471 lines
20 KiB
C++
471 lines
20 KiB
C++
// Sequential blob analysis
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// Reads parallel simulation data and performs connectivity analysis
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// and averaging on a blob-by-blob basis
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// James E. McClure 2014
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#include <stdio.h>
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#include <stdlib.h>
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#include <math.h>
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#include <iostream>
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#include <fstream>
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#include <sstream>
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#include <functional>
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#include "common/Array.h"
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#include "common/Domain.h"
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#include "common/Communication.h"
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#include "common/MPI.h"
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#include "IO/MeshDatabase.h"
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#include "IO/Mesh.h"
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#include "IO/Writer.h"
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#include "IO/netcdf.h"
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#include "analysis/analysis.h"
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#include "analysis/filters.h"
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#include "analysis/uCT.h"
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#include "analysis/distance.h"
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#include "analysis/Minkowski.h"
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#include "ProfilerApp.h"
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int main(int argc, char **argv)
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{
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// Initialize MPI
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Utilities::startup( argc, argv );
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Utilities::MPI comm( MPI_COMM_WORLD );
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int rank = comm.getRank();
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int nprocs = comm.getSize();
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{
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Utilities::setErrorHandlers();
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PROFILE_START("Main");
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//std::vector<std::string> filenames;
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if ( argc<2 ) {
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if ( rank == 0 ){
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printf("At least one filename must be specified\n");
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}
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return 1;
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}
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std::string filename = std::string(argv[1]);
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if ( rank == 0 ){
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printf("Input data file: %s\n",filename.c_str());
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}
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bool FILTER_CONNECTED_COMPONENTS = false;
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auto db = std::make_shared<Database>( filename );
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auto domain_db = db->getDatabase( "Domain" );
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auto uct_db = db->getDatabase( "uCT" );
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auto analysis_db = db->getDatabase( "Analysis" );
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// Read domain values
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auto L = domain_db->getVector<double>( "L" );
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auto size = domain_db->getVector<int>( "n" );
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auto nproc = domain_db->getVector<int>( "nproc" );
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//int BoundaryCondition = domain_db->getScalar<int>( "BC" );
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int nx = size[0];
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int ny = size[1];
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int nz = size[2];
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double Lx = L[0];
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double Ly = L[1];
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double Lz = L[2];
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int nprocx = nproc[0];
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int nprocy = nproc[1];
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int nprocz = nproc[2];
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auto InputFile = uct_db->getScalar<std::string>( "InputFile" );
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auto target = uct_db->getScalar<float>("target");
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auto background = uct_db->getScalar<float>("background");
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auto rough_cutoff = uct_db->getScalar<float>( "rough_cutoff" );
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auto lamda = uct_db->getScalar<float>( "lamda" );
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auto nlm_sigsq = uct_db->getScalar<float>( "nlm_sigsq" );
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auto nlm_depth = uct_db->getScalar<int>( "nlm_depth" );
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auto center = uct_db->getVector<int>( "center" );
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auto CylRad = uct_db->getScalar<float>( "cylinder_radius" );
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auto maxLevels = uct_db->getScalar<int>( "max_levels" );
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std::vector<int> offset( 3, 0 );
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if ( uct_db->keyExists( "offset" ) )
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offset = uct_db->getVector<int>( "offset" );
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if ( uct_db->keyExists( "filter_connected_components" ) )
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FILTER_CONNECTED_COMPONENTS = uct_db->getScalar<bool>( "filter_connected_components" );
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// Check that the number of processors >= the number of ranks
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if ( rank==0 ) {
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printf("Number of MPI ranks required: %i \n", nprocx*nprocy*nprocz);
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printf("Number of MPI ranks used: %i \n", nprocs);
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printf("Full domain size: %i x %i x %i \n",nx*nprocx,ny*nprocy,nz*nprocz);
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printf("target value = %f \n",target);
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printf("background value = %f \n",background);
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printf("cylinder center = %i, %i, %i \n",center[0],center[1],center[2]);
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printf("cylinder radius = %f \n",CylRad);
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}
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if ( nprocs < nprocx*nprocy*nprocz ){
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ERROR("Insufficient number of processors");
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}
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// Determine the maximum number of levels for the desired coarsen ratio
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int ratio[3] = {2,2,2};
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//std::vector<size_t> ratio = {4,4,4};
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// need to set up databases for each level of the mesh
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std::vector<std::shared_ptr<Database>> multidomain_db(1,domain_db);
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std::vector<int> Nx(1,nx), Ny(1,ny), Nz(1,nz);
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while ( Nx.back()%ratio[0]==0 && Nx.back()>8 &&
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Ny.back()%ratio[1]==0 && Ny.back()>8 &&
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Nz.back()%ratio[2]==0 && Nz.back()>8 &&
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(int) Nx.size() < maxLevels )
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{
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Nx.push_back( Nx.back()/ratio[0] );
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Ny.push_back( Ny.back()/ratio[1] );
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Nz.push_back( Nz.back()/ratio[2] );
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// clone the domain and create coarse version based on Nx,Ny,Nz
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auto db2 = domain_db->cloneDatabase();
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db2->putVector<int>( "n", { Nx.back(), Ny.back(), Nz.back() } );
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multidomain_db.push_back(db2);
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}
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int N_levels = Nx.size();
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// Initialize the domain
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std::vector<std::shared_ptr<Domain>> Dm(N_levels);
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for (int i=0; i<N_levels; i++) {
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// This line is no good -- will create identical Domain structures instead of
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// Need a way to define a coarse structure for the coarse domain (see above)
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Dm[i].reset( new Domain(multidomain_db[i], comm) );
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int N = (Nx[i]+2)*(Ny[i]+2)*(Nz[i]+2);
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for (int n=0; n<N; n++){
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Dm[i]->id[n] = 1;
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}
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Dm[i]->CommInit();
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}
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// array containing a distance mask
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Array<float> MASK(Nx[0]+2,Ny[0]+2,Nz[0]+2);
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MASK.fill(0);
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// Create the level data
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std::vector<Array<char>> ID(N_levels);
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std::vector<Array<float>> LOCVOL(N_levels);
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std::vector<Array<float>> Dist(N_levels);
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std::vector<Array<float>> MultiScaleSmooth(N_levels);
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std::vector<Array<float>> Mean(N_levels);
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std::vector<Array<float>> NonLocalMean(N_levels);
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std::vector<std::shared_ptr<fillHalo<double>>> fillDouble(N_levels);
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std::vector<std::shared_ptr<fillHalo<float>>> fillFloat(N_levels);
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std::vector<std::shared_ptr<fillHalo<char>>> fillChar(N_levels);
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for (int i=0; i<N_levels; i++) {
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ID[i] = Array<char>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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LOCVOL[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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Dist[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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MultiScaleSmooth[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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Mean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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NonLocalMean[i] = Array<float>(Nx[i]+2,Ny[i]+2,Nz[i]+2);
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ID[i].fill(0);
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LOCVOL[i].fill(0);
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Dist[i].fill(0);
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MultiScaleSmooth[i].fill(0);
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Mean[i].fill(0);
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NonLocalMean[i].fill(0);
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fillDouble[i].reset(new fillHalo<double>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
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fillFloat[i].reset(new fillHalo<float>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
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fillChar[i].reset(new fillHalo<char>(Dm[i]->Comm,Dm[i]->rank_info,{Nx[i],Ny[i],Nz[i]},{1,1,1},0,1) );
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}
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// Read the subvolume of interest on each processor
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PROFILE_START("ReadVolume");
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int fid = netcdf::open(InputFile,netcdf::READ);
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std::string varname("VOLUME");
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auto type = netcdf::getVarType( fid, varname );
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auto dim = netcdf::getVarDim( fid, varname );
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if ( rank == 0 ) {
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printf("Reading %s (%s)\n",varname.c_str(),netcdf::VariableTypeName(type).c_str());
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printf(" dims = %i x %i x %i \n",int(dim[0]),int(dim[1]),int(dim[2]));
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}
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{
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// Read the local data
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int x = Dm[0]->iproc()*nx + offset[0];
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int y = Dm[0]->jproc()*ny + offset[1];
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int z = Dm[0]->kproc()*nz + offset[2];
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Array<short> VOLUME = netcdf::getVar<short>( fid, varname, {x,y,z}, {nx,ny,nz}, {1,1,1} );
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// Copy the data and fill the halos
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LOCVOL[0].fill(0);
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fillFloat[0]->copy( VOLUME, LOCVOL[0] );
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fillFloat[0]->fill( LOCVOL[0] );
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}
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netcdf::close( fid );
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comm.barrier();
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PROFILE_STOP("ReadVolume");
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if (rank==0) printf("Read complete\n");
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// Filter the original data
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filter_src( *Dm[0], LOCVOL[0] );
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// Set up the mask to be distance to cylinder (crop outside cylinder)
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if (rank==0) printf("Cropping with cylinder: %i, %i, %i, radius=%f \n",Dm[0]->nprocx()*Nx[0],Dm[0]->nprocy()*Ny[0],Dm[0]->nprocz()*Nz[0],CylRad);
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for (int k=0;k<Nz[0]+2;k++) {
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for (int j=0;j<Ny[0]+2;j++) {
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for (int i=0;i<Nx[0]+2;i++) {
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//float x= float(Dm[0]->iproc()*Nx[0]+i-1);
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float y= float (Dm[0]->jproc()*Ny[0]+j-1);
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float z= float(Dm[0]->kproc()*Nz[0]+k-1);
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//float cx = float(center[0] - offset[0]);
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float cy = float(center[1] - offset[1]);
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float cz = float(center[2] - offset[2]);
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// distance from the center line
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MASK(i,j,k) = sqrt((z-cz)*(z-cz) + (y-cy)*(y-cy));
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//if (sqrt(((z-cz)*(z-cz) + (y-cy)*(y-cy)) ) > CylRad) LOCVOL[0](i,j,k)=background;
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}
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}
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}
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// Compute the means for the high/low regions
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// (should use automated mixture model to approximate histograms)
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//float THRESHOLD = 0.05*maxReduce( Dm[0]->Comm, std::max( LOCVOL[0].max(), fabs(LOCVOL[0].min()) ) );
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float THRESHOLD=0.5*(target+background);
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float mean_plus=0;
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float mean_minus=0;
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float min_value = LOCVOL[0](0);
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float max_value = LOCVOL[0](0);
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int count_plus=0;
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int count_minus=0;
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for (int k=1;k<Nz[0]+1;k++) {
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for (int j=1;j<Ny[0]+1;j++) {
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for (int i=1;i<Nx[0]+1;i++) {
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//LOCVOL[0](i,j,k) = MASK(i,j,k);
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if (MASK(i,j,k) < CylRad ){
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auto tmp = LOCVOL[0](i,j,k);
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/* if ((tmp-background)*(tmp-target) > 0){
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// direction to background / target is the same
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if (fabs(tmp-target) > fabs(tmp-background)) tmp=background; // tmp closer to background
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else tmp=target; // tmp closer to target
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}
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*/
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if ( tmp > THRESHOLD ) {
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mean_plus += tmp;
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count_plus++;
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}
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else {
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mean_minus += tmp;
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count_minus++;
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}
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if (tmp < min_value) min_value = tmp;
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if (tmp > max_value) max_value = tmp;
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}
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}
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}
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}
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count_plus = Dm[0]->Comm.sumReduce( count_plus);
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count_minus = Dm[0]->Comm.sumReduce( count_minus);
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if (rank==0) printf("minimum value=%f, max value=%f \n",min_value,max_value);
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if (rank==0) printf("plus=%i, minus=%i \n",count_plus,count_minus);
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ASSERT( count_plus > 0 && count_minus > 0 );
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comm.barrier();
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mean_plus = Dm[0]->Comm.sumReduce( mean_plus ) / count_plus;
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mean_minus = Dm[0]->Comm.sumReduce( mean_minus ) / count_minus;
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comm.barrier();
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if (rank==0) printf(" Region 1 mean (+): %f, Region 2 mean (-): %f \n",mean_plus, mean_minus);
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//if (rank==0) printf("Scale the input data (size = %i) \n",LOCVOL[0].length());
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for (size_t i=0; i<LOCVOL[0].length(); i++) {
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if ( MASK(i) > CylRad ){
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LOCVOL[0](i)=background;
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}
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if ( LOCVOL[0](i) >= THRESHOLD ) {
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auto tmp = LOCVOL[0](i)/ mean_plus;
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LOCVOL[0](i) = std::min( tmp, 1.0f );
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}
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else {
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auto tmp = -LOCVOL[0](i)/mean_minus;
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LOCVOL[0](i) = std::max( tmp, -1.0f );
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}
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//LOCVOL[0](i) = MASK(i);
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}
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// Fill the source data for the coarse meshes
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if (rank==0) printf("Coarsen the mesh for N_levels=%i \n",N_levels);
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comm.barrier();
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PROFILE_START("CoarsenMesh");
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for (int i=1; i<N_levels; i++) {
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Array<float> filter(ratio[0],ratio[1],ratio[2]);
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filter.fill(1.0f/filter.length());
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Array<float> tmp(Nx[i-1],Ny[i-1],Nz[i-1]);
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fillFloat[i-1]->copy( LOCVOL[i-1], tmp );
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Array<float> coarse = tmp.coarsen( filter );
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fillFloat[i]->copy( coarse, LOCVOL[i] );
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fillFloat[i]->fill( LOCVOL[i] );
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if (rank==0){
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printf("Coarsen level %i \n",i);
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printf(" Nx=%i, Ny=%i, Nz=%i \n",int(tmp.size(0)),int(tmp.size(1)),int(tmp.size(2)) );
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printf(" filter_x=%i, filter_y=%i, filter_z=%i \n",int(filter.size(0)),int(filter.size(1)),int(filter.size(2)) );
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printf(" ratio= %i,%i,%i \n",int(ratio[0]),int(ratio[1]),int(ratio[2]) );
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}
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comm.barrier();
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}
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PROFILE_STOP("CoarsenMesh");
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// Initialize the coarse level
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PROFILE_START("Solve coarse mesh");
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if (rank==0)
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printf("Initialize full mesh\n");
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solve( LOCVOL.back(), Mean.back(), ID.back(), Dist.back(), MultiScaleSmooth.back(),
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NonLocalMean.back(), *fillFloat.back(), *Dm.back(), nprocx,
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rough_cutoff, lamda, nlm_sigsq, nlm_depth);
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PROFILE_STOP("Solve coarse mesh");
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comm.barrier();
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// Refine the solution
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PROFILE_START("Refine distance");
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if (rank==0)
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printf("Refine mesh\n");
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for (int i=N_levels-2; i>=0; i--) {
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if (rank==0)
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printf(" Refining to level %i\n",i);
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refine( Dist[i+1], LOCVOL[i], Mean[i], ID[i], Dist[i], MultiScaleSmooth[i],
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NonLocalMean[i], *fillFloat[i], *Dm[i], nprocx, i,
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rough_cutoff, lamda, nlm_sigsq, nlm_depth);
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}
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PROFILE_STOP("Refine distance");
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comm.barrier();
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// Perform a final filter
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PROFILE_START("Filtering final domains");
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if (FILTER_CONNECTED_COMPONENTS){
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if (rank==0)
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printf("Filtering final domains\n");
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Array<float> filter_Mean, filter_Dist1, filter_Dist2;
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filter_final( ID[0], Dist[0], *fillFloat[0], *Dm[0], filter_Mean, filter_Dist1, filter_Dist2 );
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PROFILE_STOP("Filtering final domains");
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}
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//removeDisconnected( ID[0], *Dm[0] );
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// Write the distance function to a netcdf file
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/* const char* netcdf_filename = "Distance.nc";
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{
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RankInfoStruct info( rank, nprocx, nprocy, nprocz );
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std::vector<int> dim = { Nx[0]*nprocx, Ny[0]*nprocy, Nz[0]*nprocz };
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int fid = netcdf::open( netcdf_filename, netcdf::CREATE, MPI_COMM_WORLD );
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auto dims = netcdf::defDim( fid, {"X", "Y", "Z"}, dim );
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Array<float> data(Nx[0],Ny[0],Nz[0]);
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fillFloat[0]->copy( Dist[0], data );
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netcdf::write( fid, "Distance", dims, data, info );
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netcdf::close( fid );
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} */
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// Write the results
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if (rank==0) printf("Setting up visualization structure \n");
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std::vector<IO::MeshDataStruct> meshData(N_levels);
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for (size_t i=0; i<Nx.size(); i++) {
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// Mesh
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meshData[i].meshName = "image_" + std::to_string( i );
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meshData[i].mesh = std::make_shared<IO::DomainMesh>(Dm[i]->rank_info,Nx[i],Ny[i],Nz[i],Lx,Ly,Lz);
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// Source data
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auto OrigData = std::make_shared<IO::Variable>();
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OrigData->name = "Source_Data_" + std::to_string( i );
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OrigData->type = IO::VariableType::VolumeVariable;
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OrigData->dim = 1;
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OrigData->data.resize(Nx[i],Ny[i],Nz[i]);
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meshData[i].vars.push_back(OrigData);
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fillDouble[i]->copy( LOCVOL[i], OrigData->data );
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// Non-Local Mean
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auto NonLocMean = std::make_shared<IO::Variable>();
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NonLocMean->name = "NonLocal_Mean_" + std::to_string( i );
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NonLocMean->type = IO::VariableType::VolumeVariable;
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NonLocMean->dim = 1;
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NonLocMean->data.resize(Nx[i],Ny[i],Nz[i]);
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meshData[i].vars.push_back(NonLocMean);
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fillDouble[i]->copy( NonLocalMean[i], NonLocMean->data );
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// Segmented Data
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auto SegData = std::make_shared<IO::Variable>();
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SegData->name = "Segmented_Data_" + std::to_string( i );
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SegData->type = IO::VariableType::VolumeVariable;
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SegData->dim = 1;
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SegData->data.resize(Nx[i],Ny[i],Nz[i]);
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meshData[i].vars.push_back(SegData);
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fillDouble[i]->copy( ID[i], SegData->data );
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// Signed Distance
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auto DistData = std::make_shared<IO::Variable>();
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DistData->name = "Signed_Distance_" + std::to_string( i );
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DistData->type = IO::VariableType::VolumeVariable;
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DistData->dim = 1;
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DistData->data.resize(Nx[i],Ny[i],Nz[i]);
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meshData[i].vars.push_back(DistData);
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fillDouble[i]->copy( Dist[i], DistData->data );
|
|
// Smoothed Data
|
|
auto SmoothData = std::make_shared<IO::Variable>();
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|
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
|
|
comm.barrier();
|
|
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
|
|
comm.barrier();
|
|
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();
|
|
}
|
|
PROFILE_STOP("Main");
|
|
PROFILE_SAVE("lbpm_uCT_pp",true);
|
|
comm.barrier();
|
|
Utilities::shutdown();
|
|
return 0;
|
|
}
|
|
|