fe8654d7eb
Not having them could cause compiler issues.
199 lines
6.4 KiB
C++
199 lines
6.4 KiB
C++
//============================================================================
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// Copyright (c) Kitware, Inc.
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// All rights reserved.
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// See LICENSE.txt for details.
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// This software is distributed WITHOUT ANY WARRANTY; without even
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// the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR
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// PURPOSE. See the above copyright notice for more information.
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//
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// Copyright 2014 Sandia Corporation.
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// Copyright 2014 UT-Battelle, LLC.
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// Copyright 2014 Los Alamos National Security.
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//
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// Under the terms of Contract DE-AC04-94AL85000 with Sandia Corporation,
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// the U.S. Government retains certain rights in this software.
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//
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// Under the terms of Contract DE-AC52-06NA25396 with Los Alamos National
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// Laboratory (LANL), the U.S. Government retains certain rights in
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// this software.
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//============================================================================
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#ifndef VTKM_KERNEL_GAUSSIAN_H
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#define VTKM_KERNEL_GAUSSIAN_H
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#include "KernelBase.h"
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//
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// Gaussian kernel.
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// Compact support is achived by truncating the kernel beyond the cutoff radius
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// This implementation uses a factor of 5 between smoothing length and cutoff
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//
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namespace vtkm { namespace worklet {
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namespace splatkernels {
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template <int Dimensions>
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struct Gaussian : public KernelBase< Gaussian<Dimensions> >
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{
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//---------------------------------------------------------------------
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// Constructor
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// Calculate coefficients used repeatedly when evaluating the kernel
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// value or gradient
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VTKM_EXEC_CONT_EXPORT
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Gaussian(double smoothingLength)
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: KernelBase< Gaussian<Dimensions> >(smoothingLength)
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{
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Hinverse_ = 1.0/smoothingLength;
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Hinverse2_ = Hinverse_*Hinverse_;
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maxRadius_ = 5.0*smoothingLength;
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maxRadius2_ = maxRadius_*maxRadius_;
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//
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norm_ = 1.0 / pow(M_PI, static_cast<double>(Dimensions) / 2.0);
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scale_W_ = norm_ * power<Dimensions> (Hinverse_);
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scale_GradW_ = - 2.0 * power<Dimensions+1>(Hinverse_) / norm_;
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}
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//---------------------------------------------------------------------
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// return the multiplier between smoothing length and max cutoff distance
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/*constexpr */ double getDilationFactor() const { return 5.0; }
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//---------------------------------------------------------------------
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// compute w(h) for the given distance
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VTKM_EXEC_CONT_EXPORT
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double w(double distance) const
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{
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if (distance<maxDistance()) {
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// compute r/h
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double normedDist = distance * Hinverse_;
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// compute w(h)
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return scale_W_ * exp(-normedDist * normedDist);
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}
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return 0.0;
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}
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//---------------------------------------------------------------------
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// compute w(h) for the given squared distance
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VTKM_EXEC_CONT_EXPORT
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double w2(double distance2) const
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{
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if (distance2<maxSquaredDistance()) {
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// compute (r/h)^2
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double normedDist = distance2 * Hinverse2_;
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// compute w(h)
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return scale_W_ * exp(-normedDist);
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}
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return 0.0;
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}
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//---------------------------------------------------------------------
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// compute w(h) for a variable h kernel
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VTKM_EXEC_CONT_EXPORT
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double w(double h, double distance) const
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{
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if (distance<maxDistance(h)) {
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double Hinverse = 1.0/h;
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double scale_W = norm_ * power<Dimensions>(Hinverse);
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double Q = distance * Hinverse;
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return scale_W * exp(-Q*Q);
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}
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return 0;
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}
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//---------------------------------------------------------------------
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// compute w(h) for a variable h kernel using distance squared
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VTKM_EXEC_CONT_EXPORT
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double w2(double h, double distance2) const
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{
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if (distance2<maxSquaredDistance(h)) {
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double Hinverse = 1.0/h;
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double scale_W = norm_ * power<Dimensions>(Hinverse);
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double Q = distance2 * Hinverse * Hinverse;
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return scale_W * exp(-Q);
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}
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return 0;
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}
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//---------------------------------------------------------------------
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// Calculates the kernel derivative for a distance {x,y,z} vector
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// from the centre
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VTKM_EXEC_CONT_EXPORT
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vector_type gradW(double distance, const vector_type& pos) const
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{
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double Q = distance * Hinverse_;
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if (Q != 0.0)
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{
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return scale_GradW_ * exp(-Q * Q) * pos;
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}
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else {
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return vector_type(0.0);
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}
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}
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//---------------------------------------------------------------------
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// Calculates the kernel derivative for a distance {x,y,z} vector
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// from the centre using a variable h
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VTKM_EXEC_CONT_EXPORT
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vector_type gradW(double h, double distance, const vector_type& pos) const
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{
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double Hinverse = 1.0/h;
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double scale_GradW = - 2.0 * power<Dimensions+1>(Hinverse)
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/ pow(M_PI, static_cast<double>(Dimensions) / 2.0);
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double Q = distance * Hinverse;
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//!!! check this due to the fitting offset
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if (distance != 0.0)
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{
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return scale_GradW * exp(-Q * Q) * pos;
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}
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else {
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return vector_type(0.0);
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}
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}
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//---------------------------------------------------------------------
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// return the maximum distance at which this kernel is non zero
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VTKM_EXEC_CONT_EXPORT
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double maxDistance() const
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{
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return maxRadius_;
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}
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//---------------------------------------------------------------------
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// return the maximum distance at which this variable h kernel is non zero
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VTKM_EXEC_CONT_EXPORT
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double maxDistance(double h) const
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{
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return getDilationFactor()*h;
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}
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//---------------------------------------------------------------------
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// return the maximum distance at which this kernel is non zero
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VTKM_EXEC_CONT_EXPORT
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double maxSquaredDistance() const
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{
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return maxRadius2_;
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}
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//---------------------------------------------------------------------
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// return the maximum distance at which this kernel is non zero
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VTKM_EXEC_CONT_EXPORT
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double maxSquaredDistance(double h) const
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{
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return power<2>(getDilationFactor())*h*h;
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}
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private:
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double norm_;
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double Hinverse_;
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double Hinverse2_;
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double maxRadius_;
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double maxRadius2_;
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double scale_W_;
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double scale_GradW_;
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};
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}}}
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#endif
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