DRAFT : to be tested further...
- update gf<imfreq>
- write a specific mesh for matsubara frequencies
- now the cast series is :
mesh_pt --> matsubara_freq --> complex<double>
- matsubara_freq is just the matsubara frequency
- arithmetic of the mesh_pt casted to matsubara_freq
- arithmetic of matsubara_freq is casted to complex, except + and -,
which are kept as matsubara_freq.
- evaluator now accept : int, mesh_pt, and matsubara_freq
for matsubara_freq : for negative omega, use conjugation
for omega outside windows, evaluate the tail on omega.
- as a result : g( om - nu) where om, nu are 2 meshes points,
is the extrapolation outside the grid if necessary.
- updated tests
- added evaluation for tail.
- lazy_fourier and co --> fourier
- ex fourier --> make_gf_from_fourier to make a new gf
- = fourier (g) works only iif lhs is a view, like scalar.
- updated python (commented fourier method).
- 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
- the arithmetics of the gf mesh_points :
the forwarding of the second argument was missing,
leading to subtle bugs e.g. in (in matrix case) :
deltaw(iom_) << iom_ * gw.singularity()(-1) + gw.singularity()(0) - gw(iom_);
because the partial evaluation of the expression introduced a dangling const matrix_view &
instead of moving the temporary matrix_view into the final matrix expression template.
