mirror of
https://github.com/triqs/dft_tools
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164 lines
7.7 KiB
C++
164 lines
7.7 KiB
C++
/*******************************************************************************
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*
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* TRIQS: a Toolbox for Research in Interacting Quantum Systems
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*
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* Copyright (C) 2013 by O. Parcollet
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*
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* TRIQS is free software: you can redistribute it and/or modify it under the
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* terms of the GNU General Public License as published by the Free Software
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* Foundation, either version 3 of the License, or (at your option) any later
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* version.
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*
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* TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
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* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
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* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
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* details.
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*
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* You should have received a copy of the GNU General Public License along with
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* TRIQS. If not, see <http://www.gnu.org/licenses/>.
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*
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******************************************************************************/
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#ifndef TRIQS_GF_PRODUCT_H
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#define TRIQS_GF_PRODUCT_H
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#include "./tools.hpp"
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#include "./gf.hpp"
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#include "./meshes/product.hpp"
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#include "./evaluators.hpp"
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namespace triqs { namespace gfs {
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template<typename ... Ms> struct cartesian_product{
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typedef std::tuple<Ms...> type;
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static constexpr size_t size = sizeof...(Ms);
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};
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// use alias
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template<typename ... Ms> struct cartesian_product <std::tuple<Ms...>> : cartesian_product<Ms...>{};
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// the mesh is simply a cartesian product
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template<typename Opt, typename ... Ms> struct gf_mesh<cartesian_product<Ms...>,Opt> : mesh_product< gf_mesh<Ms,Opt> ... > {
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typedef mesh_product< gf_mesh<Ms,Opt> ... > B;
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typedef std::tuple<Ms...> mesh_name_t;
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gf_mesh() = default;
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gf_mesh (gf_mesh<Ms,Opt> ... ms) : B {std::move(ms)...} {}
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};
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namespace gfs_implementation {
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/// --------------------------- hdf5 ---------------------------------
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// h5 name : name1_x_name2_.....
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template<typename Opt, typename ... Ms> struct h5_name<cartesian_product<Ms...>,matrix_valued,Opt> {
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static std::string invoke(){
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return triqs::tuple::fold(
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[](std::string a, std::string b) { return a + std::string(b.empty()?"" : "_x_") + b;},
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std::make_tuple(h5_name<Ms,matrix_valued,Opt>::invoke()...),
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std::string());
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}
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};
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template<typename Opt, int R, typename ... Ms> struct h5_name<cartesian_product<Ms...>,tensor_valued<R>,Opt> : h5_name<cartesian_product<Ms...>,matrix_valued,Opt> {};
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// a slight difference with the generic case : reinterpret the data array to avoid flattening the variables
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template <typename Opt, int R, typename ... Ms>
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struct h5_rw<cartesian_product<Ms...>,tensor_valued<R>,Opt> {
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typedef gf<cartesian_product<Ms...>,tensor_valued<R>,Opt> g_t;
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static void write (h5::group gr, typename g_t::const_view_type g) {
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h5_write(gr,"data",reinterpret_linear_array(g.mesh(), g().data()));
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h5_write(gr,"singularity",g._singularity);
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h5_write(gr,"mesh",g._mesh);
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h5_write(gr,"symmetry",g._symmetry);
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}
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template<bool IsView>
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static void read (h5::group gr, gf_impl<cartesian_product<Ms...>,tensor_valued<R>,Opt,IsView,false> & g) {
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using G_t= gf_impl<cartesian_product<Ms...>,tensor_valued<R>,Opt,IsView,false> ;
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h5_read(gr,"mesh",g._mesh);
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auto arr = arrays::array<typename G_t::data_t::value_type, sizeof...(Ms)+ R>{};
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h5_read(gr,"data",arr);
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auto sh = arr.shape();
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arrays::mini_vector<size_t,R+1> sh2;
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sh2[0] = g._mesh.size();
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for (int u=1; u<R+1; ++u) sh2[u] = sh[sizeof...(Ms)-1+u];
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g._data = arrays::array<typename G_t::data_t::value_type, R+1>{sh2, std::move(arr.storage())};
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h5_read(gr,"singularity",g._singularity);
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h5_read(gr,"symmetry",g._symmetry);
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}
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};
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/// --------------------------- data access ---------------------------------
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template<typename Opt, typename ... Ms> struct data_proxy<cartesian_product<Ms...>,scalar_valued,Opt> : data_proxy_array<std::complex<double>,1> {};
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template<typename Opt, typename ... Ms> struct data_proxy<cartesian_product<Ms...>,matrix_valued,Opt> : data_proxy_array<std::complex<double>,3> {};
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template<int R, typename Opt, typename ... Ms> struct data_proxy<cartesian_product<Ms...>,tensor_valued<R>,Opt> : data_proxy_array<std::complex<double>,R+1> {};
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/// --------------------------- evaluator ---------------------------------
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/**
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* This the multi-dimensional evaluator.
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* It combine the evaluator of each components, as long as they are a linear form
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* eval(g, x) = \sum_i w_i g( n_i(x)) , with w some weight and n_i some points on the grid.
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* Mathematically, it is written as (example of evaluating g(x1,x2,x3,x4)).
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* Notation : eval(X) : g -> g(X)
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* eval(x1,x2,x3,x4) (g) = eval (x1) ( binder ( g, (), (x2,x3,x4)) )
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* binder( g, (), (x2,x3,x4)) (p1) = eval(x2)(binder (g,(p1),(x3,x4)))
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* binder( g, (p1), (x3,x4)) (p2) = eval(x3)(binder (g,(p1,p2),(x4)))
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* binder( g, (p1,p2), (x4)) (p3) = eval(x4)(binder (g,(p1,p2,p3),()))
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* binder( g, (p1,p2,p3),()) (p4) = g[p1,p2,p3,p4]
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*
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* p_i are points on the grids, x_i points in the domain.
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*
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* Unrolling the formula gives (for 2 variables, with 2 points interpolation)
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* eval(xa,xb) (g) = eval (xa) ( binder ( g, (), (xb)) ) = w_1(xa) binder ( g, (), (xb))( n_1(xa)) + w_2(xa) binder ( g, (), (xb))( n_2(xa))
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* = w_1(xa) ( eval(xb)( binder ( g, (n_1(xa) ), ()))) + 1 <-> 2
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* = w_1(xa) ( W_1(xb) * binder ( g, (n_1(xa) ), ())(N_1(xb)) + 1<->2 ) + 1 <-> 2
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* = w_1(xa) ( W_1(xb) * g[n_1(xa), N_1(xb)] + 1<->2 ) + 1 <-> 2
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* = w_1(xa) ( W_1(xb) * g[n_1(xa), N_1(xb)] + W_2(xb) * g[n_1(xa), N_2(xb)] ) + 1 <-> 2
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* which is the expected formula
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*/
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// implementation : G = gf, Tn : tuple of n points, Ev : tuple of evaluators (the evals functions), pos = counter from #args-1 =>0
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// NB : the tuple is build in reverse with respect to the previous comment.
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template<typename G, typename Tn, typename Ev, int pos> struct binder;
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template<int pos, typename G, typename Tn, typename Ev>
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binder<G,Tn,Ev,pos> make_binder(G const * g, Tn tn, Ev const & ev) { return binder<G,Tn,Ev,pos>{g, std::move(tn), ev}; }
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template<typename G, typename Tn, typename Ev, int pos> struct binder {
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G const * g; Tn tn; Ev const & evals;
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auto operator()(size_t p) const DECL_AND_RETURN( std::get<pos>(evals) ( make_binder<pos-1>(g, triqs::tuple::push_front(tn,p), evals) ));
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};
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template<typename G, typename Tn, typename Ev> struct binder<G,Tn,Ev,-1> {
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G const * g; Tn tn; Ev const & evals;
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auto operator()(size_t p) const DECL_AND_RETURN( triqs::tuple::apply(on_mesh(*g), triqs::tuple::push_front(tn,p)));
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};
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// now the multi d evaluator itself.
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template<typename Target, typename Opt, typename ... Ms>
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struct evaluator<cartesian_product<Ms...>,Target,Opt> {
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static constexpr int arity = sizeof...(Ms);
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mutable std::tuple< evaluator_fnt_on_mesh<Ms> ... > evals;
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struct _poly_lambda {// replace by a polymorphic lambda in C++14
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template<typename A, typename B, typename C> void operator()(A & a, B const & b, C const & c) const { a = A{b,c};}
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};
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template<typename G, typename ... Args>
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//std::complex<double> operator() (G const * g, Args && ... args) const {
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auto operator() (G const * g, Args && ... args) const
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-> decltype (std::get<sizeof...(Args)-1>(evals) (make_binder<sizeof...(Args)-2> (g, std::make_tuple(), evals) ))
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// when do we get C++14 decltype(auto) ...!?
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{
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static constexpr int R = sizeof...(Args);
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// build the evaluators, as a tuple of ( evaluator<Ms> ( mesh_component, args))
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triqs::tuple::call_on_zip(_poly_lambda(), evals, g->mesh().components(), std::make_tuple(args...));
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return std::get<R-1>(evals) (make_binder<R-2> (g, std::make_tuple(), evals) );
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}
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};
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} // gf_implementation
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}}
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#endif
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