3
0
mirror of https://github.com/triqs/dft_tools synced 2024-11-01 19:53:45 +01:00
dft_tools/triqs/gfs/data_proxies.hpp

156 lines
7.5 KiB
C++
Raw Normal View History

/*******************************************************************************
*
* TRIQS: a Toolbox for Research in Interacting Quantum Systems
*
* Copyright (C) 2012 by M. Ferrero, 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_DATA_PROXIES_H
#define TRIQS_GF_DATA_PROXIES_H
#include <triqs/utility/first_include.hpp>
#include <utility>
#include <triqs/arrays.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 "../arrays/matrix_tensor_proxy.hpp"
#define TRIQS_GF_DATA_PROXIES_WITH_SIMPLE_VIEWS
namespace triqs { namespace gfs {
//---------------------------- common stuff for array proxies ----------------------------------
[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
template <typename T, int D> struct data_proxy_array_common {
using storage_t = arrays::array<T, D>;
using storage_view_t = typename storage_t::view_type;
using storage_const_view_t = typename storage_t::const_view_type;
// from the shape of the mesh and the target, make the shape of the array. default is to glue them
template <typename S1, typename S2> static auto join_shape(S1 const& s1, S2 const& s2) RETURN(join(s1, s2));
[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
template<typename S, typename RHS> static void assign_to_scalar (S & data, RHS && rhs) { data() = std::forward<RHS>(rhs);}
2013-10-18 13:39:00 +02:00
template <typename ST, typename RHS> static void rebind(ST& data, RHS&& rhs) { data.rebind(rhs.data()); }
[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
};
//---------------------------- generic case array of dim R----------------------------------
template <typename T, int R> struct data_proxy_array : data_proxy_array_common<T, R> {
using B = data_proxy_array_common<T, R>;
/// The data access
#ifdef TRIQS_GF_DATA_PROXIES_WITH_SIMPLE_VIEWS
template <typename S> auto operator()(S& data, long i) const DECL_AND_RETURN(data(i, arrays::ellipsis()));
#else
auto operator()(B::storage_t& data, long i) const DECL_AND_RETURN(arrays::make_tensor_proxy(data, i));
auto operator()(B::storage_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_tensor_proxy(data, i));
auto operator()(B::storage_view_t& data, long i) const DECL_AND_RETURN(arrays::make_tensor_proxy(data, i));
auto operator()(B::storage_view_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_tensor_proxy(data, i));
auto operator()(B::storage_const_view_t& data, long i) const DECL_AND_RETURN(arrays::make_const_tensor_proxy(data, i));
auto operator()(B::storage_const_view_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_tensor_proxy(data, 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
2013-10-16 23:55:26 +02:00
#endif
};
[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
//---------------------------- 3d array : returns matrices in this case ! ----------------------------------
template <typename T> struct data_proxy_array<T, 3> : data_proxy_array_common<T, 3> {
using B = data_proxy_array_common<T, 3>;
[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
#ifdef TRIQS_GF_DATA_PROXIES_WITH_SIMPLE_VIEWS
template <typename S> auto operator()(S & data, long i) const RETURN(make_matrix_view(data(i, arrays::ellipsis())));
#else
/// The data access
auto operator()(B::storage_t& data, long i) const DECL_AND_RETURN(arrays::make_matrix_proxy(data, i));
auto operator()(B::storage_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_matrix_proxy(data, i));
auto operator()(B::storage_view_t& data, long i) const DECL_AND_RETURN(arrays::make_matrix_proxy(data, i));
auto operator()(B::storage_view_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_matrix_proxy(data, i));
auto operator()(B::storage_const_view_t& data, long i) const DECL_AND_RETURN(arrays::make_const_matrix_proxy(data, i));
auto operator()(B::storage_const_view_t const& data, long i) const DECL_AND_RETURN(arrays::make_const_matrix_proxy(data, 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
2013-10-16 23:55:26 +02:00
#endif
};
//---------------------------- 1d array ----------------------------------
template <typename T> struct data_proxy_array<T, 1> : data_proxy_array_common<T, 1> {
template <typename S> AUTO_DECL operator()(S& data, long i) const RETURN(data(i));
};
//---------------------------- multi variable ----------------------------------
template <typename T, int TotalDim> struct data_proxy_array_multivar : data_proxy_array_common<T, TotalDim> {
// using the standard technique from tuple::apply with a sequence
template <typename S, typename Tu, size_t... Is>
AUTO_DECL _impl(S& data, Tu const& tu, std14::index_sequence<Is...>) const RETURN(data(std::get<Is>(tu)..., arrays::ellipsis()));
template <typename S, typename Tu>
AUTO_DECL operator()(S& data, Tu const& tu) const RETURN(_impl(data, tu, triqs::tuple::_get_seq<Tu>()));
};
//---------------------------- multi variable ----------------------------------
template <typename T, int TotalDim> struct data_proxy_array_multivar_matrix_valued : data_proxy_array_common<T, TotalDim> {
// using the standard technique from tuple::apply with a sequence
template <typename S, typename Tu, size_t... Is>
AUTO_DECL _impl(S& data, Tu const& tu, std14::index_sequence<Is...>) const RETURN(make_matrix_view(data(std::get<Is>(tu)..., arrays::range(), arrays::range())));
template <typename S, typename Tu>
AUTO_DECL operator()(S& data, Tu const& tu) const RETURN(_impl(data, tu, triqs::tuple::_get_seq<Tu>()));
};
//---------------------------- vector ----------------------------------
2013-10-18 13:39:00 +02:00
template<typename V> struct view_proxy : public V {
view_proxy() : V(typename V::regular_type()) {}
view_proxy(V const &v) : V(v){};
view_proxy(view_proxy const & p) : V(p) {};
template<typename ... Args> explicit view_proxy(Args && ... args) : V (std::forward<Args>(args)...){}
view_proxy & operator = ( view_proxy const & cp ) { this->rebind(cp); return *this;}
view_proxy & operator = ( V const & v ) { this->rebind(v); return *this;}
using V::operator=;
//template<typename X> view_proxy & operator = (X && x) { V::operator=( std::forward<X>(x) ); return *this;}
};
template <typename T> struct data_proxy_vector {
using Tv = typename T::view_type;
using Tcv = typename T::const_view_type;
/// The storage
using storage_t = std::vector<T>;
using storage_view_t = std::vector<view_proxy<Tv>>;
using storage_const_view_t = std::vector<view_proxy<Tcv>>;
/// The data access
template <typename S> AUTO_DECL operator()(S& data, size_t i) const RETURN(data[i]);
template<typename S, typename RHS> static void assign_to_scalar (S & data, RHS && rhs) {for (size_t i =0; i<data.size(); ++i) data[i] = rhs;}
2013-10-18 13:39:00 +02:00
template <typename ST, typename RHS> static void rebind(ST& data, RHS&& rhs) { data.clear(); for (auto & x : rhs.data()) data.push_back(x);}
};
//---------------------------- lambda ----------------------------------
template <typename F> struct data_proxy_lambda {
/// The storage
using storage_t = F;
using storage_view_t = F;
using storage_const_view_t = F;
/// The data access
template <typename S, typename ... I> AUTO_DECL operator()(S& data, I const& ...i) const RETURN(data(i...));
template<typename S, typename RHS> static void assign_to_scalar (S & data, RHS && rhs) = delete;
2013-10-18 13:39:00 +02:00
template <typename ST, typename RHS> static void rebind(ST& data, RHS&& rhs) = delete;
};
}}
#endif