vtk-m/vtkm/worklet/splatkernels/Gaussian.h

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//============================================================================
// Copyright (c) Kitware, Inc.
// All rights reserved.
// See LICENSE.txt for details.
// This software is distributed WITHOUT ANY WARRANTY; without even
// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
// PURPOSE. See the above copyright notice for more information.
//
// Copyright 2014 Sandia Corporation.
// Copyright 2014 UT-Battelle, LLC.
// Copyright 2014 Los Alamos National Security.
//
// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
// the U.S. Government retains certain rights in this software.
//
// Under the terms of Contract DE-AC52-06NA25396 with Los Alamos National
// Laboratory (LANL), the U.S. Government retains certain rights in
// this software.
//============================================================================
#ifndef VTKM_KERNEL_GAUSSIAN_H
#define VTKM_KERNEL_GAUSSIAN_H
#include "KernelBase.h"
//
// Gaussian kernel.
// Compact support is achived by truncating the kernel beyond the cutoff radius
// This implementation uses a factor of 5 between smoothing length and cutoff
//
namespace vtkm { namespace worklet {
namespace splatkernels {
template <int Dimensions>
struct Gaussian : public KernelBase< Gaussian<Dimensions> >
{
//---------------------------------------------------------------------
// Constructor
// Calculate coefficients used repeatedly when evaluating the kernel
// value or gradient
VTKM_EXEC_CONT
Gaussian(double smoothingLength)
: KernelBase< Gaussian<Dimensions> >(smoothingLength)
{
Hinverse_ = 1.0/smoothingLength;
Hinverse2_ = Hinverse_*Hinverse_;
maxRadius_ = 5.0*smoothingLength;
maxRadius2_ = maxRadius_*maxRadius_;
//
norm_ = 1.0 / pow(M_PI, static_cast<double>(Dimensions) / 2.0);
scale_W_ = norm_ * PowerExpansion<Dimensions> (Hinverse_);
scale_GradW_ = - 2.0 * PowerExpansion<Dimensions+1>(Hinverse_) / norm_;
}
//---------------------------------------------------------------------
// return the multiplier between smoothing length and max cutoff distance
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VTKM_CONSTEXPR double getDilationFactor() const { return 5.0; }
//---------------------------------------------------------------------
// compute w(h) for the given distance
VTKM_EXEC_CONT
double w(double distance) const
{
if (distance<maxDistance()) {
// compute r/h
double normedDist = distance * Hinverse_;
// compute w(h)
return scale_W_ * exp(-normedDist * normedDist);
}
return 0.0;
}
//---------------------------------------------------------------------
// compute w(h) for the given squared distance
VTKM_EXEC_CONT
double w2(double distance2) const
{
if (distance2<maxSquaredDistance()) {
// compute (r/h)^2
double normedDist = distance2 * Hinverse2_;
// compute w(h)
return scale_W_ * exp(-normedDist);
}
return 0.0;
}
//---------------------------------------------------------------------
// compute w(h) for a variable h kernel
VTKM_EXEC_CONT
double w(double h, double distance) const
{
if (distance<maxDistance(h)) {
double Hinverse = 1.0/h;
double scale_W = norm_ * PowerExpansion<Dimensions>(Hinverse);
double Q = distance * Hinverse;
return scale_W * exp(-Q*Q);
}
return 0;
}
//---------------------------------------------------------------------
// compute w(h) for a variable h kernel using distance squared
VTKM_EXEC_CONT
double w2(double h, double distance2) const
{
if (distance2<maxSquaredDistance(h)) {
double Hinverse = 1.0/h;
double scale_W = norm_ * PowerExpansion<Dimensions>(Hinverse);
double Q = distance2 * Hinverse * Hinverse;
return scale_W * exp(-Q);
}
return 0;
}
//---------------------------------------------------------------------
// Calculates the kernel derivative for a distance {x,y,z} vector
// from the centre
VTKM_EXEC_CONT
vector_type gradW(double distance, const vector_type& pos) const
{
double Q = distance * Hinverse_;
if (Q != 0.0)
{
return scale_GradW_ * exp(-Q * Q) * pos;
}
else {
return vector_type(0.0);
}
}
//---------------------------------------------------------------------
// Calculates the kernel derivative for a distance {x,y,z} vector
// from the centre using a variable h
VTKM_EXEC_CONT
vector_type gradW(double h, double distance, const vector_type& pos) const
{
double Hinverse = 1.0/h;
double scale_GradW = - 2.0 * PowerExpansion<Dimensions+1>(Hinverse)
/ pow(M_PI, static_cast<double>(Dimensions) / 2.0);
double Q = distance * Hinverse;
//!!! check this due to the fitting offset
if (distance != 0.0)
{
return scale_GradW * exp(-Q * Q) * pos;
}
else {
return vector_type(0.0);
}
}
//---------------------------------------------------------------------
// return the maximum distance at which this kernel is non zero
VTKM_EXEC_CONT
double maxDistance() const
{
return maxRadius_;
}
//---------------------------------------------------------------------
// return the maximum distance at which this variable h kernel is non zero
VTKM_EXEC_CONT
double maxDistance(double h) const
{
return getDilationFactor()*h;
}
//---------------------------------------------------------------------
// return the maximum distance at which this kernel is non zero
VTKM_EXEC_CONT
double maxSquaredDistance() const
{
return maxRadius2_;
}
//---------------------------------------------------------------------
// return the maximum distance at which this kernel is non zero
VTKM_EXEC_CONT
double maxSquaredDistance(double h) const
{
return PowerExpansion<2>(getDilationFactor())*h*h;
}
private:
double norm_;
double Hinverse_;
double Hinverse2_;
double maxRadius_;
double maxRadius2_;
double scale_W_;
double scale_GradW_;
};
}}}
#endif