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dft_tools/triqs/gfs/product.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/>.
*
******************************************************************************/
#pragma once
#include "./tools.hpp"
#include "./gf.hpp"
#include "./meshes/product.hpp"
[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
2013-10-16 23:55:26 +02:00
#include "./evaluators.hpp"
namespace triqs {
namespace gfs {
template <typename... Ms> struct cartesian_product {
using type = std::tuple<Ms...>;
static constexpr size_t size = sizeof...(Ms);
};
// use alias
template <typename... Ms> struct cartesian_product<std::tuple<Ms...>> : cartesian_product<Ms...> {};
/// TODO : Put inheriting constructor, simpler...
// the mesh is simply a cartesian product
template <typename Opt, typename... Ms> struct gf_mesh<cartesian_product<Ms...>, Opt> : mesh_product<gf_mesh<Ms, Opt>...> {
// using mesh_product< gf_mesh<Ms,Opt> ... >::mesh_product< gf_mesh<Ms,Opt> ... > ;
using B = mesh_product<gf_mesh<Ms, Opt>...>;
[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
2013-10-16 23:55:26 +02:00
gf_mesh() = default;
gf_mesh(gf_mesh<Ms, Opt>... ms) : B{std::move(ms)...} {}
};
namespace gfs_implementation {
/// --------------------------- data access ---------------------------------
template <typename Opt, typename... Ms>
struct data_proxy<cartesian_product<Ms...>, scalar_valued, Opt> : data_proxy_array_multivar<std::complex<double>,
sizeof...(Ms)> {};
template <typename Opt, typename... Ms>
struct data_proxy<cartesian_product<Ms...>, matrix_valued, Opt> : data_proxy_array_multivar_matrix_valued<std::complex<double>,
2 + sizeof...(Ms)> {};
template <int R, typename Opt, typename... Ms>
struct data_proxy<cartesian_product<Ms...>, tensor_valued<R>, Opt> : data_proxy_array_multivar<std::complex<double>,
R + sizeof...(Ms)> {};
// special case ? Or make a specific container....
template <typename Opt, typename M0>
struct data_proxy<cartesian_product<M0, imtime>, matrix_valued, Opt> : data_proxy_array_multivar_matrix_valued<double, 2 + 2> {
};
[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
2013-10-16 23:55:26 +02:00
/// --------------------------- hdf5 ---------------------------------
// h5 name : name1_x_name2_.....
template <typename Opt, typename... Ms> struct h5_name<cartesian_product<Ms...>, matrix_valued, nothing, Opt> {
static std::string invoke() {
return triqs::tuple::fold([](std::string a, std::string b) { return a + std::string(b.empty() ? "" : "_x_") + b; },
std::make_tuple(h5_name<Ms, matrix_valued, nothing, Opt>::invoke()...), std::string());
}
};
template <typename Opt, int R, typename... Ms>
struct h5_name<cartesian_product<Ms...>, tensor_valued<R>, nothing, Opt> : h5_name<cartesian_product<Ms...>, matrix_valued, nothing, Opt> {};
[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
2013-10-16 23:55:26 +02:00
/// --------------------------- evaluator ---------------------------------
/**
* This the multi-dimensional evaluator.
* It combine the evaluator of each components, as long as they are a linear form
* eval(g, x) = \sum_i w_i g( n_i(x)) , with w some weight and n_i some points on the grid.
* Mathematically, it is written as (example of evaluating g(x1,x2,x3,x4)).
* Notation : eval(X) : g -> g(X)
* eval(x1,x2,x3,x4) (g) = eval (x1) ( binder ( g, (), (x2,x3,x4)) )
* binder( g, (), (x2,x3,x4)) (p1) = eval(x2)(binder (g,(p1),(x3,x4)))
* binder( g, (p1), (x3,x4)) (p2) = eval(x3)(binder (g,(p1,p2),(x4)))
* binder( g, (p1,p2), (x4)) (p3) = eval(x4)(binder (g,(p1,p2,p3),()))
* binder( g, (p1,p2,p3),()) (p4) = g[p1,p2,p3,p4]
*
* p_i are points on the grids, x_i points in the domain.
*
* Unrolling the formula gives (for 2 variables, with 2 points interpolation)
* 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))
* = w_1(xa) ( eval(xb)( binder ( g, (n_1(xa) ), ()))) + 1 <-> 2
* = w_1(xa) ( W_1(xb) * binder ( g, (n_1(xa) ), ())(N_1(xb)) + 1<->2 ) + 1 <-> 2
* = w_1(xa) ( W_1(xb) * g[n_1(xa), N_1(xb)] + 1<->2 ) + 1 <-> 2
* = 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
* which is the expected formula
*/
// implementation : G = gf, Tn : tuple of n points, Ev : tuple of evaluators (the evals functions),
// pos = counter from #args-1 =>0
// NB : the tuple is build in reverse with respect to the previous comment.
template <typename G, typename Tn, typename Ev, int pos> struct binder;
template <int pos, typename G, typename Tn, typename Ev> 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};
}
template <typename G, typename Tn, typename Ev, int pos> struct binder {
G const *g;
Tn tn;
Ev const &evals;
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)));
};
template <typename G, typename Tn, typename Ev> struct binder<G, Tn, Ev, -1> {
G const *g;
Tn tn;
Ev const &evals;
auto operator()(size_t p) const DECL_AND_RETURN(triqs::tuple::apply(on_mesh(*g), triqs::tuple::push_front(tn, p)));
};
// now the multi d evaluator itself.
template <typename Target, typename Opt, typename... Ms> struct evaluator<cartesian_product<Ms...>, Target, nothing, Opt> {
static constexpr int arity = sizeof...(Ms);
mutable std::tuple<evaluator_fnt_on_mesh<Ms>...> evals;
struct _poly_lambda { // replace by a polymorphic lambda in C++14
template <typename A, typename B, typename C> void operator()(A &a, B const &b, C const &c) const {
a = A{b, c};
}
};
template <typename G, typename... Args>
// std::complex<double> operator() (G const * g, Args && ... args) const {
auto operator()(G const *g, Args &&... args)
const -> decltype(std::get<sizeof...(Args) - 1>(evals)(make_binder<sizeof...(Args) - 2>(g, std::make_tuple(), evals)))
// when do we get C++14 decltype(auto) ...!?
{
static constexpr int R = sizeof...(Args);
// build the evaluators, as a tuple of ( evaluator<Ms> ( mesh_component, args))
triqs::tuple::for_each_zip(_poly_lambda(), evals, g->mesh().components(), std::make_tuple(args...));
return std::get<R - 1>(evals)(make_binder<R - 2>(g, std::make_tuple(), evals));
}
};
} // gf_implementation
}
}