Updating lbpm_uCT_pp.cpp

This commit is contained in:
Mark Berrill 2018-11-14 13:43:59 -05:00
parent ceb85c2cd0
commit f1239ada97
4 changed files with 840 additions and 882 deletions

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@ -24,7 +24,7 @@ namespace IO {
* silo - Silo
* @param[in] append Append any existing data (default is false)
*/
void initialize( const std::string& path="", const std::string& format="new", bool append=false );
void initialize( const std::string& path="", const std::string& format="silo", bool append=false );
/*!

File diff suppressed because it is too large Load Diff

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@ -1002,7 +1002,7 @@ Array<TYPE, FUN, Allocator> Array<TYPE, FUN, Allocator>::coarsen(
throw std::invalid_argument( "Array must be multiple of filter size" );
}
Array<TYPE, FUN, Allocator> y( S2 );
if ( d_size.ndim() <= 3 )
if ( d_size.ndim() > 3 )
throw std::logic_error( "Function programmed for more than 3 dimensions" );
const auto &Nh = filter.d_size;
for ( size_t k1 = 0; k1 < y.d_size[2]; k1++ ) {

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@ -30,463 +30,415 @@
int main(int argc, char **argv)
{
// Initialize MPI
int rank, nprocs;
MPI_Init(&argc,&argv);
MPI_Comm comm = MPI_COMM_WORLD;
MPI_Comm_rank(comm,&rank);
MPI_Comm_size(comm,&nprocs);
{
Utilities::setErrorHandlers();
PROFILE_START("Main");
//std::vector<std::string> filenames;
if ( argc<2 ) {
if ( rank == 0 ){
printf("At least one filename must be specified\n");
}
return 1;
}
std::string filename = std::string(argv[1]);
if ( rank == 0 ){
printf("Input data file: %s\n",filename.c_str());
}
auto db = std::make_shared<Database>( filename );
auto domain_db = db->getDatabase( "Domain" );
auto uct_db = db->getDatabase( "uCT" );
auto analysis_db = db->getDatabase( "Analysis" );
// Read domain values
auto L = domain_db->getVector<double>( "L" );
auto size = domain_db->getVector<int>( "n" );
auto nproc = domain_db->getVector<int>( "nproc" );
int BoundaryCondition = domain_db->getScalar<int>( "BC" );
int nx = size[0];
int ny = size[1];
int nz = size[2];
double Lx = L[0];
double Ly = L[1];
double Lz = L[2];
int nprocx = nproc[0];
int nprocy = nproc[1];
int nprocz = nproc[2];
auto InputFile=uct_db->getScalar<std::string>( "InputFile" );
auto target=uct_db->getScalar<int>("target");
auto background=uct_db->getScalar<int>("background");
auto rough_cutoff=uct_db->getScalar<float>( "rough_cutoff" );
auto lamda=uct_db->getScalar<float>( "lamda" );
auto nlm_sigsq=uct_db->getScalar<float>( "nlm_sigsq" );
auto nlm_depth=uct_db->getScalar<int>( "nlm_depth" );
auto cx=uct_db->getScalar<int>( "center_x" );
auto cy=uct_db->getScalar<int>( "center_y" );
auto cz=uct_db->getScalar<int>( "center_z" );
auto CylRad=uct_db->getScalar<float>( "cylinder_radius" );
//.......................................................................
// Reading the domain information file
//.......................................................................
// std::shared_ptr<Domain> Dm ();
//for (int i=0; i<Dm->Nx*Dm->Ny*Dm->Nz; i++) Dm->id[i] = 1;
//Dm->CommInit();
// Check that the number of processors >= the number of ranks
if ( rank==0 ) {
printf("Number of MPI ranks required: %i \n", nprocx*nprocy*nprocz);
printf("Number of MPI ranks used: %i \n", nprocs);
printf("Full domain size: %i x %i x %i \n",nx*nprocx,ny*nprocy,nz*nprocz);
}
if ( nprocs < nprocx*nprocy*nprocz ){
ERROR("Insufficient number of processors");
}
// Determine the maximum number of levels for the desired coarsen ratio
int ratio[3] = {2,2,2};
//std::vector<size_t> ratio = {4,4,4};
// need to set up databases for each level of the mesh
std:vector<Database> multidomain_db;
std::vector<int> Nx(1,nx), Ny(1,ny), Nz(1,nz);
while ( Nx.back()%ratio[0]==0 && Nx.back()>8 &&
Ny.back()%ratio[1]==0 && Ny.back()>8 &&
Nz.back()%ratio[2]==0 && Nz.back()>8 )
{
Nx.push_back( Nx.back()/ratio[0] );
Ny.push_back( Ny.back()/ratio[1] );
Nz.push_back( Nz.back()/ratio[2] );
// clone the domain and create coarse version based on Nx,Ny,Nz
//multidomain_db.push_back();
}
int N_levels = Nx.size();
// Initialize the domain
std::vector<std::shared_ptr<Domain>> Dm(N_levels);
for (int i=0; i<N_levels; i++) {
// This line is no good -- will create identical Domain structures instead of
// Need a way to define a coarse structure for the coarse domain (see above)
Dm[i].reset( new Domain(domain_db, comm) );
int N = (Nx[i]+2)*(Ny[i]+2)*(Nz[i]+2);
for (int n=0; n<N; n++){
Dm[i]->id[n] = 1;
}
Dm[i]->CommInit();
}
// array containing a distance mask
Array<float> MASK(Nx[0]+2,Ny[0]+2,Nz[0]+2);
// Create the level data
std::vector<Array<char>> ID(N_levels);
std::vector<Array<float>> LOCVOL(N_levels);
std::vector<Array<float>> Dist(N_levels);
std::vector<Array<float>> MultiScaleSmooth(N_levels);
std::vector<Array<float>> Mean(N_levels);
std::vector<Array<float>> NonLocalMean(N_levels);
std::vector<std::shared_ptr<fillHalo<double>>> fillDouble(N_levels);
std::vector<std::shared_ptr<fillHalo<float>>> fillFloat(N_levels);
std::vector<std::shared_ptr<fillHalo<char>>> fillChar(N_levels);
for (int i=0; i<N_levels; i++) {
ID[i] = Array<char>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
LOCVOL[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
Dist[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
MultiScaleSmooth[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
Mean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
NonLocalMean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
ID[i].fill(0);
LOCVOL[i].fill(0);
Dist[i].fill(0);
MultiScaleSmooth[i].fill(0);
Mean[i].fill(0);
NonLocalMean[i].fill(0);
fillDouble[i].reset(new fillHalo<double>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
fillFloat[i].reset(new fillHalo<float>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
fillChar[i].reset(new fillHalo<char>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
}
// Read the subvolume of interest on each processor
PROFILE_START("ReadVolume");
int fid = netcdf::open(InputFile,netcdf::READ);
std::string varname("VOLUME");
netcdf::VariableType type = netcdf::getVarType( fid, varname );
std::vector<size_t> dim = netcdf::getVarDim( fid, varname );
if ( rank == 0 ) {
printf("Reading %s (%s)\n",varname.c_str(),netcdf::VariableTypeName(type).c_str());
printf(" dims = %i x %i x %i \n",int(dim[0]),int(dim[1]),int(dim[2]));
}
{
RankInfoStruct info( rank, nprocx, nprocy, nprocz );
int x = info.ix*nx;
int y = info.jy*ny;
int z = info.kz*nz;
// Read the local data
Array<short> VOLUME = netcdf::getVar<short>( fid, varname, {x,y,z}, {nx,ny,nz}, {1,1,1} );
// Copy the data and fill the halos
LOCVOL[0].fill(0);
fillFloat[0]->copy( VOLUME, LOCVOL[0] );
fillFloat[0]->fill( LOCVOL[0] );
}
netcdf::close( fid );
MPI_Barrier(comm);
PROFILE_STOP("ReadVolume");
if (rank==0) printf("Read complete\n");
// Filter the original data
filter_src( *Dm[0], LOCVOL[0] );
// Set up the mask to be distance to cylinder (crop outside cylinder)
// float CylRad=900;
for (int k=0;k<Nz[0]+2;k++) {
for (int j=0;j<Ny[0]+2;j++) {
for (int i=0;i<Nx[0]+2;i++) {
int iproc = Dm[0]->iproc();
int jproc = Dm[0]->jproc();
int kproc = Dm[0]->kproc();
int x=iproc*Nx[0]+i-1;
int y=jproc*Ny[0]+j-1;
int z=kproc*Nz[0]+k-1;
//int cx = 0.5*nprocx*Nx[0];
//int cy = 0.5*nprocy*Ny[0];
//int cz = 0.5*nprocz*Nz[0];
// distance from the center line
MASK(i,j,k) = CylRad - sqrt(float((z-cz)*(z-cz) + (y-cy)*(y-cy)) );
}
}
}
// Compute the means for the high/low regions
// (should use automated mixture model to approximate histograms)
//float THRESHOLD = 0.05*maxReduce( Dm[0]->Comm, std::max( LOCVOL[0].max(), fabs(LOCVOL[0].min()) ) );
float THRESHOLD=0.5*float(target+background);
float mean_plus=0;
float mean_minus=0;
int count_plus=0;
int count_minus=0;
for (int k=1;k<Nz[0]+1;k++) {
for (int j=1;j<Ny[0]+1;j++) {
for (int i=1;i<Nx[0]+1;i++) {
if (MASK(i,j,k) > 0.f ){
auto tmp = LOCVOL[0](i,j,k);
/* if ((tmp-background)*(tmp-target) > 0){
// direction to background / target is the same
if (fabs(tmp-target) > fabs(tmp-background)) tmp=background; // tmp closer to background
else tmp=target; // tmp closer to target
}
*/
if ( tmp > THRESHOLD ) {
mean_plus += tmp;
count_plus++;
} else if ( tmp < -THRESHOLD ) {
mean_minus += tmp;
count_minus++;
}
}
}
}
}
mean_plus = sumReduce( Dm[0]->Comm, mean_plus ) / sumReduce( Dm[0]->Comm, count_plus );
mean_minus = sumReduce( Dm[0]->Comm, mean_minus ) / sumReduce( Dm[0]->Comm, count_minus );
if (rank==0) printf(" Region 1 mean (+): %f, Region 2 mean (-): %f \n",mean_plus, mean_minus);
MPI_Barrier(comm);
// Scale the source data to +-1.0
for (size_t i=0; i<LOCVOL[0].length(); i++) {
if (MASK(i) < 0.f){
LOCVOL[0](i) = 1.0;
}
else if ( LOCVOL[0](i) >= 0 ) {
LOCVOL[0](i) /= mean_plus;
LOCVOL[0](i) = std::min( LOCVOL[0](i), 1.0f );
} else {
LOCVOL[0](i) /= -mean_minus;
LOCVOL[0](i) = std::max( LOCVOL[0](i), -1.0f );
}
}
// Fill the source data for the coarse meshes
PROFILE_START("CoarsenMesh");
for (int i=1; i<N_levels; i++) {
Array<float> filter(ratio[0],ratio[1],ratio[2]);
filter.fill(1.0f/filter.length());
Array<float> tmp(Nx[i-1],Ny[i-1],Nz[i-1]);
fillFloat[i-1]->copy( LOCVOL[i-1], tmp );
Array<float> coarse = tmp.coarsen( filter );
fillFloat[i]->copy( coarse, LOCVOL[i] );
fillFloat[i]->fill( LOCVOL[i] );
if (rank==0){
printf("Coarsen level %i \n",i);
printf(" Nx=%i, Ny=%i, Nz=%i \n",int(tmp.size(0)),int(tmp.size(1)),int(tmp.size(2)) );
printf(" filter_x=%i, filter_y=%i, filter_z=%i \n",int(filter.size(0)),int(filter.size(1)),int(filter.size(2)) );
printf(" ratio= %i,%i,%i \n",int(ratio[0]),int(ratio[1]),int(ratio[2]) );
}
MPI_Barrier(comm);
}
PROFILE_STOP("CoarsenMesh");
// Initialize the coarse level
PROFILE_START("Solve full mesh");
if (rank==0)
printf("Initialize full mesh\n");
solve( LOCVOL[0], Mean[0], ID[0], Dist[0], MultiScaleSmooth[0],
NonLocalMean[0], *fillFloat[0], *Dm[0], nprocx,
rough_cutoff, lamda, nlm_sigsq, nlm_depth);
PROFILE_STOP("Solve full mesh");
MPI_Barrier(comm);
/* // Initialize the coarse level
PROFILE_START("Solve coarse mesh");
if (rank==0)
printf("Initialize coarse mesh\n");
solve( LOCVOL.back(), Mean.back(), ID.back(), Dist.back(), MultiScaleSmooth.back(),
NonLocalMean.back(), *fillFloat.back(), *Dm.back(), nprocx );
PROFILE_STOP("Solve coarse mesh");
MPI_Barrier(comm);
// Refine the solution
PROFILE_START("Refine distance");
if (rank==0)
printf("Refine mesh\n");
for (int i=int(Nx.size())-2; i>=0; i--) {
if (rank==0)
printf(" Refining to level %i\n",int(i));
refine( Dist[i+1], LOCVOL[i], Mean[i], ID[i], Dist[i], MultiScaleSmooth[i],
NonLocalMean[i], *fillFloat[i], *Dm[i], nprocx, i );
}
PROFILE_STOP("Refine distance");
MPI_Barrier(comm);
// Perform a final filter
PROFILE_START("Filtering final domains");
if (rank==0)
printf("Filtering final domains\n");
Array<float> filter_Mean, filter_Dist1, filter_Dist2;
filter_final( ID[0], Dist[0], *fillFloat[0], *Dm[0], filter_Mean, filter_Dist1, filter_Dist2 );
PROFILE_STOP("Filtering final domains");
*/
//removeDisconnected( ID[0], *Dm[0] );
/*
// Write the distance function to a netcdf file
const char* netcdf_filename = "Distance.nc";
// Initialize MPI
int rank, nprocs;
MPI_Init(&argc,&argv);
MPI_Comm comm = MPI_COMM_WORLD;
MPI_Comm_rank(comm,&rank);
MPI_Comm_size(comm,&nprocs);
{
RankInfoStruct info( rank, nprocx, nprocy, nprocz );
std::vector<int> dim = { Nx[0]*nprocx, Ny[0]*nprocy, Nz[0]*nprocz };
int fid = netcdf::open( netcdf_filename, netcdf::CREATE, MPI_COMM_WORLD );
auto dims = netcdf::defDim( fid, {"X", "Y", "Z"}, dim );
Array<float> data(Nx[0],Ny[0],Nz[0]);
fillFloat[0]->copy( Dist[0], data );
netcdf::write( fid, "Distance", dims, data, info );
Utilities::setErrorHandlers();
PROFILE_START("Main");
//std::vector<std::string> filenames;
if ( argc<2 ) {
if ( rank == 0 ){
printf("At least one filename must be specified\n");
}
return 1;
}
std::string filename = std::string(argv[1]);
if ( rank == 0 ){
printf("Input data file: %s\n",filename.c_str());
}
auto db = std::make_shared<Database>( filename );
auto domain_db = db->getDatabase( "Domain" );
auto uct_db = db->getDatabase( "uCT" );
auto analysis_db = db->getDatabase( "Analysis" );
// Read domain values
auto L = domain_db->getVector<double>( "L" );
auto size = domain_db->getVector<int>( "n" );
auto nproc = domain_db->getVector<int>( "nproc" );
int BoundaryCondition = domain_db->getScalar<int>( "BC" );
int nx = size[0];
int ny = size[1];
int nz = size[2];
double Lx = L[0];
double Ly = L[1];
double Lz = L[2];
int nprocx = nproc[0];
int nprocy = nproc[1];
int nprocz = nproc[2];
auto InputFile = uct_db->getScalar<std::string>( "InputFile" );
auto target = uct_db->getScalar<int>("target");
auto background = uct_db->getScalar<int>("background");
auto rough_cutoff = uct_db->getScalar<float>( "rough_cutoff" );
auto lamda = uct_db->getScalar<float>( "lamda" );
auto nlm_sigsq = uct_db->getScalar<float>( "nlm_sigsq" );
auto nlm_depth = uct_db->getScalar<int>( "nlm_depth" );
auto center = uct_db->getVector<int>( "center" );
auto CylRad = uct_db->getScalar<float>( "cylinder_radius" );
auto maxLevels = uct_db->getScalar<int>( "max_levels" );
std::vector<int> offset( 3, 0 );
if ( uct_db->keyExists( "offset" ) )
offset = uct_db->getVector<int>( "offset" );
// Check that the number of processors >= the number of ranks
if ( rank==0 ) {
printf("Number of MPI ranks required: %i \n", nprocx*nprocy*nprocz);
printf("Number of MPI ranks used: %i \n", nprocs);
printf("Full domain size: %i x %i x %i \n",nx*nprocx,ny*nprocy,nz*nprocz);
}
if ( nprocs < nprocx*nprocy*nprocz ){
ERROR("Insufficient number of processors");
}
// Determine the maximum number of levels for the desired coarsen ratio
int ratio[3] = {2,2,2};
//std::vector<size_t> ratio = {4,4,4};
// need to set up databases for each level of the mesh
std::vector<std::shared_ptr<Database>> multidomain_db(1,domain_db);
std::vector<int> Nx(1,nx), Ny(1,ny), Nz(1,nz);
while ( Nx.back()%ratio[0]==0 && Nx.back()>8 &&
Ny.back()%ratio[1]==0 && Ny.back()>8 &&
Nz.back()%ratio[2]==0 && Nz.back()>8 &&
(int) Nx.size() < maxLevels )
{
Nx.push_back( Nx.back()/ratio[0] );
Ny.push_back( Ny.back()/ratio[1] );
Nz.push_back( Nz.back()/ratio[2] );
// clone the domain and create coarse version based on Nx,Ny,Nz
auto db2 = domain_db->cloneDatabase();
db2->putVector<int>( "n", { Nx.back(), Ny.back(), Nz.back() } );
multidomain_db.push_back(db2);
}
int N_levels = Nx.size();
// Initialize the domain
std::vector<std::shared_ptr<Domain>> Dm(N_levels);
for (int i=0; i<N_levels; i++) {
// This line is no good -- will create identical Domain structures instead of
// Need a way to define a coarse structure for the coarse domain (see above)
Dm[i].reset( new Domain(multidomain_db[i], comm) );
int N = (Nx[i]+2)*(Ny[i]+2)*(Nz[i]+2);
for (int n=0; n<N; n++){
Dm[i]->id[n] = 1;
}
Dm[i]->CommInit();
}
// array containing a distance mask
Array<float> MASK(Nx[0]+2,Ny[0]+2,Nz[0]+2);
MASK.fill(0);
// Create the level data
std::vector<Array<char>> ID(N_levels);
std::vector<Array<float>> LOCVOL(N_levels);
std::vector<Array<float>> Dist(N_levels);
std::vector<Array<float>> MultiScaleSmooth(N_levels);
std::vector<Array<float>> Mean(N_levels);
std::vector<Array<float>> NonLocalMean(N_levels);
std::vector<std::shared_ptr<fillHalo<double>>> fillDouble(N_levels);
std::vector<std::shared_ptr<fillHalo<float>>> fillFloat(N_levels);
std::vector<std::shared_ptr<fillHalo<char>>> fillChar(N_levels);
for (int i=0; i<N_levels; i++) {
ID[i] = Array<char>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
LOCVOL[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
Dist[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
MultiScaleSmooth[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
Mean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
NonLocalMean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
ID[i].fill(0);
LOCVOL[i].fill(0);
Dist[i].fill(0);
MultiScaleSmooth[i].fill(0);
Mean[i].fill(0);
NonLocalMean[i].fill(0);
fillDouble[i].reset(new fillHalo<double>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
fillFloat[i].reset(new fillHalo<float>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
fillChar[i].reset(new fillHalo<char>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
}
// Read the subvolume of interest on each processor
PROFILE_START("ReadVolume");
int fid = netcdf::open(InputFile,netcdf::READ);
std::string varname("VOLUME");
auto type = netcdf::getVarType( fid, varname );
auto dim = netcdf::getVarDim( fid, varname );
if ( rank == 0 ) {
printf("Reading %s (%s)\n",varname.c_str(),netcdf::VariableTypeName(type).c_str());
printf(" dims = %i x %i x %i \n",int(dim[0]),int(dim[1]),int(dim[2]));
}
{
// Read the local data
int x = Dm[0]->iproc()*nx + offset[0];
int y = Dm[0]->jproc()*ny + offset[1];
int z = Dm[0]->kproc()*nz + offset[2];
Array<short> VOLUME = netcdf::getVar<short>( fid, varname, {x,y,z}, {nx,ny,nz}, {1,1,1} );
// Copy the data and fill the halos
LOCVOL[0].fill(0);
fillFloat[0]->copy( VOLUME, LOCVOL[0] );
fillFloat[0]->fill( LOCVOL[0] );
}
netcdf::close( fid );
MPI_Barrier(comm);
PROFILE_STOP("ReadVolume");
if (rank==0) printf("Read complete\n");
// Filter the original data
filter_src( *Dm[0], LOCVOL[0] );
// Set up the mask to be distance to cylinder (crop outside cylinder)
for (int k=0;k<Nz[0]+2;k++) {
for (int j=0;j<Ny[0]+2;j++) {
for (int i=0;i<Nx[0]+2;i++) {
int x=Dm[0]->iproc()*Nx[0]+i-1;
int y=Dm[0]->jproc()*Ny[0]+j-1;
int z=Dm[0]->kproc()*Nz[0]+k-1;
int cx = center[0] - offset[0];
int cy = center[1] - offset[1];
int cz = center[2] - offset[2];
// distance from the center line
MASK(i,j,k) = CylRad - sqrt(float((z-cz)*(z-cz) + (y-cy)*(y-cy)) );
}
}
}
// Compute the means for the high/low regions
// (should use automated mixture model to approximate histograms)
//float THRESHOLD = 0.05*maxReduce( Dm[0]->Comm, std::max( LOCVOL[0].max(), fabs(LOCVOL[0].min()) ) );
double THRESHOLD=0.5*(target+background);
double mean_plus=0;
double mean_minus=0;
int count_plus=0;
int count_minus=0;
for (int k=1;k<Nz[0]+1;k++) {
for (int j=1;j<Ny[0]+1;j++) {
for (int i=1;i<Nx[0]+1;i++) {
if (MASK(i,j,k) > 0.f ){
auto tmp = LOCVOL[0](i,j,k);
/* if ((tmp-background)*(tmp-target) > 0){
// direction to background / target is the same
if (fabs(tmp-target) > fabs(tmp-background)) tmp=background; // tmp closer to background
else tmp=target; // tmp closer to target
}
*/
if ( tmp > THRESHOLD ) {
mean_plus += tmp;
count_plus++;
} else if ( tmp < -THRESHOLD ) {
mean_minus += tmp;
count_minus++;
}
}
}
}
}
ASSERT( count_plus > 0 && count_minus > 0 );
mean_plus = sumReduce( Dm[0]->Comm, mean_plus ) / sumReduce( Dm[0]->Comm, count_plus );
mean_minus = sumReduce( Dm[0]->Comm, mean_minus ) / sumReduce( Dm[0]->Comm, count_minus );
if (rank==0) printf(" Region 1 mean (+): %f, Region 2 mean (-): %f \n",mean_plus, mean_minus);
MPI_Barrier(comm);
// Scale the source data to +-1.0
for (size_t i=0; i<LOCVOL[0].length(); i++) {
if (MASK(i) < 0.f){
LOCVOL[0](i) = 1.0;
}
else if ( LOCVOL[0](i) >= 0 ) {
LOCVOL[0](i) /= mean_plus;
LOCVOL[0](i) = std::min( LOCVOL[0](i), 1.0f );
} else {
LOCVOL[0](i) /= -mean_minus;
LOCVOL[0](i) = std::max( LOCVOL[0](i), -1.0f );
}
}
// Fill the source data for the coarse meshes
PROFILE_START("CoarsenMesh");
for (int i=1; i<N_levels; i++) {
Array<float> filter(ratio[0],ratio[1],ratio[2]);
filter.fill(1.0f/filter.length());
Array<float> tmp(Nx[i-1],Ny[i-1],Nz[i-1]);
fillFloat[i-1]->copy( LOCVOL[i-1], tmp );
Array<float> coarse = tmp.coarsen( filter );
fillFloat[i]->copy( coarse, LOCVOL[i] );
fillFloat[i]->fill( LOCVOL[i] );
if (rank==0){
printf("Coarsen level %i \n",i);
printf(" Nx=%i, Ny=%i, Nz=%i \n",int(tmp.size(0)),int(tmp.size(1)),int(tmp.size(2)) );
printf(" filter_x=%i, filter_y=%i, filter_z=%i \n",int(filter.size(0)),int(filter.size(1)),int(filter.size(2)) );
printf(" ratio= %i,%i,%i \n",int(ratio[0]),int(ratio[1]),int(ratio[2]) );
}
MPI_Barrier(comm);
}
PROFILE_STOP("CoarsenMesh");
// Initialize the coarse level
PROFILE_START("Solve coarse mesh");
if (rank==0)
printf("Initialize full mesh\n");
solve( LOCVOL.back(), Mean.back(), ID.back(), Dist.back(), MultiScaleSmooth.back(),
NonLocalMean.back(), *fillFloat.back(), *Dm.back(), nprocx,
rough_cutoff, lamda, nlm_sigsq, nlm_depth);
PROFILE_STOP("Solve coarse mesh");
MPI_Barrier(comm);
// Refine the solution
PROFILE_START("Refine distance");
if (rank==0)
printf("Refine mesh\n");
for (int i=N_levels-2; i>=0; i--) {
if (rank==0)
printf(" Refining to level %i\n",i);
refine( Dist[i+1], LOCVOL[i], Mean[i], ID[i], Dist[i], MultiScaleSmooth[i],
NonLocalMean[i], *fillFloat[i], *Dm[i], nprocx, i,
rough_cutoff, lamda, nlm_sigsq, nlm_depth);
}
PROFILE_STOP("Refine distance");
MPI_Barrier(comm);
// Perform a final filter
PROFILE_START("Filtering final domains");
if (rank==0)
printf("Filtering final domains\n");
Array<float> filter_Mean, filter_Dist1, filter_Dist2;
filter_final( ID[0], Dist[0], *fillFloat[0], *Dm[0], filter_Mean, filter_Dist1, filter_Dist2 );
PROFILE_STOP("Filtering final domains");
//removeDisconnected( ID[0], *Dm[0] );
// Write the distance function to a netcdf file
/* const char* netcdf_filename = "Distance.nc";
{
RankInfoStruct info( rank, nprocx, nprocy, nprocz );
std::vector<int> dim = { Nx[0]*nprocx, Ny[0]*nprocy, Nz[0]*nprocz };
int fid = netcdf::open( netcdf_filename, netcdf::CREATE, MPI_COMM_WORLD );
auto dims = netcdf::defDim( fid, {"X", "Y", "Z"}, dim );
Array<float> data(Nx[0],Ny[0],Nz[0]);
fillFloat[0]->copy( Dist[0], data );
netcdf::write( fid, "Distance", dims, data, info );
netcdf::close( fid );
} */
// Write the results
if (rank==0) printf("Setting up visualization structure \n");
std::vector<IO::MeshDataStruct> meshData(N_levels);
for (size_t i=0; i<Nx.size(); i++) {
// Mesh
meshData[i].meshName = "image_" + std::to_string( i );
meshData[i].mesh = std::make_shared<IO::DomainMesh>(Dm[i]->rank_info,Nx[i],Ny[i],Nz[i],Lx,Ly,Lz);
// Source data
auto OrigData = std::make_shared<IO::Variable>();
OrigData->name = "Source_Data_" + std::to_string( i );
OrigData->type = IO::VariableType::VolumeVariable;
OrigData->dim = 1;
OrigData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(OrigData);
fillDouble[i]->copy( LOCVOL[i], OrigData->data );
// Non-Local Mean
auto NonLocMean = std::make_shared<IO::Variable>();
NonLocMean->name = "NonLocal_Mean_" + std::to_string( i );
NonLocMean->type = IO::VariableType::VolumeVariable;
NonLocMean->dim = 1;
NonLocMean->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(NonLocMean);
fillDouble[i]->copy( NonLocalMean[i], NonLocMean->data );
// Segmented Data
auto SegData = std::make_shared<IO::Variable>();
SegData->name = "Segmented_Data_" + std::to_string( i );
SegData->type = IO::VariableType::VolumeVariable;
SegData->dim = 1;
SegData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(SegData);
fillDouble[i]->copy( ID[i], SegData->data );
// Signed Distance
auto DistData = std::make_shared<IO::Variable>();
DistData->name = "Signed_Distance_" + std::to_string( i );
DistData->type = IO::VariableType::VolumeVariable;
DistData->dim = 1;
DistData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(DistData);
fillDouble[i]->copy( Dist[i], DistData->data );
// Smoothed Data
auto SmoothData = std::make_shared<IO::Variable>();
SmoothData->name = "Smoothed_Data_" + std::to_string( i );
SmoothData->type = IO::VariableType::VolumeVariable;
SmoothData->dim = 1;
SmoothData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(SmoothData);
fillDouble[i]->copy( MultiScaleSmooth[i], SmoothData->data );
}
#if 0
std::shared_ptr<IO::Variable> filter_Mean_var( new IO::Variable() );
filter_Mean_var->name = "Mean";
filter_Mean_var->type = IO::VariableType::VolumeVariable;
filter_Mean_var->dim = 1;
filter_Mean_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Mean_var);
fillDouble[0]->copy( filter_Mean, filter_Mean_var->data );
std::shared_ptr<IO::Variable> filter_Dist1_var( new IO::Variable() );
filter_Dist1_var->name = "Dist1";
filter_Dist1_var->type = IO::VariableType::VolumeVariable;
filter_Dist1_var->dim = 1;
filter_Dist1_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Dist1_var);
fillDouble[0]->copy( filter_Dist1, filter_Dist1_var->data );
std::shared_ptr<IO::Variable> filter_Dist2_var( new IO::Variable() );
filter_Dist2_var->name = "Dist2";
filter_Dist2_var->type = IO::VariableType::VolumeVariable;
filter_Dist2_var->dim = 1;
filter_Dist2_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Dist2_var);
fillDouble[0]->copy( filter_Dist2, filter_Dist2_var->data );
#endif
MPI_Barrier(comm);
if (rank==0) printf("Writing output \n");
// Write visulization data
IO::writeData( 0, meshData, comm );
if (rank==0) printf("Finished. \n");
// Compute the Minkowski functionals
MPI_Barrier(comm);
auto Averages = std::make_shared<Minkowski>(Dm[0]);
Array <char> phase_label(Nx[0]+2,Ny[0]+2,Nz[0]+2);
Array <double> phase_distance(Nx[0]+2,Ny[0]+2,Nz[0]+2);
// Analyze the wetting fluid
for (int k=1;k<Nz[0]+1;k++) {
for (int j=1;j<Ny[0]+1;j++) {
for (int i=1;i<Nx[0]+1;i++) {
int n = k*Nx[0]*Ny[0]+j*Nx[0]+i;
if (!(Dm[0]->id[n] > 0)){
// Solid phase
phase_label(i,j,k) = 0;
}
else if (Dist[0](i,j,k) < 0.0){
// wetting phase
phase_label(i,j,k) = 1;
}
else {
// non-wetting phase
phase_label(i,j,k) = 0;
}
phase_distance(i,j,k) =2.0*double(phase_label(i,j,k))-1.0;
}
}
}
CalcDist(phase_distance,phase_label,*Dm[0]);
Averages->ComputeScalar(phase_distance,0.f);
Averages->PrintAll();
}
*/
{
// Write the results
if (rank==0) printf("Setting up visualization structure \n");
// std::vector<IO::MeshDataStruct> meshData(N_levels);
std::vector<IO::MeshDataStruct> meshData(1);
// for (size_t i=0; i<Nx.size(); i++) {
// Mesh
meshData[0].meshName = "image";
meshData[0].mesh = std::shared_ptr<IO::DomainMesh>( new IO::DomainMesh(Dm[0]->rank_info,Nx[0],Ny[0],Nz[0],Lx,Ly,Lz) );
// Source data
std::shared_ptr<IO::Variable> OrigData( new IO::Variable() );
OrigData->name = "Source Data";
OrigData->type = IO::VariableType::VolumeVariable;
OrigData->dim = 1;
OrigData->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(OrigData);
fillDouble[0]->copy( LOCVOL[0], OrigData->data );
// Non-Local Mean
std::shared_ptr<IO::Variable> NonLocMean( new IO::Variable() );
NonLocMean->name = "Non-Local Mean";
NonLocMean->type = IO::VariableType::VolumeVariable;
NonLocMean->dim = 1;
NonLocMean->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(NonLocMean);
fillDouble[0]->copy( NonLocalMean[0], NonLocMean->data );
std::shared_ptr<IO::Variable> SegData( new IO::Variable() );
SegData->name = "Segmented Data";
SegData->type = IO::VariableType::VolumeVariable;
SegData->dim = 1;
SegData->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(SegData);
fillDouble[0]->copy( ID[0], SegData->data );
// Signed Distance
std::shared_ptr<IO::Variable> DistData( new IO::Variable() );
DistData->name = "Signed Distance";
DistData->type = IO::VariableType::VolumeVariable;
DistData->dim = 1;
DistData->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(DistData);
fillDouble[0]->copy( Dist[0], DistData->data );
// Smoothed Data
std::shared_ptr<IO::Variable> SmoothData( new IO::Variable() );
SmoothData->name = "Smoothed Data";
SmoothData->type = IO::VariableType::VolumeVariable;
SmoothData->dim = 1;
SmoothData->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(SmoothData);
fillDouble[0]->copy( MultiScaleSmooth[0], SmoothData->data );
/*// Segmented Data
std::shared_ptr<IO::Variable> SegData( new IO::Variable() );
SegData->name = "Segmented Data";
SegData->type = IO::VariableType::VolumeVariable;
SegData->dim = 1;
SegData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(SegData);
fillDouble[i]->copy( ID[i], SegData->data );
// Signed Distance
std::shared_ptr<IO::Variable> DistData( new IO::Variable() );
DistData->name = "Signed Distance";
DistData->type = IO::VariableType::VolumeVariable;
DistData->dim = 1;
DistData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(DistData);
fillDouble[i]->copy( Dist[i], DistData->data );
// Smoothed Data
std::shared_ptr<IO::Variable> SmoothData( new IO::Variable() );
SmoothData->name = "Smoothed Data";
SmoothData->type = IO::VariableType::VolumeVariable;
SmoothData->dim = 1;
SmoothData->data.resize(Nx[i],Ny[i],Nz[i]);
meshData[i].vars.push_back(SmoothData);
fillDouble[i]->copy( MultiScaleSmooth[i], SmoothData->data );
}
#if 0
std::shared_ptr<IO::Variable> filter_Mean_var( new IO::Variable() );
filter_Mean_var->name = "Mean";
filter_Mean_var->type = IO::VariableType::VolumeVariable;
filter_Mean_var->dim = 1;
filter_Mean_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Mean_var);
fillDouble[0]->copy( filter_Mean, filter_Mean_var->data );
std::shared_ptr<IO::Variable> filter_Dist1_var( new IO::Variable() );
filter_Dist1_var->name = "Dist1";
filter_Dist1_var->type = IO::VariableType::VolumeVariable;
filter_Dist1_var->dim = 1;
filter_Dist1_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Dist1_var);
fillDouble[0]->copy( filter_Dist1, filter_Dist1_var->data );
std::shared_ptr<IO::Variable> filter_Dist2_var( new IO::Variable() );
filter_Dist2_var->name = "Dist2";
filter_Dist2_var->type = IO::VariableType::VolumeVariable;
filter_Dist2_var->dim = 1;
filter_Dist2_var->data.resize(Nx[0],Ny[0],Nz[0]);
meshData[0].vars.push_back(filter_Dist2_var);
fillDouble[0]->copy( filter_Dist2, filter_Dist2_var->data );
#endif
*/
MPI_Barrier(comm);
if (rank==0) printf("Writing output \n");
// Write visulization data
IO::writeData( 0, meshData, comm );
if (rank==0) printf("Finished. \n");
}
// Compute the Minkowski functionals
MPI_Barrier(comm);
std::shared_ptr<Minkowski> Averages(new Minkowski(Dm[0]));
Array <char> phase_label(Nx[0],Ny[0],Nz[0]);
Array <double> phase_distance(Nx[0],Ny[0],Nz[0]);
// Analyze the wetting fluid
for (int k=1;k<Nz[0]+1;k++) {
for (int j=1;j<Ny[0]+1;j++) {
for (int i=1;i<Nx[0]+1;i++) {
int n = k*Nx[0]*Ny[0]+j*Nx[0]+i;
if (!(Dm[0]->id[n] > 0)){
// Solid phase
phase_label(i,j,k) = 0;
}
else if (Dist[0](i,j,k) < 0.0){
// wetting phase
phase_label(i,j,k) = 1;
}
else {
// non-wetting phase
phase_label(i,j,k) = 0;
}
phase_distance(i,j,k) =2.0*double(phase_label(i,j,k))-1.0;
}
}
}
CalcDist(phase_distance,phase_label,*Dm[0]);
Averages->ComputeScalar(phase_distance,0.f);
Averages->PrintAll();
}
PROFILE_STOP("Main");
PROFILE_SAVE("lbpm_uCT_pp",true);
MPI_Barrier(comm);
MPI_Finalize();
return 0;
PROFILE_STOP("Main");
PROFILE_SAVE("lbpm_uCT_pp",true);
MPI_Barrier(comm);
MPI_Finalize();
return 0;
}