diff --git a/Base/Math/IntegratorMCMiser.h b/Base/Math/IntegratorMCMiser.h
index 7034bb306fbbceccc9fda4585193c424877be97b..4e45d3f6754c76527b30a0888212f24182db830e 100644
--- a/Base/Math/IntegratorMCMiser.h
+++ b/Base/Math/IntegratorMCMiser.h
@@ -32,7 +32,7 @@ using miser_integrand = double (T::*)(const double*, size_t, const void*) const;
//! Standard usage for integration inside a class T:
//! - Create a handle to an integrator:
//! 'auto integrator = make_integrator_miser(this, mem_function, dimension)'
-//! - Call: 'integrator.integrate(lmin, lmax, data, nbr_points)'
+//! - Call: 'integrator.integrate(lmin, lmax, data, n_points)'
template <class T>
class IntegratorMCMiser {
@@ -50,7 +50,7 @@ public:
//! perform the actual integration over the ranges [min_array, max_array]
double integrate(double* min_array, double* max_array, const void* params,
- size_t nbr_points) const;
+ size_t n_points) const;
private:
//! static function that can be passed to gsl integrator
@@ -110,7 +110,7 @@ IntegratorMCMiser<T>::~IntegratorMCMiser()
template <class T>
double IntegratorMCMiser<T>::integrate(double* min_array, double* max_array, const void* params,
- size_t nbr_points) const
+ size_t n_points) const
{
CallBackHolder cb = {m_object, m_member_function, params};
@@ -120,7 +120,7 @@ double IntegratorMCMiser<T>::integrate(double* min_array, double* max_array, con
f.params = &cb;
double result, error;
- gsl_monte_miser_integrate(&f, min_array, max_array, m_dim, nbr_points, m_random_gen,
+ gsl_monte_miser_integrate(&f, min_array, max_array, m_dim, n_points, m_random_gen,
m_gsl_workspace, &result, &error);
return result;
}
diff --git a/Img3D/Build/PositionBuilders.cpp b/Img3D/Build/PositionBuilders.cpp
index 487d20b741f298bc5d807fb7bb5246f67d89f958..1c5033792c27f4e2ed86ec566116d121305c49d6 100644
--- a/Img3D/Build/PositionBuilders.cpp
+++ b/Img3D/Build/PositionBuilders.cpp
@@ -114,14 +114,14 @@ std::vector<std::vector<double>> RandomPositionBuilder::generatePositionsImpl(do
// to compute total number of particles we use the total particle density
// and multiply by the area of the layer
- int num_particles = static_cast<int>(density * (2 * layer_size) * (2 * layer_size));
+ int n_particles = static_cast<int>(density * (2 * layer_size) * (2 * layer_size));
// random generator and distribution
std::random_device rd; // Will be used to obtain a seed for the random number engine
std::mt19937 gen(rd()); // Standard mersenne_twister_engine seeded with rd()
std::uniform_real_distribution<double> dis(0.0, 1.0);
- for (int i = 1; i <= num_particles; ++i) {
+ for (int i = 1; i <= n_particles; ++i) {
// generate random x and y coordinates
position.push_back(dis(gen) * 2 * layer_size - layer_size); // x
position.push_back(dis(gen) * 2 * layer_size - layer_size); // y
diff --git a/Param/Distrib/DistributionHandler.cpp b/Param/Distrib/DistributionHandler.cpp
index cd324cf4dd4b07c6d6129121559829a73f478781..46d207b890e7a1a0d722c75a8736595f18a26224 100644
--- a/Param/Distrib/DistributionHandler.cpp
+++ b/Param/Distrib/DistributionHandler.cpp
@@ -19,7 +19,7 @@
#include "Param/Distrib/ParameterSample.h"
DistributionHandler::DistributionHandler()
- : m_nbr_combinations(1)
+ : m_n_combinations(1)
{
}
@@ -29,19 +29,19 @@ void DistributionHandler::addParameterDistribution(const ParameterDistribution&
{
if (par_distr.nDraws() > 0) {
m_distributions.push_back(par_distr);
- m_nbr_combinations *= par_distr.nDraws();
+ m_n_combinations *= par_distr.nDraws();
m_cached_samples.push_back(par_distr.generateSamples());
}
}
size_t DistributionHandler::getTotalNumberOfSamples() const
{
- return m_nbr_combinations;
+ return m_n_combinations;
}
double DistributionHandler::setParameterValues(size_t index)
{
- ASSERT(index < m_nbr_combinations);
+ ASSERT(index < m_n_combinations);
size_t n_distr = m_distributions.size();
double weight = 1.0;
diff --git a/Param/Distrib/DistributionHandler.h b/Param/Distrib/DistributionHandler.h
index 23ab73b850eaca2fc0f275537f396f0948fc41d3..f06ffe1b251f3e29674579ae1d8dd02084020bfb 100644
--- a/Param/Distrib/DistributionHandler.h
+++ b/Param/Distrib/DistributionHandler.h
@@ -48,7 +48,7 @@ public:
std::function<void(double)> fn);
private:
- size_t m_nbr_combinations;
+ size_t m_n_combinations;
std::vector<ParameterDistribution> m_distributions;
std::map<const ParameterDistribution*, std::function<void(double)>> m_setValueFunctions;
std::vector<std::vector<ParameterSample>> m_cached_samples;
diff --git a/Param/Distrib/Distributions.cpp b/Param/Distrib/Distributions.cpp
index 5871dd0ef7e71c7580223a4fe8b6ae631b8fb7ec..b6ed6a9dc3d5e9b80ffb094901a45c09378c7687 100644
--- a/Param/Distrib/Distributions.cpp
+++ b/Param/Distrib/Distributions.cpp
@@ -48,41 +48,41 @@ IDistribution1D::IDistribution1D(const std::vector<double>& PValues)
//! Returns equidistant samples, using intrinsic parameters, weighted with probabilityDensity().
-std::vector<ParameterSample> IDistribution1D::equidistantSamples(size_t nbr_samples,
+std::vector<ParameterSample> IDistribution1D::equidistantSamples(size_t n_samples,
double sigma_factor,
const RealLimits& limits) const
{
- if (nbr_samples == 0)
+ if (n_samples == 0)
throw std::runtime_error("IDistribution1D::generateSamples: "
"number of generated samples must be bigger than zero");
if (isDelta())
return {ParameterSample(mean())};
- return generateSamplesFromValues(equidistantPoints(nbr_samples, sigma_factor, limits));
+ return generateSamplesFromValues(equidistantPoints(n_samples, sigma_factor, limits));
}
//! Returns equidistant samples from xmin to xmax, weighted with probabilityDensity().
std::vector<ParameterSample>
-IDistribution1D::equidistantSamplesInRange(size_t nbr_samples, double xmin, double xmax) const
+IDistribution1D::equidistantSamplesInRange(size_t n_samples, double xmin, double xmax) const
{
- if (nbr_samples == 0)
+ if (n_samples == 0)
throw std::runtime_error("IDistribution1D::generateSamples: "
"number of generated samples must be bigger than zero");
if (isDelta())
return {ParameterSample(mean())};
- return generateSamplesFromValues(equidistantPointsInRange(nbr_samples, xmin, xmax));
+ return generateSamplesFromValues(equidistantPointsInRange(n_samples, xmin, xmax));
}
//! Returns equidistant interpolation points from xmin to xmax.
-std::vector<double> IDistribution1D::equidistantPointsInRange(size_t nbr_samples, double xmin,
+std::vector<double> IDistribution1D::equidistantPointsInRange(size_t n_samples, double xmin,
double xmax) const
{
- if (nbr_samples < 2 || DoubleEqual(xmin, xmax))
+ if (n_samples < 2 || DoubleEqual(xmin, xmax))
return {mean()};
- std::vector<double> result(nbr_samples);
- for (size_t i = 0; i < nbr_samples; ++i)
- result[i] = xmin + i * (xmax - xmin) / (nbr_samples - 1.0);
+ std::vector<double> result(n_samples);
+ for (size_t i = 0; i < n_samples; ++i)
+ result[i] = xmin + i * (xmax - xmin) / (n_samples - 1.0);
return result;
}
@@ -152,13 +152,13 @@ double DistributionGate::probabilityDensity(double x) const
return 1.0 / (m_max - m_min);
}
-std::vector<double> DistributionGate::equidistantPoints(size_t nbr_samples, double /*sigma_factor*/,
+std::vector<double> DistributionGate::equidistantPoints(size_t n_samples, double /*sigma_factor*/,
const RealLimits& limits) const
{
double xmin = m_min;
double xmax = m_max;
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionGate::isDelta() const
@@ -209,7 +209,7 @@ double DistributionLorentz::probabilityDensity(double x) const
return m_hwhm / (m_hwhm * m_hwhm + (x - m_mean) * (x - m_mean)) / M_PI;
}
-std::vector<double> DistributionLorentz::equidistantPoints(size_t nbr_samples, double sigma_factor,
+std::vector<double> DistributionLorentz::equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits) const
{
ASSERT(m_validated);
@@ -218,7 +218,7 @@ std::vector<double> DistributionLorentz::equidistantPoints(size_t nbr_samples, d
double xmin = m_mean - sigma_factor * m_hwhm;
double xmax = m_mean + sigma_factor * m_hwhm;
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionLorentz::isDelta() const
@@ -275,7 +275,7 @@ double DistributionGaussian::probabilityDensity(double x) const
return exponential / m_std_dev / std::sqrt(M_TWOPI);
}
-std::vector<double> DistributionGaussian::equidistantPoints(size_t nbr_samples, double sigma_factor,
+std::vector<double> DistributionGaussian::equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits) const
{
ASSERT(m_validated);
@@ -284,7 +284,7 @@ std::vector<double> DistributionGaussian::equidistantPoints(size_t nbr_samples,
double xmin = m_mean - sigma_factor * m_std_dev;
double xmax = m_mean + sigma_factor * m_std_dev;
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionGaussian::isDelta() const
@@ -340,12 +340,12 @@ double DistributionLogNormal::mean() const
return m_median * std::exp(exponent);
}
-std::vector<double> DistributionLogNormal::equidistantPoints(size_t nbr_samples,
+std::vector<double> DistributionLogNormal::equidistantPoints(size_t n_samples,
double sigma_factor,
const RealLimits& limits) const
{
ASSERT(m_validated);
- if (nbr_samples < 2) {
+ if (n_samples < 2) {
std::vector<double> result;
result.push_back(m_median);
return result;
@@ -355,7 +355,7 @@ std::vector<double> DistributionLogNormal::equidistantPoints(size_t nbr_samples,
double xmin = m_median * std::exp(-sigma_factor * m_scale_param);
double xmax = m_median * std::exp(sigma_factor * m_scale_param);
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionLogNormal::isDelta() const
@@ -412,7 +412,7 @@ double DistributionCosine::probabilityDensity(double x) const
return (1.0 + std::cos((x - m_mean) / m_sigma)) / (m_sigma * M_TWOPI);
}
-std::vector<double> DistributionCosine::equidistantPoints(size_t nbr_samples, double sigma_factor,
+std::vector<double> DistributionCosine::equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits) const
{
ASSERT(m_validated);
@@ -421,7 +421,7 @@ std::vector<double> DistributionCosine::equidistantPoints(size_t nbr_samples, do
double xmin = m_mean - sigma_factor * m_sigma * M_PI_2;
double xmax = m_mean + sigma_factor * m_sigma * M_PI_2;
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionCosine::isDelta() const
@@ -491,15 +491,15 @@ double DistributionTrapezoid::probabilityDensity(double x) const
return 0.0;
}
-std::vector<double> DistributionTrapezoid::equidistantPoints(size_t nbr_samples, double,
+std::vector<double> DistributionTrapezoid::equidistantPoints(size_t n_samples, double,
const RealLimits& limits) const
{
ASSERT(m_validated);
double xmin = m_center - m_middle / 2.0 - m_left;
double xmax = xmin + m_left + m_middle + m_right;
- adjustLimitsToNonZeroSamples(xmin, xmax, nbr_samples);
+ adjustLimitsToNonZeroSamples(xmin, xmax, n_samples);
adjustMinMaxForLimits(xmin, xmax, limits);
- return equidistantPointsInRange(nbr_samples, xmin, xmax);
+ return equidistantPointsInRange(n_samples, xmin, xmax);
}
bool DistributionTrapezoid::isDelta() const
@@ -514,16 +514,16 @@ std::string DistributionTrapezoid::pythonConstructor(const std::string& units) c
}
void DistributionTrapezoid::adjustLimitsToNonZeroSamples(double& min, double& max,
- size_t nbr_samples) const
+ size_t n_samples) const
{
- if (nbr_samples <= 1)
+ if (n_samples <= 1)
return;
- size_t N = nbr_samples;
+ size_t N = n_samples;
if (m_left > 0.0)
++N;
if (m_right > 0.0)
++N;
- if (N == nbr_samples)
+ if (N == n_samples)
return;
double step = (max - min) / (N - 1);
if (m_left > 0.0)
diff --git a/Param/Distrib/Distributions.h b/Param/Distrib/Distributions.h
index 8414da5afe0765982456237e04144ca0222930c0..158ba5f99b351b6b230e7657506f545603572a30 100644
--- a/Param/Distrib/Distributions.h
+++ b/Param/Distrib/Distributions.h
@@ -42,20 +42,20 @@ public:
virtual double mean() const = 0;
//! Returns equidistant samples, using intrinsic parameters, weighted with probabilityDensity().
- std::vector<ParameterSample> equidistantSamples(size_t nbr_samples, double sigma_factor = 0.,
+ std::vector<ParameterSample> equidistantSamples(size_t n_samples, double sigma_factor = 0.,
const RealLimits& limits = {}) const;
//! Returns equidistant samples from xmin to xmax, weighted with probabilityDensity().
- std::vector<ParameterSample> equidistantSamplesInRange(size_t nbr_samples, double xmin,
+ std::vector<ParameterSample> equidistantSamplesInRange(size_t n_samples, double xmin,
double xmax) const;
//! Returns equidistant interpolation points, with range computed in distribution-specific
//! way from mean and width parameter, taking into account limits and sigma_factor.
- virtual std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ virtual std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const = 0;
//! Returns equidistant interpolation points from xmin to xmax.
- virtual std::vector<double> equidistantPointsInRange(size_t nbr_samples, double xmin,
+ virtual std::vector<double> equidistantPointsInRange(size_t n_samples, double xmin,
double xmax) const;
//! Returns true if the distribution is in the limit case of a Dirac delta distribution.
@@ -100,7 +100,7 @@ public:
double max() const { return m_max; }
//! Returns list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -138,7 +138,7 @@ public:
double hwhm() const { return m_hwhm; }
//! generate list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -179,7 +179,7 @@ public:
double getStdDev() const { return m_std_dev; }
//! generate list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -220,7 +220,7 @@ public:
double getScalePar() const { return m_scale_param; }
//! generate list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -260,7 +260,7 @@ public:
double sigma() const { return m_sigma; }
//! generate list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -306,7 +306,7 @@ public:
double getRightWidth() const { return m_right; }
//! generate list of sample values
- std::vector<double> equidistantPoints(size_t nbr_samples, double sigma_factor,
+ std::vector<double> equidistantPoints(size_t n_samples, double sigma_factor,
const RealLimits& limits = {}) const override;
bool isDelta() const override;
@@ -318,7 +318,7 @@ public:
std::string validate() const override;
private:
- void adjustLimitsToNonZeroSamples(double& min, double& max, size_t nbr_samples) const;
+ void adjustLimitsToNonZeroSamples(double& min, double& max, size_t n_samples) const;
const double& m_center;
const double& m_left;
const double& m_middle;
diff --git a/Param/Distrib/ParameterDistribution.cpp b/Param/Distrib/ParameterDistribution.cpp
index 741d503ca773257704bfaf8b6946dd959f5a3d3c..eef2678472bd7af35d2c3412f77170e8b85bdefa 100644
--- a/Param/Distrib/ParameterDistribution.cpp
+++ b/Param/Distrib/ParameterDistribution.cpp
@@ -18,10 +18,10 @@
ParameterDistribution::ParameterDistribution(WhichParameter whichParameter,
const IDistribution1D& distribution,
- size_t nbr_samples, double sigma_factor,
+ size_t n_samples, double sigma_factor,
const RealLimits& limits)
: m_whichParameter(whichParameter)
- , m_nbr_samples(nbr_samples)
+ , m_n_samples(n_samples)
, m_sigma_factor(sigma_factor)
, m_limits(limits)
, m_xmin(1.0)
@@ -31,16 +31,16 @@ ParameterDistribution::ParameterDistribution(WhichParameter whichParameter,
if (m_sigma_factor < 0.0)
throw std::runtime_error("ParameterDistribution::ParameterDistribution() -> Error."
"sigma factor cannot be negative");
- if (nbr_samples == 0)
+ if (n_samples == 0)
throw std::runtime_error("ParameterDistribution::ParameterDistribution() -> Error."
"Number of samples cannot be zero.");
}
ParameterDistribution::ParameterDistribution(WhichParameter whichParameter,
const IDistribution1D& distribution,
- size_t nbr_samples, double xmin, double xmax)
+ size_t n_samples, double xmin, double xmax)
: m_whichParameter(whichParameter)
- , m_nbr_samples(nbr_samples)
+ , m_n_samples(n_samples)
, m_sigma_factor(0.0)
, m_xmin(xmin)
, m_xmax(xmax)
@@ -49,7 +49,7 @@ ParameterDistribution::ParameterDistribution(WhichParameter whichParameter,
if (m_sigma_factor < 0.0)
throw std::runtime_error("ParameterDistribution::ParameterDistribution() -> Error."
"sigma factor cannot be negative");
- if (nbr_samples == 0)
+ if (n_samples == 0)
throw std::runtime_error("ParameterDistribution::ParameterDistribution() -> Error."
"Number of samples cannot be zero.");
if (xmin >= xmax)
@@ -59,7 +59,7 @@ ParameterDistribution::ParameterDistribution(WhichParameter whichParameter,
ParameterDistribution::ParameterDistribution(const ParameterDistribution& other)
: m_whichParameter(other.m_whichParameter)
- , m_nbr_samples(other.m_nbr_samples)
+ , m_n_samples(other.m_n_samples)
, m_sigma_factor(other.m_sigma_factor)
, m_limits(other.m_limits)
, m_xmin(other.m_xmin)
@@ -74,7 +74,7 @@ ParameterDistribution& ParameterDistribution::operator=(const ParameterDistribut
{
if (this != &other) {
this->m_whichParameter = other.m_whichParameter;
- m_nbr_samples = other.m_nbr_samples;
+ m_n_samples = other.m_n_samples;
m_sigma_factor = other.m_sigma_factor;
m_distribution.reset(other.m_distribution->clone());
m_limits = other.m_limits;
@@ -107,14 +107,14 @@ size_t ParameterDistribution::nDraws() const
{
if (m_distribution && m_distribution->isDelta())
return 1;
- return m_nbr_samples;
+ return m_n_samples;
}
std::vector<ParameterSample> ParameterDistribution::generateSamples() const
{
if (m_xmin < m_xmax)
- return m_distribution->equidistantSamplesInRange(m_nbr_samples, m_xmin, m_xmax);
- return m_distribution->equidistantSamples(m_nbr_samples, m_sigma_factor, m_limits);
+ return m_distribution->equidistantSamplesInRange(m_n_samples, m_xmin, m_xmax);
+ return m_distribution->equidistantSamples(m_n_samples, m_sigma_factor, m_limits);
}
const IDistribution1D* ParameterDistribution::getDistribution() const
diff --git a/Param/Distrib/ParameterDistribution.h b/Param/Distrib/ParameterDistribution.h
index 2e24d6d2c8072e91312eb58bbd322ab11915b0c0..b166e04505902c4b9e66199d860ec70e53d1ce46 100644
--- a/Param/Distrib/ParameterDistribution.h
+++ b/Param/Distrib/ParameterDistribution.h
@@ -34,11 +34,11 @@ public:
};
ParameterDistribution(WhichParameter whichParameter, const IDistribution1D& distribution,
- size_t nbr_samples, double sigma_factor = 0.0,
+ size_t n_samples, double sigma_factor = 0.0,
const RealLimits& limits = RealLimits());
ParameterDistribution(WhichParameter whichParameter, const IDistribution1D& distribution,
- size_t nbr_samples, double xmin, double xmax);
+ size_t n_samples, double xmin, double xmax);
ParameterDistribution(const ParameterDistribution& other);
@@ -70,7 +70,7 @@ public:
private:
WhichParameter m_whichParameter;
std::unique_ptr<IDistribution1D> m_distribution;
- size_t m_nbr_samples;
+ size_t m_n_samples;
double m_sigma_factor;
RealLimits m_limits;
double m_xmin;
diff --git a/Sim/Simulation/ISimulation.cpp b/Sim/Simulation/ISimulation.cpp
index 7debf25e21550a83518b12efb74000343ad44e5b..f049a0539d9f84193c143e7a79826928bf2be2b1 100644
--- a/Sim/Simulation/ISimulation.cpp
+++ b/Sim/Simulation/ISimulation.cpp
@@ -179,10 +179,10 @@ std::string ISimulation::unitOfParameter(ParameterDistribution::WhichParameter w
}
void ISimulation::addParameterDistribution(ParameterDistribution::WhichParameter whichParameter,
- const IDistribution1D& distribution, size_t nbr_samples,
+ const IDistribution1D& distribution, size_t n_samples,
double sigma_factor, const RealLimits& limits)
{
- ParameterDistribution par_distr(whichParameter, distribution, nbr_samples, sigma_factor,
+ ParameterDistribution par_distr(whichParameter, distribution, n_samples, sigma_factor,
limits);
addParameterDistribution(par_distr);
}
diff --git a/Sim/Simulation/ISimulation.h b/Sim/Simulation/ISimulation.h
index ae51655eaf699c41a62800f45eb073edfe75d56e..ba36f2d95f0f24119faf862c86c394550a5adc43 100644
--- a/Sim/Simulation/ISimulation.h
+++ b/Sim/Simulation/ISimulation.h
@@ -54,7 +54,7 @@ public:
void setBackground(const IBackground& bg);
void addParameterDistribution(ParameterDistribution::WhichParameter whichParameter,
- const IDistribution1D& distribution, size_t nbr_samples,
+ const IDistribution1D& distribution, size_t n_samples,
double sigma_factor = 0.0,
const RealLimits& limits = RealLimits());
void addParameterDistribution(const ParameterDistribution& par_distr);
diff --git a/Tests/Unit/Param/DistributionsTest.cpp b/Tests/Unit/Param/DistributionsTest.cpp
index c0ad96fe4c84728240cae189c62913f3b1339b49..207b99b5f7bd63472280ce34e971f76ec60fcf29 100644
--- a/Tests/Unit/Param/DistributionsTest.cpp
+++ b/Tests/Unit/Param/DistributionsTest.cpp
@@ -123,13 +123,13 @@ TEST(DistributionsTest, DistributionLorentzSamples)
{
DistributionLorentz distr(1.0, 0.1);
- const int nbr_samples(3);
+ const int n_samples(3);
// with sigma factor
const double sigma_factor(2.0);
- std::vector<ParameterSample> samples = distr.equidistantSamples(nbr_samples, sigma_factor);
+ std::vector<ParameterSample> samples = distr.equidistantSamples(n_samples, sigma_factor);
- EXPECT_EQ(samples.size(), size_t(nbr_samples));
+ EXPECT_EQ(samples.size(), size_t(n_samples));
EXPECT_EQ(samples[0].value, 1.0 - sigma_factor * 0.1);
EXPECT_EQ(samples[1].value, 1.0);
EXPECT_EQ(samples[2].value, 1.0 + sigma_factor * 0.1);
@@ -141,13 +141,13 @@ TEST(DistributionsTest, DistributionLorentzSamples)
EXPECT_EQ(samples[2].weight, d3 / (d1 + d2 + d3));
// with Limits
- samples = distr.equidistantSamples(nbr_samples, sigma_factor, RealLimits::lowerLimited(0.99));
+ samples = distr.equidistantSamples(n_samples, sigma_factor, RealLimits::lowerLimited(0.99));
EXPECT_EQ(samples[0].value, 0.99);
EXPECT_EQ(samples[1].value, samples[0].value + (samples[2].value - samples[0].value) / 2.0);
EXPECT_EQ(samples[2].value, 1.0 + sigma_factor * 0.1);
// with xmin, xmax
- samples = distr.equidistantSamplesInRange(nbr_samples, 0.8, 1.2);
+ samples = distr.equidistantSamplesInRange(n_samples, 0.8, 1.2);
EXPECT_EQ(samples[0].value, 0.8);
EXPECT_EQ(samples[1].value, 1.0);
EXPECT_EQ(samples[2].value, 1.2);
diff --git a/Tests/Unit/Param/ParameterDistributionTest.cpp b/Tests/Unit/Param/ParameterDistributionTest.cpp
index 49d55d8860359d7e334659798a1c9946abcb4e2b..f1b7acfcfc9ca92eaff1f2f71d6450a59257f08d 100644
--- a/Tests/Unit/Param/ParameterDistributionTest.cpp
+++ b/Tests/Unit/Param/ParameterDistributionTest.cpp
@@ -83,11 +83,11 @@ TEST(ParameterDistributionTest, GenerateSamples)
DistributionGaussian distribution(mean, sigma);
std::string name = "MainParameterName";
- const int nbr_samples(3);
+ const int n_samples(3);
const double sigma_factor(2.0);
// without Limits
- ParameterDistribution pardistr(name, distribution, nbr_samples, sigma_factor);
+ ParameterDistribution pardistr(name, distribution, n_samples, sigma_factor);
std::vector<ParameterSample> sample_values = pardistr.generateSamples();
EXPECT_EQ(sample_values.size(), size_t(3));
EXPECT_EQ(sample_values[0].value, mean - sigma_factor * sigma);
@@ -95,7 +95,7 @@ TEST(ParameterDistributionTest, GenerateSamples)
EXPECT_EQ(sample_values[2].value, mean + sigma_factor * sigma);
// with Limits
- ParameterDistribution pardistr2(name, distribution, nbr_samples, sigma_factor,
+ ParameterDistribution pardistr2(name, distribution, n_samples, sigma_factor,
RealLimits::lowerLimited(mean));
sample_values = pardistr2.generateSamples();
EXPECT_EQ(sample_values.size(), size_t(3));
@@ -106,7 +106,7 @@ TEST(ParameterDistributionTest, GenerateSamples)
// with xmin, xmax defined
double xmin(-1.0);
double xmax(2.0);
- ParameterDistribution pardistr3(name, distribution, nbr_samples, xmin, xmax);
+ ParameterDistribution pardistr3(name, distribution, n_samples, xmin, xmax);
sample_values = pardistr3.generateSamples();
EXPECT_EQ(sample_values.size(), size_t(3));
EXPECT_EQ(sample_values[0].value, xmin);
diff --git a/auto/Wrap/libBornAgainParam.py b/auto/Wrap/libBornAgainParam.py
index 771a8666d95db7584cf71b5dc41c1ae343081123..ba4ca1de799e53f54da075ab5de88c2f715b04a8 100644
--- a/auto/Wrap/libBornAgainParam.py
+++ b/auto/Wrap/libBornAgainParam.py
@@ -2085,20 +2085,20 @@ class IDistribution1D(libBornAgainBase.ICloneable, INode):
return _libBornAgainParam.IDistribution1D_mean(self)
def equidistantSamples(self, *args):
- r"""equidistantSamples(IDistribution1D self, size_t nbr_samples, double sigma_factor=0., RealLimits const & limits={}) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"""
+ r"""equidistantSamples(IDistribution1D self, size_t n_samples, double sigma_factor=0., RealLimits const & limits={}) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"""
return _libBornAgainParam.IDistribution1D_equidistantSamples(self, *args)
- def equidistantSamplesInRange(self, nbr_samples, xmin, xmax):
- r"""equidistantSamplesInRange(IDistribution1D self, size_t nbr_samples, double xmin, double xmax) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"""
- return _libBornAgainParam.IDistribution1D_equidistantSamplesInRange(self, nbr_samples, xmin, xmax)
+ def equidistantSamplesInRange(self, n_samples, xmin, xmax):
+ r"""equidistantSamplesInRange(IDistribution1D self, size_t n_samples, double xmin, double xmax) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"""
+ return _libBornAgainParam.IDistribution1D_equidistantSamplesInRange(self, n_samples, xmin, xmax)
def equidistantPoints(self, *args):
- r"""equidistantPoints(IDistribution1D self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(IDistribution1D self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.IDistribution1D_equidistantPoints(self, *args)
- def equidistantPointsInRange(self, nbr_samples, xmin, xmax):
- r"""equidistantPointsInRange(IDistribution1D self, size_t nbr_samples, double xmin, double xmax) -> vdouble1d_t"""
- return _libBornAgainParam.IDistribution1D_equidistantPointsInRange(self, nbr_samples, xmin, xmax)
+ def equidistantPointsInRange(self, n_samples, xmin, xmax):
+ r"""equidistantPointsInRange(IDistribution1D self, size_t n_samples, double xmin, double xmax) -> vdouble1d_t"""
+ return _libBornAgainParam.IDistribution1D_equidistantPointsInRange(self, n_samples, xmin, xmax)
def isDelta(self):
r"""isDelta(IDistribution1D self) -> bool"""
@@ -2150,7 +2150,7 @@ class DistributionGate(IDistribution1D):
return _libBornAgainParam.DistributionGate_max(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionGate self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionGate self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionGate_equidistantPoints(self, *args)
def isDelta(self):
@@ -2203,7 +2203,7 @@ class DistributionLorentz(IDistribution1D):
return _libBornAgainParam.DistributionLorentz_hwhm(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionLorentz self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionLorentz self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionLorentz_equidistantPoints(self, *args)
def isDelta(self):
@@ -2256,7 +2256,7 @@ class DistributionGaussian(IDistribution1D):
return _libBornAgainParam.DistributionGaussian_getStdDev(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionGaussian self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionGaussian self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionGaussian_equidistantPoints(self, *args)
def isDelta(self):
@@ -2312,7 +2312,7 @@ class DistributionLogNormal(IDistribution1D):
return _libBornAgainParam.DistributionLogNormal_getScalePar(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionLogNormal self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionLogNormal self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionLogNormal_equidistantPoints(self, *args)
def isDelta(self):
@@ -2365,7 +2365,7 @@ class DistributionCosine(IDistribution1D):
return _libBornAgainParam.DistributionCosine_sigma(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionCosine self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionCosine self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionCosine_equidistantPoints(self, *args)
def isDelta(self):
@@ -2426,7 +2426,7 @@ class DistributionTrapezoid(IDistribution1D):
return _libBornAgainParam.DistributionTrapezoid_getRightWidth(self)
def equidistantPoints(self, *args):
- r"""equidistantPoints(DistributionTrapezoid self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
+ r"""equidistantPoints(DistributionTrapezoid self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"""
return _libBornAgainParam.DistributionTrapezoid_equidistantPoints(self, *args)
def isDelta(self):
@@ -2456,8 +2456,8 @@ class ParameterDistribution(object):
def __init__(self, *args):
r"""
- __init__(ParameterDistribution self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t nbr_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits()) -> ParameterDistribution
- __init__(ParameterDistribution self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t nbr_samples, double xmin, double xmax) -> ParameterDistribution
+ __init__(ParameterDistribution self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t n_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits()) -> ParameterDistribution
+ __init__(ParameterDistribution self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t n_samples, double xmin, double xmax) -> ParameterDistribution
__init__(ParameterDistribution self, ParameterDistribution other) -> ParameterDistribution
"""
_libBornAgainParam.ParameterDistribution_swiginit(self, _libBornAgainParam.new_ParameterDistribution(*args))
diff --git a/auto/Wrap/libBornAgainParam_wrap.cpp b/auto/Wrap/libBornAgainParam_wrap.cpp
index 57b017853b1e41055f5a406762af33ee571354cc..a7b2dc7535ec332fafb9283c9cb95d2214ee54fa 100644
--- a/auto/Wrap/libBornAgainParam_wrap.cpp
+++ b/auto/Wrap/libBornAgainParam_wrap.cpp
@@ -34871,10 +34871,10 @@ static PyMethodDef SwigMethods[] = {
{ "IDistribution1D_clone", _wrap_IDistribution1D_clone, METH_O, "IDistribution1D_clone(IDistribution1D self) -> IDistribution1D"},
{ "IDistribution1D_probabilityDensity", _wrap_IDistribution1D_probabilityDensity, METH_VARARGS, "IDistribution1D_probabilityDensity(IDistribution1D self, double x) -> double"},
{ "IDistribution1D_mean", _wrap_IDistribution1D_mean, METH_O, "IDistribution1D_mean(IDistribution1D self) -> double"},
- { "IDistribution1D_equidistantSamples", _wrap_IDistribution1D_equidistantSamples, METH_VARARGS, "IDistribution1D_equidistantSamples(IDistribution1D self, size_t nbr_samples, double sigma_factor=0., RealLimits const & limits={}) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"},
- { "IDistribution1D_equidistantSamplesInRange", _wrap_IDistribution1D_equidistantSamplesInRange, METH_VARARGS, "IDistribution1D_equidistantSamplesInRange(IDistribution1D self, size_t nbr_samples, double xmin, double xmax) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"},
- { "IDistribution1D_equidistantPoints", _wrap_IDistribution1D_equidistantPoints, METH_VARARGS, "IDistribution1D_equidistantPoints(IDistribution1D self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
- { "IDistribution1D_equidistantPointsInRange", _wrap_IDistribution1D_equidistantPointsInRange, METH_VARARGS, "IDistribution1D_equidistantPointsInRange(IDistribution1D self, size_t nbr_samples, double xmin, double xmax) -> vdouble1d_t"},
+ { "IDistribution1D_equidistantSamples", _wrap_IDistribution1D_equidistantSamples, METH_VARARGS, "IDistribution1D_equidistantSamples(IDistribution1D self, size_t n_samples, double sigma_factor=0., RealLimits const & limits={}) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"},
+ { "IDistribution1D_equidistantSamplesInRange", _wrap_IDistribution1D_equidistantSamplesInRange, METH_VARARGS, "IDistribution1D_equidistantSamplesInRange(IDistribution1D self, size_t n_samples, double xmin, double xmax) -> std::vector< ParameterSample,std::allocator< ParameterSample > >"},
+ { "IDistribution1D_equidistantPoints", _wrap_IDistribution1D_equidistantPoints, METH_VARARGS, "IDistribution1D_equidistantPoints(IDistribution1D self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "IDistribution1D_equidistantPointsInRange", _wrap_IDistribution1D_equidistantPointsInRange, METH_VARARGS, "IDistribution1D_equidistantPointsInRange(IDistribution1D self, size_t n_samples, double xmin, double xmax) -> vdouble1d_t"},
{ "IDistribution1D_isDelta", _wrap_IDistribution1D_isDelta, METH_O, "IDistribution1D_isDelta(IDistribution1D self) -> bool"},
{ "delete_IDistribution1D", _wrap_delete_IDistribution1D, METH_O, "delete_IDistribution1D(IDistribution1D self)"},
{ "IDistribution1D_swigregister", IDistribution1D_swigregister, METH_O, NULL},
@@ -34890,7 +34890,7 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionGate_mean", _wrap_DistributionGate_mean, METH_O, "DistributionGate_mean(DistributionGate self) -> double"},
{ "DistributionGate_min", _wrap_DistributionGate_min, METH_O, "DistributionGate_min(DistributionGate self) -> double"},
{ "DistributionGate_max", _wrap_DistributionGate_max, METH_O, "DistributionGate_max(DistributionGate self) -> double"},
- { "DistributionGate_equidistantPoints", _wrap_DistributionGate_equidistantPoints, METH_VARARGS, "DistributionGate_equidistantPoints(DistributionGate self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionGate_equidistantPoints", _wrap_DistributionGate_equidistantPoints, METH_VARARGS, "DistributionGate_equidistantPoints(DistributionGate self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionGate_isDelta", _wrap_DistributionGate_isDelta, METH_O, "DistributionGate_isDelta(DistributionGate self) -> bool"},
{ "DistributionGate_validate", _wrap_DistributionGate_validate, METH_O, "DistributionGate_validate(DistributionGate self) -> std::string"},
{ "delete_DistributionGate", _wrap_delete_DistributionGate, METH_O, "delete_DistributionGate(DistributionGate self)"},
@@ -34907,7 +34907,7 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionLorentz_probabilityDensity", _wrap_DistributionLorentz_probabilityDensity, METH_VARARGS, "DistributionLorentz_probabilityDensity(DistributionLorentz self, double x) -> double"},
{ "DistributionLorentz_mean", _wrap_DistributionLorentz_mean, METH_O, "DistributionLorentz_mean(DistributionLorentz self) -> double"},
{ "DistributionLorentz_hwhm", _wrap_DistributionLorentz_hwhm, METH_O, "DistributionLorentz_hwhm(DistributionLorentz self) -> double"},
- { "DistributionLorentz_equidistantPoints", _wrap_DistributionLorentz_equidistantPoints, METH_VARARGS, "DistributionLorentz_equidistantPoints(DistributionLorentz self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionLorentz_equidistantPoints", _wrap_DistributionLorentz_equidistantPoints, METH_VARARGS, "DistributionLorentz_equidistantPoints(DistributionLorentz self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionLorentz_isDelta", _wrap_DistributionLorentz_isDelta, METH_O, "DistributionLorentz_isDelta(DistributionLorentz self) -> bool"},
{ "DistributionLorentz_validate", _wrap_DistributionLorentz_validate, METH_O, "DistributionLorentz_validate(DistributionLorentz self) -> std::string"},
{ "delete_DistributionLorentz", _wrap_delete_DistributionLorentz, METH_O, "delete_DistributionLorentz(DistributionLorentz self)"},
@@ -34924,7 +34924,7 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionGaussian_probabilityDensity", _wrap_DistributionGaussian_probabilityDensity, METH_VARARGS, "DistributionGaussian_probabilityDensity(DistributionGaussian self, double x) -> double"},
{ "DistributionGaussian_mean", _wrap_DistributionGaussian_mean, METH_O, "DistributionGaussian_mean(DistributionGaussian self) -> double"},
{ "DistributionGaussian_getStdDev", _wrap_DistributionGaussian_getStdDev, METH_O, "DistributionGaussian_getStdDev(DistributionGaussian self) -> double"},
- { "DistributionGaussian_equidistantPoints", _wrap_DistributionGaussian_equidistantPoints, METH_VARARGS, "DistributionGaussian_equidistantPoints(DistributionGaussian self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionGaussian_equidistantPoints", _wrap_DistributionGaussian_equidistantPoints, METH_VARARGS, "DistributionGaussian_equidistantPoints(DistributionGaussian self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionGaussian_isDelta", _wrap_DistributionGaussian_isDelta, METH_O, "DistributionGaussian_isDelta(DistributionGaussian self) -> bool"},
{ "DistributionGaussian_validate", _wrap_DistributionGaussian_validate, METH_O, "DistributionGaussian_validate(DistributionGaussian self) -> std::string"},
{ "delete_DistributionGaussian", _wrap_delete_DistributionGaussian, METH_O, "delete_DistributionGaussian(DistributionGaussian self)"},
@@ -34941,7 +34941,7 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionLogNormal_mean", _wrap_DistributionLogNormal_mean, METH_O, "DistributionLogNormal_mean(DistributionLogNormal self) -> double"},
{ "DistributionLogNormal_getMedian", _wrap_DistributionLogNormal_getMedian, METH_O, "DistributionLogNormal_getMedian(DistributionLogNormal self) -> double"},
{ "DistributionLogNormal_getScalePar", _wrap_DistributionLogNormal_getScalePar, METH_O, "DistributionLogNormal_getScalePar(DistributionLogNormal self) -> double"},
- { "DistributionLogNormal_equidistantPoints", _wrap_DistributionLogNormal_equidistantPoints, METH_VARARGS, "DistributionLogNormal_equidistantPoints(DistributionLogNormal self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionLogNormal_equidistantPoints", _wrap_DistributionLogNormal_equidistantPoints, METH_VARARGS, "DistributionLogNormal_equidistantPoints(DistributionLogNormal self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionLogNormal_isDelta", _wrap_DistributionLogNormal_isDelta, METH_O, "DistributionLogNormal_isDelta(DistributionLogNormal self) -> bool"},
{ "DistributionLogNormal_validate", _wrap_DistributionLogNormal_validate, METH_O, "DistributionLogNormal_validate(DistributionLogNormal self) -> std::string"},
{ "delete_DistributionLogNormal", _wrap_delete_DistributionLogNormal, METH_O, "delete_DistributionLogNormal(DistributionLogNormal self)"},
@@ -34958,7 +34958,7 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionCosine_probabilityDensity", _wrap_DistributionCosine_probabilityDensity, METH_VARARGS, "DistributionCosine_probabilityDensity(DistributionCosine self, double x) -> double"},
{ "DistributionCosine_mean", _wrap_DistributionCosine_mean, METH_O, "DistributionCosine_mean(DistributionCosine self) -> double"},
{ "DistributionCosine_sigma", _wrap_DistributionCosine_sigma, METH_O, "DistributionCosine_sigma(DistributionCosine self) -> double"},
- { "DistributionCosine_equidistantPoints", _wrap_DistributionCosine_equidistantPoints, METH_VARARGS, "DistributionCosine_equidistantPoints(DistributionCosine self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionCosine_equidistantPoints", _wrap_DistributionCosine_equidistantPoints, METH_VARARGS, "DistributionCosine_equidistantPoints(DistributionCosine self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionCosine_isDelta", _wrap_DistributionCosine_isDelta, METH_O, "DistributionCosine_isDelta(DistributionCosine self) -> bool"},
{ "DistributionCosine_validate", _wrap_DistributionCosine_validate, METH_O, "DistributionCosine_validate(DistributionCosine self) -> std::string"},
{ "delete_DistributionCosine", _wrap_delete_DistributionCosine, METH_O, "delete_DistributionCosine(DistributionCosine self)"},
@@ -34977,15 +34977,15 @@ static PyMethodDef SwigMethods[] = {
{ "DistributionTrapezoid_getLeftWidth", _wrap_DistributionTrapezoid_getLeftWidth, METH_O, "DistributionTrapezoid_getLeftWidth(DistributionTrapezoid self) -> double"},
{ "DistributionTrapezoid_getMiddleWidth", _wrap_DistributionTrapezoid_getMiddleWidth, METH_O, "DistributionTrapezoid_getMiddleWidth(DistributionTrapezoid self) -> double"},
{ "DistributionTrapezoid_getRightWidth", _wrap_DistributionTrapezoid_getRightWidth, METH_O, "DistributionTrapezoid_getRightWidth(DistributionTrapezoid self) -> double"},
- { "DistributionTrapezoid_equidistantPoints", _wrap_DistributionTrapezoid_equidistantPoints, METH_VARARGS, "DistributionTrapezoid_equidistantPoints(DistributionTrapezoid self, size_t nbr_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
+ { "DistributionTrapezoid_equidistantPoints", _wrap_DistributionTrapezoid_equidistantPoints, METH_VARARGS, "DistributionTrapezoid_equidistantPoints(DistributionTrapezoid self, size_t n_samples, double sigma_factor, RealLimits const & limits={}) -> vdouble1d_t"},
{ "DistributionTrapezoid_isDelta", _wrap_DistributionTrapezoid_isDelta, METH_O, "DistributionTrapezoid_isDelta(DistributionTrapezoid self) -> bool"},
{ "DistributionTrapezoid_validate", _wrap_DistributionTrapezoid_validate, METH_O, "DistributionTrapezoid_validate(DistributionTrapezoid self) -> std::string"},
{ "delete_DistributionTrapezoid", _wrap_delete_DistributionTrapezoid, METH_O, "delete_DistributionTrapezoid(DistributionTrapezoid self)"},
{ "DistributionTrapezoid_swigregister", DistributionTrapezoid_swigregister, METH_O, NULL},
{ "DistributionTrapezoid_swiginit", DistributionTrapezoid_swiginit, METH_VARARGS, NULL},
{ "new_ParameterDistribution", _wrap_new_ParameterDistribution, METH_VARARGS, "\n"
- "ParameterDistribution(ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t nbr_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())\n"
- "ParameterDistribution(ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t nbr_samples, double xmin, double xmax)\n"
+ "ParameterDistribution(ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t n_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())\n"
+ "ParameterDistribution(ParameterDistribution::WhichParameter whichParameter, IDistribution1D distribution, size_t n_samples, double xmin, double xmax)\n"
"new_ParameterDistribution(ParameterDistribution other) -> ParameterDistribution\n"
""},
{ "delete_ParameterDistribution", _wrap_delete_ParameterDistribution, METH_O, "delete_ParameterDistribution(ParameterDistribution self)"},
diff --git a/auto/Wrap/libBornAgainSim.py b/auto/Wrap/libBornAgainSim.py
index e67dd54f46452fc1a1f4aae1b5f8cbf2445399e0..a269a7b816460c780e282609273da22bc82b555c 100644
--- a/auto/Wrap/libBornAgainSim.py
+++ b/auto/Wrap/libBornAgainSim.py
@@ -2874,7 +2874,7 @@ class ISimulation(libBornAgainParam.INode):
def addParameterDistribution(self, *args):
r"""
- addParameterDistribution(ISimulation self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D const & distribution, size_t nbr_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())
+ addParameterDistribution(ISimulation self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D const & distribution, size_t n_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())
addParameterDistribution(ISimulation self, ParameterDistribution par_distr)
"""
return _libBornAgainSim.ISimulation_addParameterDistribution(self, *args)
diff --git a/auto/Wrap/libBornAgainSim_wrap.cpp b/auto/Wrap/libBornAgainSim_wrap.cpp
index 487cac69f0d2ea7a87ab5b45993f6b1279db8a69..8fab577f96c37f0e2bceda8fee56f0375e74db9f 100644
--- a/auto/Wrap/libBornAgainSim_wrap.cpp
+++ b/auto/Wrap/libBornAgainSim_wrap.cpp
@@ -39454,7 +39454,7 @@ static PyMethodDef SwigMethods[] = {
{ "ISimulation_simulate", _wrap_ISimulation_simulate, METH_O, "ISimulation_simulate(ISimulation self) -> SimulationResult"},
{ "ISimulation_setBackground", _wrap_ISimulation_setBackground, METH_VARARGS, "ISimulation_setBackground(ISimulation self, IBackground bg)"},
{ "ISimulation_addParameterDistribution", _wrap_ISimulation_addParameterDistribution, METH_VARARGS, "\n"
- "ISimulation_addParameterDistribution(ISimulation self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D const & distribution, size_t nbr_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())\n"
+ "ISimulation_addParameterDistribution(ISimulation self, ParameterDistribution::WhichParameter whichParameter, IDistribution1D const & distribution, size_t n_samples, double sigma_factor=0.0, RealLimits const & limits=RealLimits())\n"
"ISimulation_addParameterDistribution(ISimulation self, ParameterDistribution par_distr)\n"
""},
{ "ISimulation_options", _wrap_ISimulation_options, METH_VARARGS, "\n"