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Remove ``using namespace nda" to Avoid Namespace Pollution #12

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Remove ``using namespace nda" to Avoid Namespace Pollution #12

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4 changes: 2 additions & 2 deletions c++/cppdlr/dlr_build.hpp
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Original file line number Diff line number Diff line change
Expand Up @@ -18,10 +18,10 @@
#include <nda/nda.hpp>
#include "dlr_kernels.hpp"

using namespace nda;

namespace cppdlr {


using dcomplex = std::complex<double>;
/**
* @class fineparams
* @brief Class containing parameters for fine composite Chebyshev grid
Expand Down
4 changes: 2 additions & 2 deletions c++/cppdlr/dlr_dyson.hpp
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Expand Up @@ -32,7 +32,7 @@ namespace cppdlr {
*/

// Type of Hamiltonian, and scalar type of Hamiltonian
template <typename Ht, nda::Scalar Sh = std::conditional_t<std::floating_point<Ht>, Ht, get_value_t<Ht>>>
template <typename Ht, nda::Scalar Sh = std::conditional_t<std::floating_point<Ht>, Ht, nda::get_value_t<Ht>>>
requires(std::floating_point<Ht> || nda::MemoryMatrix<Ht>)
class dyson_it {

Expand Down Expand Up @@ -120,7 +120,7 @@ namespace cppdlr {
// Solve Dyson equation
auto g = Tg(sig.shape()); // Declare Green's function
g = rhs; // Get right hand side of Dyson equation
auto g_rs = nda::matrix_view<get_value_t<Tg>>(nda::reshape(g, norb, r * norb)); // Reshape g to be compatible w/ LAPACK
auto g_rs = nda::matrix_view<nda::get_value_t<Tg>>(nda::reshape(g, norb, r * norb)); // Reshape g to be compatible w/ LAPACK
nda::lapack::getrs(sysmat, g_rs, ipiv); // Back solve
if constexpr (std::floating_point<Ht>) { // If h is scalar, g is scalar-valued
return g;
Expand Down
4 changes: 2 additions & 2 deletions c++/cppdlr/dlr_imfreq.hpp
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Expand Up @@ -88,7 +88,7 @@ namespace cppdlr {
if (niom != g.shape(0)) throw std::runtime_error("First dim of g != # DLR imaginary frequency nodes.");

// Make a copy of the data in Fortran Layout as required by getrs
auto gf = nda::array<get_value_t<T>, get_rank<T>, F_layout>(g);
auto gf = nda::array<nda::get_value_t<T>, nda::get_rank<T>, nda::F_layout>(g);

// Reshape as matrix_view with r rows
auto gfv = nda::reshape(gf, niom, g.size() / niom);
Expand All @@ -101,7 +101,7 @@ namespace cppdlr {
auto s = nda::vector<double>(r); // Not needed
double rcond = 0; // Not needed
int rank = 0; // Not needed
nda::lapack::gelss(nda::matrix<dcomplex, F_layout>(cf2if), gfv, s, rcond, rank);
nda::lapack::gelss(nda::matrix<dcomplex, nda::F_layout>(cf2if), gfv, s, rcond, rank);
}

return gf(nda::range(r), nda::ellipsis());
Expand Down
6 changes: 3 additions & 3 deletions c++/cppdlr/dlr_imtime.hpp
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Expand Up @@ -107,7 +107,7 @@ namespace cppdlr {
if (r != g.shape(0)) throw std::runtime_error("First dim of g != DLR rank r.");

// Make a copy of the data in Fortran Layout as required by getrs
auto gf = nda::array<get_value_t<T>, get_rank<T>, F_layout>(g);
auto gf = nda::array<nda::get_value_t<T>, nda::get_rank<T>, nda::F_layout>(g);

// Reshape as matrix_view with r rows
auto gfv = nda::reshape(gf, r, g.size() / r);
Expand Down Expand Up @@ -227,14 +227,14 @@ namespace cppdlr {
// Get matrix for least squares fitting: columns are DLR basis functions
// evaluating at data points t. Must built in Fortran layout for
// compatibility with LAPACK.
auto kmat = nda::matrix<S, F_layout>(n, r); // Make sure matrix has same scalar type as g
auto kmat = nda::matrix<S, nda::F_layout>(n, r); // Make sure matrix has same scalar type as g
for (int j = 0; j < r; ++j) {
for (int i = 0; i < n; ++i) { kmat(i, j) = k_it(t(i), dlr_rf(j)); }
}

// Reshape g to matrix w/ first dimension n, and put in Fortran layout for
// compatibility w/ LAPACK
auto g_rs = nda::matrix<S, F_layout>(nda::reshape(g, n, g.size() / n));
auto g_rs = nda::matrix<S, nda::F_layout>(nda::reshape(g, n, g.size() / n));

// Solve least squares problem to obtain DLR coefficients
auto s = nda::vector<double>(r); // Singular values (not needed)
Expand Down
64 changes: 32 additions & 32 deletions c++/cppdlr/utils.hpp
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Expand Up @@ -19,9 +19,9 @@
#include <nda/nda.hpp>
#include <nda/blas.hpp>

using namespace nda;

namespace cppdlr {
using dcomplex = std::complex<double>;

/**
* Class constructor for barycheb: barycentric Lagrange interpolation at
Expand Down Expand Up @@ -101,7 +101,7 @@ namespace cppdlr {
*/

// Type T must be scalar-valued rank 2 array/array_view or matrix/matrix_view
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = get_value_t<T>>
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = nda::get_value_t<T>>
std::tuple<typename T::regular_type, nda::vector<double>, nda::vector<int>> pivrgs(T const &a, double eps) {

auto _ = nda::range::all;
Expand All @@ -116,7 +116,7 @@ namespace cppdlr {
// Compute norms of rows of input matrix, and rescale eps tolerance
auto norms = nda::vector<double>(m);
double epssq = eps * eps;
for (int j = 0; j < m; ++j) { norms(j) = real(blas::dotc(aa(j, _), aa(j, _))); }
for (int j = 0; j < m; ++j) { norms(j) = nda::real(nda::blas::dotc(aa(j, _), aa(j, _))); }

// Begin pivoted double Gram-Schmidt procedure
int jpiv = 0, jj = 0;
Expand Down Expand Up @@ -151,10 +151,10 @@ namespace cppdlr {

// Orthogonalize current rows (now the chosen pivot row) against all
// previously chosen rows
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * blas::dotc(aa(k, _), aa(j, _)); }
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * nda::blas::dotc(aa(k, _), aa(j, _)); }

// Get norm of current row
nrm = real(blas::dotc(aa(j, _), aa(j, _)));
nrm = nda::real(nda::blas::dotc(aa(j, _), aa(j, _)));
//nrm = nda::norm(aa(j, _));

// Terminate if sufficiently small, and return previously selected rows
Expand All @@ -167,8 +167,8 @@ namespace cppdlr {
// Orthogonalize remaining rows against current row
for (int k = j + 1; k < m; ++k) {
if (norms(k) <= epssq) { continue; } // Can skip rows with norm less than tolerance
aa(k, _) = aa(k, _) - aa(j, _) * blas::dotc(aa(j, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(j, _) * nda::blas::dotc(aa(j, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}
}

Expand Down Expand Up @@ -199,7 +199,7 @@ namespace cppdlr {
*/

// Type T must be scalar-valued rank 2 array/array_view or matrix/matrix_view
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = get_value_t<T>>
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = nda::get_value_t<T>>
std::tuple<typename T::regular_type, nda::vector<double>, nda::vector<int>> pivrgs_sym(T const &a, double eps) {

auto _ = nda::range::all;
Expand All @@ -218,7 +218,7 @@ namespace cppdlr {
// Compute norms of rows of input matrix, and rescale eps tolerance
auto norms = nda::vector<double>(m);
double epssq = eps * eps;
for (int j = 0; j < m; ++j) { norms(j) = real(blas::dotc(aa(j, _), aa(j, _))); }
for (int j = 0; j < m; ++j) { norms(j) = nda::real(nda::blas::dotc(aa(j, _), aa(j, _))); }

// Begin pivoted double Gram-Schmidt procedure
int jpiv = 0, jj = 0;
Expand Down Expand Up @@ -268,10 +268,10 @@ namespace cppdlr {

// Orthogonalize current row (now the first chosen pivot row) against all
// previously chosen rows
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * blas::dotc(aa(k, _), aa(j, _)); }
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * nda::blas::dotc(aa(k, _), aa(j, _)); }

// Get norm of current row
nrm = real(blas::dotc(aa(j, _), aa(j, _)));
nrm = nda::real(nda::blas::dotc(aa(j, _), aa(j, _)));

// Terminate if sufficiently small, and return previously selected rows
// (not including current row)
Expand All @@ -283,23 +283,23 @@ namespace cppdlr {
// Orthogonalize remaining rows against current row
for (int k = j + 1; k < m; ++k) {
if (norms(k) <= epssq) { continue; } // Can skip rows with norm less than tolerance
aa(k, _) = aa(k, _) - aa(j, _) * blas::dotc(aa(j, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(j, _) * nda::blas::dotc(aa(j, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}

// Orthogonalize current row (now the second chosen pivot row) against all
// previously chosen rows
for (int k = 0; k < j + 1; ++k) { aa(j + 1, _) = aa(j + 1, _) - aa(k, _) * blas::dotc(aa(k, _), aa(j + 1, _)); }
for (int k = 0; k < j + 1; ++k) { aa(j + 1, _) = aa(j + 1, _) - aa(k, _) * nda::blas::dotc(aa(k, _), aa(j + 1, _)); }

// Normalize current row
nrm = real(blas::dotc(aa(j + 1, _), aa(j + 1, _)));
nrm = nda::real(nda::blas::dotc(aa(j + 1, _), aa(j + 1, _)));
aa(j + 1, _) = aa(j + 1, _) * (1 / sqrt(nrm));

// Orthogonalize remaining rows against current row
for (int k = j + 2; k < m; ++k) {
if (norms(k) <= epssq) { continue; } // Can skip rows with norm less than tolerance
aa(k, _) = aa(k, _) - aa(j + 1, _) * blas::dotc(aa(j + 1, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(j + 1, _) * nda::blas::dotc(aa(j + 1, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}
}

Expand Down Expand Up @@ -332,7 +332,7 @@ namespace cppdlr {
*/

// Type T must be scalar-valued rank 2 array/array_view or matrix/matrix_view
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = get_value_t<T>>
template <nda::MemoryArrayOfRank<2> T, nda::Scalar S = nda::get_value_t<T>>
std::tuple<typename T::regular_type, nda::vector<double>, nda::vector<int>> pivrgs_sym(T const &a, int r) {

auto _ = nda::range::all;
Expand Down Expand Up @@ -360,7 +360,7 @@ namespace cppdlr {

// Compute norms of rows of input matrix
auto norms = nda::vector<double>(m);
for (int j = 0; j < m; ++j) { norms(j) = real(blas::dotc(aa(j, _), aa(j, _))); }
for (int j = 0; j < m; ++j) { norms(j) = nda::real(nda::blas::dotc(aa(j, _), aa(j, _))); }

// Begin pivoted double Gram-Schmidt procedure
int jpiv = 0, jj = 0;
Expand All @@ -384,14 +384,14 @@ namespace cppdlr {
//jpiv = 0; // Index of pivot row

// Normalize
nrm = real(blas::dotc(aa(0, _), aa(0, _)));
nrm = nda::real(nda::blas::dotc(aa(0, _), aa(0, _)));
aa(0, _) = aa(0, _) * (1 / sqrt(nrm));
//aa(0, _) /= sqrt(real(blas::dotc(aa(0, _), aa(0, _))));
//aa(0, _) /= sqrt(nda::real(nda::blas::dotc(aa(0, _), aa(0, _))));

// Orthogonalize remaining rows against current row
for (int k = 1; k < m; ++k) {
aa(k, _) = aa(k, _) - aa(0, _) * blas::dotc(aa(0, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(0, _) * nda::blas::dotc(aa(0, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}
}

Expand Down Expand Up @@ -433,30 +433,30 @@ namespace cppdlr {

// Orthogonalize current row (now the first chosen pivot row) against all
// previously chosen rows
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * blas::dotc(aa(k, _), aa(j, _)); }
for (int k = 0; k < j; ++k) { aa(j, _) = aa(j, _) - aa(k, _) * nda::blas::dotc(aa(k, _), aa(j, _)); }

// Normalize current row
nrm = real(blas::dotc(aa(j, _), aa(j, _)));
nrm = nda::real(nda::blas::dotc(aa(j, _), aa(j, _)));
aa(j, _) = aa(j, _) * (1 / sqrt(nrm));

// Orthogonalize remaining rows against current row
for (int k = j + 1; k < m; ++k) {
aa(k, _) = aa(k, _) - aa(j, _) * blas::dotc(aa(j, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(j, _) * nda::blas::dotc(aa(j, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}

// Orthogonalize current row (now the second chosen pivot row) against all
// previously chosen rows
for (int k = 0; k < j + 1; ++k) { aa(j + 1, _) = aa(j + 1, _) - aa(k, _) * blas::dotc(aa(k, _), aa(j + 1, _)); }
for (int k = 0; k < j + 1; ++k) { aa(j + 1, _) = aa(j + 1, _) - aa(k, _) * nda::blas::dotc(aa(k, _), aa(j + 1, _)); }

// Normalize current row
nrm = real(blas::dotc(aa(j + 1, _), aa(j + 1, _)));
nrm = nda::real(nda::blas::dotc(aa(j + 1, _), aa(j + 1, _)));
aa(j + 1, _) = aa(j + 1, _) * (1 / sqrt(nrm));

// Orthogonalize remaining rows against current row
for (int k = j + 2; k < m; ++k) {
aa(k, _) = aa(k, _) - aa(j + 1, _) * blas::dotc(aa(j + 1, _), aa(k, _));
norms(k) = real(blas::dotc(aa(k, _), aa(k, _)));
aa(k, _) = aa(k, _) - aa(j + 1, _) * nda::blas::dotc(aa(j + 1, _), aa(k, _));
norms(k) = nda::real(nda::blas::dotc(aa(k, _), aa(k, _)));
}
}

Expand Down Expand Up @@ -523,7 +523,7 @@ namespace cppdlr {
/**
* @brief Get real-valued type corresponding to type of given nda MemoryArray
*/
template <nda::MemoryArray T> using make_real_t = decltype(make_regular(real(std::declval<T>())));
template <nda::MemoryArray T> using make_real_t = decltype(make_regular(nda::real(std::declval<T>())));

/**
* @brief Get complex-valued type corresponding to type of given nda MemoryArray
Expand Down