17271733860

Committed 27 Aug 2025 03:46PM UTC coverage: 84.486% (-0.7%) from 85.204%

Build # 17271733860

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Pull #3550

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Commit Message

Merge 06ae298be into d1df80a21

Pull Request Pull Request #3550: [Point Detector] Add distribution get_pdf functionality

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73.54

/src/distribution.cpp

#include "openmc/distribution.h"

#include <algorithm> // for copy
#include <array>
#include <cmath>     // for sqrt, floor, max
#include <iterator>  // for back_inserter
#include <numeric>   // for accumulate
#include <stdexcept> // for runtime_error
#include <string>    // for string, stod

#include "openmc/error.h"
#include "openmc/math_functions.h"
#include "openmc/random_dist.h"
#include "openmc/random_lcg.h"
#include "openmc/search.h"
#include "openmc/xml_interface.h"

namespace openmc {

//==============================================================================
// DiscreteIndex implementation
//==============================================================================

DiscreteIndex::DiscreteIndex(pugi::xml_node node)
{
  auto params = get_node_array<double>(node, "parameters");
  std::size_t n = params.size() / 2;

  assign({params.data() + n, n});
}

DiscreteIndex::DiscreteIndex(span<const double> p)
{
  assign(p);
}

void DiscreteIndex::assign(span<const double> p)
{
  prob_.assign(p.begin(), p.end());

  this->init_alias();
}

void DiscreteIndex::init_alias()
{
  normalize();

  // The initialization and sampling method is based on Vose
  // (DOI: 10.1109/32.92917)
  // Vectors for large and small probabilities based on 1/n
  vector<size_t> large;
  vector<size_t> small;

  size_t n = prob_.size();

  // Set and allocate memory
  alias_.assign(n, 0);

  // Fill large and small vectors based on 1/n
  for (size_t i = 0; i < n; i++) {
    prob_[i] *= n;
    if (prob_[i] > 1.0) {
      large.push_back(i);
    } else {
      small.push_back(i);
    }
  }

  while (!large.empty() && !small.empty()) {
    int j = small.back();
    int k = large.back();

    // Remove last element of small
    small.pop_back();

    // Update probability and alias based on Vose's algorithm
    prob_[k] += prob_[j] - 1.0;
    alias_[j] = k;

    // Move large index to small vector, if it is no longer large
    if (prob_[k] < 1.0) {
      small.push_back(k);
      large.pop_back();
    }
  }
}

size_t DiscreteIndex::sample(uint64_t* seed) const
{
  // Alias sampling of discrete distribution
  size_t n = prob_.size();
  if (n > 1) {
    size_t u = prn(seed) * n;
    if (prn(seed) < prob_[u]) {
      return u;
    } else {
      return alias_[u];
    }
  } else {
    return 0;
  }
}

void DiscreteIndex::normalize()
{
  // Renormalize density function so that it sums to unity. Note that we save
  // the integral of the distribution so that if it is used as part of another
  // distribution (e.g., Mixture), we know its relative strength.
  integral_ = std::accumulate(prob_.begin(), prob_.end(), 0.0);
  for (auto& p_i : prob_) {
    p_i /= integral_;
  }
}

//==============================================================================
// Discrete implementation
//==============================================================================

Discrete::Discrete(pugi::xml_node node) : di_(node)
{
  auto params = get_node_array<double>(node, "parameters");

  std::size_t n = params.size() / 2;

  x_.assign(params.begin(), params.begin() + n);
}

Discrete::Discrete(const double* x, const double* p, size_t n) : di_({p, n})
{

  x_.assign(x, x + n);
}

double Discrete::sample(uint64_t* seed) const
{
  return x_[di_.sample(seed)];
}
// Not implemented
double Discrete::get_pdf(double x) const
{

  return -1;
}

//==============================================================================
// Uniform implementation
//==============================================================================

Uniform::Uniform(pugi::xml_node node)
{
  auto params = get_node_array<double>(node, "parameters");
  if (params.size() != 2) {
    fatal_error("Uniform distribution must have two "
                "parameters specified.");
  }

  a_ = params.at(0);
  b_ = params.at(1);
}

double Uniform::sample(uint64_t* seed) const
{
  return a_ + prn(seed) * (b_ - a_);
}
double Uniform::get_pdf(double x) const
{
  if (x <= b_ && x >= a_)
    return 1 / (b_ - a_);
  else
    return 0;
}

//==============================================================================
// PowerLaw implementation
//==============================================================================

PowerLaw::PowerLaw(pugi::xml_node node)
{
  auto params = get_node_array<double>(node, "parameters");
  if (params.size() != 3) {
    fatal_error("PowerLaw distribution must have three "
                "parameters specified.");
  }

  const double a = params.at(0);
  const double b = params.at(1);
  const double n = params.at(2);

  offset_ = std::pow(a, n + 1);
  span_ = std::pow(b, n + 1) - offset_;
  ninv_ = 1 / (n + 1);
}

double PowerLaw::sample(uint64_t* seed) const
{
  return std::pow(offset_ + prn(seed) * span_, ninv_);
}
double PowerLaw::get_pdf(double x) const
{
  return x / span_ / ninv_;
}

//==============================================================================
// Maxwell implementation
//==============================================================================

Maxwell::Maxwell(pugi::xml_node node)
{
  theta_ = std::stod(get_node_value(node, "parameters"));
}

double Maxwell::sample(uint64_t* seed) const
{
  return maxwell_spectrum(theta_, seed);
}
// Not implemented
double Maxwell::get_pdf(double x) const
{
  return -1;
}

//==============================================================================
// Watt implementation
//==============================================================================

Watt::Watt(pugi::xml_node node)
{
  auto params = get_node_array<double>(node, "parameters");
  if (params.size() != 2)
    openmc::fatal_error("Watt energy distribution must have two "
                        "parameters specified.");

  a_ = params.at(0);
  b_ = params.at(1);
}

double Watt::sample(uint64_t* seed) const
{
  return watt_spectrum(a_, b_, seed);
}
// Not implemented
double Watt::get_pdf(double x) const
{
  return -1;
}

//==============================================================================
// Normal implementation
//==============================================================================
Normal::Normal(pugi::xml_node node)
{
  auto params = get_node_array<double>(node, "parameters");
  if (params.size() != 2) {
    openmc::fatal_error("Normal energy distribution must have two "
                        "parameters specified.");
  }

  mean_value_ = params.at(0);
  std_dev_ = params.at(1);
}

double Normal::sample(uint64_t* seed) const
{
  return normal_variate(mean_value_, std_dev_, seed);
}
double Normal::get_pdf(double x) const
{
  double exponent = -0.5 * std::pow((x - mean_value_) / std_dev_, 2);
  double coefficient = 1 / (std_dev_ * std::sqrt(2 * PI));
  return coefficient * std::exp(exponent);
}

//==============================================================================
// Tabular implementation
//==============================================================================

Tabular::Tabular(pugi::xml_node node)
{
  if (check_for_node(node, "interpolation")) {
    std::string temp = get_node_value(node, "interpolation");
    if (temp == "histogram") {
      interp_ = Interpolation::histogram;
    } else if (temp == "linear-linear") {
      interp_ = Interpolation::lin_lin;
    } else {
      openmc::fatal_error(
        "Unsupported interpolation type for distribution: " + temp);
    }
  } else {
    interp_ = Interpolation::histogram;
  }

  // Read and initialize tabular distribution. If number of parameters is odd,
  // add an extra zero for the 'p' array.
  auto params = get_node_array<double>(node, "parameters");
  if (params.size() % 2 != 0) {
    params.push_back(0.0);
  }
  std::size_t n = params.size() / 2;
  const double* x = params.data();
  const double* p = x + n;
  init(x, p, n);
}

Tabular::Tabular(const double* x, const double* p, int n, Interpolation interp,
  const double* c)
  : interp_ {interp}
{
  init(x, p, n, c);
}

void Tabular::init(
  const double* x, const double* p, std::size_t n, const double* c)
{
  // Copy x/p arrays into vectors
  std::copy(x, x + n, std::back_inserter(x_));
  std::copy(p, p + n, std::back_inserter(p_));

  // Check interpolation parameter
  if (interp_ != Interpolation::histogram &&
      interp_ != Interpolation::lin_lin) {
    openmc::fatal_error("Only histogram and linear-linear interpolation "
                        "for tabular distribution is supported.");
  }

  // Calculate cumulative distribution function
  if (c) {
    std::copy(c, c + n, std::back_inserter(c_));
  } else {
    c_.resize(n);
    c_[0] = 0.0;
    for (int i = 1; i < n; ++i) {
      if (interp_ == Interpolation::histogram) {
        c_[i] = c_[i - 1] + p_[i - 1] * (x_[i] - x_[i - 1]);
      } else if (interp_ == Interpolation::lin_lin) {
        c_[i] = c_[i - 1] + 0.5 * (p_[i - 1] + p_[i]) * (x_[i] - x_[i - 1]);
      }
    }
  }

  // Normalize density and distribution functions. Note that we save the
  // integral of the distribution so that if it is used as part of another
  // distribution (e.g., Mixture), we know its relative strength.
  integral_ = c_[n - 1];
  for (int i = 0; i < n; ++i) {
    p_[i] = p_[i] / integral_;
    c_[i] = c_[i] / integral_;
  }
}

double Tabular::sample(uint64_t* seed) const
{
  // Sample value of CDF
  double c = prn(seed);

  // Find first CDF bin which is above the sampled value
  double c_i = c_[0];
  int i;
  std::size_t n = c_.size();
  for (i = 0; i < n - 1; ++i) {
    if (c <= c_[i + 1])
      break;
    c_i = c_[i + 1];
  }

  // Determine bounding PDF values
  double x_i = x_[i];
  double p_i = p_[i];

  if (interp_ == Interpolation::histogram) {
    // Histogram interpolation
    if (p_i > 0.0) {
      return x_i + (c - c_i) / p_i;
    } else {
      return x_i;
    }
  } else {
    // Linear-linear interpolation
    double x_i1 = x_[i + 1];
    double p_i1 = p_[i + 1];

    double m = (p_i1 - p_i) / (x_i1 - x_i);
    if (m == 0.0) {
      return x_i + (c - c_i) / p_i;
    } else {
      return x_i +
             (std::sqrt(std::max(0.0, p_i * p_i + 2 * m * (c - c_i))) - p_i) /
               m;
    }
  }
}

double Tabular::get_pdf(double x) const
{
  // get PDF value at x

  int i;
  std::size_t n = x_.size();
  if (x < x_[0]) {
    return 0;
  } else if (x > x_[n - 1]) {
    return 0;
  } else {
    i = lower_bound_index(x_.begin(), x_.end(), x);
  }

  // Determine bounding PDF values
  double x_i = x_[i];
  double p_i = p_[i];

  if (interp_ == Interpolation::histogram) {
    // Histogram interpolation
    return p_i;
  } else {
    // Linear-linear interpolation
    double x_i1 = x_[i + 1];
    double p_i1 = p_[i + 1];

    double m = (p_i1 - p_i) / (x_i1 - x_i);
    if (m == 0.0) {
      return p_i;
    } else {
      return p_i + (x - x_i) * m;
    }
  }
}

//==============================================================================
// Equiprobable implementation
//==============================================================================

double Equiprobable::sample(uint64_t* seed) const
{
  std::size_t n = x_.size();

  double r = prn(seed);
  int i = std::floor((n - 1) * r);

  double xl = x_[i];
  double xr = x_[i + i];
  return xl + ((n - 1) * r - i) * (xr - xl);
}
double Equiprobable::get_pdf(double x) const
{
  return -1;
}

//==============================================================================
// Mixture implementation
//==============================================================================

Mixture::Mixture(pugi::xml_node node)
{
  double cumsum = 0.0;
  for (pugi::xml_node pair : node.children("pair")) {
    // Check that required data exists
    if (!pair.attribute("probability"))
      fatal_error("Mixture pair element does not have probability.");
    if (!pair.child("dist"))
      fatal_error("Mixture pair element does not have a distribution.");

    // cummulative sum of probabilities
    double p = std::stod(pair.attribute("probability").value());

    // Save cummulative probability and distribution
    auto dist = distribution_from_xml(pair.child("dist"));
    cumsum += p * dist->integral();

    distribution_.push_back(std::make_pair(cumsum, std::move(dist)));
  }

  // Save integral of distribution
  integral_ = cumsum;

  // Normalize cummulative probabilities to 1
  for (auto& pair : distribution_) {
    pair.first /= cumsum;
  }
}

double Mixture::sample(uint64_t* seed) const
{
  // Sample value of CDF
  const double p = prn(seed);

  // find matching distribution
  const auto it = std::lower_bound(distribution_.cbegin(), distribution_.cend(),
    p, [](const DistPair& pair, double p) { return pair.first < p; });

  // This should not happen. Catch it
  assert(it != distribution_.cend());

  // Sample the chosen distribution
  return it->second->sample(seed);
}
double Mixture::get_pdf(double x) const
{
  return -1;
}

//==============================================================================
// Helper function
//==============================================================================

UPtrDist distribution_from_xml(pugi::xml_node node)
{
  if (!check_for_node(node, "type"))
    openmc::fatal_error("Distribution type must be specified.");

  // Determine type of distribution
  std::string type = get_node_value(node, "type", true, true);

  // Allocate extension of Distribution
  UPtrDist dist;
  if (type == "uniform") {
    dist = UPtrDist {new Uniform(node)};
  } else if (type == "powerlaw") {
    dist = UPtrDist {new PowerLaw(node)};
  } else if (type == "maxwell") {
    dist = UPtrDist {new Maxwell(node)};
  } else if (type == "watt") {
    dist = UPtrDist {new Watt(node)};
  } else if (type == "normal") {
    dist = UPtrDist {new Normal(node)};
  } else if (type == "discrete") {
    dist = UPtrDist {new Discrete(node)};
  } else if (type == "tabular") {
    dist = UPtrDist {new Tabular(node)};
  } else if (type == "mixture") {
    dist = UPtrDist {new Mixture(node)};
  } else if (type == "muir") {
    openmc::fatal_error(
      "'muir' distributions are now specified using the openmc.stats.muir() "
      "function in Python. Please regenerate your XML files.");
  } else {
    openmc::fatal_error("Invalid distribution type: " + type);
  }
  return dist;
}

} // namespace openmc

1	#include "openmc/distribution.h"
2
3	#include <algorithm> // for copy
4	#include <array>
5	#include <cmath> // for sqrt, floor, max
6	#include <iterator> // for back_inserter
7	#include <numeric> // for accumulate
8	#include <stdexcept> // for runtime_error
9	#include <string> // for string, stod
10
11	#include "openmc/error.h"
12	#include "openmc/math_functions.h"
13	#include "openmc/random_dist.h"
14	#include "openmc/random_lcg.h"
15	#include "openmc/search.h"
16	#include "openmc/xml_interface.h"
17
18	namespace openmc {
19
20	//==============================================================================
21	// DiscreteIndex implementation
22	//==============================================================================
23
24	DiscreteIndex::DiscreteIndex(pugi::xml_node node)	12,152✔
25	{
26	auto params = get_node_array<double>(node, "parameters");	12,152✔
27	std::size_t n = params.size() / 2;	12,152✔
28
29	assign({params.data() + n, n});	12,152✔
30	}	12,152✔
31
32	DiscreteIndex::DiscreteIndex(span<const double> p)	45,926✔
33	{
34	assign(p);	45,926✔
35	}	45,926✔
36
37	void DiscreteIndex::assign(span<const double> p)	65,251✔
38	{
39	prob_.assign(p.begin(), p.end());	65,251✔
40
41	this->init_alias();	65,251✔
42	}	65,251✔
43
44	void DiscreteIndex::init_alias()	65,251✔
45	{
46	normalize();	65,251✔
47
48	// The initialization and sampling method is based on Vose
49	// (DOI: 10.1109/32.92917)
50	// Vectors for large and small probabilities based on 1/n
51	vector<size_t> large;	65,251✔
52	vector<size_t> small;	65,251✔
53
54	size_t n = prob_.size();	65,251✔
55
56	// Set and allocate memory
57	alias_.assign(n, 0);	65,251✔
58
59	// Fill large and small vectors based on 1/n
60	for (size_t i = 0; i < n; i++) {	709,789✔
61	prob_[i] *= n;	644,538✔
62	if (prob_[i] > 1.0) {	644,538✔
63	large.push_back(i);	92,414✔
64	} else {
65	small.push_back(i);	552,124✔
66	}
67	}
68
69	while (!large.empty() && !small.empty()) {	570,681✔
70	int j = small.back();	505,430✔
71	int k = large.back();	505,430✔
72
73	// Remove last element of small
74	small.pop_back();	505,430✔
75
76	// Update probability and alias based on Vose's algorithm
77	prob_[k] += prob_[j] - 1.0;	505,430✔
78	alias_[j] = k;	505,430✔
79
80	// Move large index to small vector, if it is no longer large
81	if (prob_[k] < 1.0) {	505,430✔
82	small.push_back(k);	89,179✔
83	large.pop_back();	89,179✔
84	}
85	}
86	}	65,251✔
87
88	size_t DiscreteIndex::sample(uint64_t* seed) const	58,572,742✔
89	{
90	// Alias sampling of discrete distribution
91	size_t n = prob_.size();	58,572,742✔
92	if (n > 1) {	58,572,742✔
93	size_t u = prn(seed) * n;	13,973,994✔
94	if (prn(seed) < prob_[u]) {	13,973,994✔
95	return u;	8,571,294✔
96	} else {
97	return alias_[u];	5,402,700✔
98	}
99	} else {
100	return 0;	44,598,748✔
101	}
102	}
103
104	void DiscreteIndex::normalize()	65,251✔
105	{
106	// Renormalize density function so that it sums to unity. Note that we save
107	// the integral of the distribution so that if it is used as part of another
108	// distribution (e.g., Mixture), we know its relative strength.
109	integral_ = std::accumulate(prob_.begin(), prob_.end(), 0.0);	65,251✔
110	for (auto& p_i : prob_) {	709,789✔
111	p_i /= integral_;	644,538✔
112	}
113	}	65,251✔
114
115	//==============================================================================
116	// Discrete implementation
117	//==============================================================================
118
119	Discrete::Discrete(pugi::xml_node node) : di_(node)	12,152✔
120	{
121	auto params = get_node_array<double>(node, "parameters");	12,152✔
122
123	std::size_t n = params.size() / 2;	12,152✔
124
125	x_.assign(params.begin(), params.begin() + n);	12,152✔
126	}	12,152✔
127
128	Discrete::Discrete(const double* x, const double* p, size_t n) : di_({p, n})	45,926✔
129	{
130
131	x_.assign(x, x + n);	45,926✔
132	}	45,926✔
133
134	double Discrete::sample(uint64_t* seed) const	55,763,482✔
135	{
136	return x_[di_.sample(seed)];	55,763,482✔
137	}
138	// Not implemented
NEW 139	double Discrete::get_pdf(double x) const	×
140	{
141
NEW 142	return -1;	×
143	}
144
145	//==============================================================================
146	// Uniform implementation
147	//==============================================================================
148
149	Uniform::Uniform(pugi::xml_node node)	256✔
150	{
151	auto params = get_node_array<double>(node, "parameters");	256✔
152	if (params.size() != 2) {	256✔
153	fatal_error("Uniform distribution must have two "	×
154	"parameters specified.");
155	}
156
157	a_ = params.at(0);	256✔
158	b_ = params.at(1);	256✔
159	}	256✔
160
161	double Uniform::sample(uint64_t* seed) const	285,116✔
162	{
163	return a_ + prn(seed) * (b_ - a_);	285,116✔
164	}
NEW 165	double Uniform::get_pdf(double x) const	×
166	{
NEW 167	if (x <= b_ && x >= a_)	×
NEW 168	return 1 / (b_ - a_);	×
169	else
NEW 170	return 0;	×
171	}
172
173	//==============================================================================
174	// PowerLaw implementation
175	//==============================================================================
176
177	PowerLaw::PowerLaw(pugi::xml_node node)	32✔
178	{
179	auto params = get_node_array<double>(node, "parameters");	32✔
180	if (params.size() != 3) {	32✔
181	fatal_error("PowerLaw distribution must have three "	×
182	"parameters specified.");
183	}
184
185	const double a = params.at(0);	32✔
186	const double b = params.at(1);	32✔
187	const double n = params.at(2);	32✔
188
189	offset_ = std::pow(a, n + 1);	32✔
190	span_ = std::pow(b, n + 1) - offset_;	32✔
191	ninv_ = 1 / (n + 1);	32✔
192	}	32✔
193
194	double PowerLaw::sample(uint64_t* seed) const	2,222✔
195	{
196	return std::pow(offset_ + prn(seed) * span_, ninv_);	2,222✔
197	}
NEW 198	double PowerLaw::get_pdf(double x) const	×
199	{
NEW 200	return x / span_ / ninv_;	×
201	}
202
203	//==============================================================================
204	// Maxwell implementation
205	//==============================================================================
206
207	Maxwell::Maxwell(pugi::xml_node node)	64✔
208	{
209	theta_ = std::stod(get_node_value(node, "parameters"));	64✔
210	}	64✔
211
212	double Maxwell::sample(uint64_t* seed) const	3,883✔
213	{
214	return maxwell_spectrum(theta_, seed);	3,883✔
215	}
216	// Not implemented
NEW 217	double Maxwell::get_pdf(double x) const	×
218	{
NEW 219	return -1;	×
220	}
221
222	//==============================================================================
223	// Watt implementation
224	//==============================================================================
225
226	Watt::Watt(pugi::xml_node node)	96✔
227	{
228	auto params = get_node_array<double>(node, "parameters");	96✔
229	if (params.size() != 2)	96✔
230	openmc::fatal_error("Watt energy distribution must have two "	×
231	"parameters specified.");
232
233	a_ = params.at(0);	96✔
234	b_ = params.at(1);	96✔
235	}	96✔
236
237	double Watt::sample(uint64_t* seed) const	10,754,354✔
238	{
239	return watt_spectrum(a_, b_, seed);	10,754,354✔
240	}
241	// Not implemented
NEW 242	double Watt::get_pdf(double x) const	×
243	{
NEW 244	return -1;	×
245	}
246
247	//==============================================================================
248	// Normal implementation
249	//==============================================================================
250	Normal::Normal(pugi::xml_node node)	×
251	{
252	auto params = get_node_array<double>(node, "parameters");	×
253	if (params.size() != 2) {	×
254	openmc::fatal_error("Normal energy distribution must have two "	×
255	"parameters specified.");
256	}
257
258	mean_value_ = params.at(0);	×
259	std_dev_ = params.at(1);	×
260	}
261
262	double Normal::sample(uint64_t* seed) const	×
263	{
264	return normal_variate(mean_value_, std_dev_, seed);	×
265	}
NEW 266	double Normal::get_pdf(double x) const	×
267	{
NEW 268	double exponent = -0.5 * std::pow((x - mean_value_) / std_dev_, 2);	×
NEW 269	double coefficient = 1 / (std_dev_ * std::sqrt(2 * PI));	×
NEW 270	return coefficient * std::exp(exponent);	×
271	}
272
273	//==============================================================================
274	// Tabular implementation
275	//==============================================================================
276
277	Tabular::Tabular(pugi::xml_node node)	2,772✔
278	{
279	if (check_for_node(node, "interpolation")) {	2,772✔
280	std::string temp = get_node_value(node, "interpolation");	2,772✔
281	if (temp == "histogram") {	2,772✔
282	interp_ = Interpolation::histogram;	2,708✔
283	} else if (temp == "linear-linear") {	64✔
284	interp_ = Interpolation::lin_lin;	64✔
285	} else {
286	openmc::fatal_error(	×
287	"Unsupported interpolation type for distribution: " + temp);	×
288	}
289	} else {	2,772✔
290	interp_ = Interpolation::histogram;	×
291	}
292
293	// Read and initialize tabular distribution. If number of parameters is odd,
294	// add an extra zero for the 'p' array.
295	auto params = get_node_array<double>(node, "parameters");	2,772✔
296	if (params.size() % 2 != 0) {	2,772✔
297	params.push_back(0.0);	×
298	}
299	std::size_t n = params.size() / 2;	2,772✔
300	const double* x = params.data();	2,772✔
301	const double* p = x + n;	2,772✔
302	init(x, p, n);	2,772✔
303	}	2,772✔
304
305	Tabular::Tabular(const double* x, const double* p, int n, Interpolation interp,	43,073,646✔
306	const double* c)	43,073,646✔
307	: interp_ {interp}	43,073,646✔
308	{
309	init(x, p, n, c);	43,073,646✔
310	}	43,073,646✔
311
312	void Tabular::init(	43,076,418✔
313	const double* x, const double* p, std::size_t n, const double* c)
314	{
315	// Copy x/p arrays into vectors
316	std::copy(x, x + n, std::back_inserter(x_));	43,076,418✔
317	std::copy(p, p + n, std::back_inserter(p_));	43,076,418✔
318
319	// Check interpolation parameter
320	if (interp_ != Interpolation::histogram &&	43,076,418✔
321	interp_ != Interpolation::lin_lin) {	36,021,353✔
322	openmc::fatal_error("Only histogram and linear-linear interpolation "	×
323	"for tabular distribution is supported.");
324	}
325
326	// Calculate cumulative distribution function
327	if (c) {	43,076,418✔
328	std::copy(c, c + n, std::back_inserter(c_));	43,073,646✔
329	} else {
330	c_.resize(n);	2,772✔
331	c_[0] = 0.0;	2,772✔
332	for (int i = 1; i < n; ++i) {	36,336✔
333	if (interp_ == Interpolation::histogram) {	33,564✔
334	c_[i] = c_[i - 1] + p_[i - 1] * (x_[i] - x_[i - 1]);	33,436✔
335	} else if (interp_ == Interpolation::lin_lin) {	128✔
336	c_[i] = c_[i - 1] + 0.5 * (p_[i - 1] + p_[i]) * (x_[i] - x_[i - 1]);	128✔
337	}
338	}
339	}
340
341	// Normalize density and distribution functions. Note that we save the
342	// integral of the distribution so that if it is used as part of another
343	// distribution (e.g., Mixture), we know its relative strength.
344	integral_ = c_[n - 1];	43,076,418✔
345	for (int i = 0; i < n; ++i) {	613,375,991✔
346	p_[i] = p_[i] / integral_;	570,299,573✔
347	c_[i] = c_[i] / integral_;	570,299,573✔
348	}
349	}	43,076,418✔
350
351	double Tabular::sample(uint64_t* seed) const	569,744,642✔
352	{
353	// Sample value of CDF
354	double c = prn(seed);	569,744,642✔
355
356	// Find first CDF bin which is above the sampled value
357	double c_i = c_[0];	569,744,642✔
358	int i;
359	std::size_t n = c_.size();	569,744,642✔
360	for (i = 0; i < n - 1; ++i) {	2,006,090,681✔
361	if (c <= c_[i + 1])	2,006,090,681✔
362	break;	569,744,642✔
363	c_i = c_[i + 1];	1,436,346,039✔
364	}
365
366	// Determine bounding PDF values
367	double x_i = x_[i];	569,744,642✔
368	double p_i = p_[i];	569,744,642✔
369
370	if (interp_ == Interpolation::histogram) {	569,744,642✔
371	// Histogram interpolation
372	if (p_i > 0.0) {	3,407,965✔
373	return x_i + (c - c_i) / p_i;	3,407,965✔
374	} else {
375	return x_i;	×
376	}
377	} else {
378	// Linear-linear interpolation
379	double x_i1 = x_[i + 1];	566,336,677✔
380	double p_i1 = p_[i + 1];	566,336,677✔
381
382	double m = (p_i1 - p_i) / (x_i1 - x_i);	566,336,677✔
383	if (m == 0.0) {	566,336,677✔
384	return x_i + (c - c_i) / p_i;	307,622,181✔
385	} else {
386	return x_i +
387	(std::sqrt(std::max(0.0, p_i * p_i + 2 * m * (c - c_i))) - p_i) /	258,714,496✔
388	m;	258,714,496✔
389	}
390	}
391	}
392
NEW 393	double Tabular::get_pdf(double x) const	×
394	{
395	// get PDF value at x
396
397	int i;
NEW 398	std::size_t n = x_.size();	×
NEW 399	if (x < x_[0]) {	×
NEW 400	return 0;	×
NEW 401	} else if (x > x_[n - 1]) {	×
NEW 402	return 0;	×
403	} else {
NEW 404	i = lower_bound_index(x_.begin(), x_.end(), x);	×
405	}
406
407	// Determine bounding PDF values
NEW 408	double x_i = x_[i];	×
NEW 409	double p_i = p_[i];	×
410
NEW 411	if (interp_ == Interpolation::histogram) {	×
412	// Histogram interpolation
NEW 413	return p_i;	×
414	} else {
415	// Linear-linear interpolation
NEW 416	double x_i1 = x_[i + 1];	×
NEW 417	double p_i1 = p_[i + 1];	×
418
NEW 419	double m = (p_i1 - p_i) / (x_i1 - x_i);	×
NEW 420	if (m == 0.0) {	×
NEW 421	return p_i;	×
422	} else {
NEW 423	return p_i + (x - x_i) * m;	×
424	}
425	}
426	}
427
428	//==============================================================================
429	// Equiprobable implementation
430	//==============================================================================
431
432	double Equiprobable::sample(uint64_t* seed) const	×
433	{
434	std::size_t n = x_.size();	×
435
436	double r = prn(seed);	×
437	int i = std::floor((n - 1) * r);	×
438
439	double xl = x_[i];	×
440	double xr = x_[i + i];	×
441	return xl + ((n - 1) * r - i) * (xr - xl);	×
442	}
NEW 443	double Equiprobable::get_pdf(double x) const	×
444	{
NEW 445	return -1;	×
446	}
447
448	//==============================================================================
449	// Mixture implementation
450	//==============================================================================
451
452	Mixture::Mixture(pugi::xml_node node)	48✔
453	{
454	double cumsum = 0.0;	48✔
455	for (pugi::xml_node pair : node.children("pair")) {	192✔
456	// Check that required data exists
457	if (!pair.attribute("probability"))	144✔
458	fatal_error("Mixture pair element does not have probability.");	×
459	if (!pair.child("dist"))	144✔
460	fatal_error("Mixture pair element does not have a distribution.");	×
461
462	// cummulative sum of probabilities
463	double p = std::stod(pair.attribute("probability").value());	144✔
464
465	// Save cummulative probability and distribution
466	auto dist = distribution_from_xml(pair.child("dist"));	144✔
467	cumsum += p * dist->integral();	144✔
468
469	distribution_.push_back(std::make_pair(cumsum, std::move(dist)));	144✔
470	}	144✔
471
472	// Save integral of distribution
473	integral_ = cumsum;	48✔
474
475	// Normalize cummulative probabilities to 1
476	for (auto& pair : distribution_) {	192✔
477	pair.first /= cumsum;	144✔
478	}
479	}	48✔
480
481	double Mixture::sample(uint64_t* seed) const	3,564✔
482	{
483	// Sample value of CDF
484	const double p = prn(seed);	3,564✔
485
486	// find matching distribution
487	const auto it = std::lower_bound(distribution_.cbegin(), distribution_.cend(),	3,564✔
488	p, [](const DistPair& pair, double p) { return pair.first < p; });	7,128✔
489
490	// This should not happen. Catch it
491	assert(it != distribution_.cend());	2,916✔
492
493	// Sample the chosen distribution
494	return it->second->sample(seed);	7,128✔
495	}
NEW 496	double Mixture::get_pdf(double x) const	×
497	{
NEW 498	return -1;	×
499	}
500
501	//==============================================================================
502	// Helper function
503	//==============================================================================
504
505	UPtrDist distribution_from_xml(pugi::xml_node node)	15,409✔
506	{
507	if (!check_for_node(node, "type"))	15,409✔
508	openmc::fatal_error("Distribution type must be specified.");	×
509
510	// Determine type of distribution
511	std::string type = get_node_value(node, "type", true, true);	15,409✔
512
513	// Allocate extension of Distribution
514	UPtrDist dist;	15,409✔
515	if (type == "uniform") {	15,409✔
516	dist = UPtrDist {new Uniform(node)};	256✔
517	} else if (type == "powerlaw") {	15,153✔
518	dist = UPtrDist {new PowerLaw(node)};	32✔
519	} else if (type == "maxwell") {	15,121✔
520	dist = UPtrDist {new Maxwell(node)};	64✔
521	} else if (type == "watt") {	15,057✔
522	dist = UPtrDist {new Watt(node)};	96✔
523	} else if (type == "normal") {	14,961✔
524	dist = UPtrDist {new Normal(node)};	×
525	} else if (type == "discrete") {	14,961✔
526	dist = UPtrDist {new Discrete(node)};	12,141✔
527	} else if (type == "tabular") {	2,820✔
528	dist = UPtrDist {new Tabular(node)};	2,772✔
529	} else if (type == "mixture") {	48✔
530	dist = UPtrDist {new Mixture(node)};	48✔
531	} else if (type == "muir") {	×
532	openmc::fatal_error(	×
533	"'muir' distributions are now specified using the openmc.stats.muir() "
534	"function in Python. Please regenerate your XML files.");
535	} else {
536	openmc::fatal_error("Invalid distribution type: " + type);	×
537	}
538	return dist;	30,818✔
539	}	15,409✔
540
541	} // namespace openmc

openmc-dev / openmc / 17271733860

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