20470828925

Committed 23 Dec 2025 08:24PM UTC coverage: 81.898% (-0.04%) from 81.94%

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Merge b067e7f24 into 3f06a42ab

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

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72.91

/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)];
}

//==============================================================================
// 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::evaluate(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::evaluate(double x) const
{
  // Use accessors
  double a_val = this->a();
  double b_val = this->b();
  double n_val = this->n();

  if (x < a_val || x > b_val) {
    return 0.0; // outside support
  }

  return (n_val + 1.0) * std::pow(x, n_val) /
         (std::pow(b_val, n_val + 1.0) - std::pow(a_val, n_val + 1.0));
}

//==============================================================================
// 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);
}

//==============================================================================
// 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);
}

//==============================================================================
// 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::evaluate(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::evaluate(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];

  switch (interp_) {
  case Interpolation::histogram:
    // Histogram interpolation
    return p_i;

  case Interpolation::lin_lin: {
    // 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);
    return (m == 0.0) ? p_i : p_i + (x - x_i) * m;
  }

  default:
    fatal_error("Unsupported interpolation type in PDF evaluation.");
  }
}

//==============================================================================
// 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);
}

//==============================================================================
// 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);
}

//==============================================================================
// 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)	26,038✔
25	{
26	auto params = get_node_array<double>(node, "parameters");	26,038✔
27	std::size_t n = params.size() / 2;	26,038✔
28
29	assign({params.data() + n, n});	26,038✔
30	}	26,038✔
31
32	DiscreteIndex::DiscreteIndex(span<const double> p)	46,867✔
33	{
34	assign(p);	46,867✔
35	}	46,867✔
36
37	void DiscreteIndex::assign(span<const double> p)	80,806✔
38	{
39	prob_.assign(p.begin(), p.end());	80,806✔
40
41	this->init_alias();	80,806✔
42	}	80,806✔
43
44	void DiscreteIndex::init_alias()	80,806✔
45	{
46	normalize();	80,806✔
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;	80,806✔
52	vector<size_t> small;	80,806✔
53
54	size_t n = prob_.size();	80,806✔
55
56	// Set and allocate memory
57	alias_.assign(n, 0);	80,806✔
58
59	// Fill large and small vectors based on 1/n
60	for (size_t i = 0; i < n; i++) {	1,551,214✔
61	prob_[i] *= n;	1,470,408✔
62	if (prob_[i] > 1.0) {	1,470,408✔
63	large.push_back(i);	219,832✔
64	} else {
65	small.push_back(i);	1,250,576✔
66	}
67	}
68
69	while (!large.empty() && !small.empty()) {	1,395,913✔
70	int j = small.back();	1,315,107✔
71	int k = large.back();	1,315,107✔
72
73	// Remove last element of small
74	small.pop_back();	1,315,107✔
75
76	// Update probability and alias based on Vose's algorithm
77	prob_[k] += prob_[j] - 1.0;	1,315,107✔
78	alias_[j] = k;	1,315,107✔
79
80	// Move large index to small vector, if it is no longer large
81	if (prob_[k] < 1.0) {	1,315,107✔
82	small.push_back(k);	211,915✔
83	large.pop_back();	211,915✔
84	}
85	}
86	}	80,806✔
87
88	size_t DiscreteIndex::sample(uint64_t* seed) const	64,157,980✔
89	{
90	// Alias sampling of discrete distribution
91	size_t n = prob_.size();	64,157,980✔
92	if (n > 1) {	64,157,980✔
93	size_t u = prn(seed) * n;	15,738,116✔
94	if (prn(seed) < prob_[u]) {	15,738,116✔
95	return u;	9,304,482✔
96	} else {
97	return alias_[u];	6,433,634✔
98	}
99	} else {
100	return 0;	48,419,864✔
101	}
102	}
103
104	void DiscreteIndex::normalize()	80,806✔
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);	80,806✔
110	for (auto& p_i : prob_) {	1,551,214✔
111	p_i /= integral_;	1,470,408✔
112	}
113	}	80,806✔
114
115	//==============================================================================
116	// Discrete implementation
117	//==============================================================================
118
119	Discrete::Discrete(pugi::xml_node node) : di_(node)	26,038✔
120	{
121	auto params = get_node_array<double>(node, "parameters");	26,038✔
122
123	std::size_t n = params.size() / 2;	26,038✔
124
125	x_.assign(params.begin(), params.begin() + n);	26,038✔
126	}	26,038✔
127
128	Discrete::Discrete(const double* x, const double* p, size_t n) : di_({p, n})	46,867✔
129	{
130
131	x_.assign(x, x + n);	46,867✔
132	}	46,867✔
133
134	double Discrete::sample(uint64_t* seed) const	59,387,347✔
135	{
136	return x_[di_.sample(seed)];	59,387,347✔
137	}
138
139	//==============================================================================
140	// Uniform implementation
141	//==============================================================================
142
143	Uniform::Uniform(pugi::xml_node node)	256✔
144	{
145	auto params = get_node_array<double>(node, "parameters");	256✔
146	if (params.size() != 2) {	256!
147	fatal_error("Uniform distribution must have two "	×
148	"parameters specified.");
149	}
150
151	a_ = params.at(0);	256✔
152	b_ = params.at(1);	256✔
153	}	256✔
154
155	double Uniform::sample(uint64_t* seed) const	283,488✔
156	{
157	return a_ + prn(seed) * (b_ - a_);	283,488✔
158	}
159
NEW 160	double Uniform::evaluate(double x) const	×
161	{
NEW 162	if (x <= b_ && x >= a_)	×
NEW 163	return 1 / (b_ - a_);	×
164	else
NEW 165	return 0;	×
166	}
167
168	//==============================================================================
169	// PowerLaw implementation
170	//==============================================================================
171
172	PowerLaw::PowerLaw(pugi::xml_node node)	32✔
173	{
174	auto params = get_node_array<double>(node, "parameters");	32✔
175	if (params.size() != 3) {	32!
176	fatal_error("PowerLaw distribution must have three "	×
177	"parameters specified.");
178	}
179
180	const double a = params.at(0);	32✔
181	const double b = params.at(1);	32✔
182	const double n = params.at(2);	32✔
183
184	offset_ = std::pow(a, n + 1);	32✔
185	span_ = std::pow(b, n + 1) - offset_;	32✔
186	ninv_ = 1 / (n + 1);	32✔
187	}	32✔
188
189	double PowerLaw::sample(uint64_t* seed) const	2,222✔
190	{
191	return std::pow(offset_ + prn(seed) * span_, ninv_);	2,222✔
192	}
193
NEW 194	double PowerLaw::evaluate(double x) const	×
195	{
196	// Use accessors
NEW 197	double a_val = this->a();	×
NEW 198	double b_val = this->b();	×
NEW 199	double n_val = this->n();	×
200
NEW 201	if (x < a_val \|\| x > b_val) {	×
NEW 202	return 0.0; // outside support	×
203	}
204
NEW 205	return (n_val + 1.0) * std::pow(x, n_val) /	×
NEW 206	(std::pow(b_val, n_val + 1.0) - std::pow(a_val, n_val + 1.0));	×
207	}
208
209	//==============================================================================
210	// Maxwell implementation
211	//==============================================================================
212
213	Maxwell::Maxwell(pugi::xml_node node)	64✔
214	{
215	theta_ = std::stod(get_node_value(node, "parameters"));	64✔
216	}	64✔
217
218	double Maxwell::sample(uint64_t* seed) const	3,883✔
219	{
220	return maxwell_spectrum(theta_, seed);	3,883✔
221	}
222
223	//==============================================================================
224	// Watt implementation
225	//==============================================================================
226
227	Watt::Watt(pugi::xml_node node)	96✔
228	{
229	auto params = get_node_array<double>(node, "parameters");	96✔
230	if (params.size() != 2)	96!
231	openmc::fatal_error("Watt energy distribution must have two "	×
232	"parameters specified.");
233
234	a_ = params.at(0);	96✔
235	b_ = params.at(1);	96✔
236	}	96✔
237
238	double Watt::sample(uint64_t* seed) const	12,145,507✔
239	{
240	return watt_spectrum(a_, b_, seed);	12,145,507✔
241	}
242
243	//==============================================================================
244	// Normal implementation
245	//==============================================================================
246	Normal::Normal(pugi::xml_node node)	×
247	{
248	auto params = get_node_array<double>(node, "parameters");	×
249	if (params.size() != 2) {	×
250	openmc::fatal_error("Normal energy distribution must have two "	×
251	"parameters specified.");
252	}
253
254	mean_value_ = params.at(0);	×
255	std_dev_ = params.at(1);	×
256	}	×
257
258	double Normal::sample(uint64_t* seed) const	×
259	{
260	return normal_variate(mean_value_, std_dev_, seed);	×
261	}
262
NEW 263	double Normal::evaluate(double x) const	×
264	{
NEW 265	double exponent = -0.5 * std::pow((x - mean_value_) / std_dev_, 2);	×
NEW 266	double coefficient = 1 / (std_dev_ * std::sqrt(2 * PI));	×
NEW 267	return coefficient * std::exp(exponent);	×
268	}
269
270	//==============================================================================
271	// Tabular implementation
272	//==============================================================================
273
274	Tabular::Tabular(pugi::xml_node node)	7,320✔
275	{
276	if (check_for_node(node, "interpolation")) {	7,320!
277	std::string temp = get_node_value(node, "interpolation");	7,320✔
278	if (temp == "histogram") {	7,320✔
279	interp_ = Interpolation::histogram;	7,256✔
280	} else if (temp == "linear-linear") {	64!
281	interp_ = Interpolation::lin_lin;	64✔
282	} else {
283	openmc::fatal_error(	×
284	"Unsupported interpolation type for distribution: " + temp);	×
285	}
286	} else {	7,320✔
287	interp_ = Interpolation::histogram;	×
288	}
289
290	// Read and initialize tabular distribution. If number of parameters is odd,
291	// add an extra zero for the 'p' array.
292	auto params = get_node_array<double>(node, "parameters");	7,320✔
293	if (params.size() % 2 != 0) {	7,320!
294	params.push_back(0.0);	×
295	}
296	std::size_t n = params.size() / 2;	7,320✔
297	const double* x = params.data();	7,320✔
298	const double* p = x + n;	7,320✔
299	init(x, p, n);	7,320✔
300	}	7,320✔
301
302	Tabular::Tabular(const double* x, const double* p, int n, Interpolation interp,	46,274,153✔
303	const double* c)	46,274,153✔
304	: interp_ {interp}	46,274,153✔
305	{
306	init(x, p, n, c);	46,274,153✔
307	}	46,274,153✔
308
309	void Tabular::init(	46,281,473✔
310	const double* x, const double* p, std::size_t n, const double* c)
311	{
312	// Copy x/p arrays into vectors
313	std::copy(x, x + n, std::back_inserter(x_));	46,281,473✔
314	std::copy(p, p + n, std::back_inserter(p_));	46,281,473✔
315
316	// Check interpolation parameter
317	if (interp_ != Interpolation::histogram &&	46,281,473✔
318	interp_ != Interpolation::lin_lin) {	38,616,513!
319	openmc::fatal_error("Only histogram and linear-linear interpolation "	×
320	"for tabular distribution is supported.");
321	}
322
323	// Calculate cumulative distribution function
324	if (c) {	46,281,473✔
325	std::copy(c, c + n, std::back_inserter(c_));	46,274,153✔
326	} else {
327	c_.resize(n);	7,320✔
328	c_[0] = 0.0;	7,320✔
329	for (int i = 1; i < n; ++i) {	90,912✔
330	if (interp_ == Interpolation::histogram) {	83,592✔
331	c_[i] = c_[i - 1] + p_[i - 1] * (x_[i] - x_[i - 1]);	83,464✔
332	} else if (interp_ == Interpolation::lin_lin) {	128!
333	c_[i] = c_[i - 1] + 0.5 * (p_[i - 1] + p_[i]) * (x_[i] - x_[i - 1]);	128✔
334	}
335	}
336	}
337
338	// Normalize density and distribution functions. Note that we save the
339	// integral of the distribution so that if it is used as part of another
340	// distribution (e.g., Mixture), we know its relative strength.
341	integral_ = c_[n - 1];	46,281,473✔
342	for (int i = 0; i < n; ++i) {	665,571,341✔
343	p_[i] = p_[i] / integral_;	619,289,868✔
344	c_[i] = c_[i] / integral_;	619,289,868✔
345	}
346	}	46,281,473✔
347
348	double Tabular::sample(uint64_t* seed) const	692,006,949✔
349	{
350	// Sample value of CDF
351	double c = prn(seed);	692,006,949✔
352
353	// Find first CDF bin which is above the sampled value
354	double c_i = c_[0];	692,006,949✔
355	int i;
356	std::size_t n = c_.size();	692,006,949✔
357	for (i = 0; i < n - 1; ++i) {	2,147,483,647!
358	if (c <= c_[i + 1])	2,147,483,647✔
359	break;	692,006,949✔
360	c_i = c_[i + 1];	1,912,775,725✔
361	}
362
363	// Determine bounding PDF values
364	double x_i = x_[i];	692,006,949✔
365	double p_i = p_[i];	692,006,949✔
366
367	if (interp_ == Interpolation::histogram) {	692,006,949✔
368	// Histogram interpolation
369	if (p_i > 0.0) {	3,421,748!
370	return x_i + (c - c_i) / p_i;	3,421,748✔
371	} else {
372	return x_i;	×
373	}
374	} else {
375	// Linear-linear interpolation
376	double x_i1 = x_[i + 1];	688,585,201✔
377	double p_i1 = p_[i + 1];	688,585,201✔
378
379	double m = (p_i1 - p_i) / (x_i1 - x_i);	688,585,201✔
380	if (m == 0.0) {	688,585,201✔
381	return x_i + (c - c_i) / p_i;	321,971,350✔
382	} else {
383	return x_i +
384	(std::sqrt(std::max(0.0, p_i * p_i + 2 * m * (c - c_i))) - p_i) /	366,613,851✔
385	m;	366,613,851✔
386	}
387	}
388	}
389
NEW 390	double Tabular::evaluate(double x) const	×
391	{
392	// get PDF value at x
393
394	int i;
NEW 395	std::size_t n = x_.size();	×
NEW 396	if (x < x_[0]) {	×
NEW 397	return 0;	×
NEW 398	} else if (x > x_[n - 1]) {	×
NEW 399	return 0;	×
400	} else {
NEW 401	i = lower_bound_index(x_.begin(), x_.end(), x);	×
402	}
403
404	// Determine bounding PDF values
NEW 405	double x_i = x_[i];	×
NEW 406	double p_i = p_[i];	×
407
NEW 408	switch (interp_) {	×
NEW 409	case Interpolation::histogram:	×
410	// Histogram interpolation
NEW 411	return p_i;	×
412
NEW 413	case Interpolation::lin_lin: {	×
414	// Linear-linear interpolation
NEW 415	double x_i1 = x_[i + 1];	×
NEW 416	double p_i1 = p_[i + 1];	×
417
NEW 418	double m = (p_i1 - p_i) / (x_i1 - x_i);	×
NEW 419	return (m == 0.0) ? p_i : p_i + (x - x_i) * m;	×
420	}
421
NEW 422	default:	×
NEW 423	fatal_error("Unsupported interpolation type in PDF evaluation.");	×
424	}
425	}
426
427	//==============================================================================
428	// Equiprobable implementation
429	//==============================================================================
430
431	double Equiprobable::sample(uint64_t* seed) const	×
432	{
433	std::size_t n = x_.size();	×
434
435	double r = prn(seed);	×
436	int i = std::floor((n - 1) * r);	×
437
438	double xl = x_[i];	×
439	double xr = x_[i + i];	×
440	return xl + ((n - 1) * r - i) * (xr - xl);	×
441	}
442
443	//==============================================================================
444	// Mixture implementation
445	//==============================================================================
446
447	Mixture::Mixture(pugi::xml_node node)	48✔
448	{
449	double cumsum = 0.0;	48✔
450	for (pugi::xml_node pair : node.children("pair")) {	192✔
451	// Check that required data exists
452	if (!pair.attribute("probability"))	144!
453	fatal_error("Mixture pair element does not have probability.");	×
454	if (!pair.child("dist"))	144!
455	fatal_error("Mixture pair element does not have a distribution.");	×
456
457	// cummulative sum of probabilities
458	double p = std::stod(pair.attribute("probability").value());	144✔
459
460	// Save cummulative probability and distribution
461	auto dist = distribution_from_xml(pair.child("dist"));	144✔
462	cumsum += p * dist->integral();	144✔
463
464	distribution_.push_back(std::make_pair(cumsum, std::move(dist)));	144✔
465	}	144✔
466
467	// Save integral of distribution
468	integral_ = cumsum;	48✔
469
470	// Normalize cummulative probabilities to 1
471	for (auto& pair : distribution_) {	192✔
472	pair.first /= cumsum;	144✔
473	}
474	}	48✔
475
476	double Mixture::sample(uint64_t* seed) const	3,564✔
477	{
478	// Sample value of CDF
479	const double p = prn(seed);	3,564✔
480
481	// find matching distribution
482	const auto it = std::lower_bound(distribution_.cbegin(), distribution_.cend(),	3,564✔
483	p, [](const DistPair& pair, double p) { return pair.first < p; });	7,128✔
484
485	// This should not happen. Catch it
486	assert(it != distribution_.cend());	2,916!
487
488	// Sample the chosen distribution
489	return it->second->sample(seed);	7,128✔
490	}
491
492	//==============================================================================
493	// Helper function
494	//==============================================================================
495
496	UPtrDist distribution_from_xml(pugi::xml_node node)	33,843✔
497	{
498	if (!check_for_node(node, "type"))	33,843!
499	openmc::fatal_error("Distribution type must be specified.");	×
500
501	// Determine type of distribution
502	std::string type = get_node_value(node, "type", true, true);	33,843✔
503
504	// Allocate extension of Distribution
505	UPtrDist dist;	33,843✔
506	if (type == "uniform") {	33,843✔
507	dist = UPtrDist {new Uniform(node)};	256✔
508	} else if (type == "powerlaw") {	33,587✔
509	dist = UPtrDist {new PowerLaw(node)};	32✔
510	} else if (type == "maxwell") {	33,555✔
511	dist = UPtrDist {new Maxwell(node)};	64✔
512	} else if (type == "watt") {	33,491✔
513	dist = UPtrDist {new Watt(node)};	96✔
514	} else if (type == "normal") {	33,395!
515	dist = UPtrDist {new Normal(node)};	×
516	} else if (type == "discrete") {	33,395✔
517	dist = UPtrDist {new Discrete(node)};	26,027✔
518	} else if (type == "tabular") {	7,368✔
519	dist = UPtrDist {new Tabular(node)};	7,320✔
520	} else if (type == "mixture") {	48!
521	dist = UPtrDist {new Mixture(node)};	48✔
522	} else if (type == "muir") {	×
523	openmc::fatal_error(	×
524	"'muir' distributions are now specified using the openmc.stats.muir() "
525	"function in Python. Please regenerate your XML files.");
526	} else {
527	openmc::fatal_error("Invalid distribution type: " + type);	×
528	}
529	return dist;	67,686✔
530	}	33,843✔
531
532	} // namespace openmc

openmc-dev / openmc / 20470828925

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