MultiDimensionalInterpolation.C

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//* This file is part of the MOOSE framework
//* https://mooseframework.inl.gov
//*
//* All rights reserved, see COPYRIGHT for full restrictions
//* https://github.com/idaholab/moose/blob/master/COPYRIGHT
//*
//* Licensed under LGPL 2.1, please see LICENSE for details
//* https://www.gnu.org/licenses/lgpl-2.1.html
 
#include "MultiDimensionalInterpolation.h"
 
#include <cassert>
#include <fstream>
#include <stdexcept>
 
template <typename T>
MultiDimensionalInterpolationTempl<T>::MultiDimensionalInterpolationTempl(
    const std::vector<std::vector<Real>> & base_points, const MultiIndex<Real> & data)
  : _base_points(std::vector<std::vector<Real>>()), _data(MultiIndex<Real>({}))
{
  setData(base_points, data);
}
 
template <typename T>
MultiDimensionalInterpolationTempl<T>::MultiDimensionalInterpolationTempl()
  : _base_points(std::vector<std::vector<Real>>()), _data(MultiIndex<Real>({}))
{
}
 
template <typename T>
void
MultiDimensionalInterpolationTempl<T>::setData(const std::vector<std::vector<Real>> & base_points,
                                               const MultiIndex<Real> & data)
{
  _degenerate_interpolation = false;
  _setup_complete = false;
 
  // stash away original dimension of data because we might modify it
  _original_dim = data.dim();
  _degenerate_index.resize(_original_dim, false);
 
  // need to check this here to make sure the next loop is safe
  if (data.dim() != base_points.size())
  {
    std::ostringstream oss;
    oss << "Leading dimension of base_points " << base_points.size() << " and data dimensionality "
        << data.dim() << " are inconsistent.";
    throw std::domain_error(oss.str());
  }
 
  // first a corner case is addressed; if the size of the MultiIndex in one
  // dimension is 1, then finding the interpolation "stencil" becomes much
  // harder. It is easier to slice the data array and keep track of which values
  // to discard in the interpolation routines
  MultiIndex<Real>::size_type slice_dimensions;
  MultiIndex<Real>::size_type slice_indices;
  MultiIndex<Real>::size_type size = data.size();
  std::vector<std::vector<Real>> modified_base_points;
  for (unsigned int j = 0; j < data.dim(); ++j)
    if (size[j] == 1)
    {
      slice_dimensions.push_back(j);
      slice_indices.push_back(0);
      _degenerate_index[j] = true;
    }
    else
      modified_base_points.push_back(base_points[j]);
 
  // if all dimensions have size 1, then there is only one value to return
  // this is a weird corner case that we should support to make life easier
  // for whoever uses this object
  // In this case, we can just use the passed base_points and data objects
  // and short-cut the computation in the interpolation function
  if (modified_base_points.size() == 0)
  {
    _degenerate_interpolation = true;
    _base_points = base_points;
    _data = data;
  }
  else
  {
    // set the data
    _base_points = modified_base_points;
    _data = data.slice(slice_dimensions, slice_indices);
  }
  errorCheck();
}
 
template <typename T>
void
MultiDimensionalInterpolationTempl<T>::errorCheck()
{
  if (_base_points.size() != _data.dim())
    throw std::domain_error("Size of the leading dimension of base points must be equal to "
                            "dimensionality of data array");
 
  MultiIndex<Real>::size_type shape = _data.size();
  for (unsigned int j = 0; j < _data.dim(); ++j)
  {
    if (shape[j] != _base_points[j].size())
    {
      std::ostringstream oss;
      oss << "Dimension " << j << " has " << _base_points[j].size()
          << " base points but dimension of data array is " << shape[j];
      throw std::domain_error(oss.str());
    }
 
    for (unsigned int i = 1; i < _base_points[j].size(); ++i)
    {
      if (_base_points[j][i - 1] >= _base_points[j][i])
      {
        std::ostringstream oss;
        oss << "Base point values for dimension " << j << " are not strictly increasing: bp["
            << i - 1 << "]: " << _base_points[j][i - 1] << " bc[" << i
            << "]: " << _base_points[j][i];
        throw std::domain_error(oss.str());
      }
    }
  }
 
  // now the setup is complete
  _setup_complete = true;
}
 
template <typename T>
void
MultiDimensionalInterpolationTempl<T>::linearSearch(std::vector<T> & values,
                                                    MultiIndex<Real>::size_type & indices) const
{
  assert(values.size() == _data.dim());
 
  indices.resize(_data.dim());
  for (unsigned int d = 0; d < _data.dim(); ++d)
    indices[d] = linearSearchHelper(values[d], _base_points[d]);
}
 
template <typename T>
unsigned int
MultiDimensionalInterpolationTempl<T>::linearSearchHelper(T & x,
                                                          const std::vector<Real> & vector) const
{
// check order of vector only in debug
#ifndef NDEBUG
  for (unsigned int i = 1; i < vector.size(); ++i)
  {
    if (vector[i - 1] >= vector[i])
    {
      std::ostringstream oss;
      oss << "In linearSearchHelper vector is not strictly increasing: vector[" << i - 1
          << "]: " << vector[i - 1] << " vector[" << i << "]: " << vector[i];
      throw std::domain_error(oss.str());
    }
  }
#endif
 
  // smaller/larger cases for tabulation
  if (x < vector[0])
  {
    x = vector[0];
    return 0;
  }
  else if (x >= vector.back())
  {
    // this requires explanation because it looks like a bug (I swear it isn't, it's a feature)
    // if x >= largest base point value and the index i = vector.size() - 1 is returned, then during
    // the interpolation, i + 1 will be accessed, causing a segementation fault. Logic in the
    // interpolation is also bad because it may slow it down. By returning i = vector.size() - 2,
    // the last two values in the base point array are used, but x is reset to be equal to the last
    // value. This will give the right interpolation behavior.
    x = vector.back();
    return vector.size() - 2;
  }
 
  for (unsigned int j = 0; j < vector.size() - 2; ++j)
    if (x >= vector[j] && x < vector[j + 1])
      return j;
  return vector.size() - 2;
}
 
template <typename T>
T
MultiDimensionalInterpolationTempl<T>::multiLinearInterpolation(const std::vector<T> & x) const
{
  // ensure that object has been set up properly
  if (!_setup_complete)
    throw std::domain_error(
        "Object has been constructed properly. This happens when the empty constructor is called "
        "and setData is not called before interpolation.");
 
  // assert does not seem to be enough to check data consistency here
  // because this function is exposed to user and will be frequently used
  if (x.size() != _original_dim)
  {
    std::ostringstream oss;
    oss << "In sample the parameter x has size " << x.size() << " but data has dimension "
        << _data.dim();
    throw std::domain_error(oss.str());
  }
 
  // in this case there is only one entry that resides at flat index 0
  if (_degenerate_interpolation)
    return _data[0];
 
  // make a copy of x because linearSearch needs to be able to modify it
  // also adjust for degenerate indices that were removed
  std::vector<T> y(_data.dim());
  unsigned int l = 0;
  for (unsigned int j = 0; j < _original_dim; ++j)
    if (!_degenerate_index[j])
    {
      y[l] = x[j];
      ++l;
    }
 
  // obtain the indices using linearSearch & get flat index in _data array
  MultiIndex<Real>::size_type indices;
  linearSearch(y, indices);
 
  // pre-compute the volume of the hypercube and weights used for the interpolation
  Real volume = 1;
  std::vector<std::vector<T>> weights(_data.dim());
  for (unsigned int j = 0; j < _data.dim(); ++j)
  {
    volume *= _base_points[j][indices[j] + 1] - _base_points[j][indices[j]];
 
    // now compute weight for each dimension, note that
    // weight[j][0] = bp[j][high] - y[j]
    // weight[j][0] = y[j] - bp[j][low]
    // because in the interpolation the value on the "left" is weighted with the
    // distance of y to the "right"
    weights[j].resize(2);
    weights[j][0] = _base_points[j][indices[j] + 1] - y[j];
    weights[j][1] = y[j] - _base_points[j][indices[j]];
  }
 
  // get flat index and stride
  unsigned int flat_index = _data.flatIndex(indices);
  MultiIndex<Real>::size_type stride = _data.stride();
 
  // this is the interpolation routine, evaluation can be expensive if dim is large
 
  // first we retrieve the data around the point y
  // we use a 2^dim MultiIndex hypercube for generating the permulations
  MultiIndex<Real>::size_type shape(_data.dim(), 2);
  MultiIndex<Real> hypercube = MultiIndex<Real>(shape);
  T interpolated_value = 0;
  for (const auto & p : hypercube)
  {
    // this loop computes the flat index for the point in the
    // hypercube and accumulates the total weight for this point
    // the selection of the dimensional weight is based on the swapping
    // done in the pre-computing loop before
    unsigned int index = flat_index;
    T w = 1;
    for (unsigned int j = 0; j < p.first.size(); ++j)
    {
      index += stride[j] * p.first[j];
      w *= weights[j][p.first[j]];
    }
 
    // add the contribution of this base point to the interpolated value
    interpolated_value += w * _data[index];
  }
 
  return interpolated_value / volume;
}
 
template class MultiDimensionalInterpolationTempl<Real>;
template class MultiDimensionalInterpolationTempl<ADReal>;