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/* Copyright (c) 2025, DyssolTEC GmbH.
* All rights reserved. This file is part of Dyssol. See LICENSE file for license information. */
#pragma once
#include <cstdint>
#include <functional>
#include <string_view>
/**
* Types of distribution functions.
*/
enum class EDistributionFunction : uint8_t
{
MANUAL = 0, ///< Manual distribution.
NORMAL = 1, ///< Normal distribution.
RRSB = 2, ///< Rosin-Rammler-Sperling-Bennett distribution.
GGS = 3, ///< Gates-Gaudin-Schuhmann distribution.
LOG_NORMAL = 4, ///< Log-normal distribution.
/// \cond <- to hide in the Doxygen output
COUNT_ ///< Number of distribution functions.
/// \endcond
};
/**
* \cond <- to hide in the Doxygen output
* \brief Implementation details for distribution functions.
*/
namespace DistributionFunction
{
struct SFunctionParam
{
std::string_view name; ///< Name of the function parameter.
double initValue; ///< Initial value of the function parameter.
};
struct SFunctionDescriptor
{
std::string_view name; ///< Name of the function.
std::vector<SFunctionParam> params; ///< Parameters of the function.
};
const SFunctionDescriptor& GetFunctionDescriptor(EDistributionFunction _type);
std::vector<std::string> GetFunctionParameterNames(EDistributionFunction _type);
}
/**
* \endcond
*/
/**
* Returns the given probability density function.
* \details Available functions:
* - EDistributionFunction::NORMAL:
* \f$y(x) = \frac{1}{\sqrt{2\pi\sigma^{2}}}e^{-\frac{(x-\mu)^{2}}{2\sigma^{2}}}\f$,
*
* with \f$\sigma \neq 0\f$,
* \f$\mu\f$ - mean value,
* \f$\sigma\f$ - standard deviation.
* - EDistributionFunction::LOG_NORMAL:
* \f$y(x) = \frac{1}{x\sigma\sqrt{2\pi}}e^{-\frac{(\ln x-\mu)^{2}}{2\sigma^{2}}}\f$,
*
* with \f$\sigma > 0\f$ and \f$x > 0\f$,
* \f$\mu\f$ - mean value,
* \f$\sigma\f$ - standard deviation.
* - EDistributionFunction::RRSB:
* \f$y(x) = \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^{k}}\f$,
*
* with \f$\lambda \neq 0\f$, \f$k > 0\f$ and \f$x > 0\f$,
* \f$\lambda\f$ - characteristic size,
* \f$k\f$ - distribution modulus.
* - EDistributionFunction::GGS:
* \f$y(x) = \frac{m}{x_{max}}\left(\frac{x}{x_{max}}\right)^{m-1}\f$,
*
* with \f$x_{max} > 0\f$, \f$m > 0\f$ and \f$0 \le x \le x_{max}\f$,
* \f$x_{max}\f$ - maximum size,
* \f$m\f$ - distribution modulus.
* - EDistributionFunction::MANUAL:
* returns a vector of zeroes.
*
* Returns an empty vector if the constraints on function parameters are not satisfied.
* \param _type Type of the distribution function.
* \param _x Points for which the distribution has to be generated.
* \param _param1 First parameter of the distribution function:
* - EDistributionFunction::NORMAL: mean \f$\mu\f$
* - EDistributionFunction::LOG_NORMAL: mean \f$\mu\f$
* - EDistributionFunction::RRSB: characteristic size \f$\lambda\f$
* - EDistributionFunction::GGS: maximum size \f$x_{max}\f$
* \param _param2 Second parameter of the distribution function:
* - EDistributionFunction::NORMAL: standard deviation \f$\sigma\f$
* - EDistributionFunction::LOG_NORMAL: standard deviation \f$\sigma\f$
* - EDistributionFunction::RRSB: distribution modulus \f$k\f$
* - EDistributionFunction::GGS: distribution modulus \f$m\f$
* \return Distribution.
*/
std::vector<double> CreateDistribution(EDistributionFunction _type, const std::vector<double>& _x, double _param1, double _param2);
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