#include "./fit_tail.hpp" namespace triqs { namespace gfs { namespace local { tail fit_tail_impl(gf_view gf, const tail_view known_moments, int n_moments, int n_min, int n_max) { tail res(get_target_shape(gf)); if (known_moments.size()) for (int i = known_moments.order_min(); i <= known_moments.order_max(); i++) res(i) = known_moments(i); // if known_moments.size()==0, the lowest order to be obtained from the fit is determined by order_min in known_moments // if known_moments.size()==0, the lowest order is the one following order_max in known_moments int n_unknown_moments = n_moments - known_moments.size(); if (n_unknown_moments < 1) return known_moments; // get the number of even unknown moments: it is n_unknown_moments/2+1 if the first // moment is even and n_moments is odd; n_unknown_moments/2 otherwise int omin = known_moments.size() == 0 ? known_moments.order_min() : known_moments.order_max() + 1; // smallest unknown moment int omin_even = omin % 2 == 0 ? omin : omin + 1; int omin_odd = omin % 2 != 0 ? omin : omin + 1; int size_even = n_unknown_moments / 2; if (n_unknown_moments % 2 != 0 && omin % 2 == 0) size_even += 1; int size_odd = n_unknown_moments - size_even; int size1 = n_max - n_min + 1; // size2 is the number of moments arrays::matrix A(size1, std::max(size_even, size_odd), FORTRAN_LAYOUT); arrays::matrix B(size1, 1, FORTRAN_LAYOUT); arrays::vector S(std::max(size_even, size_odd)); const double rcond = 0.0; int rank; for (int i = 0; i < get_target_shape(gf)[0]; i++) { for (int j = 0; j < get_target_shape(gf)[1]; j++) { // fit the odd moments S.resize(size_odd); A.resize(size1,size_odd); //when resizing, gelss segfaults for (int k = 0; k < size1; k++) { auto n = n_min + k; auto iw = std::complex(gf.mesh().index_to_point(n)); B(k, 0) = imag(gf.data()(gf.mesh().index_to_linear(n), i, j)); // subtract known tail if present if (known_moments.size() > 0) B(k, 0) -= imag(slice_target(known_moments, arrays::range(i, i + 1), arrays::range(j, j + 1)).evaluate(iw)(0, 0)); for (int l = 0; l < size_odd; l++) { int order = omin_odd + 2 * l; A(k, l) = imag(pow(iw, -1.0 * order)); // set design matrix for odd moments } } arrays::lapack::gelss(A, B, S, rcond, rank); for (int m = 0; m < size_odd; m++) { res(omin_odd + 2 * m)(i, j) = B(m, 0); } // fit the even moments S.resize(size_even); A.resize(size1,size_even); //when resizing, gelss segfaults for (int k = 0; k < size1; k++) { auto n = n_min + k; auto iw = std::complex(gf.mesh().index_to_point(n)); B(k, 0) = real(gf.data()(gf.mesh().index_to_linear(n), i, j)); // subtract known tail if present if (known_moments.size() > 0) B(k, 0) -= real(slice_target(known_moments, arrays::range(i, i + 1), arrays::range(j, j + 1)).evaluate(iw)(0, 0)); for (int l = 0; l < size_even; l++) { int order = omin_even + 2 * l; A(k, l) = real(pow(iw, -1.0 * order)); // set design matrix for odd moments } } arrays::lapack::gelss(A, B, S, rcond, rank); for (int m = 0; m < size_even; m++) { res(omin_even + 2 * m)(i, j) = B(m, 0); } } } res.mask_view()=n_moments; return res; // return tail } void fit_tail(gf_view gf, tail_view known_moments, int n_moments, int n_min, int n_max, bool replace_by_fit ) { if (get_target_shape(gf) != known_moments.shape()) TRIQS_RUNTIME_ERROR << "shape of tail does not match shape of gf"; gf.singularity() = fit_tail_impl(gf, known_moments, n_moments, n_min, n_max); if (replace_by_fit) { // replace data in the fitting range by the values from the fitted tail int i = 0; for (auto iw : gf.mesh()) { // (arrays::range(n_min,n_max+1)) { if (i >= n_min) gf[iw] = gf.singularity().evaluate(iw); i++; } } } void fit_tail(gf_view> block_gf, tail_view known_moments, int n_moments, int n_min, int n_max, bool replace_by_fit ) { // for(auto &gf : block_gf) fit_tail(gf, known_moments, n_moments, n_min, n_max, replace_by_fit); for (int i = 0; i < block_gf.mesh().size(); i++) fit_tail(block_gf[i], known_moments, n_moments, n_min, n_max, replace_by_fit); } void fit_tail(gf_view gf, tail_view known_moments, int n_moments, int n_min, int n_max, bool replace_by_fit ) { fit_tail(reinterpret_scalar_valued_gf_as_matrix_valued(gf), known_moments, n_moments, n_min, n_max, replace_by_fit ); } }}} // namespace