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Commit 1a5bff88 authored by Christof Metzger-Kraus's avatar Christof Metzger-Kraus
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fixing few issues binomial distribution and cleaning up

parent acaf84b7
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Showing with 467 additions and 318 deletions
src/Distribution/Distribution.cpp
View file @ 1a5bff88
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src/Distribution/Distribution.h
View file @ 1a5bff88
......@@ -28,6 +28,7 @@
#include "Algorithms/Vektor.h"
#include "Beamlines/Beamline.h"
#include "Attributes/Attributes.h"
#include "Ippl.h"
......@@ -85,7 +86,6 @@ namespace Attrib
{
enum AttributesT {
TYPE,
DISTRIBUTION,
FNAME,
WRITETOFILE,
WEIGHT,
......@@ -207,7 +207,8 @@ namespace Attrib
{
enum LegacyAttributesT {
// DESCRIPTION OF THE DISTRIBUTION:
DEBIN = Attrib::Distribution::SIZE,
DISTRIBUTION = Attrib::Distribution::SIZE,
DEBIN,
SBIN,
SIGMAPT,
CUTOFF,
......@@ -383,6 +384,34 @@ private:
double converteVToBetaGamma(double valueIneV, double massIneV);
double convertMeVPerCToBetaGamma(double valueInMeVPerC, double massIneV);
size_t getNumberOfParticlesInFile(std::ifstream &inputFile);
class BinomialBehaviorSplitter {
public:
virtual ~BinomialBehaviorSplitter()
{ }
virtual double get(double rand) = 0;
};
class MDependentBehavior: public BinomialBehaviorSplitter {
public:
MDependentBehavior(const MDependentBehavior &rhs):
ami_m(rhs.ami_m)
{}
MDependentBehavior(double a)
{ ami_m = 1.0 / a; }
virtual double get(double rand);
private:
double ami_m;
};
class GaussianLikeBehavior: public BinomialBehaviorSplitter {
public:
virtual double get(double rand);
};
void createDistributionBinomial(size_t numberOfParticles, double massIneV);
void createDistributionFlattop(size_t numberOfParticles, double massIneV);
void createDistributionFromFile(size_t numberOfParticles, double massIneV);
......@@ -619,8 +648,155 @@ DistrTypeT::DistrTypeT Distribution::getType() const {
return distrTypeT_m;
}
inline double Distribution::getPercentageEmitted() const {
inline
double Distribution::getPercentageEmitted() const {
return (double)totalNumberEmittedParticles_m / (double)totalNumberParticles_m;
}
inline
double Distribution::getEkin() const {
return Attributes::getReal(itsAttr[Attrib::Distribution::EKIN]);
}
inline
double Distribution::getLaserEnergy() const {
return Attributes::getReal(itsAttr[Attrib::Distribution::ELASER]);
}
inline
double Distribution::getWorkFunctionRf() const {
return Attributes::getReal(itsAttr[Attrib::Distribution::W]);
}
inline
size_t Distribution::getNumberOfDarkCurrentParticles() {
return (size_t) Attributes::getReal(itsAttr[Attrib::Distribution::NPDARKCUR]);
}
inline
double Distribution::getDarkCurrentParticlesInwardMargin() {
return Attributes::getReal(itsAttr[Attrib::Distribution::INWARDMARGIN]);
}
inline
double Distribution::getEInitThreshold() {
return Attributes::getReal(itsAttr[Attrib::Distribution::EINITHR]);
}
inline
double Distribution::getWorkFunction() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNPHIW]);
}
inline
double Distribution::getFieldEnhancement() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNBETA]);
}
inline
size_t Distribution::getMaxFNemissionPartPerTri() {
return (size_t) Attributes::getReal(itsAttr[Attrib::Distribution::FNMAXEMI]);
}
inline
double Distribution::getFieldFNThreshold() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNFIELDTHR]);
}
inline
double Distribution::getFNParameterA() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNA]);
}
inline
double Distribution::getFNParameterB() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNB]);
}
inline
double Distribution::getFNParameterY() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNY]);
}
inline
double Distribution::getFNParameterVYZero() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNVYZERO]);
}
inline
double Distribution::getFNParameterVYSecond() {
return Attributes::getReal(itsAttr[Attrib::Distribution::FNVYSECOND]);
}
inline
int Distribution::getSecondaryEmissionFlag() {
return Attributes::getReal(itsAttr[Attrib::Distribution::SECONDARYFLAG]);
}
inline
bool Distribution::getEmissionMode() {
return Attributes::getBool(itsAttr[Attrib::Distribution::NEMISSIONMODE]);
}
inline
std::string Distribution::getTypeofDistribution() {
return (std::string) Attributes::getString(itsAttr[Attrib::Distribution::TYPE]);
}
inline
double Distribution::getvSeyZero() {
// return sey_0 in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VSEYZERO]);
}
inline
double Distribution::getvEZero() {
// return the energy related to sey_0 in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VEZERO]);
}
inline
double Distribution::getvSeyMax() {
// return sey max in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VSEYMAX]);
}
inline
double Distribution::getvEmax() {
// return Emax in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VEMAX]);
}
inline
double Distribution::getvKenergy() {
// return fitting parameter denotes the roughness of surface for
// impact energy in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VKENERGY]);
}
inline
double Distribution::getvKtheta() {
// return fitting parameter denotes the roughness of surface for
// impact angle in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VKTHETA]);
}
inline
double Distribution::getvVThermal() {
// thermal velocity of Maxwellian distribution of secondaries in Vaughan's model
return Attributes::getReal(itsAttr[Attrib::Distribution::VVTHERMAL]);
}
inline
double Distribution::getVw() {
// velocity scalar for parallel plate benchmark;
return Attributes::getReal(itsAttr[Attrib::Distribution::VW]);
}
inline
int Distribution::getSurfMaterial() {
// Surface material number for Furman-Pivi's Model;
return (int)Attributes::getReal(itsAttr[Attrib::Distribution::SURFMATERIAL]);
}
#endif // OPAL_Distribution_HH
\ No newline at end of file
tests/opal_src/Distribution/BinomialTest.cpp
View file @ 1a5bff88
......@@ -15,7 +15,7 @@ TEST(BinomialTest, FullSigmaTest1) {
const double expectedR22 = 0.7998;
const double expectedR33 = 2.498;
const double expectedR44 = 0.6212;
const double expectedR55 = 1.537;
const double expectedR55 = 1.537;
const double expectedR66 = 0.9457;
const double expectedR21 = -0.40993;
......@@ -35,16 +35,22 @@ TEST(BinomialTest, FullSigmaTest1) {
Distribution dist;
Attributes::setString(dist.itsAttr[Attrib::Distribution::TYPE], "BINOMIAL");
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MX], 999999999.9);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MY], 999999999.9);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MZ], 999999999.9);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAX], expectedR11 * 1e-3);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPX], expectedR22);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAY], expectedR33 * 1e-3);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPY], expectedR44);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAZ], expectedR55 * 1e-3);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPZ], expectedR66);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MX], 999999999.9);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MY], 999999999.9);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MZ], 999999999.9);
Attributes::setRealArray(dist.itsAttr[Attrib::Distribution::R], expectedR);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CORRX], expectedR21);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CORRY], expectedR43);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CORRZ], expectedR65);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R51], expectedR51);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R61], expectedR61);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R52], expectedR52);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R62], expectedR62);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::EMITTED], false);
dist.setDistType();
......@@ -61,6 +67,8 @@ TEST(BinomialTest, FullSigmaTest1) {
double R21 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.pxDist_m[0]), 1, dist.xDist_m.size()) * 1e3 /
(expectedR11 * expectedR22));
double R43 = (gsl_stats_covariance(&(dist.yDist_m[0]), 1, &(dist.pyDist_m[0]), 1, dist.yDist_m.size()) * 1e3 /
(expectedR33 * expectedR44));
double R51 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.xDist_m.size()) * 1e6 /
(expectedR11 * expectedR55));
double R52 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.pxDist_m.size()) * 1e3 /
......@@ -70,18 +78,19 @@ TEST(BinomialTest, FullSigmaTest1) {
double R62 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.pzDist_m[0]), 1, dist.pxDist_m.size()) /
(expectedR22 * expectedR66));
EXPECT_NEAR(expectedR11, R11, 0.05 * std::abs(expectedR11));
EXPECT_NEAR(expectedR22, R22, 0.05 * std::abs(expectedR22));
EXPECT_NEAR(expectedR33, R33, 0.05 * std::abs(expectedR33));
EXPECT_NEAR(expectedR44, R44, 0.05 * std::abs(expectedR44));
EXPECT_NEAR(expectedR55, R55, 0.05 * std::abs(expectedR55));
EXPECT_NEAR(expectedR66, R66, 0.05 * std::abs(expectedR66));
EXPECT_NEAR(expectedR21, R21, 0.1 * std::abs(expectedR21));
EXPECT_NEAR(expectedR51, R51, 0.1 * std::abs(expectedR51));
EXPECT_NEAR(expectedR52, R52, 0.1 * std::abs(expectedR52));
EXPECT_NEAR(expectedR61, R61, 0.1 * std::abs(expectedR61));
EXPECT_NEAR(expectedR62, R62, 0.1 * std::abs(expectedR62));
EXPECT_NEAR(R11 / expectedR11, 1.0, 0.002);
EXPECT_NEAR(R22 / expectedR22, 1.0, 0.002);
EXPECT_NEAR(R33 / expectedR33, 1.0, 0.002);
EXPECT_NEAR(R44 / expectedR44, 1.0, 0.002);
EXPECT_NEAR(R55 / expectedR55, 1.0, 0.002);
EXPECT_NEAR(R66 / expectedR66, 1.0, 0.002);
EXPECT_NEAR(R21 / expectedR21, 1.0, 0.01);
EXPECT_NEAR(R43 / expectedR43, 1.0, 0.01);
EXPECT_NEAR(R51 / expectedR51, 1.0, 0.01);
EXPECT_NEAR(R52 / expectedR52, 1.0, 0.01);
EXPECT_NEAR(R61 / expectedR61, 1.0, 0.01);
EXPECT_NEAR(R62 / expectedR62, 1.0, 0.01);
}
TEST(BinomialTest, FullSigmaTest2) {
......@@ -104,7 +113,10 @@ TEST(BinomialTest, FullSigmaTest2) {
Distribution dist;
Attributes::setString(dist.itsAttr[Attrib::Distribution::TYPE], "GAUSS");
Attributes::setString(dist.itsAttr[Attrib::Distribution::TYPE], "BINOMIAL");
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MX], 1.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MY], 1.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::MZ], 1.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAX], expectedR11 * 1e-3);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPX], expectedR22);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAY], expectedR33 * 1e-3);
......@@ -119,6 +131,7 @@ TEST(BinomialTest, FullSigmaTest2) {
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R52], expectedR52);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R62], expectedR62);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::EMITTED], false);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::WRITETOFILE], true);
dist.setDistType();
dist.checkIfEmitted();
......@@ -135,6 +148,8 @@ TEST(BinomialTest, FullSigmaTest2) {
double R21 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.pxDist_m[0]), 1, dist.xDist_m.size()) * 1e3 /
(expectedR11 * expectedR22));
double R43 = (gsl_stats_covariance(&(dist.yDist_m[0]), 1, &(dist.pyDist_m[0]), 1, dist.yDist_m.size()) * 1e3 /
(expectedR33 * expectedR44));
double R51 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.xDist_m.size()) * 1e6 /
(expectedR11 * expectedR55));
double R52 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.pxDist_m.size()) * 1e3 /
......@@ -144,16 +159,17 @@ TEST(BinomialTest, FullSigmaTest2) {
double R62 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.pzDist_m[0]), 1, dist.pxDist_m.size()) /
(expectedR22 * expectedR66));
EXPECT_NEAR(expectedR11, R11, 0.05 * std::abs(expectedR11));
EXPECT_NEAR(expectedR22, R22, 0.05 * std::abs(expectedR22));
EXPECT_NEAR(expectedR33, R33, 0.05 * std::abs(expectedR33));
EXPECT_NEAR(expectedR44, R44, 0.05 * std::abs(expectedR44));
EXPECT_NEAR(expectedR55, R55, 0.05 * std::abs(expectedR55));
EXPECT_NEAR(expectedR66, R66, 0.05 * std::abs(expectedR66));
EXPECT_NEAR(expectedR21, R21, 0.1 * std::abs(expectedR21));
EXPECT_NEAR(expectedR51, R51, 0.1 * std::abs(expectedR51));
EXPECT_NEAR(expectedR52, R52, 0.1 * std::abs(expectedR52));
EXPECT_NEAR(expectedR61, R61, 0.1 * std::abs(expectedR61));
EXPECT_NEAR(expectedR62, R62, 0.1 * std::abs(expectedR62));
EXPECT_NEAR(R11 / expectedR11, 1.0, 0.002);
EXPECT_NEAR(R22 / expectedR22, 1.0, 0.002);
EXPECT_NEAR(R33 / expectedR33, 1.0, 0.002);
EXPECT_NEAR(R44 / expectedR44, 1.0, 0.002);
EXPECT_NEAR(R55 / expectedR55, 1.0, 0.002);
EXPECT_NEAR(R66 / expectedR66, 1.0, 0.002);
EXPECT_NEAR(R21 / expectedR21, 1.0, 0.01);
EXPECT_NEAR(R43 / expectedR43, 1.0, 0.01);
EXPECT_NEAR(R51 / expectedR51, 1.0, 0.01);
EXPECT_NEAR(R52 / expectedR52, 1.0, 0.01);
EXPECT_NEAR(R61 / expectedR61, 1.0, 0.01);
EXPECT_NEAR(R62 / expectedR62, 1.0, 0.01);
}
\ No newline at end of file
tests/opal_src/Distribution/GaussTest.cpp
View file @ 1a5bff88
......@@ -9,7 +9,7 @@
#include "gsl/gsl_statistics_double.h"
TEST(GaussTest, FullSigmaTest1) {
OpalTestUtilities::SilenceTest silencer(false);
OpalTestUtilities::SilenceTest silencer(true);
const double expectedR11 = 1.978;
const double expectedR22 = 0.7998;
......@@ -41,8 +41,15 @@ TEST(GaussTest, FullSigmaTest1) {
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPY], expectedR44);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAZ], expectedR55 * 1e-3);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::SIGMAPZ], expectedR66);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFX], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFY], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFLONG], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPX], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPY], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPZ], 5.0);
Attributes::setRealArray(dist.itsAttr[Attrib::Distribution::R], expectedR);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::EMITTED], false);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::WRITETOFILE], true);
dist.setDistType();
dist.checkIfEmitted();
......@@ -58,6 +65,8 @@ TEST(GaussTest, FullSigmaTest1) {
double R21 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.pxDist_m[0]), 1, dist.xDist_m.size()) * 1e3 /
(expectedR11 * expectedR22));
double R43 = (gsl_stats_covariance(&(dist.yDist_m[0]), 1, &(dist.pyDist_m[0]), 1, dist.yDist_m.size()) * 1e3 /
(expectedR33 * expectedR44));
double R51 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.xDist_m.size()) * 1e6 /
(expectedR11 * expectedR55));
double R52 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.pxDist_m.size()) * 1e3 /
......@@ -67,22 +76,23 @@ TEST(GaussTest, FullSigmaTest1) {
double R62 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.pzDist_m[0]), 1, dist.pxDist_m.size()) /
(expectedR22 * expectedR66));
EXPECT_NEAR(expectedR11, R11, 0.05 * std::abs(expectedR11));
EXPECT_NEAR(expectedR22, R22, 0.05 * std::abs(expectedR22));
EXPECT_NEAR(expectedR33, R33, 0.05 * std::abs(expectedR33));
EXPECT_NEAR(expectedR44, R44, 0.05 * std::abs(expectedR44));
EXPECT_NEAR(expectedR55, R55, 0.05 * std::abs(expectedR55));
EXPECT_NEAR(expectedR66, R66, 0.05 * std::abs(expectedR66));
EXPECT_NEAR(expectedR21, R21, 0.1 * std::abs(expectedR21));
EXPECT_NEAR(expectedR51, R51, 0.1 * std::abs(expectedR51));
EXPECT_NEAR(expectedR52, R52, 0.1 * std::abs(expectedR52));
EXPECT_NEAR(expectedR61, R61, 0.1 * std::abs(expectedR61));
EXPECT_NEAR(expectedR62, R62, 0.1 * std::abs(expectedR62));
EXPECT_NEAR(R11 / expectedR11, 1.0, 0.002);
EXPECT_NEAR(R22 / expectedR22, 1.0, 0.002);
EXPECT_NEAR(R33 / expectedR33, 1.0, 0.002);
EXPECT_NEAR(R44 / expectedR44, 1.0, 0.002);
EXPECT_NEAR(R55 / expectedR55, 1.0, 0.002);
EXPECT_NEAR(R66 / expectedR66, 1.0, 0.002);
EXPECT_NEAR(R21 / expectedR21, 1.0, 0.02);
EXPECT_NEAR(R43 / expectedR43, 1.0, 0.02);
EXPECT_NEAR(R51 / expectedR51, 1.0, 0.05);
EXPECT_NEAR(R52 / expectedR52, 1.0, 0.02);
EXPECT_NEAR(R61 / expectedR61, 1.0, 0.02);
EXPECT_NEAR(R62 / expectedR62, 1.0, 0.02);
}
TEST(GaussTest, FullSigmaTest2) {
OpalTestUtilities::SilenceTest silencer(false);
OpalTestUtilities::SilenceTest silencer(true);
const double expectedR11 = 1.978;
const double expectedR22 = 0.7998;
......@@ -115,7 +125,14 @@ TEST(GaussTest, FullSigmaTest2) {
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R61], expectedR61);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R52], expectedR52);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::R62], expectedR62);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFX], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFY], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFLONG], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPX], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPY], 5.0);
Attributes::setReal(dist.itsAttr[Attrib::Distribution::CUTOFFPZ], 5.0);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::EMITTED], false);
Attributes::setBool(dist.itsAttr[Attrib::Distribution::WRITETOFILE], true);
dist.setDistType();
dist.checkIfEmitted();
......@@ -132,6 +149,8 @@ TEST(GaussTest, FullSigmaTest2) {
double R21 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.pxDist_m[0]), 1, dist.xDist_m.size()) * 1e3 /
(expectedR11 * expectedR22));
double R43 = (gsl_stats_covariance(&(dist.yDist_m[0]), 1, &(dist.pyDist_m[0]), 1, dist.yDist_m.size()) * 1e3 /
(expectedR33 * expectedR44));
double R51 = (gsl_stats_covariance(&(dist.xDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.xDist_m.size()) * 1e6 /
(expectedR11 * expectedR55));
double R52 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.tOrZDist_m[0]), 1, dist.pxDist_m.size()) * 1e3 /
......@@ -141,16 +160,17 @@ TEST(GaussTest, FullSigmaTest2) {
double R62 = (gsl_stats_covariance(&(dist.pxDist_m[0]), 1, &(dist.pzDist_m[0]), 1, dist.pxDist_m.size()) /
(expectedR22 * expectedR66));
EXPECT_NEAR(expectedR11, R11, 0.05 * std::abs(expectedR11));
EXPECT_NEAR(expectedR22, R22, 0.05 * std::abs(expectedR22));
EXPECT_NEAR(expectedR33, R33, 0.05 * std::abs(expectedR33));
EXPECT_NEAR(expectedR44, R44, 0.05 * std::abs(expectedR44));
EXPECT_NEAR(expectedR55, R55, 0.05 * std::abs(expectedR55));
EXPECT_NEAR(expectedR66, R66, 0.05 * std::abs(expectedR66));
EXPECT_NEAR(expectedR21, R21, 0.1 * std::abs(expectedR21));
EXPECT_NEAR(expectedR51, R51, 0.1 * std::abs(expectedR51));
EXPECT_NEAR(expectedR52, R52, 0.1 * std::abs(expectedR52));
EXPECT_NEAR(expectedR61, R61, 0.1 * std::abs(expectedR61));
EXPECT_NEAR(expectedR62, R62, 0.1 * std::abs(expectedR62));
EXPECT_NEAR(R11 / expectedR11, 1.0, 0.002);
EXPECT_NEAR(R22 / expectedR22, 1.0, 0.002);
EXPECT_NEAR(R33 / expectedR33, 1.0, 0.002);
EXPECT_NEAR(R44 / expectedR44, 1.0, 0.002);
EXPECT_NEAR(R55 / expectedR55, 1.0, 0.002);
EXPECT_NEAR(R66 / expectedR66, 1.0, 0.002);
EXPECT_NEAR(R21 / expectedR21, 1.0, 0.02);
EXPECT_NEAR(R43 / expectedR43, 1.0, 0.02);
EXPECT_NEAR(R51 / expectedR51, 1.0, 0.05);
EXPECT_NEAR(R52 / expectedR52, 1.0, 0.02);
EXPECT_NEAR(R61 / expectedR61, 1.0, 0.02);
EXPECT_NEAR(R62 / expectedR62, 1.0, 0.02);
}
\ No newline at end of file
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