- Add to the wrapper generator (add_method) the release_GIL_and_enable_signal option which :
- release the GIL
- save the python signal handler
- enable the C++ triqs signal handler instead.
- undo all of this after the code runs, or in a case of exception.
- used python include, ceval.h, line 72 comments and below.
- reworked the triqs::signal_handler.
simple C like function, no object (no need).
start, stop, received, cf header file.
- clean the call_back.cpp : only place using the signal directly
(qmc uses the callback).
in particular, remove the old BOOST CHRONO, since
the std::chrono works fine on platforms we use now.
- Use a new buffered_function to replace the complicated generator code from ALPS.
- Clean the implementation of the random_generator
- update the documentation
- update to the new python wrapper (could not be done with the previous
version, because of lack of move constructor).
- an include missing on OS X
- change serialization in the general case where hdf5 can be used,
(version of hdf5 high enough) to use only one buffer,
not a buffer per field.
- put back the mpi_param example.
- parameters : clean, add back serialization. Clean whitespace
- serialization : depending on the version of the hdf5 lib,
uses h5 or boost.
- TODO : test it on a machine with new hdf5.
- h5/make_h5.... only used in parameters.
- old boost includes before C++11
- remove boost serialization, make macro TRIQS_MAKE_NVP temporarely
- remove boost::is_complex (can be written in 2 lines...)
- move some lib in cpp
- 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
- add move semantics in expression template.
for gfs :
- rm descriptor
- Remove data from expr, rewrote assignment
which now iterates on the mesh, simply.
RHS models now FunctionOnMesh
- This fix the multiplication bug of gf
- Remove assign_no_resize/assign_with_resize from proxy
- a thin layer, using a bit boost::mpi (for the communicator mostly ...)
along the lines discussed in #12.
- implemented reduce, allreduce, bcast for arrays, simple scalars,
and any custom type that support boost serialization.
- Custom types : the operations are done recursively on members.
No change is needed in the class to use this mpi routine, as long as
serialize function is defined.
- For arrays of basic types (int, double...), a direct call to MPI C API, which works also for views
(as long as they are contiguous).
- For arrays of more complex types, we revert to boost::mpi.
- Added a simple test.
- Work still in progress :
- missing a simple scatter/gather for the arrays
- need more tests & API thinking.
- dispatch array code to array lib
- reduce is "sum" only, but do we need more.
- to use already a few c14 convenience details :
-> polymorphic std::plus, e.g.
boost::mpi::reduce (world, A,C, std::c14::plus<>(),0);
this plus determine the type by itself ...
-> errors on the type can be very cryptic on the gf.
-> add std::c14::make_unique
(equivalent of make_shared for unique_ptr).
- change : all objects are by default
stored now by reference, not by copy any more.
Unless the trait force_copy_in_expr is true.
- rvalue refs are moved into the tree
- simplifies a lot the writing of lazy method, objects.
- added a macro for methods
- tests ok. Further check needed to control absence of copies...
- improved documentation
- has_view_type_tag mechanism was replaced by a decltype long ago.
Cleaning since it seems to work on all supported compiler now.
- removed add_views : not used any more.
- Apparently there is a bug in release 3.3 of clang
(? on kondo cluster ?) that crash the compiler in compiling
a test. Pb not present in clang from svn.
Changed a little bit the libs as a workaround.
