exponential.C

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#include "exponential.H"
#include "addToRunTimeSelectionTable.H"

// * * * * * * * * * * * * * * Static Data Members * * * * * * * * * * * * * //

namespace Foam
{
namespace distributionModels
{
    defineTypeNameAndDebug(exponential, 0);
    addToRunTimeSelectionTable(distributionModel, exponential, dictionary);
}
}

// * * * * * * * * * * * * * * * * Constructors  * * * * * * * * * * * * * * //

Foam::distributionModels::exponential::exponential
(
    const dictionary& dict,
    Random& rndGen
)
:
    distributionModel(typeName, dict, rndGen),
    minValue_(readScalar(distributionModelDict_.lookup("minValue"))),
    maxValue_(readScalar(distributionModelDict_.lookup("maxValue"))),
    lambda_(readScalar(distributionModelDict_.lookup("lambda")))
{
    check();
}


Foam::distributionModels::exponential::exponential(const exponential& p)
:
    distributionModel(p),
    minValue_(p.minValue_),
    maxValue_(p.maxValue_),
    lambda_(p.lambda_)
{}


// * * * * * * * * * * * * * * * * Destructor  * * * * * * * * * * * * * * * //

Foam::distributionModels::exponential::~exponential()
{}


// * * * * * * * * * * * * * * * Member Functions  * * * * * * * * * * * * * //

Foam::scalar Foam::distributionModels::exponential::sample() const
{
    scalar y = rndGen_.sample01<scalar>();
    scalar K = exp(-lambda_*maxValue_) - exp(-lambda_*minValue_);
    return -(1.0/lambda_)*log(exp(-lambda_*minValue_) + y*K);
}


Foam::scalar Foam::distributionModels::exponential::minValue() const
{
    return minValue_;
}


Foam::scalar Foam::distributionModels::exponential::maxValue() const
{
    return maxValue_;
}


Foam::scalar Foam::distributionModels::exponential::meanValue() const
{
    return 1.0/lambda_;
}


// ************************************************************************* //