void eigen_hermitian_impl( const matrix<std::complex<T1>,D1, A1>& A, matrix<std::complex<T2>, D2, A2>& V, Otor o, const T eps = T( 1.0e-10 ) )
{
assert( A.row() == A.col() );
std::size_t const n = A.row();
auto const A_ = feng::real( A );
auto const B_ = feng::imag( A );
auto const AA = ( A_ || (-B_) ) && ( B_ || A_ );
matrix<T1,D1> VV(n+n, n+n);
matrix<T1,D1> LL(n+n, n+n);
eigen_real_symmetric( AA, VV, LL, eps);
std::vector<T1> vec(n+n);
std::copy( LL.diag_begin(), LL.diag_end(), vec.begin() );
std::sort( vec.begin(), vec.end() );
V.resize(n,n);
for ( std::size_t i = 0; i != n; ++i )
{
std::size_t const offset = std::distance( LL.diag_begin(), std::find( LL.diag_begin(), LL.diag_end(), vec[i+i] ) );
assert( offset < n+n );
feng::for_each( V.col_begin(i), V.col_end(i), VV.col_begin(offset), [](std::complex<T2>& c, T1 const r) { c.real(r); } );
feng::for_each( V.col_begin(i), V.col_end(i), VV.col_begin(offset)+n, [](std::complex<T2>& c, T1 const i) { c.imag(i); } );
*o++ = vec[i+i];
}
}//eigen_hermitian
void decode_arp(const u_char * packet) {
struct arphdr * arp_t;
arp_t = (struct arphdr*)packet;
V(1, "ARP %s ", str_arp_op(htons(arp_t->ar_op)));
VV(0, "Protocol : %s\n", str_arp_pro(htons(arp_t->ar_pro)));
VVV(1, "Hardware Type : %s\n", str_arp_hw(htons(arp_t->ar_hrd)));
const u_char * arp_data = packet + sizeof(struct arphdr);
u_char * hwd_addr = malloc(sizeof(arp_t->ar_hln));
u_char * pro_addr = malloc(sizeof(arp_t->ar_pln));
struct ether_addr mac,mac2;
struct in_addr ip, ip2;
memcpy(hwd_addr, arp_data, arp_t->ar_hln);
memcpy(pro_addr, arp_data + arp_t->ar_hln, arp_t->ar_pln);
VV(1, "From : ");
if(htons(arp_t->ar_hrd) == 0x01 && arp_t->ar_hln == ETH_ALEN) {
memcpy(&(mac.ether_addr_octet), hwd_addr, ETH_ALEN);
VV(0, "%s (", ether_ntoa(&mac));
}
if(htons(arp_t->ar_pro) == 0x0800 && arp_t->ar_pln == 0x04) {
memcpy(&(ip.s_addr), pro_addr, arp_t->ar_pln);
VV(0, "%s)\n", inet_ntoa(ip));
}
memcpy(hwd_addr, arp_data + arp_t->ar_hln + arp_t->ar_pln, arp_t->ar_hln);
memcpy(pro_addr, arp_data + 2*arp_t->ar_hln + arp_t->ar_pln, arp_t->ar_pln);
VV(1, "To : ");
if(htons(arp_t->ar_hrd) == 0x01 && arp_t->ar_hln == ETH_ALEN) {
memcpy(&(mac2.ether_addr_octet), hwd_addr, ETH_ALEN);
VV(0, "%s (", ether_ntoa(&mac2));
}
if(htons(arp_t->ar_pro) == 0x0800 && arp_t->ar_pln == 0x04) {
memcpy(&(ip2.s_addr), pro_addr, arp_t->ar_pln);
VV(0, "%s)\n", inet_ntoa(ip2));
}
char buffer[128];
bzero(buffer, 128);
char * yellow_str;
if(htons(arp_t->ar_op) == ARPOP_REQUEST) {
sprintf(buffer, "Who is %s ? (to %s [%s])\n",
inet_ntoa(ip2), inet_ntoa(ip), ether_ntoa(&mac));
} else if(htons(arp_t->ar_op) == ARPOP_REPLY) {
sprintf(buffer, "%s is %s\n", inet_ntoa(ip), ether_ntoa(&mac));
}
if(strcmp(buffer, "")) {
yellow_str = yellow(buffer);
V(1, yellow_str);
free(yellow_str);
}
free(hwd_addr);
free(pro_addr);
}
void decode_ip6(const u_char * packet) {
char str_sip[INET6_ADDRSTRLEN];
char str_dip[INET6_ADDRSTRLEN];
char buffer[128];
char * yellow_plen, *yellow_nxt, *b_sip, *b_dip;
const struct ip6_hdr * ip_t;
ip_t = (struct ip6_hdr *)(packet);
inet_ntop(AF_INET6, &(ip_t->ip6_src), str_sip, INET6_ADDRSTRLEN);
inet_ntop(AF_INET6, &(ip_t->ip6_dst), str_dip, INET6_ADDRSTRLEN);
b_sip = bold(str_sip);
b_dip = bold(str_dip);
sprintf(buffer, "%d", htons(ip_t->ip6_ctlun.ip6_un1.ip6_un1_plen));
yellow_plen = yellow(buffer);
sprintf(buffer, "%x", ip_t->ip6_ctlun.ip6_un1.ip6_un1_plen);
yellow_nxt = yellow(buffer);
V(1, "IPv6 - %s --> %s\n", b_sip, b_dip);
uint32_t flow = htonl(ip_t->ip6_ctlun.ip6_un1.ip6_un1_flow);
VV(1, "Payload Length : %s - Next Header : %s\n", yellow_plen, yellow_nxt);
/* htons(ip_t->ip6_ctlun.ip6_un1.ip6_un1_plen), ip_t->ip6_ctlun.ip6_un1.ip6_un1_nxt); */
VVV(1,"Version : %d - Traffic Class : %x - Flow Label : %x - Hop Limit : %d\n",
flow >> 28, (flow & 0x0ff00000) >> 20, flow & 0x000fffff, ip_t->ip6_ctlun.ip6_un1.ip6_un1_hlim);
int offset = sizeof(struct ip6_hdr);
switch(ip_t->ip6_ctlun.ip6_un1.ip6_un1_nxt) {
case 0x06:
decode_tcp(packet + offset);
break;
case 0x11:
decode_udp(packet + offset);
break;
}
free(yellow_plen);
free(yellow_nxt);
free(b_sip);
free(b_dip);
}
NOX::Abstract::Group::ReturnType
LOCA::BorderedSolver::Nested::applyTranspose(
const NOX::Abstract::MultiVector& X,
const NOX::Abstract::MultiVector::DenseMatrix& Y,
NOX::Abstract::MultiVector& U,
NOX::Abstract::MultiVector::DenseMatrix& V) const
{
int num_cols = X.numVectors();
Teuchos::RCP<NOX::Abstract::MultiVector> XX =
unbordered_grp->getX().createMultiVector(num_cols);
Teuchos::RCP<NOX::Abstract::MultiVector> UU =
unbordered_grp->getX().createMultiVector(num_cols);
NOX::Abstract::MultiVector::DenseMatrix YY(myWidth, num_cols);
NOX::Abstract::MultiVector::DenseMatrix VV(myWidth, num_cols);
NOX::Abstract::MultiVector::DenseMatrix YY1(Teuchos::View, YY,
underlyingWidth, num_cols,
0, 0);
NOX::Abstract::MultiVector::DenseMatrix YY2(Teuchos::View, YY,
numConstraints, num_cols,
underlyingWidth, 0);
NOX::Abstract::MultiVector::DenseMatrix VV1(Teuchos::View, VV,
underlyingWidth, num_cols,
0, 0);
NOX::Abstract::MultiVector::DenseMatrix VV2(Teuchos::View, VV,
numConstraints, num_cols,
underlyingWidth, 0);
grp->extractSolutionComponent(X, *XX);
grp->extractParameterComponent(false, X, YY1);
YY2.assign(Y);
NOX::Abstract::Group::ReturnType status =
solver->applyTranspose(*XX, YY, *UU, VV);
V.assign(VV2);
grp->loadNestedComponents(*UU, VV1, U);
return status;
}
int VBX_T(vbw_vec_reverse_test)()
{
unsigned int aN[] = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 12, 15, 16, 17, 20, 25, 31, 32, 33, 35, 40, 48, 60, 61, 62, 63, 64, 64, 65,
66, 67, 68, 70, 80, 90, 99, 100, 101, 110, 128, 128, 144, 144, 160, 160, 176, 176, 192, 192, 224, 224,
256, 256, 288, 288, 320, 320, 352, 352, 384, 384, 400, 450, 512, 550, 600, 650, 700, 768, 768, 900,
900, 1023, 1024, 1200, 1400, 1600, 1800, 2048, 2048, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800,
2900, 3000, 3100, 3200, 3300, 3400, 3500, 3600, 3700, 3800, 3900, 4000, 4096, 4096, 4100, 4200, 4300,
4400, 4500, 4600, 4700, 4800, 4900, 5000, 6000, 7000, 8000, 8192, 8192, 9000, 10000, 11000, 12000,
13000, 14000, 15000, 16000, 16384, 16384, 20000, 25000, 30000, 32767, 32768, 32768, 35000, 40000,
45000, 50000, 55000, 60000, 65000, 65535, 65536, 65536 };
int retval;
unsigned int N;
unsigned int NBYTES;
unsigned int NREPS = 100;
unsigned int i,k;
vbx_timestamp_t start=0,finish=0;
vbx_mxp_t *this_mxp = VBX_GET_THIS_MXP();
const unsigned int VBX_SCRATCHPAD_SIZE = this_mxp->scratchpad_size;
for( i=0; i<sizeof(aN)/4; i++ ) {
N = aN[i];
//printf( "testing with vector size %d\n", N );
NBYTES = sizeof(vbx_sp_t)*N;
if( 2*NBYTES > VBX_SCRATCHPAD_SIZE ) continue;
vbx_sp_t *vsrc = vbx_sp_malloc( NBYTES );
vbx_sp_t *vdst = vbx_sp_malloc( NBYTES );
//printf("bytes alloc: %d\n", NBYTES );
if( !vsrc ) VBX_EXIT(-1);
if( !vdst ) VBX_EXIT(-1);
#if ( VBX_TEMPLATE_T == BYTESIZE_DEF | VBX_TEMPLATE_T == UBYTESIZE_DEF )
unsigned int mask = 0x007F;
#elif ( VBX_TEMPLATE_T == HALFSIZE_DEF | VBX_TEMPLATE_T == UHALFSIZE_DEF )
unsigned int mask = 0x7FFF;
#else
unsigned int mask = 0xFFFF;
#endif
vbx_set_vl( N );
vbx( SV(T), VMOV, vdst, -1, 0 ); // Fill the destination vector with -1
vbx( SE(T), VAND, vsrc, mask, 0 ); // Fill the source vector with enumerated values
//VBX_T(print_vector)( "vsrcInit", vsrc, N );
//VBX_T(print_vector)( "vdstInit", vdst, N );
/** measure performance of function call **/
vbx_sync();
start = vbx_timestamp();
for(k=0; k<NREPS; k++ ) {
retval = VBX_T(vbw_vec_reverse)( vdst, vsrc, N );
vbx_sync();
}
finish = vbx_timestamp();
printf( "length %d (%s):\tvbware sp f():\t%llu", N, VBX_EXPAND_AND_QUOTE(BYTEHALFWORD), (unsigned long long) vbx_mxp_cycles((finish-start)/NREPS) );
//VBX_T(print_vector)( "vsrcPost", vsrc, N );
//VBX_T(print_vector)( "vdstPost", vdst, N );
#if VERIFY_VBWARE_ALGORITHM
VBX_T(verify_vector)( vsrc, vdst, N );
#else
printf(" [VERIFY OFF]");
#endif
printf("\treturn value: %X", retval);
vbx_set_vl( N );
vbx( SE(T), VAND, vsrc, mask, 0 ); // Reset the source vector
/** measure performance of simple algorithm **/
vbx_sync();
vbx_set_vl( 1 );
vbx_set_2D( N, -sizeof(vbx_sp_t), sizeof(vbx_sp_t), 0 );
start = vbx_timestamp();
for(k=0; k<NREPS; k++ ) {
vbx_2D( VV(T), VMOV, vdst+N-1, vsrc, 0 );
vbx_sync();
}
finish = vbx_timestamp();
printf( "\tsimple (vl=1):\t%llu", (unsigned long long) vbx_mxp_cycles((finish-start)/NREPS) );
#if VERIFY_SIMPLE_ALGORITHM
VBX_T(verify_vector)( vsrc, vdst, N );
#else
printf(" [VERIFY OFF]");
#endif
printf("\tcycles\n");
vbx_sp_free();
}
vbx_sp_free();
printf("All tests passed successfully.\n");
return 0;
}
void IBFECentroidPostProcessor::reconstructVariables(double data_time)
{
EquationSystems* equation_systems = d_fe_data_manager->getEquationSystems();
const MeshBase& mesh = equation_systems->get_mesh();
const int dim = mesh.mesh_dimension();
AutoPtr<QBase> qrule = QBase::build(QGAUSS, NDIM, CONSTANT);
// Set up all system data required to evaluate the mesh functions.
System& X_system = equation_systems->get_system<System>(IBFEMethod::COORDS_SYSTEM_NAME);
const DofMap& X_dof_map = X_system.get_dof_map();
for (unsigned d = 0; d < NDIM; ++d)
TBOX_ASSERT(X_dof_map.variable_type(d) == X_dof_map.variable_type(0));
std::vector<std::vector<unsigned int> > X_dof_indices(NDIM);
AutoPtr<FEBase> X_fe(FEBase::build(dim, X_dof_map.variable_type(0)));
X_fe->attach_quadrature_rule(qrule.get());
const std::vector<libMesh::Point>& q_point = X_fe->get_xyz();
const std::vector<std::vector<double> >& phi_X = X_fe->get_phi();
const std::vector<std::vector<VectorValue<double> > >& dphi_X = X_fe->get_dphi();
X_system.solution->localize(*X_system.current_local_solution);
NumericVector<double>& X_data = *(X_system.current_local_solution);
X_data.close();
for (std::set<unsigned int>::const_iterator cit = d_var_fcn_systems.begin();
cit != d_var_fcn_systems.end();
++cit)
{
System& system = equation_systems->get_system(*cit);
system.update();
}
const unsigned int num_scalar_vars = d_scalar_var_systems.size();
std::vector<const DofMap*> scalar_var_dof_maps(num_scalar_vars);
std::vector<std::vector<unsigned int> > scalar_var_dof_indices(num_scalar_vars);
std::vector<NumericVector<double>*> scalar_var_data(num_scalar_vars);
std::vector<unsigned int> scalar_var_system_num(num_scalar_vars);
std::vector<std::vector<NumericVector<double>*> > scalar_var_fcn_data(num_scalar_vars);
for (unsigned int k = 0; k < num_scalar_vars; ++k)
{
scalar_var_dof_maps[k] = &d_scalar_var_systems[k]->get_dof_map();
scalar_var_data[k] = d_scalar_var_systems[k]->solution.get();
scalar_var_system_num[k] = d_scalar_var_systems[k]->number();
scalar_var_fcn_data[k].reserve(d_scalar_var_fcn_systems[k].size());
for (std::vector<unsigned int>::const_iterator cit =
d_scalar_var_fcn_systems[k].begin();
cit != d_scalar_var_fcn_systems[k].end();
++cit)
{
System& system = equation_systems->get_system(*cit);
scalar_var_fcn_data[k].push_back(system.current_local_solution.get());
}
}
const unsigned int num_vector_vars = d_vector_var_systems.size();
std::vector<const DofMap*> vector_var_dof_maps(num_vector_vars);
std::vector<std::vector<std::vector<unsigned int> > > vector_var_dof_indices(
num_vector_vars);
std::vector<NumericVector<double>*> vector_var_data(num_vector_vars);
std::vector<unsigned int> vector_var_system_num(num_vector_vars);
std::vector<std::vector<NumericVector<double>*> > vector_var_fcn_data(num_vector_vars);
for (unsigned int k = 0; k < num_vector_vars; ++k)
{
vector_var_dof_maps[k] = &d_vector_var_systems[k]->get_dof_map();
vector_var_dof_indices[k].resize(d_vector_var_dims[k]);
vector_var_data[k] = d_vector_var_systems[k]->solution.get();
vector_var_system_num[k] = d_vector_var_systems[k]->number();
vector_var_fcn_data[k].reserve(d_vector_var_fcn_systems[k].size());
for (std::vector<unsigned int>::const_iterator cit =
d_vector_var_fcn_systems[k].begin();
cit != d_vector_var_fcn_systems[k].end();
++cit)
{
System& system = equation_systems->get_system(*cit);
vector_var_fcn_data[k].push_back(system.current_local_solution.get());
}
}
const unsigned int num_tensor_vars = d_tensor_var_systems.size();
std::vector<const DofMap*> tensor_var_dof_maps(num_tensor_vars);
std::vector<boost::multi_array<std::vector<unsigned int>, 2> > tensor_var_dof_indices(
num_tensor_vars);
std::vector<NumericVector<double>*> tensor_var_data(num_tensor_vars);
std::vector<unsigned int> tensor_var_system_num(num_tensor_vars);
std::vector<std::vector<NumericVector<double>*> > tensor_var_fcn_data(num_tensor_vars);
for (unsigned int k = 0; k < num_tensor_vars; ++k)
{
tensor_var_dof_maps[k] = &d_tensor_var_systems[k]->get_dof_map();
typedef boost::multi_array<std::vector<unsigned int>, 2> array_type;
array_type::extent_gen extents;
tensor_var_dof_indices[k].resize(extents[d_tensor_var_dims[k]][d_tensor_var_dims[k]]);
tensor_var_data[k] = d_tensor_var_systems[k]->solution.get();
tensor_var_system_num[k] = d_tensor_var_systems[k]->number();
tensor_var_fcn_data[k].reserve(d_tensor_var_fcn_systems[k].size());
for (std::vector<unsigned int>::const_iterator cit =
d_tensor_var_fcn_systems[k].begin();
cit != d_tensor_var_fcn_systems[k].end();
++cit)
{
System& system = equation_systems->get_system(*cit);
tensor_var_fcn_data[k].push_back(system.current_local_solution.get());
}
}
// Reconstruct the variables via simple function evaluation.
TensorValue<double> FF_qp, VV;
libMesh::Point X_qp;
VectorValue<double> V;
double v;
boost::multi_array<double, 2> X_node;
const MeshBase::const_element_iterator el_begin = mesh.active_local_elements_begin();
const MeshBase::const_element_iterator el_end = mesh.active_local_elements_end();
for (MeshBase::const_element_iterator el_it = el_begin; el_it != el_end; ++el_it)
{
Elem* const elem = *el_it;
X_fe->reinit(elem);
for (unsigned int d = 0; d < NDIM; ++d)
{
X_dof_map.dof_indices(elem, X_dof_indices[d], d);
}
const unsigned int n_qp = qrule->n_points();
TBOX_ASSERT(n_qp == 1);
const unsigned int qp = 0;
get_values_for_interpolation(X_node, X_data, X_dof_indices);
interpolate(X_qp, qp, X_node, phi_X);
jacobian(FF_qp, qp, X_node, dphi_X);
const libMesh::Point& s_qp = q_point[qp];
// Scalar-valued variables.
for (unsigned int k = 0; k < num_scalar_vars; ++k)
{
scalar_var_dof_maps[k]->dof_indices(elem, scalar_var_dof_indices[k], 0);
d_scalar_var_fcns[k](v,
FF_qp,
X_qp,
s_qp,
elem,
scalar_var_fcn_data[k],
data_time,
d_scalar_var_fcn_ctxs[k]);
scalar_var_data[k]->set(scalar_var_dof_indices[k][0], v);
}
// Vector-valued variables.
for (unsigned int k = 0; k < num_vector_vars; ++k)
{
for (unsigned int i = 0; i < d_vector_var_dims[k]; ++i)
{
vector_var_dof_maps[k]->dof_indices(elem, vector_var_dof_indices[k][i], i);
}
d_vector_var_fcns[k](V,
FF_qp,
X_qp,
s_qp,
elem,
vector_var_fcn_data[k],
data_time,
d_vector_var_fcn_ctxs[k]);
for (unsigned int i = 0; i < d_vector_var_dims[k]; ++i)
{
vector_var_data[k]->set(vector_var_dof_indices[k][i][0], V(i));
}
}
// Tensor-valued variables.
for (unsigned int k = 0; k < num_tensor_vars; ++k)
{
for (unsigned int i = 0; i < d_tensor_var_dims[k]; ++i)
{
for (unsigned int j = 0; j < d_tensor_var_dims[k]; ++j)
{
tensor_var_dof_maps[k]->dof_indices(
elem, tensor_var_dof_indices[k][i][j], j + i * d_tensor_var_dims[k]);
}
}
d_tensor_var_fcns[k](VV,
FF_qp,
X_qp,
s_qp,
elem,
tensor_var_fcn_data[k],
data_time,
d_tensor_var_fcn_ctxs[k]);
for (unsigned int i = 0; i < d_tensor_var_dims[k]; ++i)
{
for (unsigned int j = 0; j < d_tensor_var_dims[k]; ++j)
{
tensor_var_data[k]->set(tensor_var_dof_indices[k][i][j][0], VV(i, j));
}
}
}
}
// Close all vectors.
for (unsigned int k = 0; k < num_scalar_vars; ++k)
{
scalar_var_data[k]->close();
}
for (unsigned int k = 0; k < num_vector_vars; ++k)
{
vector_var_data[k]->close();
}
for (unsigned int k = 0; k < num_tensor_vars; ++k)
{
tensor_var_data[k]->close();
}
return;
} // reconstructVariables
/// Sets up the location and orientation of the leap in order to properly
/// transform relatively located leap events into absolutely located ones.
void SetLocAndOrientation (Vect const& orig, Vect const& nrm, Vect const& ov)
{ origin = VV (orig);
normal = VV(nrm);
over = VV(ov);
transform = Leap::Matrix (over, normal, over . cross (normal), origin);
}
/*
* ====================================================================
* Make a (pseudo)inverse of a dense matrix using CLAPACK SVD.
*
* Note the different definitions of a matrix here (double **) and
* in other routines (double *).
*
* The matrix A must be of size at least [max(rows,cols)][max(rows,cols)]
* since the pseudoinverse (and not its transpose) is returned in A.
*
* Singular value decomposition is used to calculate the psuedo-inverse.
* If #rows=#cols then (an approximation of) the inverse is found.
* Otherwise an approximation of the pseudoinverse is obtained.
*
* The return value of this routine is the estimated rank of the system.
* ==================================================================== */
int pinv(double **A, int rows, int cols)
{
char fctName[] = "pinv_new";
int i,j,k;
int nsv, rank;
double tol;
/* Variables needed for interaction with CLAPACK routines
* (FORTRAN style) */
double *amat, *svals, *U, *V, *work;
long int lda, ldu, ldvt, lwork, m,n,info;
/* Short-hands for this routine only [undef'ed at end of routine] */
#define AA(i,j) amat[i + j * (int) lda]
#define UU(i,j) U[i + j * (int) ldu]
#define VV(i,j) V[i + j * (int) ldvt]
/* Set up arrays for call to CLAPACK routine */
/* Use leading dimensions large enough to calculate both under-
* and over-determined systems of equations. The way this is
* calculated, we need to store U*(Z^-1^T) in U. The array U must
* have at least cols columns to perform this operation, and at
* least rows columns to store the initial U. However, for an under-
* determined system the last cols-rows columns of U*(Z^-1^T) will be
* zero. Exploiting this, the size of the array U is just mxm */
m = (long int) rows;
n = (long int) cols;
lda = m;
ldu = m;
ldvt = n;
nsv = MIN(rows,cols);
/* Allocate memory for:
* amat : The matrix to factor
* svals : Vector of singular values
* U : mxm unitary matrix
* V : nxn unitary matrix */
amat = (double *) calloc((int) m*n+1, sizeof(double));
svals = (double *) calloc((int) m+n, sizeof(double));
U = (double *) calloc((int) ldu*m+1,sizeof(double));
V = (double *) calloc((int) ldvt*n+1, sizeof(double));
/* In matrix form amat = U * diag(svals) * V^T */
/* Leading dimensions [number of rows] of the above arrays */
/* Work storage */
lwork = 100*(m+n);
work = (double *) calloc(lwork, sizeof(double));
/* Copy A to temporary storage */
for (j=0; j<cols; j++) {
for (i=0; i<rows; i++) {
AA(i,j) = A[i][j];
}
}
{
char jobu,jobvt;
jobu = 'A'; /* Calculate all columns of matrix U */
jobvt = 'A'; /* Calculate all columns of matrix V^T */
/* Note that dgesvd_ returns V^T rather than V */
dgesvd_(&jobu, &jobvt, &m, &n,
amat, &lda, svals, U, &ldu, V, &ldvt,
work, &lwork, &info);
/* Test info from dgesvd */
if (info) {
if (info<0) {
printf("%s: ERROR: clapack routine 'dgesvd_' complained about\n"
" illegal value of argument # %d\n",fctName,(int) -info);
_EXIT_;
}
else{
printf("%s: ERROR: clapack routine 'dgesvd_' complained that\n"
" %d superdiagonals didn't converge.\n",
fctName,(int) info);
_EXIT_;
}
}
}
/* Test the the singular values are returned in correct ordering
* (This is done because there were problems with this with a former
* implementation [using f2c'ed linpack] when high optimization
* was used) */
if (svals[0]<0.0) {
printf("%s: ERROR: First singular value returned by clapack \n"
" is negative: %16.6e.\n",fctName,svals[0]);
_EXIT_;
}
for (i=1; i<nsv; i++){
if ( svals[i] > svals[i-1] ) {
printf("%s: ERROR: Singular values returned by clapack \n"
" are not approprately ordered!\n"
" svals[%d] = %16.6e > svals[%d] = %16.6e",
fctName,i,svals[i],i-1,svals[i-1]);
_EXIT_;
}
}
/* Test rank of matrix by examining the singular values. */
/* The singular values of the matrix is sorted in decending order,
* so that svals[i] >= svals[i+1]. The first that is zero (to some
* precision) yields information on the rank (to some precision) */
rank = nsv;
tol = DBL_EPSILON * svals[0] * MAX(rows,cols);
for (i=0; i<nsv; i++){
if ( svals[i] <= tol ){
rank = i;
break;
}
}
/* Compute (pseudo-) inverse matrix using the computed SVD:
* A = U*S*V'
* A^+ = V * (Zi) U'
* = V * (U * (Zi)')'
* = { (U * Zi') * V' }'
* Here Zi is the "inverse" of the diagonal matrix S, i.e. Zi is
* diagonal and has the same size as S'. The non-zero entries in
* Zi is calculated from the non-zero entries in S as Zi_ii=1/S_ii
* Note that Zi' is of the same size as S.
* The last line here is used in the present computation. This
* notation avoids any need to transpose the output from the CLAPACK
* rotines (which deliver U, S and V). */
/* Inverse of [non-zero part of] diagonal matrix */
tol = 1.0e-10 * svals[0];
for (i=0; i< nsv; i++) {
if (svals[i] < tol)
svals[i] = 0.0;
else
svals[i] = 1.0 / svals[i];
}
/* Calculate UZ = U * Zi', ie. scale COLUMN j in U by
* the j'th singular value [nsv columns only - since the diagonal
* matrix in general is not square]. If rows>cols then the last
* columns in UZ wil be zero (no need to compute). */
for (j=0; j<nsv; j++){
for (i=0; i<rows; i++){
UU(i,j) *= svals[j];
}
}
/* U*Zi' is stored in array U. It has size (rows x nsv).
* If cols>rows, then it should be though of as the larger matrix
* (rows x cols) with zero columns added on the right. */
/* Zero out the full array A to avoid confusion upon return.
* This is not abosolutely necessary, zeroin out A[j][i] could be
* part of the next loop. */
for (i=0; i< MAX(cols,rows); i++) {
for (j=0; j< MAX(cols,rows); j++) {
A[i][j] = 0.0;
}
}
/* Matrix-matrix multiply (U*Zi') * V'.
* Only the first nsv columns in U*Zi' are non-zero, so the inner
* most loop will go to k=nsv (-1).
* The result will be the transpose of the (psuedo-) inverse of A,
* so store directly in A'=A[j][i].
* A[j][i] = sum_k (U*Zi)[i][k] * V'[k][j] : */
for (i=0; i< rows; i++) {
for (j=0; j< cols; j++) {
/*A[j][i] = 0.0; */ /* Include if A is not zeroed out above */
for (k=0;k<nsv;k++){
A[j][i] += UU(i,k)*VV(k,j);
}
}
}
/* Free memory allocated in this routine */
FREE(amat);
FREE(svals);
FREE(U);
FREE(V);
FREE(work);
return rank;
/* Undefine macros for this routine */
#undef AA
#undef UU
#undef VV
} /* End of routine pinv */
/* Subroutine */ int dhseqr_(char *job, char *compz, integer *n, integer *ilo,
integer *ihi, doublereal *h, integer *ldh, doublereal *wr,
doublereal *wi, doublereal *z, integer *ldz, doublereal *work,
integer *lwork, integer *info)
{
/* -- LAPACK routine (version 2.0) --
Univ. of Tennessee, Univ. of California Berkeley, NAG Ltd.,
Courant Institute, Argonne National Lab, and Rice University
September 30, 1994
Purpose
=======
DHSEQR computes the eigenvalues of a real upper Hessenberg matrix H
and, optionally, the matrices T and Z from the Schur decomposition
H = Z T Z**T, where T is an upper quasi-triangular matrix (the Schur
form), and Z is the orthogonal matrix of Schur vectors.
Optionally Z may be postmultiplied into an input orthogonal matrix Q,
so that this routine can give the Schur factorization of a matrix A
which has been reduced to the Hessenberg form H by the orthogonal
matrix Q: A = Q*H*Q**T = (QZ)*T*(QZ)**T.
Arguments
=========
JOB (input) CHARACTER*1
= 'E': compute eigenvalues only;
= 'S': compute eigenvalues and the Schur form T.
COMPZ (input) CHARACTER*1
= 'N': no Schur vectors are computed;
= 'I': Z is initialized to the unit matrix and the matrix Z
of Schur vectors of H is returned;
= 'V': Z must contain an orthogonal matrix Q on entry, and
the product Q*Z is returned.
N (input) INTEGER
The order of the matrix H. N >= 0.
ILO (input) INTEGER
IHI (input) INTEGER
It is assumed that H is already upper triangular in rows
and columns 1:ILO-1 and IHI+1:N. ILO and IHI are normally
set by a previous call to DGEBAL, and then passed to SGEHRD
when the matrix output by DGEBAL is reduced to Hessenberg
form. Otherwise ILO and IHI should be set to 1 and N
respectively.
1 <= ILO <= IHI <= N, if N > 0; ILO=1 and IHI=0, if N=0.
H (input/output) DOUBLE PRECISION array, dimension (LDH,N)
On entry, the upper Hessenberg matrix H.
On exit, if JOB = 'S', H contains the upper quasi-triangular
matrix T from the Schur decomposition (the Schur form);
2-by-2 diagonal blocks (corresponding to complex conjugate
pairs of eigenvalues) are returned in standard form, with
H(i,i) = H(i+1,i+1) and H(i+1,i)*H(i,i+1) < 0. If JOB = 'E',
the contents of H are unspecified on exit.
LDH (input) INTEGER
The leading dimension of the array H. LDH >= max(1,N).
WR (output) DOUBLE PRECISION array, dimension (N)
WI (output) DOUBLE PRECISION array, dimension (N)
The real and imaginary parts, respectively, of the computed
eigenvalues. If two eigenvalues are computed as a complex
conjugate pair, they are stored in consecutive elements of
WR and WI, say the i-th and (i+1)th, with WI(i) > 0 and
WI(i+1) < 0. If JOB = 'S', the eigenvalues are stored in the
same order as on the diagonal of the Schur form returned in
H, with WR(i) = H(i,i) and, if H(i:i+1,i:i+1) is a 2-by-2
diagonal block, WI(i) = sqrt(H(i+1,i)*H(i,i+1)) and
WI(i+1) = -WI(i).
Z (input/output) DOUBLE PRECISION array, dimension (LDZ,N)
If COMPZ = 'N': Z is not referenced.
If COMPZ = 'I': on entry, Z need not be set, and on exit, Z
contains the orthogonal matrix Z of the Schur vectors of H.
If COMPZ = 'V': on entry Z must contain an N-by-N matrix Q,
which is assumed to be equal to the unit matrix except for
the submatrix Z(ILO:IHI,ILO:IHI); on exit Z contains Q*Z.
Normally Q is the orthogonal matrix generated by DORGHR after
the call to DGEHRD which formed the Hessenberg matrix H.
LDZ (input) INTEGER
The leading dimension of the array Z.
LDZ >= max(1,N) if COMPZ = 'I' or 'V'; LDZ >= 1 otherwise.
WORK (workspace) DOUBLE PRECISION array, dimension (N)
LWORK (input) INTEGER
This argument is currently redundant.
INFO (output) INTEGER
= 0: successful exit
< 0: if INFO = -i, the i-th argument had an illegal value
> 0: if INFO = i, DHSEQR failed to compute all of the
eigenvalues in a total of 30*(IHI-ILO+1) iterations;
elements 1:ilo-1 and i+1:n of WR and WI contain those
eigenvalues which have been successfully computed.
=====================================================================
Decode and test the input parameters
Parameter adjustments
Function Body */
/* Table of constant values */
static doublereal c_b9 = 0.;
static doublereal c_b10 = 1.;
static integer c__4 = 4;
static integer c_n1 = -1;
static integer c__2 = 2;
static integer c__8 = 8;
static integer c__15 = 15;
static logical c_false = FALSE_;
static integer c__1 = 1;
/* System generated locals */
address a__1[2];
integer h_dim1, h_offset, z_dim1, z_offset, i__1, i__2, i__3[2], i__4,
i__5;
doublereal d__1, d__2;
char ch__1[2];
/* Builtin functions
Subroutine */ int s_cat(char *, char **, integer *, integer *, ftnlen);
/* Local variables */
static integer maxb;
static doublereal absw;
static integer ierr;
static doublereal unfl, temp, ovfl;
static integer i, j, k, l;
static doublereal s[225] /* was [15][15] */, v[16];
extern /* Subroutine */ int dscal_(integer *, doublereal *, doublereal *,
integer *);
extern logical lsame_(char *, char *);
extern /* Subroutine */ int dgemv_(char *, integer *, integer *,
doublereal *, doublereal *, integer *, doublereal *, integer *,
doublereal *, doublereal *, integer *);
static integer itemp;
extern /* Subroutine */ int dcopy_(integer *, doublereal *, integer *,
doublereal *, integer *);
static integer i1, i2;
static logical initz, wantt, wantz;
extern doublereal dlapy2_(doublereal *, doublereal *);
extern /* Subroutine */ int dlabad_(doublereal *, doublereal *);
static integer ii, nh;
extern doublereal dlamch_(char *);
extern /* Subroutine */ int dlarfg_(integer *, doublereal *, doublereal *,
integer *, doublereal *);
static integer nr, ns;
extern integer idamax_(integer *, doublereal *, integer *);
static integer nv;
extern doublereal dlanhs_(char *, integer *, doublereal *, integer *,
doublereal *);
extern /* Subroutine */ int dlahqr_(logical *, logical *, integer *,
integer *, integer *, doublereal *, integer *, doublereal *,
doublereal *, integer *, integer *, doublereal *, integer *,
integer *);
static doublereal vv[16];
extern /* Subroutine */ int dlacpy_(char *, integer *, integer *,
doublereal *, integer *, doublereal *, integer *);
extern integer ilaenv_(integer *, char *, char *, integer *, integer *,
integer *, integer *, ftnlen, ftnlen);
extern /* Subroutine */ int dlaset_(char *, integer *, integer *,
doublereal *, doublereal *, doublereal *, integer *),
dlarfx_(char *, integer *, integer *, doublereal *, doublereal *,
doublereal *, integer *, doublereal *), xerbla_(char *,
integer *);
static doublereal smlnum;
static integer itn;
static doublereal tau;
static integer its;
static doublereal ulp, tst1;
#define S(I) s[(I)]
#define WAS(I) was[(I)]
#define V(I) v[(I)]
#define VV(I) vv[(I)]
#define WR(I) wr[(I)-1]
#define WI(I) wi[(I)-1]
#define WORK(I) work[(I)-1]
#define H(I,J) h[(I)-1 + ((J)-1)* ( *ldh)]
#define Z(I,J) z[(I)-1 + ((J)-1)* ( *ldz)]
wantt = lsame_(job, "S");
initz = lsame_(compz, "I");
wantz = initz || lsame_(compz, "V");
*info = 0;
if (! lsame_(job, "E") && ! wantt) {
*info = -1;
} else if (! lsame_(compz, "N") && ! wantz) {
*info = -2;
} else if (*n < 0) {
*info = -3;
} else if (*ilo < 1 || *ilo > max(1,*n)) {
*info = -4;
} else if (*ihi < min(*ilo,*n) || *ihi > *n) {
*info = -5;
} else if (*ldh < max(1,*n)) {
*info = -7;
} else if (*ldz < 1 || wantz && *ldz < max(1,*n)) {
*info = -11;
}
if (*info != 0) {
i__1 = -(*info);
xerbla_("DHSEQR", &i__1);
return 0;
}
/* Initialize Z, if necessary */
if (initz) {
dlaset_("Full", n, n, &c_b9, &c_b10, &Z(1,1), ldz);
}
/* Store the eigenvalues isolated by DGEBAL. */
i__1 = *ilo - 1;
for (i = 1; i <= *ilo-1; ++i) {
WR(i) = H(i,i);
WI(i) = 0.;
/* L10: */
}
i__1 = *n;
for (i = *ihi + 1; i <= *n; ++i) {
WR(i) = H(i,i);
WI(i) = 0.;
/* L20: */
}
/* Quick return if possible. */
if (*n == 0) {
return 0;
}
if (*ilo == *ihi) {
WR(*ilo) = H(*ilo,*ilo);
WI(*ilo) = 0.;
return 0;
}
/* Set rows and columns ILO to IHI to zero below the first
subdiagonal. */
i__1 = *ihi - 2;
for (j = *ilo; j <= *ihi-2; ++j) {
i__2 = *n;
for (i = j + 2; i <= *n; ++i) {
H(i,j) = 0.;
/* L30: */
}
/* L40: */
}
nh = *ihi - *ilo + 1;
/* Determine the order of the multi-shift QR algorithm to be used.
Writing concatenation */
i__3[0] = 1, a__1[0] = job;
i__3[1] = 1, a__1[1] = compz;
s_cat(ch__1, a__1, i__3, &c__2, 2L);
ns = ilaenv_(&c__4, "DHSEQR", ch__1, n, ilo, ihi, &c_n1, 6L, 2L);
/* Writing concatenation */
i__3[0] = 1, a__1[0] = job;
i__3[1] = 1, a__1[1] = compz;
s_cat(ch__1, a__1, i__3, &c__2, 2L);
maxb = ilaenv_(&c__8, "DHSEQR", ch__1, n, ilo, ihi, &c_n1, 6L, 2L);
if (ns <= 2 || ns > nh || maxb >= nh) {
/* Use the standard double-shift algorithm */
dlahqr_(&wantt, &wantz, n, ilo, ihi, &H(1,1), ldh, &WR(1), &WI(1)
, ilo, ihi, &Z(1,1), ldz, info);
return 0;
}
maxb = max(3,maxb);
/* Computing MIN */
i__1 = min(ns,maxb);
ns = min(i__1,15);
/* Now 2 < NS <= MAXB < NH.
Set machine-dependent constants for the stopping criterion.
If norm(H) <= sqrt(OVFL), overflow should not occur. */
unfl = dlamch_("Safe minimum");
ovfl = 1. / unfl;
dlabad_(&unfl, &ovfl);
ulp = dlamch_("Precision");
smlnum = unfl * (nh / ulp);
/* I1 and I2 are the indices of the first row and last column of H
to which transformations must be applied. If eigenvalues only are
being computed, I1 and I2 are set inside the main loop. */
if (wantt) {
i1 = 1;
i2 = *n;
}
/* ITN is the total number of multiple-shift QR iterations allowed. */
itn = nh * 30;
/* The main loop begins here. I is the loop index and decreases from
IHI to ILO in steps of at most MAXB. Each iteration of the loop
works with the active submatrix in rows and columns L to I.
Eigenvalues I+1 to IHI have already converged. Either L = ILO or
H(L,L-1) is negligible so that the matrix splits. */
i = *ihi;
L50:
l = *ilo;
if (i < *ilo) {
goto L170;
}
/* Perform multiple-shift QR iterations on rows and columns ILO to I
until a submatrix of order at most MAXB splits off at the bottom
because a subdiagonal element has become negligible. */
i__1 = itn;
for (its = 0; its <= itn; ++its) {
/* Look for a single small subdiagonal element. */
i__2 = l + 1;
for (k = i; k >= l+1; --k) {
tst1 = (d__1 = H(k-1,k-1), abs(d__1)) + (d__2 =
H(k,k), abs(d__2));
if (tst1 == 0.) {
i__4 = i - l + 1;
tst1 = dlanhs_("1", &i__4, &H(l,l), ldh, &WORK(1));
}
/* Computing MAX */
d__2 = ulp * tst1;
if ((d__1 = H(k,k-1), abs(d__1)) <= max(d__2,
smlnum)) {
goto L70;
}
/* L60: */
}
L70:
l = k;
if (l > *ilo) {
/* H(L,L-1) is negligible. */
H(l,l-1) = 0.;
}
/* Exit from loop if a submatrix of order <= MAXB has split off
. */
if (l >= i - maxb + 1) {
goto L160;
}
/* Now the active submatrix is in rows and columns L to I. If
eigenvalues only are being computed, only the active submatr
ix
need be transformed. */
if (! wantt) {
i1 = l;
i2 = i;
}
if (its == 20 || its == 30) {
/* Exceptional shifts. */
i__2 = i;
for (ii = i - ns + 1; ii <= i; ++ii) {
WR(ii) = ((d__1 = H(ii,ii-1), abs(d__1)) + (
d__2 = H(ii,ii), abs(d__2))) * 1.5;
WI(ii) = 0.;
/* L80: */
}
} else {
/* Use eigenvalues of trailing submatrix of order NS as
shifts. */
dlacpy_("Full", &ns, &ns, &H(i-ns+1,i-ns+1),
ldh, s, &c__15);
dlahqr_(&c_false, &c_false, &ns, &c__1, &ns, s, &c__15, &WR(i -
ns + 1), &WI(i - ns + 1), &c__1, &ns, &Z(1,1), ldz, &
ierr);
if (ierr > 0) {
/* If DLAHQR failed to compute all NS eigenvalues
, use the
unconverged diagonal elements as the remaining
shifts. */
i__2 = ierr;
for (ii = 1; ii <= ierr; ++ii) {
WR(i - ns + ii) = S(ii + ii * 15 - 16);
WI(i - ns + ii) = 0.;
/* L90: */
}
}
}
/* Form the first column of (G-w(1)) (G-w(2)) . . . (G-w(ns))
where G is the Hessenberg submatrix H(L:I,L:I) and w is
the vector of shifts (stored in WR and WI). The result is
stored in the local array V. */
V(0) = 1.;
i__2 = ns + 1;
for (ii = 2; ii <= ns+1; ++ii) {
V(ii - 1) = 0.;
/* L100: */
}
nv = 1;
i__2 = i;
for (j = i - ns + 1; j <= i; ++j) {
if (WI(j) >= 0.) {
if (WI(j) == 0.) {
/* real shift */
i__4 = nv + 1;
dcopy_(&i__4, v, &c__1, vv, &c__1);
i__4 = nv + 1;
d__1 = -WR(j);
dgemv_("No transpose", &i__4, &nv, &c_b10, &H(l,l), ldh, vv, &c__1, &d__1, v, &c__1);
++nv;
} else if (WI(j) > 0.) {
/* complex conjugate pair of shifts */
i__4 = nv + 1;
dcopy_(&i__4, v, &c__1, vv, &c__1);
i__4 = nv + 1;
d__1 = WR(j) * -2.;
dgemv_("No transpose", &i__4, &nv, &c_b10, &H(l,l), ldh, v, &c__1, &d__1, vv, &c__1);
i__4 = nv + 1;
itemp = idamax_(&i__4, vv, &c__1);
/* Computing MAX */
d__2 = (d__1 = VV(itemp - 1), abs(d__1));
temp = 1. / max(d__2,smlnum);
i__4 = nv + 1;
dscal_(&i__4, &temp, vv, &c__1);
absw = dlapy2_(&WR(j), &WI(j));
temp = temp * absw * absw;
i__4 = nv + 2;
i__5 = nv + 1;
dgemv_("No transpose", &i__4, &i__5, &c_b10, &H(l,l), ldh, vv, &c__1, &temp, v, &c__1);
nv += 2;
}
/* Scale V(1:NV) so that max(abs(V(i))) = 1. If V
is zero,
reset it to the unit vector. */
itemp = idamax_(&nv, v, &c__1);
temp = (d__1 = V(itemp - 1), abs(d__1));
if (temp == 0.) {
V(0) = 1.;
i__4 = nv;
for (ii = 2; ii <= nv; ++ii) {
V(ii - 1) = 0.;
/* L110: */
}
} else {
temp = max(temp,smlnum);
d__1 = 1. / temp;
dscal_(&nv, &d__1, v, &c__1);
}
}
/* L120: */
}
/* Multiple-shift QR step */
i__2 = i - 1;
for (k = l; k <= i-1; ++k) {
/* The first iteration of this loop determines a reflect
ion G
from the vector V and applies it from left and right
to H,
thus creating a nonzero bulge below the subdiagonal.
Each subsequent iteration determines a reflection G t
o
restore the Hessenberg form in the (K-1)th column, an
d thus
chases the bulge one step toward the bottom of the ac
tive
submatrix. NR is the order of G.
Computing MIN */
i__4 = ns + 1, i__5 = i - k + 1;
nr = min(i__4,i__5);
if (k > l) {
dcopy_(&nr, &H(k,k-1), &c__1, v, &c__1);
}
dlarfg_(&nr, v, &V(1), &c__1, &tau);
if (k > l) {
H(k,k-1) = V(0);
i__4 = i;
for (ii = k + 1; ii <= i; ++ii) {
H(ii,k-1) = 0.;
/* L130: */
}
}
V(0) = 1.;
/* Apply G from the left to transform the rows of the ma
trix in
columns K to I2. */
i__4 = i2 - k + 1;
dlarfx_("Left", &nr, &i__4, v, &tau, &H(k,k), ldh, &
WORK(1));
/* Apply G from the right to transform the columns of th
e
matrix in rows I1 to min(K+NR,I).
Computing MIN */
i__5 = k + nr;
i__4 = min(i__5,i) - i1 + 1;
dlarfx_("Right", &i__4, &nr, v, &tau, &H(i1,k), ldh, &
WORK(1));
if (wantz) {
/* Accumulate transformations in the matrix Z */
dlarfx_("Right", &nh, &nr, v, &tau, &Z(*ilo,k),
ldz, &WORK(1));
}
/* L140: */
}
/* L150: */
}
/* Failure to converge in remaining number of iterations */
*info = i;
return 0;
L160:
/* A submatrix of order <= MAXB in rows and columns L to I has split
off. Use the double-shift QR algorithm to handle it. */
dlahqr_(&wantt, &wantz, n, &l, &i, &H(1,1), ldh, &WR(1), &WI(1), ilo,
ihi, &Z(1,1), ldz, info);
if (*info > 0) {
return 0;
}
/* Decrement number of remaining iterations, and return to start of
the main loop with a new value of I. */
itn -= its;
i = l - 1;
goto L50;
L170:
return 0;
/* End of DHSEQR */
} /* dhseqr_ */
IGL_INLINE bool igl::copyleft::boolean::mesh_boolean(
const Eigen::PlainObjectBase<DerivedVA> & VA,
const Eigen::PlainObjectBase<DerivedFA> & FA,
const Eigen::PlainObjectBase<DerivedVB> & VB,
const Eigen::PlainObjectBase<DerivedFB> & FB,
const WindingNumberOp& wind_num_op,
const KeepFunc& keep,
const ResolveFunc& resolve_fun,
Eigen::PlainObjectBase<DerivedVC > & VC,
Eigen::PlainObjectBase<DerivedFC > & FC,
Eigen::PlainObjectBase<DerivedJ > & J)
{
#ifdef MESH_BOOLEAN_TIMING
const auto & tictoc = []() -> double
{
static double t_start = igl::get_seconds();
double diff = igl::get_seconds()-t_start;
t_start += diff;
return diff;
};
const auto log_time = [&](const std::string& label) -> void {
std::cout << "mesh_boolean." << label << ": "
<< tictoc() << std::endl;
};
tictoc();
#endif
typedef typename DerivedVC::Scalar Scalar;
//typedef typename DerivedFC::Scalar Index;
typedef CGAL::Epeck Kernel;
typedef Kernel::FT ExactScalar;
typedef Eigen::Matrix<Scalar,Eigen::Dynamic,3> MatrixX3S;
//typedef Eigen::Matrix<Index,Eigen::Dynamic,Eigen::Dynamic> MatrixXI;
typedef Eigen::Matrix<typename DerivedJ::Scalar,Eigen::Dynamic,1> VectorXJ;
// Generate combined mesh.
typedef Eigen::Matrix<
ExactScalar,
Eigen::Dynamic,
Eigen::Dynamic,
DerivedVC::IsRowMajor> MatrixXES;
MatrixXES V;
DerivedFC F;
VectorXJ CJ;
{
DerivedVA VV(VA.rows() + VB.rows(), 3);
DerivedFC FF(FA.rows() + FB.rows(), 3);
VV << VA, VB;
FF << FA, FB.array() + VA.rows();
//// Handle annoying empty cases
//if(VA.size()>0)
//{
// VV<<VA;
//}
//if(VB.size()>0)
//{
// VV<<VB;
//}
//if(FA.size()>0)
//{
// FF<<FA;
//}
//if(FB.size()>0)
//{
// FF<<FB.array()+VA.rows();
//}
resolve_fun(VV, FF, V, F, CJ);
}
#ifdef MESH_BOOLEAN_TIMING
log_time("resolve_self_intersection");
#endif
// Compute winding numbers on each side of each facet.
const size_t num_faces = F.rows();
Eigen::MatrixXi W;
Eigen::VectorXi labels(num_faces);
std::transform(CJ.data(), CJ.data()+CJ.size(), labels.data(),
[&](int i) { return i<FA.rows() ? 0:1; });
bool valid = true;
if (num_faces > 0)
{
valid = valid &
igl::copyleft::cgal::propagate_winding_numbers(V, F, labels, W);
} else
{
W.resize(0, 4);
}
assert((size_t)W.rows() == num_faces);
if (W.cols() == 2)
{
assert(FB.rows() == 0);
Eigen::MatrixXi W_tmp(num_faces, 4);
W_tmp << W, Eigen::MatrixXi::Zero(num_faces, 2);
W = W_tmp;
} else {
assert(W.cols() == 4);
}
#ifdef MESH_BOOLEAN_TIMING
log_time("propagate_input_winding_number");
#endif
// Compute resulting winding number.
Eigen::MatrixXi Wr(num_faces, 2);
for (size_t i=0; i<num_faces; i++)
{
Eigen::MatrixXi w_out(1,2), w_in(1,2);
w_out << W(i,0), W(i,2);
w_in << W(i,1), W(i,3);
Wr(i,0) = wind_num_op(w_out);
Wr(i,1) = wind_num_op(w_in);
}
#ifdef MESH_BOOLEAN_TIMING
log_time("compute_output_winding_number");
#endif
// Extract boundary separating inside from outside.
auto index_to_signed_index = [&](size_t i, bool ori) -> int
{
return (i+1)*(ori?1:-1);
};
//auto signed_index_to_index = [&](int i) -> size_t {
// return abs(i) - 1;
//};
std::vector<int> selected;
for(size_t i=0; i<num_faces; i++)
{
auto should_keep = keep(Wr(i,0), Wr(i,1));
if (should_keep > 0)
{
selected.push_back(index_to_signed_index(i, true));
} else if (should_keep < 0)
{
selected.push_back(index_to_signed_index(i, false));
}
}
const size_t num_selected = selected.size();
DerivedFC kept_faces(num_selected, 3);
DerivedJ kept_face_indices(num_selected, 1);
for (size_t i=0; i<num_selected; i++)
{
size_t idx = abs(selected[i]) - 1;
if (selected[i] > 0)
{
kept_faces.row(i) = F.row(idx);
} else
{
kept_faces.row(i) = F.row(idx).reverse();
}
kept_face_indices(i, 0) = CJ[idx];
}
#ifdef MESH_BOOLEAN_TIMING
log_time("extract_output");
#endif
// Finally, remove duplicated faces and unreferenced vertices.
{
DerivedFC G;
DerivedJ JJ;
igl::resolve_duplicated_faces(kept_faces, G, JJ);
igl::slice(kept_face_indices, JJ, 1, J);
#ifdef DOUBLE_CHECK_EXACT_OUTPUT
{
// Sanity check on exact output.
igl::copyleft::cgal::RemeshSelfIntersectionsParam params;
params.detect_only = true;
params.first_only = true;
MatrixXES dummy_VV;
DerivedFC dummy_FF, dummy_IF;
Eigen::VectorXi dummy_J, dummy_IM;
igl::copyleft::cgal::SelfIntersectMesh<
Kernel,
MatrixXES, DerivedFC,
MatrixXES, DerivedFC,
DerivedFC,
Eigen::VectorXi,
Eigen::VectorXi
> checker(V, G, params,
dummy_VV, dummy_FF, dummy_IF, dummy_J, dummy_IM);
if (checker.count != 0)
{
throw "Self-intersection not fully resolved.";
}
}
#endif
MatrixX3S Vs(V.rows(), V.cols());
for (size_t i=0; i<(size_t)V.rows(); i++)
{
for (size_t j=0; j<(size_t)V.cols(); j++)
{
igl::copyleft::cgal::assign_scalar(V(i,j), Vs(i,j));
}
}
Eigen::VectorXi newIM;
igl::remove_unreferenced(Vs,G,VC,FC,newIM);
}
#ifdef MESH_BOOLEAN_TIMING
log_time("clean_up");
#endif
return valid;
}
IGL_INLINE void igl::copyleft::boolean::mesh_boolean(
const Eigen::PlainObjectBase<DerivedVA> & VA,
const Eigen::PlainObjectBase<DerivedFA> & FA,
const Eigen::PlainObjectBase<DerivedVB> & VB,
const Eigen::PlainObjectBase<DerivedFB> & FB,
const WindingNumberOp& wind_num_op,
const KeepFunc& keep,
const ResolveFunc& resolve_fun,
Eigen::PlainObjectBase<DerivedVC > & VC,
Eigen::PlainObjectBase<DerivedFC > & FC,
Eigen::PlainObjectBase<DerivedJ > & J) {
typedef typename DerivedVC::Scalar Scalar;
//typedef typename DerivedFC::Scalar Index;
typedef CGAL::Epeck Kernel;
typedef Kernel::FT ExactScalar;
typedef Eigen::Matrix<Scalar,Eigen::Dynamic,3> MatrixX3S;
//typedef Eigen::Matrix<Index,Eigen::Dynamic,Eigen::Dynamic> MatrixXI;
typedef Eigen::Matrix<typename DerivedJ::Scalar,Eigen::Dynamic,1> VectorXJ;
// Generate combined mesh.
typedef Eigen::Matrix<
ExactScalar,
Eigen::Dynamic,
Eigen::Dynamic,
DerivedVC::IsRowMajor> MatrixXES;
MatrixXES V;
DerivedFC F;
VectorXJ CJ;
{
DerivedVA VV(VA.rows() + VB.rows(), 3);
DerivedFC FF(FA.rows() + FB.rows(), 3);
VV << VA, VB;
FF << FA, FB.array() + VA.rows();
//// Handle annoying empty cases
//if(VA.size()>0)
//{
// VV<<VA;
//}
//if(VB.size()>0)
//{
// VV<<VB;
//}
//if(FA.size()>0)
//{
// FF<<FA;
//}
//if(FB.size()>0)
//{
// FF<<FB.array()+VA.rows();
//}
resolve_fun(VV, FF, V, F, CJ);
}
// Compute winding numbers on each side of each facet.
const size_t num_faces = F.rows();
Eigen::MatrixXi W;
Eigen::VectorXi labels(num_faces);
std::transform(CJ.data(), CJ.data()+CJ.size(), labels.data(),
[&](int i) { return i<FA.rows() ? 0:1; });
igl::copyleft::cgal::propagate_winding_numbers(V, F, labels, W);
assert((size_t)W.rows() == num_faces);
if (W.cols() == 2) {
assert(FB.rows() == 0);
Eigen::MatrixXi W_tmp(num_faces, 4);
W_tmp << W, Eigen::MatrixXi::Zero(num_faces, 2);
W = W_tmp;
} else {
assert(W.cols() == 4);
}
// Compute resulting winding number.
Eigen::MatrixXi Wr(num_faces, 2);
for (size_t i=0; i<num_faces; i++) {
Eigen::MatrixXi w_out(1,2), w_in(1,2);
w_out << W(i,0), W(i,2);
w_in << W(i,1), W(i,3);
Wr(i,0) = wind_num_op(w_out);
Wr(i,1) = wind_num_op(w_in);
}
// Extract boundary separating inside from outside.
auto index_to_signed_index = [&](size_t i, bool ori) -> int{
return (i+1)*(ori?1:-1);
};
//auto signed_index_to_index = [&](int i) -> size_t {
// return abs(i) - 1;
//};
std::vector<int> selected;
for(size_t i=0; i<num_faces; i++) {
auto should_keep = keep(Wr(i,0), Wr(i,1));
if (should_keep > 0) {
selected.push_back(index_to_signed_index(i, true));
} else if (should_keep < 0) {
selected.push_back(index_to_signed_index(i, false));
}
}
const size_t num_selected = selected.size();
DerivedFC kept_faces(num_selected, 3);
DerivedJ kept_face_indices(num_selected, 1);
for (size_t i=0; i<num_selected; i++) {
size_t idx = abs(selected[i]) - 1;
if (selected[i] > 0) {
kept_faces.row(i) = F.row(idx);
} else {
kept_faces.row(i) = F.row(idx).reverse();
}
kept_face_indices(i, 0) = CJ[idx];
}
// Finally, remove duplicated faces and unreferenced vertices.
{
DerivedFC G;
DerivedJ JJ;
igl::resolve_duplicated_faces(kept_faces, G, JJ);
igl::slice(kept_face_indices, JJ, 1, J);
MatrixX3S Vs(V.rows(), V.cols());
for (size_t i=0; i<(size_t)V.rows(); i++)
{
for (size_t j=0; j<(size_t)V.cols(); j++)
{
igl::copyleft::cgal::assign_scalar(V(i,j), Vs(i,j));
}
}
Eigen::VectorXi newIM;
igl::remove_unreferenced(Vs,G,VC,FC,newIM);
}
}
