mirror of
https://github.com/triqs/dft_tools
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101 lines
5.3 KiB
C++
101 lines
5.3 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_CURRY_H
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#define TRIQS_GF_CURRY_H
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#include "./product.hpp"
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namespace triqs { namespace gfs {
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template<typename F> struct lambda_valued {};
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namespace gfs_implementation {
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/// --------------------------- data access ---------------------------------
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template<typename Opt, typename F, typename M> struct data_proxy<M,lambda_valued<F>,Opt> : data_proxy_lambda<F> {};
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/// --------------------------- Factories ---------------------------------
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template<typename F, typename Opt, typename ... Ms>
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struct factories<cartesian_product<Ms...>, lambda_valued<F>, Opt> {};
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/// --------------------------- partial_eval ---------------------------------
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// partial_eval<0> (g, 1) returns : x -> g(1,x)
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// partial_eval<1> (g, 3) returns : x -> g(x,3)
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// a technical trait: from a tuple of mesh, return the mesh (either M if it is a tuple of size 1, or the corresponding cartesian_product<M..>).
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template<typename ... Ms> struct cart_prod_impl;
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template<typename ... Ms> using cart_prod = typename cart_prod_impl<Ms...>::type;
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template<typename ... Ms> struct cart_prod_impl<std::tuple<Ms...>> { using type = cartesian_product<Ms...>;};
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template<typename M> struct cart_prod_impl<std::tuple<M>> { typedef M type;}; //using type = M;};
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template<typename M0, typename M1, typename ...M> auto rm_tuple_of_size_one(std::tuple<M0,M1,M...> const & t) DECL_AND_RETURN(t);
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template<typename M> auto rm_tuple_of_size_one(std::tuple<M> const & t) DECL_AND_RETURN(std::get<0>(t));
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template<int ... pos, typename Opt, typename Target, bool B, typename IT, typename ... Ms>
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gf_view< typename cart_prod_impl< triqs::tuple::filter_out_t<std::tuple<Ms...>, pos...>>::type ,Target, Opt>
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partial_eval(gf_impl< cartesian_product<Ms...>, Target,Opt,B> const & g, IT index) {
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// meshes of the returned gf_view : just drop the mesh of the evaluated variables
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auto meshes_tuple_partial = triqs::tuple::filter_out<pos...>(g.mesh().components());
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// a view of the array of g, with the dimension sizeof...(Ms)
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auto arr = reinterpret_linear_array(g.mesh(),g.data());
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// now rebuild a tuple of the size sizeof...(Ms), containing the indices and range at the position of evaluated variables.
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auto arr_args = triqs::tuple::inverse_filter<sizeof...(Ms),pos...>(index, arrays::range());
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// from it, we make a slice of the array of g, corresponding to the data of the returned gf_view
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auto arr2 = triqs::tuple::apply(arr, arr_args);
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// finally, we build the view on this data.
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using r_t = gf_view< cart_prod< triqs::tuple::filter_out_t<std::tuple<Ms...>, pos...>> ,Target, Opt>;
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return r_t{ rm_tuple_of_size_one(meshes_tuple_partial), arr2, typename r_t::singularity_non_view_t{}, typename r_t::symmetry_t{} };
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}
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/// --------------------------- curry ---------------------------------
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// curry<0>(g) returns : x-> y... -> g(x,y...)
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// curry<1>(g) returns : y-> x,z... -> g(x,y,z...)
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// to adapt the partial_eval as a polymorphic lambda (replace by a lambda in c++14)
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template<typename Gview, int ... pos> struct curry_polymorphic_lambda {
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Gview g;
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template<typename ...I> auto operator()(I ... i) const DECL_AND_RETURN(partial_eval<pos...>(g,std::make_tuple(i...)));
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friend int get_shape(curry_polymorphic_lambda const&) { return 0;}// no shape here, but needed for compilation
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//void resize(int){}
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};
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// curry function ...
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template<int ... pos, typename Target, typename Opt, bool B, typename ... Ms>
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gf_view<cart_prod< triqs::tuple::filter_t<std::tuple<Ms...>,pos...> >,
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lambda_valued<curry_polymorphic_lambda<gf_view<cartesian_product<Ms...>, Target,Opt>,pos...>>,
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Opt>
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curry (gf_impl<cartesian_product<Ms...>, Target,Opt,B> const & g) {
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// pick up the meshed corresponding to the curryed variables
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auto meshes_tuple = triqs::tuple::filter<pos...>(g.mesh().components());
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// building the view
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return {rm_tuple_of_size_one(meshes_tuple),curry_polymorphic_lambda<gf_view<cartesian_product<Ms...>, Target,Opt>, pos ...>{g}, nothing(), nothing()};
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//using m_t = gf_mesh< cart_prod< triqs::tuple::filter_t<std::tuple<Ms...>,pos...>>>;
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//return {triqs::tuple::apply_construct<m_t>(meshes_tuple),curry_polymorphic_lambda<gf_view<cartesian_product<Ms...>, Target,Opt>, pos ...>{g}, nothing(), nothing()};
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};
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} // gf_implementation
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using gfs_implementation::partial_eval;
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using gfs_implementation::curry;
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}}
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#endif
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