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// ************************************************************************************************
//
// BornAgain: simulate and fit reflection and scattering
//
//! @file Sample/Interface/AutocorrelationModels.cpp
//! @brief Implement AutocorrelationModel classes.
//!
//! @homepage http://www.bornagainproject.org
//! @license GNU General Public License v3 or higher (see COPYING)
//! @copyright Forschungszentrum Jülich GmbH 2024
//! @authors Scientific Computing Group at MLZ (see CITATION, AUTHORS)
//
// ************************************************************************************************
#include "Sample/Interface/AutocorrelationModels.h"
#include "Base/Py/PyFmt.h"
#include "Base/Util/Assert.h"
#include <numbers>
using std::numbers::pi;
AutocorrelationModel::AutocorrelationModel(double maxSpatFrequency)
: m_max_spatial_frequency(maxSpatFrequency)
{
}
std::vector<std::string> AutocorrelationModel::validationErrs() const
{
std::vector<std::string> errs;
requestGt0(errs, m_max_spatial_frequency, "maxSpatialFrequency");
return errs;
}
std::string AutocorrelationModel::pythonArguments() const
{
return Py::Fmt::printArguments(
{{m_max_spatial_frequency, AutocorrelationModel::parDefs()[0].unit}});
}
//-------------------------------------------------------------------------------------------------
//! @param sigma: height scale of the roughness in nanometers (usually close to rms)
//! @param hurstParameter: hurst parameter which describes how jagged the interface,
//! dimensionless (0.0, 1.0], where 0.0 gives more spikes, 1.0 more smoothness
//! @param lateralCorrLength: lateral correlation length of the roughness in nanometers
SelfAffineFractalModel::SelfAffineFractalModel(double sigma, double hurst, double lateralCorrLength,
double maxSpatFrequency)
: AutocorrelationModel(maxSpatFrequency)
, m_sigma(sigma)
, m_hurst_parameter(hurst)
, m_lateral_corr_length(lateralCorrLength)
{
validateOrThrow();
}
SelfAffineFractalModel* SelfAffineFractalModel::clone() const
{
return new SelfAffineFractalModel(m_sigma, m_hurst_parameter, m_lateral_corr_length,
m_max_spatial_frequency);
}
std::vector<ParaMeta> SelfAffineFractalModel::parDefs() const
{
std::vector<ParaMeta> result = {{"Sigma", "nm"}, {"Hurst", ""}, {"CorrLength", "nm"}};
const auto base_pars = AutocorrelationModel::parDefs();
result.insert(result.end(), base_pars.begin(), base_pars.end());
return result;
}
std::string SelfAffineFractalModel::validate() const
{
std::vector<std::string> errs = AutocorrelationModel::validationErrs();
requestGe0(errs, m_sigma, "sigma");
requestIn(errs, m_hurst_parameter, "hurst", 0, 1);
requestGe0(errs, m_lateral_corr_length, "lateralCorrLength");
if (!errs.empty())
return jointError(errs);
m_validated = true;
return "";
}
std::string SelfAffineFractalModel::pythonArguments() const
{
return Py::Fmt::printArguments({{m_sigma, parDefs()[0].unit},
{m_hurst_parameter, parDefs()[1].unit},
{m_lateral_corr_length, parDefs()[2].unit}})
+ ", " + AutocorrelationModel::pythonArguments();
}
//! Power spectral density of the surface roughness is a result of two-dimensional
//! Fourier transform of the correlation function of the roughness profile.
//!
//! Based on Palasantzas, Phys Rev B, 48, 14472 (1993)
double SelfAffineFractalModel::spectralFunction(double spatial_f) const
{
ASSERT(m_validated);
if (spatial_f > m_max_spatial_frequency)
return 0;
const double Qpar2 = pow(2 * pi * spatial_f, 2);
const double H = m_hurst_parameter;
const double clength2 = m_lateral_corr_length * m_lateral_corr_length;
return 4.0 * pi * H * m_sigma * m_sigma * clength2 * std::pow(1 + Qpar2 * clength2, -1 - H);
}
double SelfAffineFractalModel::rms() const
{
// integration of spectral function: rms^2 = Integrate[PSD(f) * 2*pi*f, {f, 0, max_frequency}]
const double H = m_hurst_parameter;
const double val = 2 * pi * m_lateral_corr_length * m_max_spatial_frequency;
return m_sigma * std::sqrt(1. - std::pow(1 + val * val, -H));
}
//-------------------------------------------------------------------------------------------------
LinearGrowthModel::LinearGrowthModel(double particle_volume, double damp1, double damp2,
double damp3, double damp4, double maxSpatFrequency)
: AutocorrelationModel(maxSpatFrequency)
, m_cluster_volume(particle_volume)
, m_damp1(damp1)
, m_damp2(damp2)
, m_damp3(damp3)
, m_damp4(damp4)
{
validateOrThrow();
}
LinearGrowthModel* LinearGrowthModel::clone() const
{
return new LinearGrowthModel(m_cluster_volume, m_damp1, m_damp2, m_damp3, m_damp4,
m_max_spatial_frequency);
}
std::string LinearGrowthModel::validate() const
{
std::vector<std::string> errs = AutocorrelationModel::validationErrs();
requestGe0(errs, m_cluster_volume, "particleVolume");
requestGe0(errs, m_damp1, "dampingExp1");
requestGe0(errs, m_damp2, "dampingExp2");
requestGe0(errs, m_damp3, "dampingExp3");
requestGe0(errs, m_damp4, "dampingExp4");
if (!errs.empty())
return jointError(errs);
m_validated = true;
return "";
}
std::vector<ParaMeta> LinearGrowthModel::parDefs() const
{
std::vector<ParaMeta> result = {{"Particle volume", "nm^3"},
{"Damping 1", ""},
{"Damping 2", "nm"},
{"Damping 3", "nm^2"},
{"Damping 4", "nm^3"}};
const auto base_pars = AutocorrelationModel::parDefs();
result.insert(result.end(), base_pars.begin(), base_pars.end());
return result;
}
std::string LinearGrowthModel::pythonArguments() const
{
return Py::Fmt::printArguments({{m_cluster_volume, parDefs()[0].unit},
{m_damp1, parDefs()[1].unit},
{m_damp2, parDefs()[2].unit},
{m_damp3, parDefs()[3].unit},
{m_damp4, parDefs()[4].unit}})
+ ", " + AutocorrelationModel::pythonArguments();
;
}
double LinearGrowthModel::damping(double spatial_f) const
{
return m_damp1 * spatial_f + m_damp2 * std::pow(spatial_f, 2) + m_damp3 * std::pow(spatial_f, 3)
+ m_damp4 * std::pow(spatial_f, 4);
}
double LinearGrowthModel::spectralFunction(double spectrum_below, double thickness,
double spatial_f) const
{
ASSERT(m_validated);
if (spatial_f > m_max_spatial_frequency)
return 0;
if (thickness == 0)
return spectrum_below;
double inherited_spectrum = spectrum_below;
double growth_spectrum = m_cluster_volume * thickness;
const double damp = damping(spatial_f);
if (damp > 0) {
const double exponent = std::exp(-damp * thickness);
inherited_spectrum = spectrum_below * exponent;
growth_spectrum = m_cluster_volume * (1. - exponent) / damp;
}
return inherited_spectrum + growth_spectrum;
}
double LinearGrowthModel::crosscorrSpectrum(double spectrum_below, double thickness,
double spatial_f) const
{
ASSERT(m_validated);
const double damp = damping(spatial_f);
if (damp == 0 || thickness == 0)
return spectrum_below;
return spectrum_below * std::exp(-damp * thickness);
}
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