulation.Aivika
import Simulation.Aivika.SystemDynamics
import Simulation.Aivika.Experiment
specs = Specs { spcStartTime = 0,
spcStopTime = 500,
spcDT = 0.1,
spcMethod = RungeKutta4,
spcGeneratorType = SimpleGenerator }
-- | This is an analog of 'V.generateM' included in the Haskell platform.
generateArray :: (Ix i, Monad m) => (i, i) -> (i -> m a) -> m (Array i a)
generateArray bnds generator =
do ps <- forM (range bnds) $ \i ->
do x <- generator i
return (i, x)
return $ array bnds ps
model :: Int -> Simulation ExperimentData
model n =
mdo m <- generateArray (1, n) $ \i ->
integ (q + k * (c!(i - 1) - c!i) + k * (c!(i + 1) - c!i)) 0
let c =
array (0, n + 1) [(i, if (i == 0) || (i == n + 1)
then 0
else (m!i / v)) | i <- [0 .. n + 1]]
q = 1
k = 2
v = 0.75
experimentDataInStartTime
[("t", seriesEntity "time" time),
("m", seriesEntity "M" m),
("c", seriesEntity "C" c)]
experiment :: FileRenderer r => Experiment r
experiment =
defaultExperiment {
experimentSpecs = specs,
experimentRunCount = 1,
experimentTitle = "Linear Array",
experimentDescription = "Model Linear Array as described in " ++
"the examples included in Berkeley-Madonna.",
experimentGenerators =
[outputView defaultExperimentSpecsView,
outputView $ defaultTableView {
tableSeries = ["t", "m", "c"] },
outputView $ defaultFinalTableView {
finalTableSeries = ["m", "c"] },
outputView $ defaultLastValueView {
lastValueSeries = ["t", "m", "c"] },
outputView $ defaultTimingStatsView {
timingStatsSeries = ["t", "m", "c"] },
outputView $ defaultFinalStatsView {
finalStatsSeries = ["m", "c"] } ] }
main = runExperiment experiment HtmlRenderer (model 51)