mirror of
https://github.com/cosmo-sims/MUSIC.git
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1439 lines
39 KiB
C++
1439 lines
39 KiB
C++
/*
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random.hh - This file is part of MUSIC -
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a code to generate multi-scale initial conditions
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for cosmological simulations
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Copyright (C) 2010 Oliver Hahn
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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*/
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//#define DEGRADE_RAND1
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//#define DEGRADE_RAND2
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#ifndef __RANDOM_HH
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#define __RANDOM_HH
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#include <fstream>
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#include <algorithm>
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#include <gsl/gsl_rng.h>
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#include <gsl/gsl_randist.h>
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#include <omp.h>
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#ifdef SINGLE_PRECISION
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#ifdef SINGLETHREAD_FFTW
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#include <srfftw.h>
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#else
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#include <srfftw_threads.h>
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#endif
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#else
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#ifdef SINGLETHREAD_FFTW
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#include <drfftw.h>
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#else
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#include <drfftw_threads.h>
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#endif
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#endif
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#include "general.hh"
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#include "constraints.hh"
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#include "mesh.hh"
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#include "mg_operators.hh"
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/*!
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* @brief encapsulates all things random number generator related
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*/
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template< typename T >
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class random_numbers
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{
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public:
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unsigned
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res_, //!< resolution of the full mesh
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cubesize_, //!< size of one independent random number cube
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ncubes_; //!< number of random number cubes to cover the full mesh
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long baseseed_; //!< base seed from which cube seeds are computed
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//! vector of 3D meshes (the random number cubes) with random numbers
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std::vector< Meshvar<T>* > rnums_;
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protected:
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//! fills a subcube with random numbers
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double fill_cube( int i, int j, int k)
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{
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gsl_rng *RNG = gsl_rng_alloc( gsl_rng_mt19937 );
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i = (i+ncubes_)%ncubes_;
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j = (j+ncubes_)%ncubes_;
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k = (k+ncubes_)%ncubes_;
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long icube = (i*ncubes_+j)*ncubes_+k;
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long cubeseed = baseseed_+icube; //... each cube gets its unique seed
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gsl_rng_set( RNG, cubeseed );
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if( rnums_[icube] == NULL )
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rnums_[icube] = new Meshvar<T>( cubesize_, 0, 0, 0 );
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double mean = 0.0;
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for( int ii=0; ii<(int)cubesize_; ++ii )
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for( int jj=0; jj<(int)cubesize_; ++jj )
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for( int kk=0; kk<(int)cubesize_; ++kk )
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{
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(*rnums_[icube])(ii,jj,kk) = gsl_ran_ugaussian_ratio_method( RNG );
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mean += (*rnums_[icube])(ii,jj,kk);
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}
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gsl_rng_free( RNG );
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return mean/(cubesize_*cubesize_*cubesize_);
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}
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//! subtract a constant from an entire cube
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void subtract_from_cube( int i, int j, int k, double val )
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{
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i = (i+ncubes_)%ncubes_;
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j = (j+ncubes_)%ncubes_;
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k = (k+ncubes_)%ncubes_;
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long icube = (i*ncubes_+j)*ncubes_+k;
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for( int ii=0; ii<(int)cubesize_; ++ii )
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for( int jj=0; jj<(int)cubesize_; ++jj )
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for( int kk=0; kk<(int)cubesize_; ++kk )
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(*rnums_[icube])(ii,jj,kk) -= val;
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}
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//! copy random numbers from a cube to a full grid array
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template< class C >
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void copy_cube( int i, int j, int k, C& dat )
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{
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int offi, offj, offk;
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offi = i*cubesize_;
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offj = j*cubesize_;
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offk = k*cubesize_;
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i = (i+ncubes_)%ncubes_;
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j = (j+ncubes_)%ncubes_;
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k = (k+ncubes_)%ncubes_;
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long icube = (i*ncubes_+j)*ncubes_+k;
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for( int ii=0; ii<(int)cubesize_; ++ii )
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for( int jj=0; jj<(int)cubesize_; ++jj )
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for( int kk=0; kk<(int)cubesize_; ++kk )
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dat(offi+ii,offj+jj,offk+kk) = (*rnums_[icube])(ii,jj,kk);
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}
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//! free the memory associated with a subcube
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void free_cube( int i, int j, int k )
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{
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i = (i+ncubes_)%ncubes_;
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j = (j+ncubes_)%ncubes_;
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k = (k+ncubes_)%ncubes_;
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long icube = (i*ncubes_+j)*ncubes_+k;
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if( rnums_[icube] != NULL )
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{
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delete rnums_[icube];
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rnums_[icube] = NULL;
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}
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}
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//! initialize member variables and allocate memory
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void initialize( void )
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{
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ncubes_ = std::max((int)((double)res_/cubesize_),1);
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if( res_ < cubesize_ )
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{
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ncubes_ = 1;
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cubesize_ = res_;
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}
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std::cout << " - Generating random numbers w/ sample cube size of " << cubesize_ << std::endl;
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rnums_.assign( ncubes_*ncubes_*ncubes_, NULL );
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}
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//! fill a cubic subvolume of the full grid with random numbers
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double fill_subvolume( int *i0, int *n )
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{
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int i0cube[3], ncube[3];
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i0cube[0] = (int)((double)i0[0]/cubesize_);
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i0cube[1] = (int)((double)i0[1]/cubesize_);
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i0cube[2] = (int)((double)i0[2]/cubesize_);
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ncube[0] = (int)((double)(i0[0]+n[0])/cubesize_+1.0)-i0cube[0];
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ncube[1] = (int)((double)(i0[1]+n[1])/cubesize_+1.0)-i0cube[1];
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ncube[2] = (int)((double)(i0[2]+n[2])/cubesize_+1.0)-i0cube[2];
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double mean = 0.0;
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std::cerr << i0[0] << ", " << i0[1] << ", " << i0[2] << "\n";
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std::cerr << n[0] << ", " << n[1] << ", " << n[2] << "\n";
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std::cerr << i0cube[0] << ", " << i0cube[1] << ", " << i0cube[2] << "\n";
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std::cerr << ncube[0] << ", " << ncube[1] << ", " << ncube[2] << "\n";
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#pragma omp parallel for reduction(+:mean)
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for( int i=i0cube[0]; i<i0cube[0]+ncube[0]; ++i )
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for( int j=i0cube[1]; j<i0cube[1]+ncube[1]; ++j )
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for( int k=i0cube[2]; k<i0cube[2]+ncube[2]; ++k )
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{
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int ii(i),jj(j),kk(k);
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ii = (ii+ncubes_)%ncubes_;
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jj = (jj+ncubes_)%ncubes_;
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kk = (kk+ncubes_)%ncubes_;
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mean += fill_cube(ii, jj, kk);
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}
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return mean/(ncube[0]*ncube[1]*ncube[2]);
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}
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//! fill an entire grid with random numbers
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double fill_all( void )
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{
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double sum = 0.0;
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#pragma omp parallel for reduction(+:sum)
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for( int i=0; i<(int)ncubes_; ++i )
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for( int j=0; j<(int)ncubes_; ++j )
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for( int k=0; k<(int)ncubes_; ++k )
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{
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int ii(i),jj(j),kk(k);
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ii = (ii+ncubes_)%ncubes_;
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jj = (jj+ncubes_)%ncubes_;
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kk = (kk+ncubes_)%ncubes_;
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sum+=fill_cube(ii, jj, kk);
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}
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//... subtract mean
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#pragma omp parallel for reduction(+:sum)
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for( int i=0; i<(int)ncubes_; ++i )
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for( int j=0; j<(int)ncubes_; ++j )
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for( int k=0; k<(int)ncubes_; ++k )
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{
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int ii(i),jj(j),kk(k);
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ii = (ii+ncubes_)%ncubes_;
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jj = (jj+ncubes_)%ncubes_;
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kk = (kk+ncubes_)%ncubes_;
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subtract_from_cube(ii,jj,kk,sum/(ncubes_*ncubes_*ncubes_));
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}
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/////////////////////////////////////////////////////
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#if defined(DEGRADE_RAND1)
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{
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std::cerr << " - degrading field for 1 level...(" << res_ << ")\n";
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//unsigned ixoff=51,iyoff=51,izoff=51;
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//unsigned nx=52, ny=52, nz=52;
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int ixoff=102, iyoff=102, izoff=102;
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int nx=104, ny=104, nz=104;
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#pragma omp parallel for
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for( int ix=0; ix<(int)res_; ix+=2 )
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for( int iy=0; iy<(int)res_; iy+=2 )
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for( int iz=0; iz<(int)res_; iz+=2 )
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{
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if( ix>=2*ixoff && ix < 2*ixoff+nx
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&& iy>=2*iyoff && iy < 2*iyoff+ny
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&& iz>=2*izoff && iz < 2*izoff+nz )
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{
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continue;
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}
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double avg = 0.125*((*this)(ix,iy,iz)+(*this)(ix+1,iy,iz)+(*this)(ix,iy+1,iz)+(*this)(ix,iy,iz+1)+
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(*this)(ix+1,iy+1,iz)+(*this)(ix+1,iy,iz+1)+(*this)(ix,iy+1,iz+1)+(*this)(ix+1,iy+1,iz+1));
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(*this)(ix,iy,iz) = avg;
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(*this)(ix+1,iy,iz) = avg;
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(*this)(ix,iy+1,iz) = avg;
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(*this)(ix,iy,iz+1) = avg;
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(*this)(ix+1,iy+1,iz) = avg;
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(*this)(ix+1,iy,iz+1) = avg;
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(*this)(ix,iy+1,iz+1) = avg;
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(*this)(ix+1,iy+1,iz+1) = avg;
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}
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}
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#elif defined(DEGRADE_RAND2)
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{
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std::cerr << " - degrading field for 2 level...(" << res_ << ")\n";
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/*unsigned ixoff2=102,iyoff2=102,izoff2=102;
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unsigned nx2=52, ny2=52, nz2=52;
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unsigned ixoff1=86,iyoff1=86,izoff1=86;
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unsigned nx1=168,ny1=168,nz1=168;*/
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int ixoff2=86,iyoff2=86,izoff2=86;
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int nx2=84,ny2=84,nz2=84;
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int ixoff1=102,iyoff1=102,izoff1=102;
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int nx1=104,ny1=104,nz1=104;
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//unsigned ixoff2=51,iyoff2=51,izoff2=51;
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//unsigned nx2=52, ny2=52, nz2=52;
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//unsigned ixoff1=34,iyoff1=34,izoff1=34;
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//unsigned nx1=120,ny1=120,nz1=120;
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//unsigned ixoff2=84, iyoff2=84, izoff2=84;
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//unsigned nx2=90, ny2=90, nz2=90;
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//unsigned ixoff1=100, iyoff1=100, izoff1=100;
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//unsigned nx1=112, ny1=112, nz1=112;
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/*unsigned ixoff2=100, iyoff2=100, izoff2=100;
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unsigned nx2=112, ny2=112, nz2=112;
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unsigned ixoff1=84, iyoff1=84, izoff1=84;
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unsigned nx1=180, ny1=180, nz1=180;*/
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#pragma omp parallel for
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for( int ix=0; ix<(int)res_; ix+=2 )
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for( int iy=0; iy<(int)res_; iy+=2 )
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for( int iz=0; iz<(int)res_; iz+=2 )
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{
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if( ix>=2*ixoff2 && ix < 2*ixoff2+nx2
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&& iy>=2*iyoff2 && iy < 2*iyoff2+ny2
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&& iz>=2*izoff2 && iz < 2*izoff2+nz2 )
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{
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continue;
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}
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double avg = 0.125*((*this)(ix,iy,iz)+(*this)(ix+1,iy,iz)+(*this)(ix,iy+1,iz)+(*this)(ix,iy,iz+1)+
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(*this)(ix+1,iy+1,iz)+(*this)(ix+1,iy,iz+1)+(*this)(ix,iy+1,iz+1)+(*this)(ix+1,iy+1,iz+1));
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(*this)(ix,iy,iz) = avg;
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(*this)(ix+1,iy,iz) = avg;
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(*this)(ix,iy+1,iz) = avg;
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(*this)(ix,iy,iz+1) = avg;
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(*this)(ix+1,iy+1,iz) = avg;
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(*this)(ix+1,iy,iz+1) = avg;
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(*this)(ix,iy+1,iz+1) = avg;
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(*this)(ix+1,iy+1,iz+1) = avg;
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}
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#pragma omp parallel for
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for( int ix=0; ix<(int)res_; ix+=4 )
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for( int iy=0; iy<(int)res_; iy+=4 )
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for( int iz=0; iz<(int)res_; iz+=4 )
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{
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if( ix>=2*ixoff1 && ix < 2*ixoff1+nx1
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&& iy>=2*iyoff1 && iy < 2*iyoff1+ny1
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&& iz>=2*izoff1 && iz < 2*izoff1+nz1 )
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{
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continue;
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}
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double avg = 0.0;//0.125*((*this)(ix,iy,iz)+(*this)(ix+1,iy,iz)+(*this)(ix,iy+1,iz)+(*this)(ix,iy,iz+1)+
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//(*this)(ix+1,iy+1,iz)+(*this)(ix+1,iy,iz+1)+(*this)(ix,iy+1,iz+1)+(*this)(ix+1,iy+1,iz+1));
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for( int i=0; i<4; ++i )
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for( int j=0; j<4; ++j )
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for( int k=0; k<4; ++k )
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avg += (*this)(ix+i,iy+j,iz+k);
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avg /=4.*4.*4.;
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for( int i=0; i<4; ++i )
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for( int j=0; j<4; ++j )
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for( int k=0; k<4; ++k )
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(*this)(ix+i,iy+j,iz+k) = avg;
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}
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}
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#endif
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/////////////////////////////////////////////////////
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return sum/(ncubes_*ncubes_*ncubes_);
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}
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public:
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template< class C >
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double fill_all( C& dat )
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{
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double sum = 0.0;
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std::cerr << "CHECK\n";
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#pragma omp parallel for reduction(+:sum)
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for( int i=0; i<(int)ncubes_; ++i )
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for( int j=0; j<(int)ncubes_; ++j )
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for( int k=0; k<(int)ncubes_; ++k )
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{
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int ii(i),jj(j),kk(k);
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ii = (ii+ncubes_)%ncubes_;
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jj = (jj+ncubes_)%ncubes_;
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kk = (kk+ncubes_)%ncubes_;
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sum+=fill_cube(ii, jj, kk);
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copy_cube(ii,jj,kk,dat);
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free_cube(ii, jj, kk);
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}
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return sum/(ncubes_*ncubes_*ncubes_);
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}
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void print_allocated( void )
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{
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unsigned ncount = 0, ntot = rnums_.size();
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for( int i=0; i<rnums_.size(); ++i )
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if( rnums_[i]!=NULL ) ncount++;
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std::cerr << " -> " << ncount << " of " << ntot << " random number cubes currently allocated\n";
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}
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public:
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//! constructor
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random_numbers( unsigned res, unsigned cubesize, long baseseed, int *x0, int *lx )
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: res_( res ), cubesize_( cubesize ), ncubes_( 1 ), baseseed_( baseseed )
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{
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std::cout << " - Generating random numbers (1) with seed " << baseseed << std::endl;
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initialize();
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fill_subvolume( x0, lx );
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}
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//... create constrained new field
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random_numbers( random_numbers<T>& rc, unsigned cubesize, long baseseed, bool kspace=false, int *x0_=NULL, int *lx_=NULL, bool zeromean=true )
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: res_( 2*rc.res_ ), cubesize_( cubesize ), ncubes_( 1 ), baseseed_( baseseed )
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{
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//double mean = 0.0;
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initialize();
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int x0[3],lx[3];
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if( x0_==NULL || lx_==NULL )
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{
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for(int i=0;i<3;++i ){
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x0[i]=0;
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lx[i]=res_;
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}
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fill_all();
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}
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else
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{
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for(int i=0;i<3;++i ){
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x0[i]=x0_[i];
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lx[i]=lx_[i];
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}
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fill_subvolume( x0, lx );
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}
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if( kspace )
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{
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std::cout << " - Generating a constrained random number set with seed " << baseseed << "\n"
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<< " using coarse mode replacement...\n";
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int nx=lx[0], ny=lx[1], nz=lx[2],
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nxc=lx[0]/2, nyc=lx[1]/2, nzc=lx[2]/2;
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fftw_real
|
|
*rcoarse = new fftw_real[nxc*nyc*(nzc+2)],
|
|
*rfine = new fftw_real[nx*ny*(nz+2)];
|
|
|
|
fftw_complex
|
|
*ccoarse = reinterpret_cast<fftw_complex*> (rcoarse),
|
|
*cfine = reinterpret_cast<fftw_complex*> (rfine);
|
|
|
|
rfftwnd_plan
|
|
pc = rfftw3d_create_plan( nxc, nyc, nzc, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE|FFTW_IN_PLACE),
|
|
pf = rfftw3d_create_plan( nx, ny, nz, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE|FFTW_IN_PLACE),
|
|
ipf = rfftw3d_create_plan( nx, ny, nz, FFTW_COMPLEX_TO_REAL, FFTW_ESTIMATE|FFTW_IN_PLACE);
|
|
|
|
#pragma omp parallel for
|
|
for( int i=0; i<nx; i++ )
|
|
for( int j=0; j<ny; j++ )
|
|
for( int k=0; k<nz; k++ )
|
|
{
|
|
unsigned q = (i*ny+j)*(nz+2)+k;
|
|
rfine[q] = (*this)(x0[0]+i,x0[1]+j,x0[2]+k);
|
|
}
|
|
|
|
#pragma omp parallel for
|
|
for( int i=0; i<nxc; i++ )
|
|
for( int j=0; j<nyc; j++ )
|
|
for( int k=0; k<nzc; k++ )
|
|
{
|
|
unsigned q = (i*nyc+j)*(nzc+2)+k;
|
|
rcoarse[q] = rc(x0[0]/2+i,x0[1]/2+j,x0[2]/2+k);
|
|
}
|
|
|
|
#ifndef SINGLETHREAD_FFTW
|
|
rfftwnd_threads_one_real_to_complex( omp_get_max_threads(), pc, rcoarse, NULL );
|
|
rfftwnd_threads_one_real_to_complex( omp_get_max_threads(), pf, rfine, NULL );
|
|
#else
|
|
rfftwnd_one_real_to_complex( pc, rcoarse, NULL );
|
|
rfftwnd_one_real_to_complex( pf, rfine, NULL );
|
|
#endif
|
|
|
|
|
|
double fftnorm = 1.0/(nx*ny*nz);
|
|
|
|
#pragma omp parallel for
|
|
for( int i=0; i<nxc; i++ )
|
|
for( int j=0; j<nyc; j++ )
|
|
for( int k=0; k<nzc/2+1; k++ )
|
|
{
|
|
int ii(i),jj(j),kk(k);
|
|
|
|
if( i > nxc/2 ) ii += nx/2;
|
|
if( j > nyc/2 ) jj += ny/2;
|
|
|
|
unsigned qc,qf;
|
|
qc = (i*nyc+j)*(nzc/2+1)+k;
|
|
qf = (ii*ny+jj)*(nz/2+1)+kk;
|
|
|
|
cfine[qf].re = sqrt(8.0)*ccoarse[qc].re;
|
|
cfine[qf].im = sqrt(8.0)*ccoarse[qc].im;
|
|
}
|
|
|
|
delete[] rcoarse;
|
|
|
|
#pragma omp parallel for
|
|
for( int i=0; i<nx; i++ )
|
|
for( int j=0; j<ny; j++ )
|
|
for( int k=0; k<nz/2+1; k++ )
|
|
{
|
|
unsigned q = (i*ny+j)*(nz/2+1)+k;
|
|
cfine[q].re *= fftnorm;
|
|
cfine[q].im *= fftnorm;
|
|
}
|
|
|
|
#ifndef SINGLETHREAD_FFTW
|
|
rfftwnd_threads_one_complex_to_real( omp_get_max_threads(), ipf, cfine, NULL );
|
|
#else
|
|
rfftwnd_one_complex_to_real( ipf, cfine, NULL );
|
|
#endif
|
|
|
|
for( int i=0; i<nx; i++ )
|
|
for( int j=0; j<ny; j++ )
|
|
for( int k=0; k<nz; k++ )
|
|
{
|
|
unsigned q = (i*ny+j)*(nz+2)+k;
|
|
(*this)(x0[0]+i,x0[1]+j,x0[2]+k) = rfine[q];
|
|
}
|
|
|
|
delete[] rfine;
|
|
|
|
}
|
|
else
|
|
{
|
|
std::cout << " - Generating a constrained random number set with seed " << baseseed << "\n"
|
|
<< " using Hoffman-Ribak constraints...\n";
|
|
|
|
double fac = 1./sqrt(8.0);
|
|
|
|
for( int i=x0[0],ii=x0[0]/2; ii<lx[0]; i+=2,++ii )
|
|
for( int j=x0[1],jj=x0[1]/2; jj<lx[1]; j+=2,++jj )
|
|
for( int k=x0[2],kk=x0[2]/2; kk<lx[2]; k+=2,++kk )
|
|
{
|
|
double topval = rc(ii,jj,kk);
|
|
double locmean = 0.125*((*this)(i,j,k)+(*this)(i+1,j,k)+(*this)(i,j+1,k)+(*this)(i,j,k+1)+
|
|
(*this)(i+1,j+1,k)+(*this)(i+1,j,k+1)+(*this)(i,j+1,k+1)+(*this)(i+1,j+1,k+1));
|
|
double dif = fac*topval-locmean;
|
|
|
|
(*this)(i,j,k) += dif;
|
|
(*this)(i+1,j,k) += dif;
|
|
(*this)(i,j+1,k) += dif;
|
|
(*this)(i,j,k+1) += dif;
|
|
(*this)(i+1,j+1,k) += dif;
|
|
(*this)(i+1,j,k+1) += dif;
|
|
(*this)(i,j+1,k+1) += dif;
|
|
(*this)(i+1,j+1,k+1) += dif;
|
|
|
|
}
|
|
}
|
|
}
|
|
|
|
|
|
//! constructor
|
|
random_numbers( unsigned res, unsigned cubesize, long baseseed, bool zeromean=true )
|
|
: res_( res ), cubesize_( cubesize ), ncubes_( 1 ), baseseed_( baseseed )
|
|
{
|
|
std::cout << " - Generating random numbers (2) with seed " << baseseed << std::endl;
|
|
|
|
double mean = 0.0;
|
|
initialize();
|
|
mean = fill_all();
|
|
|
|
if( zeromean )
|
|
{
|
|
mean = 0.0;
|
|
for(unsigned i=0; i<res_; ++i )
|
|
for( unsigned j=0; j<res_; ++j )
|
|
for( unsigned k=0; k<res_; ++k )
|
|
mean += (*this)(i,j,k);
|
|
mean *= 1.0/(res_*res_*res_);
|
|
|
|
for(unsigned i=0; i<res_; ++i )
|
|
for( unsigned j=0; j<res_; ++j )
|
|
for( unsigned k=0; k<res_; ++k )
|
|
(*this)(i,j,k) = (*this)(i,j,k) - mean;
|
|
}
|
|
|
|
}
|
|
|
|
//! constructor
|
|
random_numbers( unsigned res, std::string randfname )
|
|
: res_( res ), cubesize_( res ), ncubes_(1)
|
|
{
|
|
rnums_.push_back( new Meshvar<T>( res, 0, 0, 0 ) );
|
|
|
|
std::ifstream ifs(randfname.c_str(), std::ios::binary);
|
|
if( !ifs )
|
|
throw std::runtime_error(std::string("Could not open random number file \'")+randfname+std::string("\'!"));
|
|
|
|
unsigned vartype;
|
|
unsigned nx,ny,nz,blksz;
|
|
int iseed;
|
|
long seed;
|
|
//ifs.read( (char*)&vartype, sizeof(unsigned) );
|
|
|
|
//... read header .../
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
|
|
if( blksz != 4*sizeof(int) )
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
ifs.read( reinterpret_cast<char*> (&nx), sizeof(unsigned) );
|
|
ifs.read( reinterpret_cast<char*> (&ny), sizeof(unsigned) );
|
|
ifs.read( reinterpret_cast<char*> (&nz), sizeof(unsigned) );
|
|
ifs.read( reinterpret_cast<char*> (&iseed), sizeof(int) );
|
|
seed = (long)iseed;
|
|
|
|
if( nx!=res_ || ny!=res_ || nz!=res_ )
|
|
{
|
|
char errmsg[128];
|
|
sprintf(errmsg,"White noise file dimensions do not match level dimensions: %ux%ux%u vs. %u**3",nx,ny,nz,res_);
|
|
throw std::runtime_error(errmsg);
|
|
|
|
}
|
|
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
|
|
//... read data ...//
|
|
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
if( blksz == nx*ny*sizeof(float) )
|
|
vartype = 4;
|
|
else if( blksz == nx*ny*sizeof(double) )
|
|
vartype = 8;
|
|
else
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
ifs.seekg(-sizeof(int),std::ios::cur);
|
|
|
|
std::vector<float> in_float;
|
|
std::vector<double> in_double;
|
|
|
|
std::cout << " - Random number file \'" << randfname << "\'\n"
|
|
<< " contains " << nx*ny*nz << " numbers. Reading..." << std::endl;
|
|
|
|
double sum = 0.0, sum2 = 0.0;
|
|
unsigned count = 0;
|
|
|
|
if( vartype == 4 )
|
|
{
|
|
for( int ii=0; ii<(int)nz; ++ii )
|
|
{
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
if( blksz != nx*ny*sizeof(float) )
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
in_float.assign(nx*ny,0.0f);
|
|
ifs.read( (char*)&in_float[0], nx*ny*sizeof(float) );
|
|
|
|
for( int jj=0,q=0; jj<(int)ny; ++jj )
|
|
for( int kk=0; kk<(int)nx; ++kk ){
|
|
sum += in_float[q];
|
|
sum2 += in_float[q]*in_float[q];
|
|
++count;
|
|
|
|
(*rnums_[0])(kk,jj,ii) = -in_float[q++];
|
|
}
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
if( blksz != nx*ny*sizeof(float) )
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
}
|
|
}
|
|
else if( vartype == 8 )
|
|
{
|
|
for( int ii=0; ii<(int)nz; ++ii )
|
|
{
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
if( blksz != nx*ny*sizeof(double) )
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
in_double.assign(nx*ny,0.0f);
|
|
ifs.read( (char*)&in_double[0], nx*ny*sizeof(double) );
|
|
|
|
for( int jj=0,q=0; jj<(int)ny; ++jj )
|
|
for( int kk=0; kk<(int)nx; ++kk )
|
|
{
|
|
sum += in_double[q];
|
|
sum2 += in_double[q]*in_double[q];
|
|
++count;
|
|
(*rnums_[0])(kk,jj,ii) = -in_double[q++];
|
|
}
|
|
ifs.read( reinterpret_cast<char*> (&blksz), sizeof(int) );
|
|
if( blksz != nx*ny*sizeof(double) )
|
|
throw std::runtime_error("corrupt random number file");
|
|
|
|
}
|
|
}
|
|
|
|
double mean, var;
|
|
mean = sum/count;
|
|
var = sum2/count-mean*mean;
|
|
|
|
std::cout << " - Random numbers in file have \n"
|
|
<< " mean = " << mean << " and var = " << var << std::endl;
|
|
|
|
}
|
|
|
|
//... create constrained new field
|
|
/*random_numbers( random_numbers<T>& rc, unsigned cubesize, long baseseed, bool kspace=false, bool zeromean=true )
|
|
: res_( 2*rc.res_ ), cubesize_( cubesize ), ncubes_( 1 ), baseseed_( baseseed )
|
|
{
|
|
double mean = 0.0;
|
|
initialize();
|
|
mean = fill_all();
|
|
|
|
if( zeromean )
|
|
{
|
|
for(unsigned i=0; i<res_; ++i )
|
|
for( unsigned j=0; j<res_; ++j )
|
|
for( unsigned k=0; k<res_; ++k )
|
|
(*this)(i,j,k) -= mean;
|
|
}
|
|
|
|
if( kspace )
|
|
{
|
|
}
|
|
else
|
|
{
|
|
std::cout << " - Generating a constrained random number set with seed " << baseseed << "...\n";
|
|
|
|
double fac = 1./sqrt(8.0);
|
|
|
|
for( unsigned i=0,ii=0; i<res_; i+=2,++ii )
|
|
for( unsigned j=0,jj=0; j<res_; j+=2,++jj )
|
|
for( unsigned k=0,kk=0; k<res_; k+=2,++kk )
|
|
{
|
|
double topval = rc(ii,jj,kk);
|
|
double locmean = 0.125*((*this)(i,j,k)+(*this)(i+1,j,k)+(*this)(i,j+1,k)+(*this)(i,j,k+1)+
|
|
(*this)(i+1,j+1,k)+(*this)(i+1,j,k+1)+(*this)(i,j+1,k+1)+(*this)(i+1,j+1,k+1));
|
|
double dif = fac*topval-locmean;
|
|
|
|
(*this)(i,j,k) += dif;
|
|
(*this)(i+1,j,k) += dif;
|
|
(*this)(i,j+1,k) += dif;
|
|
(*this)(i,j,k+1) += dif;
|
|
(*this)(i+1,j+1,k) += dif;
|
|
(*this)(i+1,j,k+1) += dif;
|
|
(*this)(i,j+1,k+1) += dif;
|
|
(*this)(i+1,j+1,k+1) += dif;
|
|
|
|
}
|
|
|
|
}
|
|
}*/
|
|
|
|
//... copy construct by averaging down
|
|
explicit random_numbers( /*const*/ random_numbers <T>& rc, bool kdegrade = true )
|
|
{
|
|
//if( res > rc.m_res || (res/rc.m_res)%2 != 0 )
|
|
// throw std::runtime_error("Invalid restriction in random number container copy constructor.");
|
|
|
|
double sum = 0.0, sum2 = 0.0;
|
|
unsigned count = 0;
|
|
|
|
if( kdegrade )
|
|
{
|
|
std::cout << " - Generating a coarse white noise field by k-space degrading\n";
|
|
//... initialize properties of container
|
|
res_ = rc.res_/2;
|
|
cubesize_ = res_;
|
|
ncubes_ = 1;
|
|
baseseed_ = -2;
|
|
|
|
if( sizeof(fftw_real)!=sizeof(T) )
|
|
throw std::runtime_error("type mismatch with fftw_real in k-space averaging");
|
|
|
|
fftw_real
|
|
*rfine = new fftw_real[rc.res_*rc.res_*2*(rc.res_/2+1)],
|
|
*rcoarse = new fftw_real[res_*res_*2*(res_/2+1)];
|
|
|
|
fftw_complex
|
|
*ccoarse = reinterpret_cast<fftw_complex*> (rcoarse),
|
|
*cfine = reinterpret_cast<fftw_complex*> (rfine);
|
|
|
|
int nx(rc.res_), ny(rc.res_), nz(rc.res_), nxc(res_), nyc(res_), nzc(res_);
|
|
|
|
rfftwnd_plan
|
|
pf = rfftw3d_create_plan( nx, ny, nz, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE|FFTW_IN_PLACE),
|
|
ipc = rfftw3d_create_plan( nxc, nyc, nzc, FFTW_COMPLEX_TO_REAL, FFTW_ESTIMATE|FFTW_IN_PLACE);
|
|
|
|
for( int i=0; i<nx; i++ )
|
|
for( int j=0; j<ny; j++ )
|
|
for( int k=0; k<nz; k++ )
|
|
{
|
|
unsigned q = (i*ny+j)*(nz+2)+k;
|
|
rfine[q] = rc(i,j,k);
|
|
}
|
|
|
|
|
|
#ifndef SINGLETHREAD_FFTW
|
|
rfftwnd_threads_one_real_to_complex( omp_get_max_threads(), pf, rfine, NULL );
|
|
#else
|
|
rfftwnd_one_real_to_complex( pf, rfine, NULL );
|
|
#endif
|
|
|
|
double fftnorm = 1.0/(nxc*nyc*nzc);
|
|
|
|
for( int i=0; i<nxc; i++ )
|
|
for( int j=0; j<nyc; j++ )
|
|
for( int k=0; k<nzc/2+1; k++ )
|
|
{
|
|
int ii(i),jj(j),kk(k);
|
|
|
|
if( i > nxc/2 ) ii += nx/2;
|
|
if( j > nyc/2 ) jj += ny/2;
|
|
|
|
unsigned qc,qf;
|
|
|
|
qc = (i*nyc+j)*(nzc/2+1)+k;
|
|
qf = (ii*ny+jj)*(nz/2+1)+kk;
|
|
|
|
ccoarse[qc].re = 1.0/sqrt(8.0)*cfine[qf].re*fftnorm;
|
|
ccoarse[qc].im = 1.0/sqrt(8.0)*cfine[qf].im*fftnorm;
|
|
}
|
|
|
|
delete[] rfine;
|
|
|
|
#ifndef SINGLETHREAD_FFTW
|
|
rfftwnd_threads_one_complex_to_real( omp_get_max_threads(), ipc, ccoarse, NULL );
|
|
#else
|
|
rfftwnd_one_complex_to_real( ipc, ccoarse, NULL );
|
|
#endif
|
|
rnums_.push_back( new Meshvar<T>( res_, 0, 0, 0 ) );
|
|
|
|
for( int i=0; i<nxc; i++ )
|
|
for( int j=0; j<nyc; j++ )
|
|
for( int k=0; k<nzc; k++ )
|
|
{
|
|
unsigned q = (i*nyc+j)*(nzc+2)+k;
|
|
(*rnums_[0])(i,j,k) = rcoarse[q];
|
|
sum += (*rnums_[0])(i,j,k);
|
|
sum2+= (*rnums_[0])(i,j,k) * (*rnums_[0])(i,j,k);
|
|
++count;
|
|
}
|
|
|
|
delete[] rcoarse;
|
|
|
|
rfftwnd_destroy_plan(pf);
|
|
rfftwnd_destroy_plan(ipc);
|
|
|
|
}
|
|
else
|
|
{
|
|
std::cout << " - Generating a coarse white noise field by averaging\n";
|
|
if( rc.rnums_.size() == 1 )
|
|
{
|
|
//... initialize properties of container
|
|
res_ = rc.res_/2;
|
|
cubesize_ = res_;
|
|
ncubes_ = 1;
|
|
baseseed_ = -2;
|
|
|
|
//... use restriction to get consistent random numbers on coarser grid
|
|
mg_straight gop;
|
|
rnums_.push_back( new Meshvar<T>( res_, 0, 0, 0 ) );
|
|
gop.restrict( *rc.rnums_[0], *rnums_[0] );
|
|
|
|
|
|
for( unsigned i=0; i< rnums_[0]->size(0); ++i )
|
|
for( unsigned j=0; j< rnums_[0]->size(1); ++j )
|
|
for( unsigned k=0; k< rnums_[0]->size(2); ++k )
|
|
{
|
|
(*rnums_[0])(i,j,k) *= sqrt(8); //.. maintain that var(delta)=1
|
|
sum += (*rnums_[0])(i,j,k);
|
|
sum2+= (*rnums_[0])(i,j,k) * (*rnums_[0])(i,j,k);
|
|
++count;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
//... initialize properties of container
|
|
res_ = rc.res_/2;
|
|
cubesize_ = res_;
|
|
ncubes_ = 1;
|
|
baseseed_ = -2;
|
|
|
|
rnums_.push_back( new Meshvar<T>( res_, 0, 0, 0 ) );
|
|
double fac = 1.0/sqrt(8);
|
|
|
|
for( unsigned i=0,ii=0; i<rc.res_; i+=2,++ii )
|
|
for( unsigned j=0,jj=0; j<rc.res_; j+=2,++jj )
|
|
for( unsigned k=0,kk=0; k<rc.res_; k+=2,++kk )
|
|
{
|
|
(*rnums_[0])(ii,jj,kk) = fac *
|
|
( rc(i,j,k)+rc(i+1,j,k)+rc(i,j+1,k)+rc(i,j,k+1)+
|
|
rc(i+1,j+1,k)+rc(i+1,j,k+1)+rc(i,j+1,k+1)+rc(i+1,j+1,k+1));
|
|
|
|
sum += (*rnums_[0])(ii,jj,kk);
|
|
sum2+= (*rnums_[0])(ii,jj,kk) * (*rnums_[0])(ii,jj,kk);
|
|
++count;
|
|
}
|
|
|
|
}
|
|
}
|
|
|
|
double rmean, rvar;
|
|
rmean = sum/count;
|
|
rvar = sum2/count-rmean*rmean;
|
|
|
|
std::cout << " - Restricted random numbers have\n"
|
|
<< " mean = " << rmean << ", var = " << rvar << std::endl;
|
|
}
|
|
|
|
|
|
~random_numbers()
|
|
{
|
|
for( unsigned i=0; i<rnums_.size(); ++i )
|
|
if( rnums_[i] != NULL )
|
|
delete rnums_[i];
|
|
rnums_.clear();
|
|
}
|
|
|
|
T& operator()( int i, int j, int k )
|
|
{
|
|
int ic, jc, kc, is, js, ks;
|
|
|
|
if( ncubes_ == 0 )
|
|
throw std::runtime_error("random_numbers: internal error, not properly initialized");
|
|
|
|
//... determine cube
|
|
ic = (int)((double)i/cubesize_ + ncubes_) % ncubes_;
|
|
jc = (int)((double)j/cubesize_ + ncubes_) % ncubes_;
|
|
kc = (int)((double)k/cubesize_ + ncubes_) % ncubes_;
|
|
|
|
long icube = (ic*ncubes_+jc)*ncubes_+kc;
|
|
|
|
if( rnums_[ icube ] == NULL )
|
|
{
|
|
//... cube has not been precomputed. fill now with random numbers
|
|
//std::cerr << "*";
|
|
rnums_[ icube ] = new Meshvar<T>( cubesize_, 0, 0, 0 );
|
|
fill_cube(ic, jc, kc);
|
|
}
|
|
|
|
//... determine cell in cube
|
|
is = (i - ic * cubesize_ + cubesize_) % cubesize_;
|
|
js = (j - jc * cubesize_ + cubesize_) % cubesize_;
|
|
ks = (k - kc * cubesize_ + cubesize_) % cubesize_;
|
|
|
|
return (*rnums_[ icube ])(is,js,ks);
|
|
}
|
|
|
|
|
|
};
|
|
|
|
#define DEF_RAN_CUBE_SIZE 32
|
|
|
|
template< typename rng, typename T >
|
|
class random_number_generator
|
|
{
|
|
protected:
|
|
config_file * pcf_;
|
|
refinement_hierarchy * prefh_;
|
|
constraint_set constraints;
|
|
|
|
int levelmin_, levelmax_;
|
|
int levelmin_seed_;
|
|
std::vector<long> rngseeds_;
|
|
std::vector<std::string> rngfnames_;
|
|
|
|
unsigned ran_cube_size_;
|
|
|
|
bool is_number(const std::string& s)
|
|
{
|
|
for (unsigned i = 0; i < s.length(); i++)
|
|
if (!std::isdigit(s[i])&&s[i]!='-' )
|
|
return false;
|
|
|
|
return true;
|
|
}
|
|
|
|
void parse_rand_parameters( void )
|
|
{
|
|
//... parse random number options
|
|
for( int i=0; i<=100; ++i )
|
|
{
|
|
char seedstr[128];
|
|
std::string tempstr;
|
|
sprintf(seedstr,"seed[%d]",i);
|
|
if( pcf_->containsKey( "random", seedstr ) )
|
|
tempstr = pcf_->getValue<std::string>( "random", seedstr );
|
|
else
|
|
tempstr = std::string("-2");
|
|
|
|
if( is_number( tempstr ) )
|
|
{
|
|
long ltemp;
|
|
pcf_->convert( tempstr, ltemp );
|
|
rngfnames_.push_back( "" );
|
|
rngseeds_.push_back( ltemp );
|
|
}else{
|
|
rngfnames_.push_back( tempstr );
|
|
rngseeds_.push_back(-1);
|
|
std::cout << " - Random numbers for level " << std::setw(3) << i << " will be read from file.\n";
|
|
}
|
|
|
|
}
|
|
|
|
//.. determine for which levels random seeds/random number files are given
|
|
levelmin_seed_ = -1;
|
|
for( unsigned ilevel = 0; ilevel < rngseeds_.size(); ++ilevel )
|
|
{
|
|
if( levelmin_seed_ < 0 && (rngfnames_[ilevel].size() > 0 || rngseeds_[ilevel] > 0) )
|
|
levelmin_seed_ = ilevel;
|
|
}
|
|
|
|
}
|
|
|
|
void correct_avg( int icoarse, int ifine )
|
|
{
|
|
int shift[3], levelmin_poisson;
|
|
shift[0] = pcf_->getValue<int>("setup","shift_x");
|
|
shift[1] = pcf_->getValue<int>("setup","shift_y");
|
|
shift[2] = pcf_->getValue<int>("setup","shift_z");
|
|
|
|
levelmin_poisson = pcf_->getValue<unsigned>("setup","levelmin");
|
|
|
|
int lfacc = 1<<(icoarse-levelmin_poisson);
|
|
|
|
char fncoarse[128], fnfine[128];
|
|
sprintf(fncoarse,"wnoise_%04d.bin",icoarse);
|
|
sprintf(fnfine,"wnoise_%04d.bin",ifine);
|
|
|
|
std::ifstream
|
|
iffine( fnfine, std::ios::binary ),
|
|
ifcoarse( fncoarse, std::ios::binary );
|
|
|
|
int nc[3], i0c[3], nf[3], i0f[3];
|
|
if( icoarse != levelmin_ )
|
|
{
|
|
nc[0] = 2*prefh_->size(icoarse, 0);
|
|
nc[1] = 2*prefh_->size(icoarse, 1);
|
|
nc[2] = 2*prefh_->size(icoarse, 2);
|
|
i0c[0] = prefh_->offset_abs(icoarse, 0) - lfacc*shift[0] - nc[0]/4;
|
|
i0c[1] = prefh_->offset_abs(icoarse, 1) - lfacc*shift[1] - nc[1]/4;
|
|
i0c[2] = prefh_->offset_abs(icoarse, 2) - lfacc*shift[2] - nc[2]/4;
|
|
|
|
}
|
|
else
|
|
{
|
|
nc[0] = prefh_->size(icoarse, 0);
|
|
nc[1] = prefh_->size(icoarse, 1);
|
|
nc[2] = prefh_->size(icoarse, 2);
|
|
i0c[0] = - lfacc*shift[0];
|
|
i0c[1] = - lfacc*shift[1];
|
|
i0c[2] = - lfacc*shift[2];
|
|
}
|
|
nf[0] = 2*prefh_->size(ifine, 0);
|
|
nf[1] = 2*prefh_->size(ifine, 1);
|
|
nf[2] = 2*prefh_->size(ifine, 2);
|
|
i0f[0] = prefh_->offset_abs(ifine, 0) - 2*lfacc*shift[0] - nf[0]/4;
|
|
i0f[1] = prefh_->offset_abs(ifine, 1) - 2*lfacc*shift[1] - nf[1]/4;
|
|
i0f[2] = prefh_->offset_abs(ifine, 2) - 2*lfacc*shift[2] - nf[2]/4;
|
|
|
|
//.................................
|
|
|
|
int nxc,nyc,nzc,nxf,nyf,nzf;
|
|
iffine.read( reinterpret_cast<char*> (&nxf), sizeof(unsigned) );
|
|
iffine.read( reinterpret_cast<char*> (&nyf), sizeof(unsigned) );
|
|
iffine.read( reinterpret_cast<char*> (&nzf), sizeof(unsigned) );
|
|
|
|
ifcoarse.read( reinterpret_cast<char*> (&nxc), sizeof(unsigned) );
|
|
ifcoarse.read( reinterpret_cast<char*> (&nyc), sizeof(unsigned) );
|
|
ifcoarse.read( reinterpret_cast<char*> (&nzc), sizeof(unsigned) );
|
|
|
|
if( nxf!=nf[0] || nyf!=nf[1] || nzf!=nf[2] || nxc!=nc[0] || nyc!=nc[1] || nzc!=nc[2] )
|
|
throw std::runtime_error("White noise file mismatch. This should not happen. Notify a developer!");
|
|
|
|
int nxd(nxf/2),nyd(nyf/2),nzd(nzf/2);
|
|
std::vector<T> deg_rand( nxd*nyd*nzd, 0.0 );
|
|
double fac = 1.0/sqrt(8.0);
|
|
|
|
for( int i=0; i<nxf; i+=2 )
|
|
{
|
|
std::vector<T> fine_rand( 2*nyf*nzf, 0.0 );
|
|
iffine.read( reinterpret_cast<char*> (&fine_rand[0]), 2*nyf*nzf*sizeof(T) );
|
|
|
|
for( int j=0; j<nyf; j+=2 )
|
|
for( int k=0; k<nzf; k+=2 )
|
|
{
|
|
unsigned qc = ((i/2)*nyd+(j/2))*nzd+(k/2);
|
|
unsigned qf[8];
|
|
qf[0] = (0*nyf+j+0)*nzf+k+0;
|
|
qf[1] = (0*nyf+j+0)*nzf+k+1;
|
|
qf[2] = (0*nyf+j+1)*nzf+k+0;
|
|
qf[3] = (0*nyf+j+1)*nzf+k+1;
|
|
qf[4] = (1*nyf+j+0)*nzf+k+0;
|
|
qf[5] = (1*nyf+j+0)*nzf+k+1;
|
|
qf[6] = (1*nyf+j+1)*nzf+k+0;
|
|
qf[7] = (1*nyf+j+1)*nzf+k+1;
|
|
|
|
for( int q=0; q<8; ++q )
|
|
deg_rand[qc] += fac*fine_rand[qf[q]];
|
|
|
|
}
|
|
}
|
|
|
|
//... now deg_rand holds the oct-averaged fine field, store this in the coarse field
|
|
std::vector<T> coarse_rand(nxc*nyc*nzc,0.0);
|
|
ifcoarse.read( reinterpret_cast<char*> (&coarse_rand[0]), nxc*nyc*nzc*sizeof(T) );
|
|
|
|
int di,dj,dk;
|
|
|
|
di = i0f[0]/2-i0c[0];
|
|
dj = i0f[1]/2-i0c[1];
|
|
dk = i0f[2]/2-i0c[2];
|
|
|
|
#pragma omp parallel for
|
|
for( int i=0; i<nxd; i++ )
|
|
for( int j=0; j<nyd; j++ )
|
|
for( int k=0; k<nxd; k++ )
|
|
{
|
|
//unsigned qc = (((i+di+nxc)%nxc)*nyc+(((j+dj+nyc)%nyc)))*nzc+((k+dk+nzc)%nzc);
|
|
|
|
if( i+di < 0 || i+di >= nxc || j+dj < 0 || j+dj >= nyc || k+dk < 0 || k+dk >= nzc )
|
|
continue;
|
|
|
|
unsigned qc = ((i+di)*nyc+(j+dj))*nzc+(k+dk);
|
|
unsigned qcd = (i*nyd+j)*nzd+k;
|
|
|
|
coarse_rand[qc] = deg_rand[qcd];
|
|
}
|
|
|
|
deg_rand.clear();
|
|
|
|
ifcoarse.close();
|
|
std::ofstream ofcoarse( fncoarse, std::ios::binary|std::ios::trunc );
|
|
ofcoarse.write( reinterpret_cast<char*> (&nxc), sizeof(unsigned) );
|
|
ofcoarse.write( reinterpret_cast<char*> (&nyc), sizeof(unsigned) );
|
|
ofcoarse.write( reinterpret_cast<char*> (&nzc), sizeof(unsigned) );
|
|
ofcoarse.write( reinterpret_cast<char*> (&coarse_rand[0]), nxc*nyc*nzc*sizeof(T) );
|
|
ofcoarse.close();
|
|
}
|
|
|
|
void store_rnd( int ilevel, rng* prng )
|
|
{
|
|
int shift[3], levelmin_poisson;
|
|
shift[0] = pcf_->getValue<int>("setup","shift_x");
|
|
shift[1] = pcf_->getValue<int>("setup","shift_y");
|
|
shift[2] = pcf_->getValue<int>("setup","shift_z");
|
|
|
|
levelmin_poisson = pcf_->getValue<unsigned>("setup","levelmin");
|
|
|
|
int lfac = 1<<(ilevel-levelmin_poisson);
|
|
|
|
|
|
std::vector<T> data;
|
|
if( ilevel == levelmin_ )
|
|
{
|
|
int N = 1<<levelmin_;
|
|
int i0,j0,k0;
|
|
|
|
i0 = -lfac*shift[0];
|
|
j0 = -lfac*shift[1];
|
|
k0 = -lfac*shift[2];
|
|
|
|
char fname[128];
|
|
sprintf(fname,"wnoise_%04d.bin",ilevel);
|
|
|
|
std::ofstream ofs(fname,std::ios::binary|std::ios::trunc);
|
|
|
|
ofs.write( reinterpret_cast<char*> (&N), sizeof(unsigned) );
|
|
ofs.write( reinterpret_cast<char*> (&N), sizeof(unsigned) );
|
|
ofs.write( reinterpret_cast<char*> (&N), sizeof(unsigned) );
|
|
|
|
data.assign( N*N, 0.0 );
|
|
for( int i=0; i<N; ++i )
|
|
{
|
|
for( int j=0; j<N; ++j )
|
|
for( int k=0; k<N; ++k )
|
|
data[j*N+k] = (*prng)(i+i0,j+j0,k+k0);
|
|
|
|
ofs.write(reinterpret_cast<char*> (&data[0]), N*N*sizeof(T) );
|
|
}
|
|
ofs.close();
|
|
}
|
|
else
|
|
{
|
|
int nx,ny,nz;
|
|
int i0,j0,k0;
|
|
|
|
nx = 2*prefh_->size(ilevel, 0);
|
|
ny = 2*prefh_->size(ilevel, 1);
|
|
nz = 2*prefh_->size(ilevel, 2);
|
|
i0 = prefh_->offset_abs(ilevel, 0) - lfac*shift[0] - nx/4;
|
|
j0 = prefh_->offset_abs(ilevel, 1) - lfac*shift[1] - nx/4;
|
|
k0 = prefh_->offset_abs(ilevel, 2) - lfac*shift[2] - nx/4;
|
|
|
|
char fname[128];
|
|
sprintf(fname,"wnoise_%04d.bin",ilevel);
|
|
|
|
std::ofstream ofs(fname,std::ios::binary|std::ios::trunc);
|
|
|
|
ofs.write( reinterpret_cast<char*> (&nx), sizeof(unsigned) );
|
|
ofs.write( reinterpret_cast<char*> (&ny), sizeof(unsigned) );
|
|
ofs.write( reinterpret_cast<char*> (&nz), sizeof(unsigned) );
|
|
|
|
data.assign( ny*nz, 0.0 );
|
|
for( int i=0; i<nx; ++i )
|
|
{
|
|
for( int j=0; j<ny; ++j )
|
|
for( int k=0; k<nz; ++k )
|
|
data[j*ny+k] = (*prng)(i+i0,j+j0,k+k0);
|
|
|
|
ofs.write(reinterpret_cast<char*> (&data[0]), ny*nz*sizeof(T) );
|
|
}
|
|
ofs.close();
|
|
|
|
}
|
|
|
|
}
|
|
|
|
void compute_random_numbers( void )
|
|
{
|
|
bool kavg = pcf_->getValueSafe<bool>("random","kaveraging",true);
|
|
|
|
std::vector< rng* > randc(std::max(levelmax_,levelmin_seed_)+1,(rng*)NULL);
|
|
|
|
//--- FILL ALL WHITE NOISE ARRAYS FOR WHICH WE NEED THE FULL FIELD ---//
|
|
|
|
//... seeds are given for a level coarser than levelmin
|
|
if( levelmin_seed_ < levelmin_ )
|
|
{
|
|
if( rngfnames_[levelmin_seed_].size() > 0 )
|
|
randc[levelmin_seed_]
|
|
= new rng( 1<<levelmin_seed_, rngfnames_[levelmin_seed_] );
|
|
else
|
|
randc[levelmin_seed_]
|
|
= new rng( 1<<levelmin_seed_, ran_cube_size_, rngseeds_[levelmin_seed_], true );
|
|
|
|
for( int i=levelmin_seed_+1; i<=levelmin_; ++i )
|
|
{
|
|
#warning add possibility to read noise from file also here!
|
|
randc[i] = new rng( *randc[i-1], ran_cube_size_, rngseeds_[i], kavg );
|
|
delete randc[i-1];
|
|
randc[i-1] = NULL;
|
|
}
|
|
}
|
|
|
|
//... seeds are given for a level finer than levelmin, obtain by averaging
|
|
if( levelmin_seed_ > levelmin_ )
|
|
{
|
|
randc[levelmin_seed_] = new rng( 1<<levelmin_seed_, ran_cube_size_, rngseeds_[levelmin_seed_], true );//, x0, lx );
|
|
|
|
for( int ilevel = levelmin_seed_-1; ilevel >= (int)levelmin_; --ilevel ){
|
|
if( rngseeds_[ilevel-levelmin_] > 0 )
|
|
std::cerr << " - Warning: random seed for level " << ilevel << " will be ignored.\n"
|
|
<< " consistency requires that it is obtained by restriction from level " << levelmin_seed_ << std::endl;
|
|
|
|
|
|
|
|
//if( levelmin_ == levelmax_ )
|
|
if( ilevel >= levelmax_ )
|
|
randc[ilevel] = new rng( *randc[ilevel+1], kavg );
|
|
else
|
|
randc[ilevel] = new rng( *randc[ilevel+1], false );
|
|
|
|
if( ilevel+1 > levelmax_ )
|
|
{
|
|
delete randc[ilevel+1];
|
|
randc[ilevel+1] = NULL;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
//--- GENERATE AND STORE ALL LEVELS, INCLUDING REFINEMENTS ---//
|
|
|
|
//... levelmin
|
|
if( randc[levelmin_] == NULL )
|
|
if( rngfnames_[levelmin_].size() > 0 )
|
|
randc[levelmin_] = new rng( 1<<levelmin_, rngfnames_[levelmin_] );
|
|
else
|
|
randc[levelmin_] = new rng( 1<<levelmin_, ran_cube_size_, rngseeds_[levelmin_], true );
|
|
|
|
store_rnd( levelmin_, randc[levelmin_] );
|
|
|
|
|
|
|
|
//... refinement levels
|
|
for( int ilevel=levelmin_+1; ilevel<=levelmax_; ++ilevel )
|
|
{
|
|
int lx[3], x0[3];
|
|
int shift[3], levelmin_poisson;
|
|
shift[0] = pcf_->getValue<int>("setup","shift_x");
|
|
shift[1] = pcf_->getValue<int>("setup","shift_y");
|
|
shift[2] = pcf_->getValue<int>("setup","shift_z");
|
|
|
|
levelmin_poisson = pcf_->getValue<unsigned>("setup","levelmin");
|
|
|
|
int lfac = 1<<(ilevel-levelmin_poisson);
|
|
|
|
lx[0] = 2*prefh_->size(ilevel, 0);
|
|
lx[1] = 2*prefh_->size(ilevel, 1);
|
|
lx[2] = 2*prefh_->size(ilevel, 2);
|
|
x0[0] = prefh_->offset_abs(ilevel, 0) - lfac*shift[0] - lx[0]/4;
|
|
x0[1] = prefh_->offset_abs(ilevel, 1) - lfac*shift[1] - lx[1]/4;
|
|
x0[2] = prefh_->offset_abs(ilevel, 2) - lfac*shift[2] - lx[2]/4;
|
|
|
|
if( randc[ilevel] == NULL )
|
|
{
|
|
randc[ilevel] = new rng( *randc[ilevel-1], ran_cube_size_, rngseeds_[ilevel], kavg, x0, lx );
|
|
store_rnd( ilevel, randc[ilevel] );
|
|
delete randc[ilevel-1];
|
|
randc[ilevel-1] = NULL;
|
|
}
|
|
else
|
|
{
|
|
store_rnd( ilevel, randc[ilevel] );
|
|
delete randc[ilevel-1];
|
|
randc[ilevel-1] = NULL;
|
|
}
|
|
}
|
|
|
|
delete randc[levelmax_];
|
|
randc[levelmax_] = NULL;
|
|
|
|
//... make sure that the coarse grid contains oct averages where it overlaps with a fine grid
|
|
for( int ilevel=levelmax_; ilevel>levelmin_; --ilevel )
|
|
correct_avg( ilevel-1, ilevel );
|
|
|
|
|
|
|
|
|
|
|
|
//.. we do not have random numbers for a coarse level, generate them
|
|
/*if( levelmax_rand_ >= (int)levelmin_ )
|
|
{
|
|
std::cerr << "lmaxread >= (int)levelmin\n";
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randc[levelmax_rand_] = new rng( (unsigned)pow(2,levelmax_rand_), rngfnames_[levelmax_rand_] );
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for( int ilevel = levelmax_rand_-1; ilevel >= (int)levelmin_; --ilevel )
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randc[ilevel] = new rng( *randc[ilevel+1] );
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}*/
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}
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|
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public:
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random_number_generator( config_file& cf, refinement_hierarchy& refh )
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|
: pcf_( &cf ), prefh_( &refh ), constraints( cf )
|
|
{
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|
levelmin_ = prefh_->levelmin();
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|
levelmax_ = prefh_->levelmax();
|
|
|
|
ran_cube_size_ = pcf_->getValueSafe<unsigned>("random","cubesize",DEF_RAN_CUBE_SIZE);
|
|
|
|
parse_rand_parameters();
|
|
|
|
compute_random_numbers();
|
|
}
|
|
|
|
~random_number_generator()
|
|
{ }
|
|
|
|
template< typename array >
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|
void load( array& A, int ilevel )
|
|
{
|
|
char fname[128];
|
|
sprintf(fname,"wnoise_%04d.bin",ilevel);
|
|
|
|
std::ifstream ifs( fname, std::ios::binary );
|
|
if( !ifs.good() )
|
|
{
|
|
LOGERR("White noise file \'%s\'was not found.",fname);
|
|
throw std::runtime_error("A white noise file was not found. This is an internal inconsistency. Inform a developer!");
|
|
|
|
}
|
|
|
|
int nx,ny,nz;
|
|
ifs.read( reinterpret_cast<char*> (&nx), sizeof(int) );
|
|
ifs.read( reinterpret_cast<char*> (&ny), sizeof(int) );
|
|
ifs.read( reinterpret_cast<char*> (&nz), sizeof(int) );
|
|
|
|
if( nx!=A.size(0) || ny!=A.size(1) || nz!=A.size(2) )
|
|
{
|
|
LOGERR("White noise file is not aligned with array. File: [%d,%d,%d]. Mem: [%d,%d,%d].",nx,ny,nz,A.size(0),A.size(1),A.size(2));
|
|
throw std::runtime_error("White noise file is not aligned with array. This is an internal inconsistency. Inform a developer!");
|
|
}
|
|
|
|
for( int i=0; i<nx; ++i )
|
|
{
|
|
std::vector<T> slice( ny*nz, 0.0 );
|
|
ifs.read( reinterpret_cast<char*> ( &slice[0] ), nx*nz*sizeof(T) );
|
|
|
|
for( int j=0; j<ny; ++j )
|
|
for( int k=0; k<nz; ++k )
|
|
A(i,j,k) = slice[j*nz+k];
|
|
}
|
|
ifs.close();
|
|
|
|
}
|
|
};
|
|
|
|
|
|
#endif //__RANDOM_HH
|
|
|