#include "openmc/distribution.h"

#include "openmc/random_lcg.h"

#include <catch2/catch_test_macros.hpp>

#include <catch2/matchers/catch_matchers_floating_point.hpp>

#include <cmath>

#include <pugixml.hpp>

TEST_CASE("Test alias method sampling of a discrete distribution")

{

constexpr int n_samples = 1000000;

double x[5] = {-1.6, 1.1, 20.3, 4.7, 0.9};

double p[5] = {0.2, 0.1, 0.65, 0.02, 0.03};

// Initialize distribution

openmc::Discrete dist(x, p, 5);

uint64_t seed = openmc::init_seed(0, 0);

// Calculate expected distribution mean

double mean = 0.0;

for (size_t i = 0; i < 5; i++) {

mean += x[i] * p[i];

}

// Sample distribution and calculate mean, standard deviation, and number of

// x[0] sampled

double dist_mean = 0.0;

double std = 0.0;

int counter = 0;

for (size_t i = 0; i < n_samples; i++) {

auto sample = dist.sample(&seed);

std += sample * sample / n_samples;

dist_mean += sample;

if (sample == x[0])

counter++;

}

dist_mean /= n_samples;

std -= dist_mean * dist_mean;

// Require sampled distribution mean is within 4 standard deviations of the

// expected mean

REQUIRE(std::abs(dist_mean - mean) < 4 * std);

// Require counter of number of x[0] is within the 95% confidence interval

// assuming a Poisson distribution of 200,000

REQUIRE(std::abs((double)counter / n_samples - p[0]) <

1.96 * std::sqrt(p[0] / n_samples));

}

TEST_CASE("Test alias sampling method for pugixml constructor")

{

// XML doc node for Discrete contructor

pugi::xml_document doc;

pugi::xml_node energy = doc.append_child("energy");

pugi::xml_node parameters = energy.append_child("parameters");

parameters.append_child(pugi::node_pcdata)

.set_value("800 500000 30000 0.1 0.6 0.3");

// Initialize discrete distribution and seed

openmc::Discrete dist(energy);

uint64_t seed = openmc::init_seed(0, 0);

auto sample = dist.sample(&seed);

// Assertions

REQUIRE(dist.x().size() == 3);

REQUIRE(dist.prob().size() == 3);

REQUIRE(dist.alias().size() == 3);

openmc::vector<double> correct_x = {800, 500000, 30000};

openmc::vector<double> correct_prob = {0.3, 1.0, 0.9};

openmc::vector<size_t> correct_alias = {1, 0, 1};

for (size_t i = 0; i < 3; i++) {

REQUIRE(dist.x()[i] == correct_x[i]);

REQUIRE_THAT(

dist.prob()[i], Catch::Matchers::WithinAbs(correct_prob[i], 1e-12));

REQUIRE(dist.alias()[i] == correct_alias[i]);

}

}