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dft_tools/triqs/gfs/imfreq.hpp

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/*******************************************************************************
*
* TRIQS: a Toolbox for Research in Interacting Quantum Systems
*
* Copyright (C) 2012-2013 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 "./local/tail.hpp"
#include "./local/no_tail.hpp"
#include "./meshes/matsubara_freq.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 {
struct imfreq {};
template <typename Opt> struct gf_mesh<imfreq, Opt> : matsubara_freq_mesh {
template <typename... T> gf_mesh(T &&... x) : matsubara_freq_mesh(std::forward<T>(x)...) {}
// using matsubara_freq_mesh::matsubara_freq_mesh;
};
// singularity
template <> struct gf_default_singularity<imfreq, matrix_valued> {
using type = tail;
};
template <> struct gf_default_singularity<imfreq, scalar_valued> {
using type = tail;
};
namespace gfs_implementation {
/// --------------------------- hdf5 ---------------------------------
template <typename S, typename Opt> struct h5_name<imfreq, matrix_valued, S, Opt> {
static std::string invoke() { return "ImFreq"; }
};
/// --------------------------- data access ---------------------------------
template <typename Opt> struct data_proxy<imfreq, matrix_valued, Opt> : data_proxy_array<std::complex<double>, 3> {};
template <typename Opt> struct data_proxy<imfreq, scalar_valued, Opt> : data_proxy_array<std::complex<double>, 1> {};
/// --------------------------- evaluator ---------------------------------
[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
// simple evaluation : take the point on the grid...
template <> struct evaluator_fnt_on_mesh<imfreq> {
long n;
double w;
evaluator_fnt_on_mesh() = default;
template <typename MeshType> evaluator_fnt_on_mesh(MeshType const &m, long p) { n = p; w=1; }
template <typename MeshType> evaluator_fnt_on_mesh(MeshType const &m, matsubara_freq const &p) {
if ((p.n >= m.first_index()) && (p.n < m.size()+m.first_index())) {w=1; n =p.n;}
else {w=0; n=0;}
}
template <typename F> AUTO_DECL operator()(F const &f) const RETURN(w*f(n));
};
[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 -------------------
// handle the case where the matsu. freq is out of grid...
[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
struct _eval_imfreq_base_impl {
static constexpr int arity = 1;
template <typename G> int sh(G const * g) const { return (g->mesh().domain().statistic == Fermion ? 1 : 0);}
// int -> replace by matsubara_freq
template <typename G>
AUTO_DECL operator()(G const *g, int n) const
RETURN((*g)(matsubara_freq(n, g->mesh().domain().beta, g->mesh().domain().statistic)));
template <typename G> typename G::singularity_t operator()(G const *g, tail_view t) const {
return compose(g->singularity(),t);
//return g->singularity();
}
};
// --- various 4 specializations
// scalar_valued, tail
template <typename Opt> struct evaluator<imfreq, scalar_valued, tail, Opt> : _eval_imfreq_base_impl {
using _eval_imfreq_base_impl::operator();
template <typename G> std::complex<double> operator()(G const *g, matsubara_freq const &f) const {
if (g->mesh().positive_only()) { // only positive Matsubara frequencies
if ((f.n >= 0) && (f.n < g->mesh().size())) return (*g)[f.n];
if ((f.n < 0) && ((-f.n - this->sh(g)) < g->mesh().size())) return conj((*g)[-f.n - this->sh(g)]);
} else {
if ((f.n >= g->mesh().first_index()) && (f.n < g->mesh().size() + g->mesh().first_index())) return (*g)[f.n];
}
return evaluate(g->singularity(),f)(0, 0);
}
};
// scalar_valued, no tail
template <typename Opt> struct evaluator<imfreq, scalar_valued, nothing, Opt> : _eval_imfreq_base_impl {
using _eval_imfreq_base_impl::operator();
template <typename G> std::complex<double> operator()(G const *g, matsubara_freq const &f) const {
if (g->mesh().positive_only()) { // only positive Matsubara frequencies
if ((f.n >= 0) && (f.n < g->mesh().size())) return (*g)[f.n];
if ((f.n < 0) && ((-f.n - this->sh(g)) < g->mesh().size())) return conj((*g)[-f.n - this->sh(g)]);
} else {
if ((f.n >= g->mesh().first_index()) && (f.n < g->mesh().size() + g->mesh().first_index())) return (*g)[f.n];
}
return 0;
}
};
// matrix_valued, tail
template <typename Opt> struct evaluator<imfreq, matrix_valued, tail, Opt> : _eval_imfreq_base_impl {
using _eval_imfreq_base_impl::operator();
template <typename G> arrays::matrix_const_view<std::complex<double>> operator()(G const *g, matsubara_freq const &f) const {
if (g->mesh().positive_only()) { // only positive Matsubara frequencies
if ((f.n >= 0) && (f.n < g->mesh().size())) return (*g)[f.n]();
if ((f.n < 0) && ((-f.n - this->sh(g)) < g->mesh().size()))
return arrays::matrix<std::complex<double>>{conj((*g)[-f.n - this->sh(g)]())};
} else {
if ((f.n >= g->mesh().first_index()) && (f.n < g->mesh().size() + g->mesh().first_index())) return (*g)[f.n];
}
return evaluate(g->singularity(), f);
}
};
// matrix_valued, no tail
template <typename Opt> struct evaluator<imfreq, matrix_valued, nothing, Opt> : _eval_imfreq_base_impl {
using _eval_imfreq_base_impl::operator();
template <typename G> arrays::matrix_const_view<std::complex<double>> operator()(G const *g, matsubara_freq const &f) const {
if (g->mesh().positive_only()) { // only positive Matsubara frequencies
if ((f.n >= 0) && (f.n < g->mesh().size())) return (*g)[f.n]();
if ((f.n < 0) && ((-f.n - this->sh(g)) < g->mesh().size()))
return arrays::matrix<std::complex<double>>{conj((*g)[-f.n - this->sh(g)]())};
} else {
if ((f.n >= g->mesh().first_index()) && (f.n < g->mesh().size() + g->mesh().first_index())) return (*g)[f.n];
}
auto r = arrays::matrix<std::complex<double>>{get_target_shape(*g)};
r() = 0;
return r;
}
};
} // gfs_implementation
// specific operations (for legacy python code).
// +=, -= with a matrix
inline void operator+=(gf_view<imfreq> g, arrays::matrix<std::complex<double>> m) {
for (int u = 0; u < int(first_dim(g.data())); ++u) g.data()(u, arrays::ellipsis()) += m;
g.singularity()(0) += m;
}
inline void operator-=(gf_view<imfreq> g, arrays::matrix<std::complex<double>> m) {
for (int u = 0; u < int(first_dim(g.data())); ++u) g.data()(u, arrays::ellipsis()) -= m;
g.singularity()(0) -= m;
}
}
}