#include "openmc/scattdata.h"

#include <algorithm>

#include <cmath>

#include <numeric>

#include "openmc/tensor.h"

#include "openmc/constants.h"

#include "openmc/error.h"

#include "openmc/math_functions.h"

#include "openmc/random_lcg.h"

#include "openmc/settings.h"

namespace openmc {

//==============================================================================

// ScattData base-class methods

//==============================================================================

void ScattData::base_init(int order, const tensor::Tensor<int>& in_gmin,

const tensor::Tensor<int>& in_gmax, const double_2dvec& in_energy,

const double_2dvec& in_mult)

{

size_t groups = in_energy.size();

gmin = in_gmin;

gmax = in_gmax;

energy.resize(groups);

mult.resize(groups);

dist.resize(groups);

for (int gin = 0; gin < groups; gin++) {

// Store the inputted data

energy[gin] = in_energy[gin];

mult[gin] = in_mult[gin];

// Make sure the multiplicity does not have 0s

for (int go = 0; go < mult[gin].size(); go++) {

if (mult[gin][go] == 0.) {

mult[gin][go] = 1.;

}

}

// Make sure the energy is normalized

double norm = std::accumulate(energy[gin].begin(), energy[gin].end(), 0.);

if (norm != 0.) {

for (auto& n : energy[gin])

n /= norm;

}

// Initialize the distribution data

dist[gin].resize(in_gmax[gin] - in_gmin[gin] + 1);

for (auto& v : dist[gin]) {

v.resize(order);

}

}

}

//==============================================================================

void ScattData::base_combine(size_t max_order, size_t order_dim,

const vector<ScattData*>& those_scatts, const vector<double>& scalars,

tensor::Tensor<int>& in_gmin, tensor::Tensor<int>& in_gmax,

double_2dvec& sparse_mult, double_3dvec& sparse_scatter)

{

size_t groups = those_scatts[0]->energy.size();

// Now allocate and zero our storage spaces

tensor::Tensor<double> this_nuscatt_matrix =

tensor::zeros<double>({groups, groups, order_dim});

tensor::Tensor<double> this_nuscatt_P0 =

tensor::zeros<double>({groups, groups});

tensor::Tensor<double> this_scatt_P0 =

tensor::zeros<double>({groups, groups});

tensor::Tensor<double> this_mult = tensor::ones<double>({groups, groups});

// Build the dense scattering and multiplicity matrices

for (int i = 0; i < those_scatts.size(); i++) {

ScattData* that = those_scatts[i];

// Build the dense matrix for that object

tensor::Tensor<double> that_matrix = that->get_matrix(max_order);

// Now add that to this for the nu-scatter matrix

this_nuscatt_matrix += scalars[i] * that_matrix;

// Do the same with the P0 matrices

for (int gin = 0; gin < groups; gin++) {

for (int go = 0; go < groups; go++) {

this_nuscatt_P0(gin, go) +=

scalars[i] * that->get_xs(MgxsType::NU_SCATTER, gin, &go, nullptr);

this_scatt_P0(gin, go) +=

scalars[i] * that->get_xs(MgxsType::SCATTER, gin, &go, nullptr);

}

}

}

// Now we have the dense nuscatt and scatt, we can easily compute the

// multiplicity matrix by dividing the two and fixing any nans

this_mult = tensor::nan_to_num(this_nuscatt_P0 / this_scatt_P0);

// We have the data, now we need to convert to a jagged array and then use

// the initialize function to store it on the object.

for (int gin = 0; gin < groups; gin++) {

// Find the minimum and maximum group boundaries

int gmin_;

for (gmin_ = 0; gmin_ < groups; gmin_++) {

bool non_zero = false;

for (int l = 0; l < this_nuscatt_matrix.shape(2); l++) {

if (this_nuscatt_matrix(gin, gmin_, l) != 0.) {

non_zero = true;

break;

}

}

if (non_zero)

break;

}

int gmax_;

for (gmax_ = groups - 1; gmax_ >= 0; gmax_--) {

bool non_zero = false;

for (int l = 0; l < this_nuscatt_matrix.shape(2); l++) {

if (this_nuscatt_matrix(gin, gmax_, l) != 0.) {

non_zero = true;

break;

}

}

if (non_zero)

break;

}

// treat the case of all values being 0

if (gmin_ > gmax_) {

gmin_ = gin;

gmax_ = gin;

}

// Store the group bounds

in_gmin[gin] = gmin_;

in_gmax[gin] = gmax_;

// Store the data in the compressed format

sparse_scatter[gin].resize(gmax_ - gmin_ + 1);

sparse_mult[gin].resize(gmax_ - gmin_ + 1);

int i_gout = 0;

for (int gout = gmin_; gout <= gmax_; gout++) {

sparse_scatter[gin][i_gout].resize(this_nuscatt_matrix.shape(2));

for (int l = 0; l < this_nuscatt_matrix.shape(2); l++) {

sparse_scatter[gin][i_gout][l] = this_nuscatt_matrix(gin, gout, l);

}

sparse_mult[gin][i_gout] = this_mult(gin, gout);

i_gout++;

}

}

}

//==============================================================================

void ScattData::sample_energy(int gin, int& gout, int& i_gout, uint64_t* seed)

{

// Sample the outgoing group

double xi = prn(seed);

double prob = 0.;

i_gout = 0;

for (gout = gmin[gin]; gout < gmax[gin]; ++gout) {

prob += energy[gin][i_gout];

if (xi < prob)

break;

++i_gout;

}

}

//==============================================================================

double ScattData::get_xs(

MgxsType xstype, int gin, const int* gout, const double* mu)

{

// Set the outgoing group offset index as needed

int i_gout = 0;

if (gout != nullptr) {

// short circuit the function if gout is from a zero portion of the

// scattering matrix

if ((*gout < gmin[gin]) || (*gout > gmax[gin])) { // > gmax?

return 0.;

}

i_gout = *gout - gmin[gin];

}

double val = scattxs[gin];

switch (xstype) {

case MgxsType::NU_SCATTER:

if (gout != nullptr)

val *= energy[gin][i_gout];

break;

case MgxsType::SCATTER:

if (gout != nullptr) {

val *= energy[gin][i_gout] / mult[gin][i_gout];

} else {

val /= std::inner_product(

mult[gin].begin(), mult[gin].end(), energy[gin].begin(), 0.0);

}

break;

case MgxsType::NU_SCATTER_FMU:

if ((gout != nullptr) && (mu != nullptr)) {

val *= energy[gin][i_gout] * calc_f(gin, *gout, *mu);

} else {

// This is not an expected path (asking for f_mu without asking for a

// group or mu is not useful

fatal_error("Invalid call to get_xs");

}

break;

case MgxsType::SCATTER_FMU:

if ((gout != nullptr) && (mu != nullptr)) {

val *= energy[gin][i_gout] * calc_f(gin, *gout, *mu) / mult[gin][i_gout];

} else {

// This is not an expected path (asking for f_mu without asking for a

// group or mu is not useful

fatal_error("Invalid call to get_xs");

}

break;

default:

break;

}

return val;

}

//==============================================================================

// ScattDataLegendre methods

//==============================================================================

void ScattDataLegendre::init(const tensor::Tensor<int>& in_gmin,

const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,

const double_3dvec& coeffs)

{

size_t groups = coeffs.size();

size_t order = coeffs[0][0].size();

// make a copy of coeffs that we can use to both extract data and normalize

double_3dvec matrix = coeffs;

// Get the scattering cross section value by summing the un-normalized P0

// coefficient in the variable matrix over all outgoing groups.

scattxs = tensor::zeros<double>({groups});

for (int gin = 0; gin < groups; gin++) {

int num_groups = in_gmax[gin] - in_gmin[gin] + 1;

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

scattxs[gin] += matrix[gin][i_gout][0];

}

}

// Build the energy transfer matrix from data in the variable matrix while

// also normalizing the variable matrix itself

// (forcing the CDF of f(mu=1) == 1)

double_2dvec in_energy;

in_energy.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = in_gmax[gin] - in_gmin[gin] + 1;

in_energy[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

double norm = matrix[gin][i_gout][0];

in_energy[gin][i_gout] = norm;

if (norm != 0.) {

for (auto& n : matrix[gin][i_gout])

n /= norm;

}

}

}

// Initialize the base class attributes

ScattData::base_init(order, in_gmin, in_gmax, in_energy, in_mult);

// Set the distribution (sdata.dist) values and initialize max_val

max_val.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = gmax[gin] - gmin[gin] + 1;

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

dist[gin][i_gout] = matrix[gin][i_gout];

}

max_val[gin].resize(num_groups);

for (auto& n : max_val[gin])

n = 0.;

}

// Now update the maximum value

update_max_val();

}

//==============================================================================

void ScattDataLegendre::update_max_val()

{

size_t groups = max_val.size();

// Step through the polynomial with fixed number of points to identify the

// maximal value

int Nmu = 1001;

double dmu = 2. / (Nmu - 1);

for (int gin = 0; gin < groups; gin++) {

int num_groups = gmax[gin] - gmin[gin] + 1;

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

for (int imu = 0; imu < Nmu; imu++) {

double mu;

if (imu == 0) {

mu = -1.;

} else if (imu == (Nmu - 1)) {

mu = 1.;

} else {

mu = -1. + (imu - 1) * dmu;

}

// Calculate probability

double f = evaluate_legendre(

dist[gin][i_gout].size() - 1, dist[gin][i_gout].data(), mu);

// if this is a new maximum, store it

if (f > max_val[gin][i_gout])

max_val[gin][i_gout] = f;

} // end imu loop

// Since we may not have caught the true max, add 10% margin

max_val[gin][i_gout] *= 1.1;

}

}

}

//==============================================================================

double ScattDataLegendre::calc_f(int gin, int gout, double mu)

{

double f;

if ((gout < gmin[gin]) || (gout > gmax[gin])) {

f = 0.;

} else {

int i_gout = gout - gmin[gin];

f = evaluate_legendre(

dist[gin][i_gout].size() - 1, dist[gin][i_gout].data(), mu);

}

return f;

}

//==============================================================================

void ScattDataLegendre::sample(

int gin, int& gout, double& mu, double& wgt, uint64_t* seed)

{

// Sample the outgoing energy using the base-class method

int i_gout;

sample_energy(gin, gout, i_gout, seed);

// Now we can sample mu using the scattering kernel using rejection

// sampling from a rectangular bounding box

double M = max_val[gin][i_gout];

int samples;

for (samples = 0; samples < MAX_SAMPLE; ++samples) {

mu = 2. * prn(seed) - 1.;

double f = calc_f(gin, gout, mu);

if (f > 0.) {

double u = prn(seed) * M;

if (u <= f)

break;

}

}

if (samples == MAX_SAMPLE) {

fatal_error("Maximum number of Legendre expansion samples reached!");

}

// Update the weight to reflect neutron multiplicity

wgt *= mult[gin][i_gout];

}

//==============================================================================

void ScattDataLegendre::combine(

const vector<ScattData*>& those_scatts, const vector<double>& scalars)

{

// Find the max order in the data set and make sure we can combine the sets

size_t max_order = 0;

for (int i = 0; i < those_scatts.size(); i++) {

// Lets also make sure these items are combineable

ScattDataLegendre* that = dynamic_cast<ScattDataLegendre*>(those_scatts[i]);

if (!that) {

fatal_error("Cannot combine the ScattData objects!");

}

size_t that_order = that->get_order();

if (that_order > max_order)

max_order = that_order;

}

size_t groups = those_scatts[0]->energy.size();

tensor::Tensor<int> in_gmin({groups}, 0);

tensor::Tensor<int> in_gmax({groups}, 0);

double_3dvec sparse_scatter(groups);

double_2dvec sparse_mult(groups);

// The rest of the steps do not depend on the type of angular representation

// so we use a base class method to sum up xs and create new energy and mult

// matrices

size_t order_dim = max_order + 1;

ScattData::base_combine(max_order, order_dim, those_scatts, scalars, in_gmin,

in_gmax, sparse_mult, sparse_scatter);

// Got everything we need, store it.

init(in_gmin, in_gmax, sparse_mult, sparse_scatter);

}

//==============================================================================

tensor::Tensor<double> ScattDataLegendre::get_matrix(size_t max_order)

{

// Get the sizes and initialize the data to 0

size_t groups = energy.size();

size_t order_dim = max_order + 1;

tensor::Tensor<double> matrix =

tensor::zeros<double>({groups, groups, order_dim});

for (int gin = 0; gin < groups; gin++) {

for (int i_gout = 0; i_gout < energy[gin].size(); i_gout++) {

int gout = i_gout + gmin[gin];

for (int l = 0; l < order_dim; l++) {

matrix(gin, gout, l) =

scattxs[gin] * energy[gin][i_gout] * dist[gin][i_gout][l];

}

}

}

return matrix;

}

//==============================================================================

// ScattDataHistogram methods

//==============================================================================

void ScattDataHistogram::init(const tensor::Tensor<int>& in_gmin,

const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,

const double_3dvec& coeffs)

{

size_t groups = coeffs.size();

size_t order = coeffs[0][0].size();

// make a copy of coeffs that we can use to both extract data and normalize

double_3dvec matrix = coeffs;

// Get the scattering cross section value by summing the distribution

// over all the histogram bins in angle and outgoing energy groups

scattxs = tensor::zeros<double>({groups});

for (int gin = 0; gin < groups; gin++) {

for (int i_gout = 0; i_gout < matrix[gin].size(); i_gout++) {

scattxs[gin] += std::accumulate(

matrix[gin][i_gout].begin(), matrix[gin][i_gout].end(), 0.);

}

}

// Build the energy transfer matrix from data in the variable matrix

double_2dvec in_energy;

in_energy.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = in_gmax[gin] - in_gmin[gin] + 1;

in_energy[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

double norm = std::accumulate(

matrix[gin][i_gout].begin(), matrix[gin][i_gout].end(), 0.);

in_energy[gin][i_gout] = norm;

if (norm != 0.) {

for (auto& n : matrix[gin][i_gout])

n /= norm;

}

}

}

// Initialize the base class attributes

ScattData::base_init(order, in_gmin, in_gmax, in_energy, in_mult);

// Build the angular distribution mu values

mu = tensor::linspace(-1., 1., order + 1);

dmu = 2. / order;

// Calculate f(mu) and integrate it so we can avoid rejection sampling

fmu.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = gmax[gin] - gmin[gin] + 1;

fmu[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

fmu[gin][i_gout].resize(order);

// The variable matrix contains f(mu); so directly assign it

fmu[gin][i_gout] = matrix[gin][i_gout];

// Integrate the histogram

dist[gin][i_gout][0] = dmu * matrix[gin][i_gout][0];

for (int imu = 1; imu < order; imu++) {

dist[gin][i_gout][imu] =

dmu * matrix[gin][i_gout][imu] + dist[gin][i_gout][imu - 1];

}

// Now re-normalize for integral to unity

double norm = dist[gin][i_gout][order - 1];

if (norm > 0.) {

for (int imu = 0; imu < order; imu++) {

fmu[gin][i_gout][imu] /= norm;

dist[gin][i_gout][imu] /= norm;

}

}

}

}

}

//==============================================================================

double ScattDataHistogram::calc_f(int gin, int gout, double mu)

{

double f;

if ((gout < gmin[gin]) || (gout > gmax[gin])) {

f = 0.;

} else {

// Find mu bin

int i_gout = gout - gmin[gin];

int imu;

if (mu == 1.) {

// use size -2 to have the index one before the end

imu = this->mu.shape(0) - 2;

} else {

imu = std::floor((mu + 1.) / dmu + 1.) - 1;

}

f = fmu[gin][i_gout][imu];

}

return f;

}

//==============================================================================

void ScattDataHistogram::sample(

int gin, int& gout, double& mu, double& wgt, uint64_t* seed)

{

// Sample the outgoing energy using the base-class method

int i_gout;

sample_energy(gin, gout, i_gout, seed);

// Determine the outgoing cosine bin

double xi = prn(seed);

int imu;

if (xi < dist[gin][i_gout][0]) {

imu = 0;

} else {

imu =

std::upper_bound(dist[gin][i_gout].begin(), dist[gin][i_gout].end(), xi) -

dist[gin][i_gout].begin();

}

// Randomly select mu within the imu bin

mu = prn(seed) * dmu + this->mu[imu];

mu = std::clamp(mu, -1., 1.);

// Update the weight to reflect neutron multiplicity

wgt *= mult[gin][i_gout];

}

//==============================================================================

tensor::Tensor<double> ScattDataHistogram::get_matrix(size_t max_order)

{

// Get the sizes and initialize the data to 0

size_t groups = energy.size();

// We ignore the requested order for Histogram and Tabular representations

size_t order_dim = get_order();

tensor::Tensor<double> matrix({groups, groups, order_dim}, 0);

for (int gin = 0; gin < groups; gin++) {

for (int i_gout = 0; i_gout < energy[gin].size(); i_gout++) {

int gout = i_gout + gmin[gin];

for (int l = 0; l < order_dim; l++) {

matrix(gin, gout, l) =

scattxs[gin] * energy[gin][i_gout] * fmu[gin][i_gout][l];

}

}

}

return matrix;

}

//==============================================================================

void ScattDataHistogram::combine(

const vector<ScattData*>& those_scatts, const vector<double>& scalars)

{

// Find the max order in the data set and make sure we can combine the sets

size_t max_order = those_scatts[0]->get_order();

for (int i = 0; i < those_scatts.size(); i++) {

// Lets also make sure these items are combineable

ScattDataHistogram* that =

dynamic_cast<ScattDataHistogram*>(those_scatts[i]);

if (!that) {

fatal_error("Cannot combine the ScattData objects!");

}

if (max_order != that->get_order()) {

fatal_error("Cannot combine the ScattData objects!");

}

}

size_t groups = those_scatts[0]->energy.size();

tensor::Tensor<int> in_gmin({groups}, 0);

tensor::Tensor<int> in_gmax({groups}, 0);

double_3dvec sparse_scatter(groups);

double_2dvec sparse_mult(groups);

// The rest of the steps do not depend on the type of angular representation

// so we use a base class method to sum up xs and create new energy and mult

// matrices

size_t order_dim = max_order;

ScattData::base_combine(max_order, order_dim, those_scatts, scalars, in_gmin,

in_gmax, sparse_mult, sparse_scatter);

// Got everything we need, store it.

init(in_gmin, in_gmax, sparse_mult, sparse_scatter);

}

//==============================================================================

// ScattDataTabular methods

//==============================================================================

void ScattDataTabular::init(const tensor::Tensor<int>& in_gmin,

const tensor::Tensor<int>& in_gmax, const double_2dvec& in_mult,

const double_3dvec& coeffs)

{

size_t groups = coeffs.size();

size_t order = coeffs[0][0].size();

// make a copy of coeffs that we can use to both extract data and normalize

double_3dvec matrix = coeffs;

// Build the angular distribution mu values

mu = tensor::linspace(-1., 1., order);

dmu = 2. / (order - 1);

// Get the scattering cross section value by integrating the distribution

// over all mu points and then combining over all outgoing groups

scattxs = tensor::zeros<double>({groups});

for (int gin = 0; gin < groups; gin++) {

for (int i_gout = 0; i_gout < matrix[gin].size(); i_gout++) {

for (int imu = 1; imu < order; imu++) {

scattxs[gin] +=

0.5 * dmu * (matrix[gin][i_gout][imu - 1] + matrix[gin][i_gout][imu]);

}

}

}

// Build the energy transfer matrix from data in the variable matrix

double_2dvec in_energy(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = in_gmax[gin] - in_gmin[gin] + 1;

in_energy[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

double norm = 0.;

for (int imu = 1; imu < order; imu++) {

norm +=

0.5 * dmu * (matrix[gin][i_gout][imu - 1] + matrix[gin][i_gout][imu]);

}

in_energy[gin][i_gout] = norm;

}

}

// Initialize the base class attributes

ScattData::base_init(order, in_gmin, in_gmax, in_energy, in_mult);

// Calculate f(mu) and integrate it so we can avoid rejection sampling

fmu.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = gmax[gin] - gmin[gin] + 1;

fmu[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

fmu[gin][i_gout].resize(order);

// The variable matrix contains f(mu); so directly assign it

fmu[gin][i_gout] = matrix[gin][i_gout];

// Ensure positivity

for (auto& val : fmu[gin][i_gout]) {

if (val < 0.)

val = 0.;

}

// Now re-normalize for numerical integration issues and to take care of

// the above negative fix-up. Also accrue the CDF

double norm = 0.;

for (int imu = 1; imu < order; imu++) {

norm += 0.5 * dmu * (fmu[gin][i_gout][imu - 1] + fmu[gin][i_gout][imu]);

// incorporate to the CDF

dist[gin][i_gout][imu] = norm;

}

// now do the normalization

if (norm > 0.) {

for (int imu = 0; imu < order; imu++) {

fmu[gin][i_gout][imu] /= norm;

dist[gin][i_gout][imu] /= norm;

}

}

}

}

}

//==============================================================================

double ScattDataTabular::calc_f(int gin, int gout, double mu)

{

double f;

if ((gout < gmin[gin]) || (gout > gmax[gin])) {

f = 0.;

} else {

// Find mu bin

int i_gout = gout - gmin[gin];

int imu;

if (mu == 1.) {

// use size -2 to have the index one before the end

imu = this->mu.shape(0) - 2;

} else {

imu = std::floor((mu + 1.) / dmu + 1.) - 1;

}

double r = (mu - this->mu[imu]) / (this->mu[imu + 1] - this->mu[imu]);

f = (1. - r) * fmu[gin][i_gout][imu] + r * fmu[gin][i_gout][imu + 1];

}

return f;

}

//==============================================================================

void ScattDataTabular::sample(

int gin, int& gout, double& mu, double& wgt, uint64_t* seed)

{

// Sample the outgoing energy using the base-class method

int i_gout;

sample_energy(gin, gout, i_gout, seed);

// Determine the outgoing cosine bin

int NP = this->mu.shape(0);

double xi = prn(seed);

double c_k = dist[gin][i_gout][0];

int k;

for (k = 0; k < NP - 1; k++) {

double c_k1 = dist[gin][i_gout][k + 1];

if (xi < c_k1)

break;

c_k = c_k1;

}

// Check to make sure k is <= NP - 1

k = std::min(k, NP - 2);

// Find the pdf values we want

double p0 = fmu[gin][i_gout][k];

double mu0 = this->mu[k];

double p1 = fmu[gin][i_gout][k + 1];

double mu1 = this->mu[k + 1];

if (p0 == p1) {

mu = mu0 + (xi - c_k) / p0;

} else {

double frac = (p1 - p0) / (mu1 - mu0);

mu =

mu0 +

(std::sqrt(std::max(0., p0 * p0 + 2. * frac * (xi - c_k))) - p0) / frac;

}

mu = std::clamp(mu, -1., 1.);

// Update the weight to reflect neutron multiplicity

wgt *= mult[gin][i_gout];

}

//==============================================================================

tensor::Tensor<double> ScattDataTabular::get_matrix(size_t max_order)

{

// Get the sizes and initialize the data to 0

size_t groups = energy.size();

// We ignore the requested order for Histogram and Tabular representations

size_t order_dim = get_order();

tensor::Tensor<double> matrix =

tensor::zeros<double>({groups, groups, order_dim});

for (int gin = 0; gin < groups; gin++) {

for (int i_gout = 0; i_gout < energy[gin].size(); i_gout++) {

int gout = i_gout + gmin[gin];

for (int l = 0; l < order_dim; l++) {

matrix(gin, gout, l) =

scattxs[gin] * energy[gin][i_gout] * fmu[gin][i_gout][l];

}

}

}

return matrix;

}

//==============================================================================

void ScattDataTabular::combine(

const vector<ScattData*>& those_scatts, const vector<double>& scalars)

{

// Find the max order in the data set and make sure we can combine the sets

size_t max_order = those_scatts[0]->get_order();

for (int i = 0; i < those_scatts.size(); i++) {

// Lets also make sure these items are combineable

ScattDataTabular* that = dynamic_cast<ScattDataTabular*>(those_scatts[i]);

if (!that) {

fatal_error("Cannot combine the ScattData objects!");

}

if (max_order != that->get_order()) {

fatal_error("Cannot combine the ScattData objects!");

}

}

size_t groups = those_scatts[0]->energy.size();

tensor::Tensor<int> in_gmin({groups}, 0);

tensor::Tensor<int> in_gmax({groups}, 0);

double_3dvec sparse_scatter(groups);

double_2dvec sparse_mult(groups);

// The rest of the steps do not depend on the type of angular representation

// so we use a base class method to sum up xs and create new energy and mult

// matrices

size_t order_dim = max_order;

ScattData::base_combine(max_order, order_dim, those_scatts, scalars, in_gmin,

in_gmax, sparse_mult, sparse_scatter);

// Got everything we need, store it.

init(in_gmin, in_gmax, sparse_mult, sparse_scatter);

}

//==============================================================================

// module-level methods

//==============================================================================

void convert_legendre_to_tabular(ScattDataLegendre& leg, ScattDataTabular& tab)

{

// See if the user wants us to figure out how many points to use

int n_mu = settings::legendre_to_tabular_points;

if (n_mu == C_NONE) {

// then we will use 2 pts if its P0, or the default if a higher order

// TODO use an error minimization algorithm that also picks n_mu

if (leg.get_order() == 0) {

n_mu = 2;

} else {

n_mu = DEFAULT_NMU;

}

}

tab.base_init(n_mu, leg.gmin, leg.gmax, leg.energy, leg.mult);

tab.scattxs = leg.scattxs;

// Build mu and dmu

tab.mu = tensor::linspace(-1., 1., n_mu);

tab.dmu = 2. / (n_mu - 1);

// Calculate f(mu) and integrate it so we can avoid rejection sampling

size_t groups = tab.energy.size();

tab.fmu.resize(groups);

for (int gin = 0; gin < groups; gin++) {

int num_groups = tab.gmax[gin] - tab.gmin[gin] + 1;

tab.fmu[gin].resize(num_groups);

for (int i_gout = 0; i_gout < num_groups; i_gout++) {

tab.fmu[gin][i_gout].resize(n_mu);

for (int imu = 0; imu < n_mu; imu++) {

tab.fmu[gin][i_gout][imu] =

evaluate_legendre(leg.dist[gin][i_gout].size() - 1,

leg.dist[gin][i_gout].data(), tab.mu[imu]);

}

// Ensure positivity

for (auto& val : tab.fmu[gin][i_gout]) {

if (val < 0.)

val = 0.;

}

// Now re-normalize for numerical integration issues and to take care of

// the above negative fix-up. Also accrue the CDF

double norm = 0.;

tab.dist[gin][i_gout][0] = 0.;

for (int imu = 1; imu < n_mu; imu++) {

norm += 0.5 * tab.dmu *

(tab.fmu[gin][i_gout][imu - 1] + tab.fmu[gin][i_gout][imu]);

// incorporate to the CDF

tab.dist[gin][i_gout][imu] = norm;

}

// now do the normalization

if (norm > 0.) {

for (int imu = 0; imu < n_mu; imu++) {

tab.fmu[gin][i_gout][imu] /= norm;

tab.dist[gin][i_gout][imu] /= norm;

}

}

}

}

}

} // namespace openmc