void InertiaSensorFilter::covOfSigmaPoints()
{
cov = tensor(sigmaPoints[0] - x) +
(tensor(sigmaPoints[1] - x) + tensor(sigmaPoints[2] - x)) +
(tensor(sigmaPoints[3] - x) + tensor(sigmaPoints[4] - x));
cov *= 0.5f;
}
static void test_static_index_list()
{
Tensor<float, 4> tensor(2,3,5,7);
tensor.setRandom();
constexpr auto reduction_axis = make_index_list(0, 1, 2);
VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 0);
VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1);
VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 2);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[0]), 0);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[1]), 1);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[2]), 2);
EIGEN_STATIC_ASSERT((internal::array_get<0>(reduction_axis) == 0), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((internal::array_get<1>(reduction_axis) == 1), YOU_MADE_A_PROGRAMMING_MISTAKE);
EIGEN_STATIC_ASSERT((internal::array_get<2>(reduction_axis) == 2), YOU_MADE_A_PROGRAMMING_MISTAKE);
Tensor<float, 1> result = tensor.sum(reduction_axis);
for (int i = 0; i < result.size(); ++i) {
float expected = 0.0f;
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 3; ++k) {
for (int l = 0; l < 5; ++l) {
expected += tensor(j,k,l,i);
}
}
}
VERIFY_IS_APPROX(result(i), expected);
}
}
void InertiaSensorFilter::covOfSigmaReadingsAndSigmaPoints()
{
readingsSigmaPointsCov = (
(tensor(sigmaReadings[1] - readingMean, l.c[0]) + tensor(sigmaReadings[2] - readingMean, l.c[1])) +
(tensor(sigmaReadings[3] - readingMean, -l.c[0]) + tensor(sigmaReadings[4] - readingMean, -l.c[1])));
readingsSigmaPointsCov *= 0.5f;
}
/**
Returns nullptr if settings incorrect.
*/
std::unique_ptr<Configurator> FractureConfiguratorFactory::configurator(const Settings& s) const
{
std::unique_ptr<Configurator> pConf(nullptr);
const string c = s.json["configuration"].get<string>();
if (c == string("uniform")) {
const double am = s.json["mechanical aperture"].get<double>();
if (s.json["hydraulic aperture"].size() == 9) // tensor
pConf.reset(new UniformFractureConfigurator(tensor("hydraulic aperture", s),
tensor("permeability", s),
tensor("conductivity", s),
am));
else // scalar
pConf.reset(new UniformFractureConfigurator(s.json["hydraulic aperture"].get<double>(), am));
}
else if (c == string("regional uniform")) {
const auto ams = s.json["mechanical aperture"].get<vector<double>>();
const auto frnames = s.json["fracture regions"].get<vector<string>>();
if (s.json.count("permeability")) { // tensor
vector<string> props = { "hydraulic aperture" , "permeability", "conductivity" };
vector<vector<TensorVariable<3>>> vals(props.size(), vector<TensorVariable<3>>(ams.size(), TensorVariable<3>()));
for (size_t i(0); i < props.size(); ++i) {
auto jvals = s.json[props[i].c_str()].get<vector<vector<double>>>();
for (size_t j(0); j < ams.size(); ++j)
vals[i][j] = tensor(jvals.at(j));
}
pConf.reset(new RegionalUniformFractureConfigurator(vals.at(0), vals.at(1), vals.at(2), ams, frnames));
}
else { // scalar
const auto ahs = s.json["hydraulic aperture"].get<vector<double>>();
pConf.reset(new RegionalUniformFractureConfigurator(ahs, ams, frnames));
}
}
return pConf;
}
static void test_dynamic_index_list()
{
Tensor<float, 4> tensor(2,3,5,7);
tensor.setRandom();
int dim1 = 2;
int dim2 = 1;
int dim3 = 0;
auto reduction_axis = make_index_list(dim1, dim2, dim3);
VERIFY_IS_EQUAL(internal::array_get<0>(reduction_axis), 2);
VERIFY_IS_EQUAL(internal::array_get<1>(reduction_axis), 1);
VERIFY_IS_EQUAL(internal::array_get<2>(reduction_axis), 0);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[0]), 2);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[1]), 1);
VERIFY_IS_EQUAL(static_cast<DenseIndex>(reduction_axis[2]), 0);
Tensor<float, 1> result = tensor.sum(reduction_axis);
for (int i = 0; i < result.size(); ++i) {
float expected = 0.0f;
for (int j = 0; j < 2; ++j) {
for (int k = 0; k < 3; ++k) {
for (int l = 0; l < 5; ++l) {
expected += tensor(j,k,l,i);
}
}
}
VERIFY_IS_APPROX(result(i), expected);
}
}
void InertiaSensorFilter::covOfSigmaReadings()
{
readingsCov = (tensor(sigmaReadings[0] - readingMean) +
(tensor(sigmaReadings[1] - readingMean) + tensor(sigmaReadings[2] - readingMean)) +
(tensor(sigmaReadings[3] - readingMean) + tensor(sigmaReadings[4] - readingMean)));
readingsCov *= 0.5f;
}
void InertialDataFilter::covOfSigmaPoints()
{
cov = tensor(sigmaPoints[0] - mean) +
tensor(sigmaPoints[1] - mean) +
tensor(sigmaPoints[2] - mean) +
tensor(sigmaPoints[3] - mean) +
tensor(sigmaPoints[4] - mean);
cov *= 0.5f;
}
Matrix2x2<> AngleEstimator::covOfSigmaPZgyroAndSigmaPX(const Vector2<>& mean) const
{
Matrix2x2<> result(tensor(sigmaPZgyro[1] - mean, l.c[0]));
result += tensor(sigmaPZgyro[2] - mean, l.c[1]);
result += tensor(sigmaPZgyro[3] - mean, -l.c[0]);
result += tensor(sigmaPZgyro[4] - mean, -l.c[1]);
result /= 2.;
return result;
}
Matrix3x2<> AngleEstimator::covOfSigmaPZaccAndSigmaPX(const Vector3<>& mean) const
{
Matrix3x2<> result(tensor(sigmaPZacc[1] - mean, l.c[0]));
result += tensor(sigmaPZacc[2] - mean, l.c[1]);
result += tensor(sigmaPZacc[3] - mean, -l.c[0]);
result += tensor(sigmaPZacc[4] - mean, -l.c[1]);
result /= 2.;
return result;
}
Matrix3x2f InertialDataFilter::covOfSigmaReadingsAndSigmaPoints()
{
Matrix3x2f readingsSigmaPointsCov =
tensor(sigmaReadings[1] - readingMean, l.col(0)) +
tensor(sigmaReadings[2] - readingMean, l.col(1)) +
tensor(sigmaReadings[3] - readingMean, -l.col(0)) +
tensor(sigmaReadings[4] - readingMean, -l.col(1));
readingsSigmaPointsCov *= 0.5f;
return readingsSigmaPointsCov;
}
Matrix3x3<> AngleEstimator::covOfSigmaPZacc(const Vector3<>& mean) const
{
Matrix3x3<> result(tensor(sigmaPZacc[0] - mean));
result += tensor(sigmaPZacc[1] - mean);
result += tensor(sigmaPZacc[2] - mean);
result += tensor(sigmaPZacc[3] - mean);
result += tensor(sigmaPZacc[4] - mean);
result /= 2.;
return result;
}
Matrix2x2<> AngleEstimator::covOfSigmaPX(const RotationMatrix& mean) const
{
Matrix2x2<> result(tensor(sigmaPX[0] - mean));
result += tensor(sigmaPX[1] - mean);
result += tensor(sigmaPX[2] - mean);
result += tensor(sigmaPX[3] - mean);
result += tensor(sigmaPX[4] - mean);
result /= 2.;
return result;
}
Matrix3f InertialDataFilter::covOfSigmaReadings()
{
Matrix3f readingsCov =
tensor(Vector3f(sigmaReadings[0] - readingMean)) +
tensor(Vector3f(sigmaReadings[1] - readingMean)) +
tensor(Vector3f(sigmaReadings[2] - readingMean)) +
tensor(Vector3f(sigmaReadings[3] - readingMean)) +
tensor(Vector3f(sigmaReadings[4] - readingMean));
readingsCov *= 0.5f;
return readingsCov;
}
void Foam::coordinateRotationOFext::calcTransform
(
const vector& axis1,
const vector& axis2,
const axisOrder& order
)
{
vector a = axis1 / mag(axis1);
vector b = axis2;
// Absorb minor non-orthogonality into axis2
b = b - (b & a)*a;
if (mag(b) < SMALL)
{
FatalErrorIn("coordinateRotation::calcTransform()")
<< "axis1, axis2 appear co-linear: "
<< axis1 << ", " << axis2 << endl
<< abort(FatalError);
}
b = b / mag(b);
vector c = a ^ b;
// the global -> local transformation
tensor Rtr;
switch (order)
{
case e1e2:
Rtr = tensor(a, b, c);
break;
case e2e3:
Rtr = tensor(c, a, b);
break;
case e3e1:
Rtr = tensor(b, c, a);
break;
default:
FatalErrorIn("coordinateRotation::calcTransform()")
<< "programmer error" << endl
<< abort(FatalError);
// To satisfy compiler warnings
Rtr = tensor::zero;
break;
}
// the local -> global transformation
tensor::operator=( Rtr.T() );
}
bool MxQuadric3::optimize(Vec3& v, const Vec3& v1,
const Vec3& v2, const Vec3& v3) const
{
Vec3 d13 = v1 - v3;
Vec3 d23 = v2 - v3;
Mat3 A = tensor();
Vec3 B = vector();
Vec3 Ad13 = A*d13;
Vec3 Ad23 = A*d23;
Vec3 Av3 = A*v3;
double d13_d23 = (d13*Ad23) + (d23*Ad13);
double v3_d13 = (d13*Av3) + (v3*Ad13);
double v3_d23 = (d23*Av3) + (v3*Ad23);
double d23Ad23 = d23*Ad23;
double d13Ad13 = d13*Ad13;
double denom = d13Ad13*d23Ad23 - 2*d13_d23;
if( FEQ(denom, 0.0, 1e-12) )
return false;
double a = ( d23Ad23*(2*(B*d13) + v3_d13) -
d13_d23*(2*(B*d23) + v3_d23) ) / -denom;
double b = ( d13Ad13*(2*(B*d23) + v3_d23) -
d13_d23*(2*(B*d13) + v3_d13) ) / -denom;
if( a<0.0 ) a=0.0; else if( a>1.0 ) a=1.0;
if( b<0.0 ) b=0.0; else if( b>1.0 ) b=1.0;
v = a*d13 + b*d23 + v3;
return true;
}
EPState::EPState( )
//ZC05/2004: Eo(30000.0), E_Young(30000.0), nu_Poisson(0.3), rho_mass_density(0.0),
//ZC05/2004 Converged(false),
//ZC05/2004 Elasticflag(0),Ev(0.0),nuhv(0.0),Ghv(0.0),
//ZC05/2004 eo(0.85), ec(0.80), Lambda(0.025), po(100.0), e(0.85), psi(0.05), a(0.5)
: Converged(false), e(0.85), psi(0.05) //ZC05/2004
{
//Eo = 30000.0;
//E_Young = 30000.0;
//nu_Poisson = 0.3;
//rho_mass_density = 0.0;
Eep = tensor( 4, def_dim_4, 0.0 );
integratorFlag = 0;//ForwardEuler assumed
Delta_lambda = 0.0;
NScalarVar = MaxNScalarVar;
for (int i =0; i < NScalarVar; i++) {
ScalarVar_commit[i] = 0.0;
ScalarVar[i] = 0.0;
ScalarVar_init[i] = 0.0;
}
NTensorVar = MaxNTensorVar;
for (int j =0; j < NTensorVar; j++) {
TensorVar[j] = stresstensor(0.0);
TensorVar_commit[j] = stresstensor(0.0);
TensorVar_init[j] = stresstensor(0.0);
}
//Converged = false;
}
Tensor variable_data_factory(const Type& type, PyObject* args, PyObject* kwargs) {
static PythonArgParser parser({
"new(Tensor other, *, bool requires_grad=False)",
"new(PyObject* data, *, int64_t device=-1, bool requires_grad=False)",
});
PyObject* parsed_args[3];
auto r = parser.parse(args, kwargs, parsed_args);
if (r.idx == 0) {
return set_requires_grad(new_with_tensor_copy(type, r.tensor(0)), r.toBool(1));
return set_requires_grad(new_with_tensor(type, r.tensor(0)), r.toBool(1));
} else if (r.idx == 1) {
return set_requires_grad(new_from_data(type, r.toInt64(1), r.pyobject(0)), r.toBool(2));
}
throw std::runtime_error("variable(): invalid arguments");
}
Tensor tensor_new(const Type& type, PyObject* args, PyObject* kwargs) {
static PythonArgParser parser({
"new(*, int64_t device=-1)",
"new(IntList size, *, int64_t device=-1)",
"new(Storage storage)",
"new(*, int64_t cdata)|hidden",
"new(Tensor other)",
"new(PyObject* data, *, int64_t device=-1)",
});
PyObject* parsed_args[2];
auto r = parser.parse(args, kwargs, parsed_args);
if (r.idx == 0) {
AutoGPU auto_gpu(r.toInt64(0));
return type.tensor();
} else if (r.idx == 1) {
PyObject* arg = parsed_args[0];
if (!THPSize_Check(arg) && PyTuple_GET_SIZE(args) >= 1 && arg == PyTuple_GET_ITEM(args, 0)) {
// new(sequence) binds to this signature but should be treated differently
// unless the sequences is a torch.Size
return new_from_sequence(type, r.toInt64(1), r.pyobject(0));
}
return new_with_sizes(type, r.toInt64(1), r.intlist(0));
} else if (r.idx == 2) {
return new_with_storage(type, *r.storage(0));
} else if (r.idx == 3) {
auto cdata = reinterpret_cast<void*>(r.toInt64(0));
return type.unsafeTensorFromTH(cdata, true);
} else if (r.idx == 4) {
return new_with_tensor(type, r.tensor(0));
} else if (r.idx == 5) {
return new_from_sequence(type, r.toInt64(1), r.pyobject(0));
}
throw std::runtime_error("new(): invalid arguments");
}
void tree1(int k, int i, int matrix[7][4], int E[7][4][4], int pivots[7][4],
int rge[7][4], int u[7], int s[7], int zero[3])
{
//lookup table for indices
static int first[3][2] = {{0,1}, {0,2}, {2,2}};
static int index[17][2] =
{
{0,1}, {1,0}, {0,0}, {1,1},
{2,0}, {3,0}, {2,1}, {3,1},
{0,2}, {1,2}, {0,3}, {1,3},
{2,2}, {3,2}, {2,3}, {3,3},
{4,4}
};
//set u,s indices
if (k == 0) {
u[k] = first[i][0];
s[k] = first[i][1];
}
else {
u[k] = index[i][0];
s[k] = index[i][1];
}
//handle zero case
zero[k] = (i == 16);
tensor(matrix[k], u[k], s[k]); //fill raw matrix
RGE_Solver(k, matrix[k], E, rge[k], pivots); //Running Gaussian Elimination
}
void btCompoundShape::calculatePrincipalAxisTransform(btScalar* masses, btTransform& principal, btVector3& inertia) const
{
int n = m_children.size();
btScalar totalMass = 0;
btVector3 center(0, 0, 0);
int k;
for (k = 0; k < n; k++)
{
btAssert(masses[k]>0);
center += m_children[k].m_transform.getOrigin() * masses[k];
totalMass += masses[k];
}
btAssert(totalMass>0);
center /= totalMass;
principal.setOrigin(center);
btMatrix3x3 tensor(0, 0, 0, 0, 0, 0, 0, 0, 0);
for ( k = 0; k < n; k++)
{
btVector3 i;
m_children[k].m_childShape->calculateLocalInertia(masses[k], i);
const btTransform& t = m_children[k].m_transform;
btVector3 o = t.getOrigin() - center;
//compute inertia tensor in coordinate system of compound shape
btMatrix3x3 j = t.getBasis().transpose();
j[0] *= i[0];
j[1] *= i[1];
j[2] *= i[2];
j = t.getBasis() * j;
//add inertia tensor
tensor[0] += j[0];
tensor[1] += j[1];
tensor[2] += j[2];
//compute inertia tensor of pointmass at o
btScalar o2 = o.length2();
j[0].setValue(o2, 0, 0);
j[1].setValue(0, o2, 0);
j[2].setValue(0, 0, o2);
j[0] += o * -o.x();
j[1] += o * -o.y();
j[2] += o * -o.z();
//add inertia tensor of pointmass
tensor[0] += masses[k] * j[0];
tensor[1] += masses[k] * j[1];
tensor[2] += masses[k] * j[2];
}
tensor.diagonalize(principal.getBasis(), btScalar(0.00001), 20);
inertia.setValue(tensor[0][0], tensor[1][1], tensor[2][2]);
}
void
ColumnMajorMatrixTest::fillMatrix()
{
TensorValue<Real> tensor( 0, 0, 0, 0, 0, 0, 0, 0, 0 );
ColumnMajorMatrix & a_ref = *a;
a_ref.fill( tensor );
CPPUNIT_ASSERT( tensor(0, 0) == a_ref(0, 0) );
CPPUNIT_ASSERT( tensor(1, 0) == a_ref(1, 0) );
CPPUNIT_ASSERT( tensor(2, 0) == a_ref(2, 0) );
CPPUNIT_ASSERT( tensor(0, 1) == a_ref(0, 1) );
CPPUNIT_ASSERT( tensor(1, 1) == a_ref(1, 1) );
CPPUNIT_ASSERT( tensor(2, 1) == a_ref(2, 1) );
CPPUNIT_ASSERT( tensor(0, 2) == a_ref(0, 2) );
CPPUNIT_ASSERT( tensor(1, 2) == a_ref(1, 2) );
CPPUNIT_ASSERT( tensor(2, 2) == a_ref(2, 2) );
}
Foam::energyDissipationRate::energyDissipationRate
(
const word& name,
const objectRegistry& obr,
const dictionary& dict,
const bool loadFromFiles
)
:
name_(name),
fieldName_(dict.lookup("fieldName")),
obr_(obr),
active_(true)
{
// Check if the available mesh is an fvMesh, otherwise deactivate
if (!isA<fvMesh>(obr_))
{
active_ = false;
WarningIn
(
"energyDissipationRate::energyDissipationRate"
"("
"const word&, "
"const objectRegistry&, "
"const dictionary&, "
"const bool"
")"
) << "No fvMesh available, deactivating." << nl
<< endl;
}
read(dict);
if (active_)
{
const fvMesh& mesh = refCast<const fvMesh>(obr_);
volTensorField* energyDissipationRateTensorPtr
(
new volTensorField
(
IOobject
(
fieldName_,
mesh.time().timeName(),
mesh,
IOobject::NO_READ,
IOobject::NO_WRITE
),
mesh,
dimensionedTensor("0", sqr(dimLength)/pow(dimTime,3), tensor(0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0))
)
);
mesh.objectRegistry::store(energyDissipationRateTensorPtr);
}
}
Foam::tensor Foam::molecule::rotationTensorZ(scalar phi) const
{
return tensor
(
Foam::cos(phi), -Foam::sin(phi), 0,
Foam::sin(phi), Foam::cos(phi), 0,
0, 0, 1
);
}
bool MxQuadric3::optimize(Vec3& v) const
{
Mat3 Ainv;
double det = invert(Ainv, tensor());
if( FEQ(det, 0.0, 1e-12) )
return false;
v = -(Ainv*vector());
return true;
}
void
ColumnMajorMatrixTest::tensorConstructor()
{
TensorValue<Real> tensor( 1, 4, 7,
2, 5, 8,
3, 6, 9 );
ColumnMajorMatrix test( tensor );
CPPUNIT_ASSERT( test == *a );
CPPUNIT_ASSERT( test.numEntries() == 9 );
}
void
ColumnMajorMatrixTest::tensorAssignOperator()
{
ColumnMajorMatrix test(3, 3);
TensorValue<Real> tensor( 1, 4, 7,
2, 5, 8,
3, 6, 9 );
test = tensor;
CPPUNIT_ASSERT( test == *a );
CPPUNIT_ASSERT( test.numEntries() == 9 );
}
void Foam::RBD::joints::Ryxz::jcalc
(
joint::XSvc& J,
const scalarField& q,
const scalarField& w,
const scalarField& qDot
) const
{
vector qj(q.block<vector>(qIndex_));
scalar s0 = sin(qj.x());
scalar c0 = cos(qj.x());
scalar s1 = sin(qj.y());
scalar c1 = cos(qj.y());
scalar s2 = sin(qj.z());
scalar c2 = cos(qj.z());
J.X.E() = tensor
(
c2*c0 + s2*s1*s0, s2*c1, -c2*s0 + s2*s1*c0,
-s2*c0 + c2*s1*s0, c2*c1, s2*s0 + c2*s1*c0,
c1*s0, -s1, c1*c0
);
J.X.r() = Zero;
J.S = Zero;
J.S.xx() = s2*c1;
J.S.xy() = c2;
J.S.yx() = c2*c1;
J.S.yy() = -s2;
J.S.zx() = -s1;
J.S.zz() = 1;
vector qDotj(qDot.block<vector>(qIndex_));
J.v = J.S & qDotj;
J.c = spatialVector
(
c2*c1*qDotj.z()*qDotj.x()
- s2*s1*qDotj.y()*qDotj.x()
- s2*qDotj.z()*qDotj.y(),
-s2*c1*qDotj.z()*qDotj.x()
- c2*s1*qDotj.y()*qDotj.x()
- c2*qDotj.z()*qDotj.y(),
-c1*qDotj.y()*qDotj.x(),
0,
0,
0
);
}
void tree3(int k, int i, int VT[7][4], int UT[7][4], int rge[7][4],
int E[7][4][4], int pivots[7][4], int inZero[3], int inU[7],
int inS[7], int inV[7], int outU[7], int outV[7], int outS[7],
int outT[7])
{
static int alt[96][3] =
{ //u:0, v:1, t:2, s = 4
{0,0,0}, {0,0,1}, {0,0,2}, {0,0,3}, {0,1,0}, {0,1,1}, {0,1,2}, {0,1,3},
{0,2,0}, {0,2,1}, {0,2,2}, {0,2,3}, {0,3,0}, {0,3,1}, {0,3,2}, {0,3,3},
{0,4,0}, {0,4,1}, {0,4,2}, {0,4,3}, {1,0,0}, {1,0,1}, {1,0,2}, {1,0,3},
{1,1,0}, {1,1,1}, {1,1,2}, {1,1,3}, {1,2,0}, {1,2,1}, {1,2,2}, {1,2,3},
{1,3,0}, {1,3,1}, {1,3,2}, {1,3,3}, {1,4,0}, {1,4,1}, {1,4,2}, {1,4,3},
{2,0,0}, {2,0,1}, {2,0,2}, {2,0,3}, {2,1,0}, {2,1,1}, {2,1,2}, {2,1,3},
{2,2,0}, {2,2,1}, {2,2,2}, {2,2,3}, {2,3,0}, {2,3,1}, {2,3,2}, {2,3,3},
{2,4,0}, {2,4,1}, {2,4,2}, {2,4,3}, {3,0,0}, {3,0,1}, {3,0,2}, {3,0,3},
{3,1,0}, {3,1,1}, {3,1,2}, {3,1,3}, {3,2,0}, {3,2,1}, {3,2,2}, {3,2,3},
{3,3,0}, {3,3,1}, {3,3,2}, {3,3,3}, {3,4,0}, {3,4,1}, {3,4,2}, {3,4,3},
{4,0,0}, {4,0,1}, {4,0,2}, {4,0,3}, {4,1,0}, {4,1,1}, {4,1,2}, {4,1,3},
{4,2,0}, {4,2,1}, {4,2,2}, {4,2,3}, {4,3,0}, {4,3,1}, {4,3,2}, {4,3,3}
};
if (inZero[k] == 1) {
outU[k] = alt[i][0];
outV[k] = alt[i][1];
outS[k] = 4;
outT[k] = alt[i][2];
}
else {
outU[k] = inU[k];
outV[k] = inV[k];
outS[k] = inS[k];
outT[k] = i;
}
tensor(VT[k], outV[k], outT[k]);
tensor(UT[k], outU[k], outT[k]);
RGE_Solver(k, VT[k], E, rge[k], pivots);
}
void Foam::barycentricParticle::tetTransform
(
vector& centre,
tensor& A
) const
{
vector base, vertex1, vertex2;
tetGeometry(centre, base, vertex1, vertex2);
A = tensor
(
base - centre,
vertex1 - centre,
vertex2 - centre
).T();
}
void
OutputTestMaterial::computeQpProperties()
{
Point p = _q_point[_qp];
Real v = std::floor(_variable[_qp] + 0.5);
Real x = std::ceil(p(0));
Real y = std::ceil(p(1));
_real_property[_qp] = v + y + x + _factor;
RealVectorValue vec(v + x, v + y);
_vector_property[_qp] = vec;
RealTensorValue tensor(v, x * y, 0, -x * y, y * y);
_tensor_property[_qp] = tensor;
}