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// ************************************************************************************************
//
// BornAgain: simulate and fit reflection and scattering
//
//! @file Device/Resolution/ConvolutionDetectorResolution.cpp
//! @brief Implements class ConvolutionDetectorResolution.
//!
//! @homepage http://www.bornagainproject.org
//! @license GNU General Public License v3 or higher (see COPYING)
//! @copyright Forschungszentrum Jülich GmbH 2018
//! @authors Scientific Computing Group at MLZ (see CITATION, AUTHORS)
//
// ************************************************************************************************
#include "Device/Resolution/ConvolutionDetectorResolution.h"
#include "Base/Axis/Frame.h"
#include "Base/Axis/Scale.h"
#include "Base/Util/Assert.h"
#include "Device/Data/Datafield.h"
#include "Device/Resolution/Convolve.h"
ConvolutionDetectorResolution::ConvolutionDetectorResolution(cumulative_DF_1d res_function_1d)
: m_rank(1)
, m_res_function_1d(res_function_1d)
{
}
ConvolutionDetectorResolution::ConvolutionDetectorResolution(
const IResolutionFunction2D& res_function_2d)
: m_rank(2)
, m_res_function_1d(nullptr)
{
setResolutionFunction(res_function_2d);
}
ConvolutionDetectorResolution::~ConvolutionDetectorResolution() = default;
ConvolutionDetectorResolution::ConvolutionDetectorResolution(
const ConvolutionDetectorResolution& other)
: m_rank(other.m_rank)
, m_res_function_1d(other.m_res_function_1d)
{
if (other.m_res_function_2d)
setResolutionFunction(*other.m_res_function_2d);
}
ConvolutionDetectorResolution* ConvolutionDetectorResolution::clone() const
{
return new ConvolutionDetectorResolution(*this);
}
std::vector<const INode*> ConvolutionDetectorResolution::nodeChildren() const
{
return std::vector<const INode*>() << m_res_function_2d;
}
void ConvolutionDetectorResolution::execDetectorResolution(Datafield* df) const
{
ASSERT(df->rank() == m_rank);
if (m_rank == 1)
apply1dConvolution(df);
else if (m_rank == 2)
apply2dConvolution(df);
else
ASSERT_NEVER;
}
void ConvolutionDetectorResolution::setResolutionFunction(const IResolutionFunction2D& resFunc)
{
m_res_function_2d.reset(resFunc.clone());
}
void ConvolutionDetectorResolution::apply1dConvolution(Datafield* df) const
{
ASSERT(m_res_function_1d);
ASSERT(df->rank() == 1);
const Scale& axis = df->axis(0);
const size_t n = df->size();
if (n < 2)
return; // No convolution for sets of zero or one element
// Construct kernel vector from resolution function
ASSERT(axis.size() == n);
double step_size = std::abs(axis.binCenter(0) - axis.binCenter(n - 1)) / (n - 1);
double mid_value = axis.binCenter(n / 2); // because Convolve expects zero at midpoint
std::vector<double> kernel;
for (size_t index = 0; index < n; ++index)
kernel.push_back(getIntegratedPDF1d(axis.binCenter(index) - mid_value, step_size));
// Calculate convolution
std::vector<double> result;
Convolve().fftconvolve1D(df->flatVector(), kernel, result);
// Truncate negative values that can arise because of finite precision of Fourier Transform
for (double& e : result)
e = std::max(0.0, e);
df->setVector(result);
}
void ConvolutionDetectorResolution::apply2dConvolution(Datafield* df) const
{
ASSERT(m_res_function_2d);
ASSERT(df->rank() == 2);
const Scale& X = df->axis(0);
const Scale& Y = df->axis(1);
const size_t nx = X.size();
const size_t ny = Y.size();
if (nx < 2 && ny < 2)
return;
// Construct kernel vector from resolution function
double2d_t kernel;
kernel.resize(ny);
double mid_value1 = X.binCenter(nx / 2); // because Convolve expects zero at midpoint
double mid_value2 = Y.binCenter(ny / 2); // because Convolve expects zero at midpoint
double dx = std::abs(X.binCenter(0) - X.binCenter(nx - 1)) / (nx - 1);
double dy = std::abs(Y.binCenter(0) - Y.binCenter(ny - 1)) / (ny - 1);
for (size_t iy = 0; iy < ny; ++iy) {
double y = Y.binCenter(iy) - mid_value2;
std::vector<double> row_vector;
row_vector.resize(nx, 0.0);
for (size_t ix = 0; ix < nx; ++ix) {
double x = X.binCenter(ix) - mid_value1;
row_vector[ix] = getIntegratedPDF2d(x, dx, y, dy);
}
kernel[iy] = row_vector;
}
// Calculate convolution
double2d_t result;
Convolve().fftconvolve2D(df->values2D(), kernel, result);
df->setVector2D(result);
}
double ConvolutionDetectorResolution::getIntegratedPDF1d(double x, double step) const
{
double halfstep = step / 2.0;
double xmin = x - halfstep;
double xmax = x + halfstep;
ASSERT(m_res_function_1d != nullptr);
return m_res_function_1d(xmax) - m_res_function_1d(xmin);
}
double ConvolutionDetectorResolution::getIntegratedPDF2d(double x, double step_x, double y,
double step_y) const
{
double halfstepx = step_x / 2.0;
double halfstepy = step_y / 2.0;
double xmin = x - halfstepx;
double xmax = x + halfstepx;
double ymin = y - halfstepy;
double ymax = y + halfstepy;
double result =
m_res_function_2d->evaluateCDF(xmax, ymax) - m_res_function_2d->evaluateCDF(xmax, ymin)
- m_res_function_2d->evaluateCDF(xmin, ymax) + m_res_function_2d->evaluateCDF(xmin, ymin);
return result;
}
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