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207 lines
7.4 KiB
C++
207 lines
7.4 KiB
C++
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/*************************************************************************************
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Grid physics library, www.github.com/paboyle/Grid
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Source file: ./tests/debug/Test_reweight_dwf_eofa.cc
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Copyright (C) 2017
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Author: Peter Boyle <paboyle@ph.ed.ac.uk>
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Author: paboyle <paboyle@ph.ed.ac.uk>
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Author: David Murphy <dmurphy@phys.columbia.edu>
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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 2 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 along
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with this program; if not, write to the Free Software Foundation, Inc.,
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51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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See the full license in the file "LICENSE" in the top level distribution directory
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*************************************************************************************/
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/* END LEGAL */
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#include <Grid/Grid.h>
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using namespace std;
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using namespace Grid;
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using namespace Grid::QCD;
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// parameters for test
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const std::vector<int> grid_dim = { 8, 8, 8, 8 };
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const int Ls = 8;
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const int Nhits = 25;
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const int max_iter = 5000;
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const RealD mf = 0.1;
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const RealD mb = 0.11;
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const RealD M5 = 1.8;
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const RealD stop_tol = 1.0e-12;
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RealD mean(const std::vector<RealD>& data)
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{
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int N = data.size();
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RealD mean(0.0);
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for(int i=0; i<N; ++i){ mean += data[i]; }
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return mean/RealD(N);
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}
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RealD jack_mean(const std::vector<RealD>& data, int sample)
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{
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int N = data.size();
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RealD mean(0.0);
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for(int i=0; i<N; ++i){ if(i != sample){ mean += data[i]; } }
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return mean/RealD(N-1);
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}
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RealD jack_std(const std::vector<RealD>& jacks, RealD mean)
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{
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int N = jacks.size();
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RealD std(0.0);
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for(int i=0; i<N; ++i){ std += std::pow(jacks[i]-mean, 2.0); }
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return std::sqrt(RealD(N-1)/RealD(N)*std);
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}
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std::vector<RealD> jack_stats(const std::vector<RealD>& data)
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{
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int N = data.size();
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std::vector<RealD> jack_samples(N);
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std::vector<RealD> jack_stats(2);
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jack_stats[0] = mean(data);
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for(int i=0; i<N; i++){ jack_samples[i] = jack_mean(data,i); }
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jack_stats[1] = jack_std(jack_samples, jack_stats[0]);
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return jack_stats;
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}
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int main(int argc, char **argv)
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{
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Grid_init(&argc, &argv);
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// Initialize spacetime grid
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std::cout << GridLogMessage << "Lattice dimensions: "
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<< grid_dim << " Ls: " << Ls << std::endl;
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GridCartesian* UGrid = SpaceTimeGrid::makeFourDimGrid(grid_dim,
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GridDefaultSimd(Nd, vComplex::Nsimd()), GridDefaultMpi());
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GridRedBlackCartesian* UrbGrid = SpaceTimeGrid::makeFourDimRedBlackGrid(UGrid);
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GridCartesian* FGrid = SpaceTimeGrid::makeFiveDimGrid(Ls, UGrid);
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GridRedBlackCartesian* FrbGrid = SpaceTimeGrid::makeFiveDimRedBlackGrid(Ls, UGrid);
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// Set up RNGs
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std::vector<int> seeds4({1, 2, 3, 4});
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std::vector<int> seeds5({5, 6, 7, 8});
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GridParallelRNG RNG5(FGrid);
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RNG5.SeedFixedIntegers(seeds5);
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GridParallelRNG RNG4(UGrid);
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RNG4.SeedFixedIntegers(seeds4);
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// Random gauge field
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LatticeGaugeField Umu(UGrid);
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SU3::HotConfiguration(RNG4, Umu);
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// Initialize RHMC fermion operators
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DomainWallFermionR Ddwf_f(Umu, *FGrid, *FrbGrid, *UGrid, *UrbGrid, mf, M5);
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DomainWallFermionR Ddwf_b(Umu, *FGrid, *FrbGrid, *UGrid, *UrbGrid, mb, M5);
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SchurDiagMooeeOperator<DomainWallFermionR, LatticeFermion> MdagM(Ddwf_f);
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SchurDiagMooeeOperator<DomainWallFermionR, LatticeFermion> VdagV(Ddwf_b);
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// Degree 12 rational approximations to x^(1/4) and x^(-1/4)
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double lo = 0.0001;
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double hi = 95.0;
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int precision = 64;
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int degree = 12;
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AlgRemez remez(lo, hi, precision);
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std::cout << GridLogMessage << "Generating degree " << degree << " for x^(1/4)" << std::endl;
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remez.generateApprox(degree, 1, 4);
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MultiShiftFunction PowerQuarter(remez, stop_tol, false);
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MultiShiftFunction PowerNegQuarter(remez, stop_tol, true);
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// Stochastically estimate reweighting factor via RHMC
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RealD scale = std::sqrt(0.5);
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std::vector<RealD> rw_rhmc(Nhits);
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ConjugateGradientMultiShift<LatticeFermion> msCG_V(max_iter, PowerQuarter);
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ConjugateGradientMultiShift<LatticeFermion> msCG_M(max_iter, PowerNegQuarter);
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std::cout.precision(12);
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for(int hit=0; hit<Nhits; hit++){
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// Gaussian source
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LatticeFermion Phi (Ddwf_f.FermionGrid());
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LatticeFermion PhiOdd (Ddwf_f.FermionRedBlackGrid());
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std::vector<LatticeFermion> tmp(2, Ddwf_f.FermionRedBlackGrid());
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gaussian(RNG5, Phi);
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Phi = Phi*scale;
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pickCheckerboard(Odd, PhiOdd, Phi);
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// evaluate -log(rw)
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msCG_V(VdagV, PhiOdd, tmp[0]);
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msCG_M(MdagM, tmp[0], tmp[1]);
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rw_rhmc[hit] = norm2(tmp[1]) - norm2(PhiOdd);
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std::cout << std::endl << "==================================================" << std::endl;
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std::cout << " --- RHMC: Hit " << hit << ": rw = " << rw_rhmc[hit];
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std::cout << std::endl << "==================================================" << std::endl << std::endl;
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}
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// Initialize EOFA fermion operators
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RealD shift_L = 0.0;
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RealD shift_R = -1.0;
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int pm = 1;
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DomainWallEOFAFermionR Deofa_L(Umu, *FGrid, *FrbGrid, *UGrid, *UrbGrid, mf, mf, mb, shift_L, pm, M5);
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DomainWallEOFAFermionR Deofa_R(Umu, *FGrid, *FrbGrid, *UGrid, *UrbGrid, mb, mf, mb, shift_R, pm, M5);
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MdagMLinearOperator<DomainWallEOFAFermionR, LatticeFermion> LdagL(Deofa_L);
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MdagMLinearOperator<DomainWallEOFAFermionR, LatticeFermion> RdagR(Deofa_R);
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// Stochastically estimate reweighting factor via EOFA
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RealD k = Deofa_L.k;
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std::vector<RealD> rw_eofa(Nhits);
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ConjugateGradient<LatticeFermion> CG(stop_tol, max_iter);
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SchurRedBlackDiagMooeeSolve<LatticeFermion> SchurSolver(CG);
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for(int hit=0; hit<Nhits; hit++){
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// Gaussian source
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LatticeFermion Phi (Deofa_L.FermionGrid());
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LatticeFermion spProj_Phi(Deofa_L.FermionGrid());
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std::vector<LatticeFermion> tmp(2, Deofa_L.FermionGrid());
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gaussian(RNG5, Phi);
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Phi = Phi*scale;
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// evaluate -log(rw)
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// LH term
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for(int s=0; s<Ls; ++s){ axpby_ssp_pminus(spProj_Phi, 0.0, Phi, 1.0, Phi, s, s); }
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Deofa_L.Omega(spProj_Phi, tmp[0], -1, 0);
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G5R5(tmp[1], tmp[0]);
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tmp[0] = zero;
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SchurSolver(Deofa_L, tmp[1], tmp[0]);
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Deofa_L.Omega(tmp[0], tmp[1], -1, 1);
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rw_eofa[hit] = -k*innerProduct(spProj_Phi,tmp[1]).real();
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// RH term
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for(int s=0; s<Ls; ++s){ axpby_ssp_pplus(spProj_Phi, 0.0, Phi, 1.0, Phi, s, s); }
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Deofa_R.Omega(spProj_Phi, tmp[0], 1, 0);
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G5R5(tmp[1], tmp[0]);
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tmp[0] = zero;
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SchurSolver(Deofa_R, tmp[1], tmp[0]);
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Deofa_R.Omega(tmp[0], tmp[1], 1, 1);
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rw_eofa[hit] += k*innerProduct(spProj_Phi,tmp[1]).real();
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std::cout << std::endl << "==================================================" << std::endl;
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std::cout << " --- EOFA: Hit " << hit << ": rw = " << rw_eofa[hit];
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std::cout << std::endl << "==================================================" << std::endl << std::endl;
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}
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std::vector<RealD> rhmc_result = jack_stats(rw_rhmc);
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std::vector<RealD> eofa_result = jack_stats(rw_eofa);
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std::cout << std::endl << "RHMC: rw = " << rhmc_result[0] << " +/- " << rhmc_result[1] << std::endl;
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std::cout << std::endl << "EOFA: rw = " << eofa_result[0] << " +/- " << eofa_result[1] << std::endl;
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Grid_finalize();
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}
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