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music-panphasia/densities.cc

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/*
densities.cc - This file is part of MUSIC -
a code to generate multi-scale initial conditions
for cosmological simulations
Copyright (C) 2010 Oliver Hahn
*/
#include <cstring>
#include "densities.hh"
#include "convolution_kernel.hh"
// TODO: this should be a larger number by default, just to maintain consistency with old default
#define DEF_RAN_CUBE_SIZE 32
double blend_sharpness = 0.9;//0.95; //0.5;
double Blend_Function(double k, double kmax) {
float kabs = fabs(k);
// return (kabs>kmax)? 0.0 : 1.0;
return 0.5*(1.0-std::erf((kabs-kmax)));
// float kabs = fabs(k);
// double const eps = blend_sharpness;
// float kp = (1.0f - 2.0f * eps) * kmax;
// if (kabs >= kmax)
// return 0.;
// if (kabs > kp)
// return 1.0f / (expf((kp - kmax) / (k - kp) + (kp - kmax) / (k - kmax)) + 1.0f);
// return 1.0f;
}
template <typename m1, typename m2> void fft_coarsen(m1 &v, m2 &V) {
size_t nxf = v.size(0), nyf = v.size(1), nzf = v.size(2), nzfp = nzf + 2;
size_t nxF = V.size(0), nyF = V.size(1), nzF = V.size(2), nzFp = nzF + 2;
fftw_real *rcoarse = new fftw_real[nxF * nyF * nzFp];
fftw_complex *ccoarse = reinterpret_cast<fftw_complex *>(rcoarse);
fftw_real *rfine = new fftw_real[nxf * nyf * nzfp];
fftw_complex *cfine = reinterpret_cast<fftw_complex *>(rfine);
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_plan pf = fftwf_plan_dft_r2c_3d(nxf, nyf, nzf, rfine, cfine, FFTW_ESTIMATE),
ipc = fftwf_plan_dft_c2r_3d(nxF, nyF, nzF, ccoarse, rcoarse, FFTW_ESTIMATE);
#else
fftw_plan pf = fftw_plan_dft_r2c_3d(nxf, nyf, nzf, rfine, cfine, FFTW_ESTIMATE),
ipc = fftw_plan_dft_c2r_3d(nxF, nyF, nzF, ccoarse, rcoarse, FFTW_ESTIMATE);
#endif
#else
rfftwnd_plan pf = rfftw3d_create_plan(nxf, nyf, nzf, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE | FFTW_IN_PLACE),
ipc = rfftw3d_create_plan(nxF, nyF, nzF, FFTW_COMPLEX_TO_REAL, FFTW_ESTIMATE | FFTW_IN_PLACE);
#endif
#pragma omp parallel for
for (int i = 0; i < (int)nxf; i++)
for (int j = 0; j < (int)nyf; j++)
for (int k = 0; k < (int)nzf; k++) {
size_t q = ((size_t)i * nyf + (size_t)j) * nzfp + (size_t)k;
rfine[q] = v(i, j, k);
assert(!std::isnan(rfine[q]));
}
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_execute(pf);
#else
fftw_execute(pf);
#endif
#else
#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
#endif
real_t fftnorm = 1.0 / ((real_t)nxF * (real_t)nyF * (real_t)nzF);
#pragma omp parallel for
for (int i = 0; i < (int)nxF; i++)
for (int j = 0; j < (int)nyF; j++)
for (int k = 0; k < (int)nzF / 2 + 1; k++) {
int ii(i), jj(j), kk(k);
if (i > (int)nxF / 2)
ii += (int)nxf / 2;
if (j > (int)nyF / 2)
jj += (int)nyf / 2;
size_t qc, qf;
real_t kx = (i <= (int)nxF / 2) ? (real_t)i : (real_t)(i - (real_t)nxF);
real_t ky = (j <= (int)nyF / 2) ? (real_t)j : (real_t)(j - (real_t)nyF);
real_t kz = (k <= (int)nzF / 2) ? (real_t)k : (real_t)(k - (real_t)nzF);
qc = ((size_t)i * nyF + (size_t)j) * (nzF / 2 + 1) + (size_t)k;
qf = ((size_t)ii * nyf + (size_t)jj) * (nzf / 2 + 1) + (size_t)kk;
std::complex<real_t> val_fine(RE(cfine[qf]), IM(cfine[qf]));
real_t phase = (kx / nxF + ky / nyF + kz / nzF) * 0.5 * M_PI;
std::complex<real_t> val_phas(cos(phase), sin(phase));
#ifdef SINGLE_PRECISION
val_fine *= val_phas * fftnorm / 8.0f; // sqrt(8.0);
#else
val_fine *= val_phas * fftnorm / 8.0; // sqrt(8.0);
#endif
RE(ccoarse[qc]) = val_fine.real();
IM(ccoarse[qc]) = val_fine.imag();
assert(!std::isnan(RE(ccoarse[qc])) && !std::isnan(IM(ccoarse[qc])) );
}
delete[] rfine;
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_execute(ipc);
#else
fftw_execute(ipc);
#endif
#else
#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
#endif
#pragma omp parallel for
for (int i = 0; i < (int)nxF; i++)
for (int j = 0; j < (int)nyF; j++)
for (int k = 0; k < (int)nzF; k++) {
size_t q = ((size_t)i * nyF + (size_t)j) * nzFp + (size_t)k;
V(i, j, k) = rcoarse[q];
}
delete[] rcoarse;
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_destroy_plan(pf);
fftwf_destroy_plan(ipc);
#else
fftw_destroy_plan(pf);
fftw_destroy_plan(ipc);
#endif
#else
rfftwnd_destroy_plan(pf);
rfftwnd_destroy_plan(ipc);
#endif
}
//#define NO_COARSE_OVERLAP
template <typename m1, typename m2> void fft_interpolate(m1 &V, m2 &v, bool from_basegrid = false) {
int oxf = v.offset(0), oyf = v.offset(1), ozf = v.offset(2);
size_t nxf = v.size(0), nyf = v.size(1), nzf = v.size(2), nzfp = nzf + 2;
size_t nxF = V.size(0), nyF = V.size(1), nzF = V.size(2);
if (!from_basegrid) {
#ifdef NO_COARSE_OVERLAP
oxf += nxF / 4;
oyf += nyF / 4;
ozf += nzF / 4;
#else
oxf += nxF / 4 - nxf / 8;
oyf += nyF / 4 - nyf / 8;
ozf += nzF / 4 - nzf / 8;
} else {
oxf -= nxf / 8;
oyf -= nyf / 8;
ozf -= nzf / 8;
#endif
}
LOGUSER("FFT interpolate: offset=%d,%d,%d size=%d,%d,%d", oxf, oyf, ozf, nxf, nyf, nzf);
// cut out piece of coarse grid that overlaps the fine:
assert(nxf % 2 == 0 && nyf % 2 == 0 && nzf % 2 == 0);
size_t nxc = nxf / 2, nyc = nyf / 2, nzc = nzf / 2, nzcp = nzf / 2 + 2;
fftw_real *rcoarse = new fftw_real[nxc * nyc * nzcp];
fftw_complex *ccoarse = reinterpret_cast<fftw_complex *>(rcoarse);
fftw_real *rfine = new fftw_real[nxf * nyf * nzfp];
fftw_complex *cfine = reinterpret_cast<fftw_complex *>(rfine);
// copy coarse data to rcoarse[.]
memset(rcoarse, 0, sizeof(fftw_real) * nxc * nyc * nzcp);
memset(rfine, 0, sizeof(fftw_real) * nxf * nyf * nzfp);
#ifdef NO_COARSE_OVERLAP
#pragma omp parallel for
for (int i = 0; i < (int)nxc / 2; ++i)
for (int j = 0; j < (int)nyc / 2; ++j)
for (int k = 0; k < (int)nzc / 2; ++k) {
int ii(i + nxc / 4);
int jj(j + nyc / 4);
int kk(k + nzc / 4);
size_t q = ((size_t)ii * nyc + (size_t)jj) * nzcp + (size_t)kk;
rcoarse[q] = V(oxf + i, oyf + j, ozf + k);
assert( !std::isnan( rcoarse[q] ) );
}
#else
#pragma omp parallel for
for (int i = 0; i < (int)nxc; ++i)
for (int j = 0; j < (int)nyc; ++j)
for (int k = 0; k < (int)nzc; ++k) {
int ii(i);
int jj(j);
int kk(k);
size_t q = ((size_t)ii * nyc + (size_t)jj) * nzcp + (size_t)kk;
if( from_basegrid )
rcoarse[q] = V((oxf + i+nxF)%nxF, (oyf + j+nyF)%nyF, (ozf + k+nzF)%nzF);
else
rcoarse[q] = V(oxf + i, oyf + j, ozf + k);
}
#endif
#pragma omp parallel for
for (int i = 0; i < (int)nxf; ++i)
for (int j = 0; j < (int)nyf; ++j)
for (int k = 0; k < (int)nzf; ++k) {
size_t q = ((size_t)i * nyf + (size_t)j) * nzfp + (size_t)k;
rfine[q] = v(i, j, k);
assert( !std::isnan(rfine[q]) );
}
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_plan pc = fftwf_plan_dft_r2c_3d(nxc, nyc, nzc, rcoarse, ccoarse, FFTW_ESTIMATE),
pf = fftwf_plan_dft_r2c_3d(nxf, nyf, nzf, rfine, cfine, FFTW_ESTIMATE),
ipf = fftwf_plan_dft_c2r_3d(nxf, nyf, nzf, cfine, rfine, FFTW_ESTIMATE);
fftwf_execute(pc);
fftwf_execute(pf);
#else
fftw_plan pc = fftw_plan_dft_r2c_3d(nxc, nyc, nzc, rcoarse, ccoarse, FFTW_ESTIMATE),
pf = fftw_plan_dft_r2c_3d(nxf, nyf, nzf, rfine, cfine, FFTW_ESTIMATE),
ipf = fftw_plan_dft_c2r_3d(nxf, nyf, nzf, cfine, rfine, FFTW_ESTIMATE);
fftw_execute(pc);
fftw_execute(pf);
#endif
#else
rfftwnd_plan pc = rfftw3d_create_plan(nxc, nyc, nzc, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE | FFTW_IN_PLACE),
pf = rfftw3d_create_plan(nxf, nyf, nzf, FFTW_REAL_TO_COMPLEX, FFTW_ESTIMATE | FFTW_IN_PLACE),
ipf = rfftw3d_create_plan(nxf, nyf, nzf, FFTW_COMPLEX_TO_REAL, FFTW_ESTIMATE | FFTW_IN_PLACE);
#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
#endif
/*************************************************/
//.. perform actual interpolation
real_t fftnorm = 1.0 / ((real_t)nxf * (real_t)nyf * (real_t)nzf);
real_t ref_vol_fac = 8.0;
real_t phasefac = -0.5;
// this enables filtered splicing of coarse and fine modes
for (int i = 0; i < (int)nxc; i++) {
for (int j = 0; j < (int)nyc; j++) {
for (int k = 0; k < (int)nzc / 2 + 1; k++) {
int ii(i), jj(j), kk(k);
if (i > (int)nxc / 2)
ii += (int)nxf / 2;
if (j > (int)nyc / 2)
jj += (int)nyf / 2;
if (k > (int)nzc / 2)
kk += (int)nzf / 2;
size_t qc, qf;
qc = ((size_t)i * (size_t)nyc + (size_t)j) * (nzc / 2 + 1) + (size_t)k;
qf = ((size_t)ii * (size_t)nyf + (size_t)jj) * (nzf / 2 + 1) + (size_t)kk;
real_t kx = (i <= (int)nxc / 2) ? (real_t)i : (real_t)(i - (int)nxc);
real_t ky = (j <= (int)nyc / 2) ? (real_t)j : (real_t)(j - (int)nyc);
real_t kz = (k <= (int)nzc / 2) ? (real_t)k : (real_t)(k - (int)nzc);
real_t phase = phasefac * (kx / nxc + ky / nyc + kz / nzc) * M_PI;
std::complex<real_t> val_phas(cos(phase), sin(phase));
std::complex<real_t> val(RE(ccoarse[qc]), IM(ccoarse[qc]));
val *= ref_vol_fac * val_phas;
real_t blend_coarse = Blend_Function(sqrt(kx * kx + ky * ky + kz * kz), nxc / 2);
real_t blend_fine = 1.0 - blend_coarse;
assert( !std::isnan(blend_fine) );
assert( !std::isnan(blend_coarse) );
if( std::isnan(IM(cfine[qf])) || std::isnan(IM(ccoarse[qc])) ){
fprintf(stderr,"%f %f , %f %f , %d %d %d\n",RE(cfine[qf]),IM(cfine[qf]), RE(ccoarse[qc]), IM(ccoarse[qc]), i,j,k );
}
RE(cfine[qf]) = blend_fine * RE(cfine[qf]) + blend_coarse * val.real();
IM(cfine[qf]) = blend_fine * IM(cfine[qf]) + blend_coarse * val.imag();
assert( !std::isnan(IM(cfine[qf])) );
}
}
}
delete[] rcoarse;
/*************************************************/
#ifdef FFTW3
#ifdef SINGLE_PRECISION
fftwf_execute(ipf);
fftwf_destroy_plan(pf);
fftwf_destroy_plan(pc);
fftwf_destroy_plan(ipf);
#else
fftw_execute(ipf);
fftw_destroy_plan(pf);
fftw_destroy_plan(pc);
fftw_destroy_plan(ipf);
#endif
#else
#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
fftwnd_destroy_plan(pf);
fftwnd_destroy_plan(pc);
fftwnd_destroy_plan(ipf);
#endif
// copy back and normalize
#pragma omp parallel for
for (int i = 0; i < (int)nxf; ++i)
for (int j = 0; j < (int)nyf; ++j)
for (int k = 0; k < (int)nzf; ++k) {
size_t q = ((size_t)i * nyf + (size_t)j) * nzfp + (size_t)k;
v(i, j, k) = rfine[q] * fftnorm;
}
delete[] rfine;
}
/*******************************************************************************************/
/*******************************************************************************************/
/*******************************************************************************************/
void GenerateDensityUnigrid(config_file &cf, transfer_function *ptf, tf_type type, refinement_hierarchy &refh,
noise_generator &rand, grid_hierarchy &delta, bool smooth, bool shift) {
unsigned levelmin, levelmax, levelminPoisson;
levelminPoisson = cf.getValue<unsigned>("setup", "levelmin");
levelmin = cf.getValueSafe<unsigned>("setup", "levelmin_TF", levelminPoisson);
levelmax = cf.getValue<unsigned>("setup", "levelmax");
bool kspace = cf.getValue<bool>("setup", "kspace_TF");
unsigned nbase = 1 << levelmin;
std::cerr << " - Running unigrid version\n";
LOGUSER("Running unigrid density convolution...");
//... select the transfer function to be used
convolution::kernel_creator *the_kernel_creator;
if (kspace) {
std::cout << " - Using k-space transfer function kernel.\n";
LOGUSER("Using k-space transfer function kernel.");
#ifdef SINGLE_PRECISION
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_k_float"];
#else
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_k_double"];
#endif
} else {
std::cout << " - Using real-space transfer function kernel.\n";
LOGUSER("Using real-space transfer function kernel.");
#ifdef SINGLE_PRECISION
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_real_float"];
#else
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_real_double"];
#endif
}
//... initialize convolution kernel
convolution::kernel *the_tf_kernel = the_kernel_creator->create(cf, ptf, refh, type);
//...
std::cout << " - Performing noise convolution on level " << std::setw(2) << levelmax << " ..." << std::endl;
LOGUSER("Performing noise convolution on level %3d", levelmax);
//... create convolution mesh
DensityGrid<real_t> *top = new DensityGrid<real_t>(nbase, nbase, nbase);
//... fill with random numbers
rand.load(*top, levelmin);
//... load convolution kernel
the_tf_kernel->fetch_kernel(levelmin, false);
//... perform convolution
convolution::perform<real_t>(the_tf_kernel, reinterpret_cast<void *>(top->get_data_ptr()), shift);
//... clean up kernel
delete the_tf_kernel;
//... create multi-grid hierarchy
delta.create_base_hierarchy(levelmin);
//... copy convolved field to multi-grid hierarchy
top->copy(*delta.get_grid(levelmin));
//... delete convolution grid
delete top;
}
/*******************************************************************************************/
/*******************************************************************************************/
/*******************************************************************************************/
void GenerateDensityHierarchy(config_file &cf, transfer_function *ptf, tf_type type, refinement_hierarchy &refh,
noise_generator &rand, grid_hierarchy &delta, bool smooth, bool shift) {
unsigned levelmin, levelmax, levelminPoisson;
std::vector<long> rngseeds;
std::vector<std::string> rngfnames;
bool debugnoise, fourier_splicing;
double tstart, tend;
#ifdef _OPENMP
tstart = omp_get_wtime();
#else
tstart = (double)clock() / CLOCKS_PER_SEC;
#endif
levelminPoisson = cf.getValue<unsigned>("setup", "levelmin");
levelmin = cf.getValueSafe<unsigned>("setup", "levelmin_TF", levelminPoisson);
levelmax = cf.getValue<unsigned>("setup", "levelmax");
debugnoise = cf.getValueSafe<bool>("setup", "debugnoise", false);
blend_sharpness = cf.getValueSafe<double>("setup", "kspace_filter", blend_sharpness);
fourier_splicing = cf.getValueSafe<bool>( "setup","fourier_splicing", true );
unsigned nbase = 1 << levelmin;
convolution::kernel_creator *the_kernel_creator;
#ifdef SINGLE_PRECISION
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_k_float"];
#else
the_kernel_creator = convolution::get_kernel_map()["tf_kernel_k_double"];
#endif
convolution::kernel *the_tf_kernel = the_kernel_creator->create(cf, ptf, refh, type);
/***** PERFORM CONVOLUTIONS *****/
//... create and initialize density grids with white noise
DensityGrid<real_t> *top(NULL);
PaddedDensitySubGrid<real_t> *coarse(NULL), *fine(NULL);
int nlevels = (int)levelmax - (int)levelmin + 1;
// do coarse level
top = new DensityGrid<real_t>(nbase, nbase, nbase);
LOGINFO("Performing noise convolution on level %3d (shift=%d)", levelmin, shift);
if ((debugnoise && levelmin == levelmax) || !debugnoise)
rand.load(*top, levelmin);
convolution::perform<real_t>(the_tf_kernel->fetch_kernel(levelmin, false),
reinterpret_cast<void *>(top->get_data_ptr()), shift);
delta.create_base_hierarchy(levelmin);
top->copy(*delta.get_grid(levelmin));
for (int i = 1; i < nlevels; ++i) {
LOGINFO("Performing noise convolution on level %3d...", levelmin + i);
/////////////////////////////////////////////////////////////////////////
//... add new refinement patch
LOGUSER("Allocating refinement patch");
LOGUSER(" offset=(%5d,%5d,%5d)", refh.offset(levelmin + i, 0), refh.offset(levelmin + i, 1),
refh.offset(levelmin + i, 2));
LOGUSER(" size =(%5d,%5d,%5d)", refh.size(levelmin + i, 0), refh.size(levelmin + i, 1),
refh.size(levelmin + i, 2));
fine = new PaddedDensitySubGrid<real_t>(refh.offset(levelmin + i, 0), refh.offset(levelmin + i, 1),
refh.offset(levelmin + i, 2), refh.size(levelmin + i, 0),
refh.size(levelmin + i, 1), refh.size(levelmin + i, 2));
/////////////////////////////////////////////////////////////////////////
// load white noise for patch
if ((debugnoise && levelmin + i == levelmax) || !debugnoise)
rand.load(*fine, levelmin + i);
convolution::perform<real_t>(the_tf_kernel->fetch_kernel(levelmin + i, true),
reinterpret_cast<void *>(fine->get_data_ptr()), shift);
if( fourier_splicing ){
if( i==1 )
fft_interpolate( *top, *fine, true );
else
fft_interpolate( *coarse, *fine, false );
}
delta.add_patch(refh.offset(levelmin + i, 0), refh.offset(levelmin + i, 1), refh.offset(levelmin + i, 2),
refh.size(levelmin + i, 0), refh.size(levelmin + i, 1), refh.size(levelmin + i, 2));
fine->copy_unpad(*delta.get_grid(levelmin + i));
if (i == 1)
delete top;
else
delete coarse;
coarse = fine;
}
delete coarse;
delete the_tf_kernel;
#ifdef _OPENMP
tend = omp_get_wtime();
if (true) // verbosity > 1 )
std::cout << " - Density calculation took " << tend - tstart << "s with " << omp_get_max_threads() << " threads."
<< std::endl;
#else
tend = (double)clock() / CLOCKS_PER_SEC;
if (true) // verbosity > 1 )
std::cout << " - Density calculation took " << tend - tstart << "s." << std::endl;
#endif
LOGUSER("Finished computing the density field in %fs", tend - tstart);
}
/*******************************************************************************************/
/*******************************************************************************************/
/*******************************************************************************************/
void normalize_density(grid_hierarchy &delta) {
// return;
long double sum = 0.0;
unsigned levelmin = delta.levelmin(), levelmax = delta.levelmax();
{
size_t nx, ny, nz;
nx = delta.get_grid(levelmin)->size(0);
ny = delta.get_grid(levelmin)->size(1);
nz = delta.get_grid(levelmin)->size(2);
#pragma omp parallel for reduction(+ : sum)
for (int ix = 0; ix < (int)nx; ++ix)
for (size_t iy = 0; iy < ny; ++iy)
for (size_t iz = 0; iz < nz; ++iz)
sum += (*delta.get_grid(levelmin))(ix, iy, iz);
sum /= (double)(nx * ny * nz);
}
std::cout << " - Top grid mean density is off by " << sum << ", correcting..." << std::endl;
LOGUSER("Grid mean density is %g. Correcting...", sum);
for (unsigned i = levelmin; i <= levelmax; ++i) {
size_t nx, ny, nz;
nx = delta.get_grid(i)->size(0);
ny = delta.get_grid(i)->size(1);
nz = delta.get_grid(i)->size(2);
#pragma omp parallel for
for (int ix = 0; ix < (int)nx; ++ix)
for (size_t iy = 0; iy < ny; ++iy)
for (size_t iz = 0; iz < nz; ++iz)
(*delta.get_grid(i))(ix, iy, iz) -= sum;
}
}
void coarsen_density(const refinement_hierarchy &rh, GridHierarchy<real_t> &u, bool kspace) {
unsigned levelmin_TF = u.levelmin();
if( kspace ){
for( int i=levelmin_TF; i>=(int)rh.levelmin(); --i )
fft_coarsen( *(u.get_grid(i)), *(u.get_grid(i-1)) );
}else{
for( int i=levelmin_TF; i>=(int)rh.levelmin(); --i )
mg_straight().restrict( *(u.get_grid(i)), *(u.get_grid(i-1)) );
}
for (unsigned i = 1; i <= rh.levelmax(); ++i) {
if (rh.offset(i, 0) != u.get_grid(i)->offset(0) || rh.offset(i, 1) != u.get_grid(i)->offset(1) ||
rh.offset(i, 2) != u.get_grid(i)->offset(2) || rh.size(i, 0) != u.get_grid(i)->size(0) ||
rh.size(i, 1) != u.get_grid(i)->size(1) || rh.size(i, 2) != u.get_grid(i)->size(2)) {
// u.cut_patch(i, rh.offset_abs(i, 0), rh.offset_abs(i, 1), rh.offset_abs(i, 2), rh.size(i, 0), rh.size(i, 1),
// rh.size(i, 2));
//cut_patch_enforce_top_density
u.cut_patch_enforce_top_density(i, rh.offset_abs(i, 0), rh.offset_abs(i, 1), rh.offset_abs(i, 2), rh.size(i, 0), rh.size(i, 1),
rh.size(i, 2));
}
}
for (int i = rh.levelmax(); i > 0; --i)
mg_straight().restrict(*(u.get_grid(i)), *(u.get_grid(i - 1)));
}
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