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https://github.com/openmc-dev/openmc.git
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Got it all working, next would like to take advantage of the xtensor features to reduce lines of code
This commit is contained in:
Adam G Nelson
parent
4e92988433
commit
35def7aac2
9 changed files with 200 additions and 332 deletions
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@ -22,7 +22,7 @@ ScattData::base_init(int order, const xt::xtensor<int, 1>& in_gmin,
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const xt::xtensor<int, 1>& in_gmax, const double_2dvec& in_energy,
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const double_2dvec& in_mult)
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{
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int groups = in_energy.size();
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size_t groups = in_energy.size();
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gmin = in_gmin;
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gmax = in_gmax;
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@ -53,12 +53,12 @@ ScattData::base_init(int order, const xt::xtensor<int, 1>& in_gmin,
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//==============================================================================
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void
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ScattData::base_combine(int max_order,
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const std::vector<ScattData*>& those_scatts, const double_1dvec& scalars,
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ScattData::base_combine(size_t max_order,
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const std::vector<ScattData*>& those_scatts, const std::vector<double>& scalars,
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xt::xtensor<int, 1>& in_gmin, xt::xtensor<int, 1>& in_gmax, double_2dvec& sparse_mult,
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double_3dvec& sparse_scatter)
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{
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int groups = those_scatts[0] -> energy.size();
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size_t groups = those_scatts[0] -> energy.size();
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// Now allocate and zero our storage spaces
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xt::xtensor<double, 3> this_matrix({groups, groups, max_order}, 0.);
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@ -108,7 +108,7 @@ ScattData::base_combine(int max_order,
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}
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// Combine mult_numer and mult_denom into the combined multiplicity matrix
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xt::xtensor<double, 2> this_mult = xt::ones<double>({groups, groups});
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xt::xtensor<double, 2> this_mult({groups, groups}, 1.);
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for (int gin = 0; gin < groups; gin++) {
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for (int gout = 0; gout < groups; gout++) {
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if (mult_denom(gin, gout) > 0.) {
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@ -247,8 +247,8 @@ ScattDataLegendre::init(const xt::xtensor<int, 1>& in_gmin,
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const xt::xtensor<int, 1>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs)
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{
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int groups = coeffs.size();
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int order = coeffs[0][0].size();
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size_t groups = coeffs.size();
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size_t order = coeffs[0][0].size();
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// make a copy of coeffs that we can use to both extract data and normalize
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double_3dvec matrix = coeffs;
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@ -303,7 +303,7 @@ ScattDataLegendre::init(const xt::xtensor<int, 1>& in_gmin,
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void
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ScattDataLegendre::update_max_val()
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{
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int groups = max_val.size();
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size_t groups = max_val.size();
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// Step through the polynomial with fixed number of points to identify the
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// maximal value
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int Nmu = 1001;
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@ -386,25 +386,25 @@ ScattDataLegendre::sample(int gin, int& gout, double& mu, double& wgt)
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void
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ScattDataLegendre::combine(const std::vector<ScattData*>& those_scatts,
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const double_1dvec& scalars)
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const std::vector<double>& scalars)
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{
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// Find the max order in the data set and make sure we can combine the sets
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int max_order = 0;
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size_t max_order = 0;
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for (int i = 0; i < those_scatts.size(); i++) {
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// Lets also make sure these items are combineable
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ScattDataLegendre* that = dynamic_cast<ScattDataLegendre*>(those_scatts[i]);
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if (!that) {
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fatal_error("Cannot combine the ScattData objects!");
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}
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int that_order = that->get_order();
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size_t that_order = that->get_order();
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if (that_order > max_order) max_order = that_order;
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}
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max_order++; // Add one since this is a Legendre
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int groups = those_scatts[0] -> energy.size();
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size_t groups = those_scatts[0] -> energy.size();
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xt::xtensor<int, 1> in_gmin({groups});
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xt::xtensor<int, 1> in_gmax({groups});
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xt::xtensor<int, 1> in_gmin({groups}, 0);
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xt::xtensor<int, 1> in_gmax({groups}, 0);
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double_3dvec sparse_scatter(groups);
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double_2dvec sparse_mult(groups);
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@ -421,11 +421,11 @@ ScattDataLegendre::combine(const std::vector<ScattData*>& those_scatts,
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//==============================================================================
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xt::xtensor<double, 3>
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ScattDataLegendre::get_matrix(int max_order)
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ScattDataLegendre::get_matrix(size_t max_order)
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{
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// Get the sizes and initialize the data to 0
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int groups = energy.size();
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int order_dim = max_order + 1;
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size_t groups = energy.size();
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size_t order_dim = max_order + 1;
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xt::xtensor<double, 3> matrix({groups, groups, order_dim}, 0.);
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for (int gin = 0; gin < groups; gin++) {
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@ -449,8 +449,8 @@ ScattDataHistogram::init(const xt::xtensor<int, 1>& in_gmin,
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const xt::xtensor<int, 1>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs)
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{
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int groups = coeffs.size();
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int order = coeffs[0][0].size();
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size_t groups = coeffs.size();
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size_t order = coeffs[0][0].size();
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// make a copy of coeffs that we can use to both extract data and normalize
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double_3dvec matrix = coeffs;
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@ -579,13 +579,13 @@ ScattDataHistogram::sample(int gin, int& gout, double& mu, double& wgt)
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//==============================================================================
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xt::xtensor<double, 3>
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ScattDataHistogram::get_matrix(int max_order)
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ScattDataHistogram::get_matrix(size_t max_order)
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{
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// Get the sizes and initialize the data to 0
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int groups = energy.size();
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size_t groups = energy.size();
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// We ignore the requested order for Histogram and Tabular representations
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int order_dim = get_order();
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xt::xtensor<double, 3> matrix = xt::zeros<double>({groups, groups, order_dim});
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size_t order_dim = get_order();
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xt::xtensor<double, 3> matrix({groups, groups, order_dim}, 0);
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for (int gin = 0; gin < groups; gin++) {
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for (int i_gout = 0; i_gout < energy[gin].size(); i_gout++) {
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@ -603,10 +603,10 @@ ScattDataHistogram::get_matrix(int max_order)
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void
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ScattDataHistogram::combine(const std::vector<ScattData*>& those_scatts,
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const double_1dvec& scalars)
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const std::vector<double>& scalars)
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{
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// Find the max order in the data set and make sure we can combine the sets
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int max_order = those_scatts[0]->get_order();
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size_t max_order = those_scatts[0]->get_order();
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for (int i = 0; i < those_scatts.size(); i++) {
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// Lets also make sure these items are combineable
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ScattDataHistogram* that = dynamic_cast<ScattDataHistogram*>(those_scatts[i]);
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@ -618,10 +618,10 @@ ScattDataHistogram::combine(const std::vector<ScattData*>& those_scatts,
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}
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}
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int groups = those_scatts[0] -> energy.size();
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size_t groups = those_scatts[0] -> energy.size();
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xt::xtensor<int, 1> in_gmin({groups});
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xt::xtensor<int, 1> in_gmax({groups});
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xt::xtensor<int, 1> in_gmin({groups}, 0);
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xt::xtensor<int, 1> in_gmax({groups}, 0);
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double_3dvec sparse_scatter(groups);
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double_2dvec sparse_mult(groups);
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@ -644,8 +644,8 @@ ScattDataTabular::init(const xt::xtensor<int, 1>& in_gmin,
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const xt::xtensor<int, 1>& in_gmax, const double_2dvec& in_mult,
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const double_3dvec& coeffs)
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{
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int groups = coeffs.size();
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int order = coeffs[0][0].size();
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size_t groups = coeffs.size();
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size_t order = coeffs[0][0].size();
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// make a copy of coeffs that we can use to both extract data and normalize
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double_3dvec matrix = coeffs;
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@ -796,12 +796,12 @@ ScattDataTabular::sample(int gin, int& gout, double& mu, double& wgt)
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//==============================================================================
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xt::xtensor<double, 3>
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ScattDataTabular::get_matrix(int max_order)
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ScattDataTabular::get_matrix(size_t max_order)
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{
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// Get the sizes and initialize the data to 0
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int groups = energy.size();
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size_t groups = energy.size();
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// We ignore the requested order for Histogram and Tabular representations
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int order_dim = get_order();
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size_t order_dim = get_order();
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xt::xtensor<double, 3> matrix({groups, groups, order_dim}, 0.);
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for (int gin = 0; gin < groups; gin++) {
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@ -820,10 +820,10 @@ ScattDataTabular::get_matrix(int max_order)
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void
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ScattDataTabular::combine(const std::vector<ScattData*>& those_scatts,
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const double_1dvec& scalars)
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const std::vector<double>& scalars)
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{
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// Find the max order in the data set and make sure we can combine the sets
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int max_order = those_scatts[0]->get_order();
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size_t max_order = those_scatts[0]->get_order();
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for (int i = 0; i < those_scatts.size(); i++) {
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// Lets also make sure these items are combineable
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ScattDataTabular* that = dynamic_cast<ScattDataTabular*>(those_scatts[i]);
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@ -835,10 +835,10 @@ ScattDataTabular::combine(const std::vector<ScattData*>& those_scatts,
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}
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}
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int groups = those_scatts[0] -> energy.size();
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size_t groups = those_scatts[0] -> energy.size();
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xt::xtensor<int, 1> in_gmin({groups});
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xt::xtensor<int, 1> in_gmax({groups});
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xt::xtensor<int, 1> in_gmin({groups}, 0);
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xt::xtensor<int, 1> in_gmax({groups}, 0);
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double_3dvec sparse_scatter(groups);
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double_2dvec sparse_mult(groups);
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@ -879,7 +879,7 @@ convert_legendre_to_tabular(ScattDataLegendre& leg, ScattDataTabular& tab,
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tab.dmu = 2. / (n_mu - 1);
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// Calculate f(mu) and integrate it so we can avoid rejection sampling
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int groups = tab.energy.size();
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size_t groups = tab.energy.size();
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tab.fmu.resize(groups);
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for (int gin = 0; gin < groups; gin++) {
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int num_groups = tab.gmax[gin] - tab.gmin[gin] + 1;
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