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dft_tools/triqs/gfs/curry.hpp

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/*******************************************************************************
*
* TRIQS: a Toolbox for Research in Interacting Quantum Systems
*
* Copyright (C) 2013 by O. Parcollet
*
* TRIQS is free software: you can redistribute it and/or modify it under the
* terms of the GNU General Public License as published by the Free Software
* Foundation, either version 3 of the License, or (at your option) any later
* version.
*
* TRIQS is distributed in the hope that it will be useful, but WITHOUT ANY
* WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
* FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
* details.
*
* You should have received a copy of the GNU General Public License along with
* TRIQS. If not, see <http://www.gnu.org/licenses/>.
*
******************************************************************************/
#ifndef TRIQS_GF_CURRY_H
#define TRIQS_GF_CURRY_H
#include "./product.hpp"
namespace triqs { namespace gfs {
template<typename F> struct lambda_valued {};
namespace gfs_implementation {
/// --------------------------- data access ---------------------------------
template<typename Opt, typename F, typename M> struct data_proxy<M,lambda_valued<F>,Opt> : data_proxy_lambda<F> {};
/// --------------------------- Factories ---------------------------------
template<typename F, typename Opt, typename ... Ms>
struct factories<cartesian_product<Ms...>, lambda_valued<F>, Opt> {};
/// --------------------------- partial_eval ---------------------------------
// partial_eval<0> (g, 1) returns : x -> g(1,x)
// partial_eval<1> (g, 3) returns : x -> g(x,3)
// 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..>).
template<typename ... Ms> struct cart_prod_impl;
template<typename ... Ms> using cart_prod = typename cart_prod_impl<Ms...>::type;
template<typename ... Ms> struct cart_prod_impl<std::tuple<Ms...>> { using type = cartesian_product<Ms...>;};
template<typename M> struct cart_prod_impl<std::tuple<M>> { typedef M type;}; //using type = M;};
template<typename M0, typename M1, typename ...M> auto rm_tuple_of_size_one(std::tuple<M0,M1,M...> const & t) DECL_AND_RETURN(t);
template<typename M> auto rm_tuple_of_size_one(std::tuple<M> const & t) DECL_AND_RETURN(std::get<0>(t));
// as_tuple leaves a tuple intact and wrap everything else in a tuple...
template<typename T> std::tuple<T> as_tuple(T && x) { return std::tuple<T> {std::forward<T>(x)};}
template<typename ... T> std::tuple<T...> as_tuple(std::tuple<T...> && x) { return std::forward<T...>(x);}
template<typename ... T> std::tuple<T...> const & as_tuple(std::tuple<T...> const & x) { return x;}
template<typename ... T> std::tuple<T...> & as_tuple(std::tuple<T...> & x) { return x;}
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template<int ... pos, typename Opt, typename Target, bool B, bool IsConst, typename IT, typename ... Ms>
gf_view< typename cart_prod_impl< triqs::tuple::filter_out_t<std::tuple<Ms...>, pos...>>::type ,Target, Opt,IsConst>
partial_eval(gf_impl< cartesian_product<Ms...>, Target,Opt,B,IsConst> const & g, IT index) {
// meshes of the returned gf_view : just drop the mesh of the evaluated variables
auto meshes_tuple_partial = triqs::tuple::filter_out<pos...>(g.mesh().components());
// a view of the array of g, with the dimension sizeof...(Ms)
auto arr = reinterpret_linear_array(g.mesh(),g.data()); // NO the second () forces a view
// now rebuild a tuple of the size sizeof...(Ms), containing the indices and range at the position of evaluated variables.
auto arr_args = triqs::tuple::inverse_filter<sizeof...(Ms),pos...>(as_tuple(index), arrays::range());
// from it, we make a slice of the array of g, corresponding to the data of the returned gf_view
auto arr2 = triqs::tuple::apply(arr, arr_args);
// 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,IsConst>;
return r_t{ rm_tuple_of_size_one(meshes_tuple_partial), arr2, typename r_t::singularity_non_view_t{}, typename r_t::symmetry_t{} };
}
/// --------------------------- curry ---------------------------------
// curry<0>(g) returns : x-> y... -> g(x,y...)
// curry<1>(g) returns : y-> x,z... -> g(x,y,z...)
// to adapt the partial_eval as a polymorphic lambda (replace by a lambda in c++14)
template<typename Gview, int ... pos> struct curry_polymorphic_lambda {
Gview g;
template<typename ...I> auto operator()(I ... i) const DECL_AND_RETURN(partial_eval<pos...>(g,std::make_tuple(i...)));
[API change] gf : factories -> constructors - Make more general constructors for the gf. gf( mesh, target_shape_t) - remove the old make_gf for the basic gf. - 2 var non generic gf removed. - clean evaluator - add tensor_valued - add a simple vertex test. - clean specialisation - Fix bug introduced in 1906dc3 - forgot to resize the gf in new version of operator = - Fix make_singularity in gf.hpp - clean resize in operator = - update h5 read/write for block gf - changed a bit the general trait to save *all* the gf. - allows a more general specialization, then a correct for blocks - NOT FINISHED : need to save the block indice for python. How to reread ? Currently it read the blocks names and reconstitute the mesh from it. Is it sufficient ? - clean block constructors - block constructors simplest possible : an int for the number of blocks - rest in free factories. - fixed the generic constructor from GfType for the regular type : only enable iif GfType is ImmutableGreenFunction - multivar. fix linear index in C, and h5 format - linear index now correctly flatten in C mode (was in fortran mode), using a simple reverse of the tuple in the folding. - fix the h5 read write of the multivar fonctions in order to write an array on dimension # variables + dim_target i.e. without flattening the indices of the meshes. Easier for later data analysis, e.g. in Python. - merge matrix/tensor_valued. improve factories - matrix_valued now = tensor_valued<2> (simplifies generic code for h5). - factories_one_var -> factories : this is the generic case ... only a few specialization, code is simpler. - clef expression call with rvalue for *this - generalize matrix_proxy to tensor and clean - clean exception catch in tests - exception catching catch in need in test because the silly OS X does not print anything, just "exception occurred". Very convenient for the developer... - BUT, one MUST add return 1, or the make test will *pass* !! - --> systematically replace the catch by a macro TRIQS_CATCH_AND_ABORT which return a non zero error code. - exception : curry_and_fourier which does not work at this stage (mesh incompatible). - gf: clean draft of gf 2 times - comment the python interface for the moment. - rm useless tests
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friend int get_shape(curry_polymorphic_lambda const&) { return 0;}// no shape here, but needed for compilation
//void resize(int){}
};
// curry function ...
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template <int... pos, typename Target, typename Opt, bool IsConst, typename... Ms>
gf_view<cart_prod<triqs::tuple::filter_t<std::tuple<Ms...>, pos...>>,
lambda_valued<curry_polymorphic_lambda<gf_view<cartesian_product<Ms...>, Target, Opt,IsConst>, pos...>>, Opt, IsConst>
curry(gf_view<cartesian_product<Ms...>, Target, Opt, IsConst> g) {
// pick up the meshed corresponding to the curryed variables
auto meshes_tuple = triqs::tuple::filter<pos...>(g.mesh().components());
// 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,IsConst>, pos ...>{g}, nothing(), nothing()};
//using m_t = gf_mesh< cart_prod< triqs::tuple::filter_t<std::tuple<Ms...>,pos...>>>;
//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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template <int... pos, typename Target, typename Opt, typename... Ms>
auto curry(gf<cartesian_product<Ms...>, Target, Opt> & g) DECL_AND_RETURN(curry<pos...>(g()));
template <int... pos, typename Target, typename Opt, typename... Ms>
auto curry(gf<cartesian_product<Ms...>, Target, Opt> const & g) DECL_AND_RETURN(curry<pos...>(g()));
} // gf_implementation
using gfs_implementation::partial_eval;
using gfs_implementation::curry;
}}
#endif