106 lines
3.6 KiB
C++
106 lines
3.6 KiB
C++
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/*+
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ARES/HADES/BORG Package -- ./libLSS/data/window3d_post.hpp
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Copyright (C) 2014-2020 Guilhem Lavaux <guilhem.lavaux@iap.fr>
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Copyright (C) 2009-2020 Jens Jasche <jens.jasche@fysik.su.se>
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Additional contributions from:
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Guilhem Lavaux <guilhem.lavaux@iap.fr> (2023)
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+*/
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#ifndef __LIBLSS_DATA_WINDOW_3D_POST_HPP
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#define __LIBLSS_DATA_WINDOW_3D_POST_HPP
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#include <cassert>
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#include <functional>
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#include "libLSS/mpi/generic_mpi.hpp"
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#include "libLSS/tools/openmp.hpp"
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#include <CosmoTool/algo.hpp>
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#include <boost/array.hpp>
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#include <numeric>
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#include <cmath>
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#include "libLSS/tools/console.hpp"
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#include "libLSS/samplers/core/types_samplers.hpp"
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#include "libLSS/tools/mpi_fftw_helper.hpp"
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#include "libLSS/tools/fusewrapper.hpp"
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namespace LibLSS {
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namespace convolveDetails {
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template<typename RealArray>
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void buildKernel(FFTW_Manager_3d<double>& mgr, RealArray& real_data) {
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constexpr double KernelCore[2] = {1, 0.5};
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decltype(mgr.N0) N[3] = {mgr.N0, mgr.N1, mgr.N2};
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for (int i = -1; i <= 1; i++) {
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size_t ri = (i < 0) ? (N[0]+i) : i;
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if (!mgr.on_core(ri))
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continue;
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size_t ai = std::abs(i);
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for (int j = -1; j<= 1; j++) {
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size_t rj = (j < 0) ? (N[1]+j) : j;
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size_t aj = std::abs(j);
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for (int k = -1; k <= 1; k++) {
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size_t rk = (k < 0) ? (N[2]+k) : k;
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size_t ak = std::abs(k);
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real_data[ri][rj][rk] = KernelCore[ai]*KernelCore[aj]*KernelCore[ak];
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}
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}
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}
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}
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}
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template<typename SelectionArray, typename T>
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void convolve_selection_cic(MPI_Communication *comm, SelectionArray& sel_array, T const* N)
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{
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typedef FFTW_Manager_3d<double> DFT_Manager;
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typedef typename DFT_Manager::plan_type plan_type;
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ConsoleContext<LOG_DEBUG> ctx("convolution of selection function");
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DFT_Manager mgr(N[0], N[1], N[2], comm);
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Uninit_FFTW_Real_Array real_data_p(mgr.extents_real(), mgr.allocator_real);
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Uninit_FFTW_Complex_Array complex_data_p(mgr.extents_complex(), mgr.allocator_complex);
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Uninit_FFTW_Complex_Array kernel_data_p(mgr.extents_complex(), mgr.allocator_complex);
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auto real_data = real_data_p.get_array();
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auto complex_data = complex_data_p.get_array();
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auto kernel_data = kernel_data_p.get_array();
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auto wc = fwrap(complex_data);
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auto wr = fwrap(real_data);
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auto kc = fwrap(kernel_data);
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ctx.print("Create plans");
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plan_type analysis_plan = mgr.create_r2c_plan(real_data.data(), complex_data.data());
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plan_type synthesis_plan = mgr.create_c2r_plan(complex_data.data(), real_data.data());
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ctx.print("Kernel building");
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wr = 0;
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convolveDetails::buildKernel(mgr, real_data);
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mgr.execute_r2c(analysis_plan, real_data.data(), kernel_data.data());
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ctx.print("Convolve");
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LibLSS::copy_array(real_data, sel_array);
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mgr.execute_r2c(analysis_plan, real_data.data(), complex_data.data());
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wc = wc * kc * (1.0/(N[0]*N[1]*N[2]));
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mgr.execute_c2r(synthesis_plan, complex_data.data(), real_data.data());
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auto S = fwrap(sel_array) ;
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// This is a mask operation, if the condition is true, then S is copied on itself (thus noop).
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// If the condition is false, then the value is cleared with zero. The wrapping of constant
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// is not trivial at the moment.
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S = mask((S>0)&&(wr>=0), S, fwrap(S.fautowrap(0)));
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ctx.print("Cleaning up");
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mgr.destroy_plan(analysis_plan);
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mgr.destroy_plan(synthesis_plan);
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}
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}
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#endif
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