Python implementation of the TT-Toolbox. It contains several important packages for working with the Tensor Train (TT) format in Python. Most of the computational routines are done in Fortran, and are wrapped with f2py tool.
##Downloading the code This installation works with git submodules, so you should be sure, that you got them all right. The best way is to work with this repository is to use git with a version >= 1.6.5. Then, to clone the repository, you can simply run
git clone --recursive git://github.com/oseledets/ttpy.git
cd ttpy
git submodule foreach git checkout master
To update to a newer version, run
git pull --recurse-submodules
This command will update the submodules as well.
##Installing the package The installation of the package is done via setup.py scripts.
The installation script is a very newbie one, but it seems to work. Go to the tt directory and run
python setup.py build_ext --inplace
This builds the package tt with submodules amr, eigb, kls. For some of the examples the quadgauss package is required, so go to quadgauss directory and run setup.py there
##BLAS and so on
Almost all of the packages depend on the BLAS/LAPACK libraries, but the code is not explicitly linked against them, so if you do not have the BLAS/LAPACK in the global namespace of your Python interpreter, you will encounter "undefined symbols" error during the import of the tt package. There are two possibilities how to deal with this situation
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Use the Enthought Python Distribution (EPD) (non-Free version, but it is free for academics). It loads the well-tuned MKL library into the global namespace, and you do not have to do anything.
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Use ctypes to load the BLAS (preferrably MKL) dynamic library. If you have MKL installed somewhere on your PATH, you can simply use
import ctypes
ctypes.CDLL("libmkl_rt.so", ctypes.RTLD_GLOBAL)before the tt import
They have the following functionality
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tt : The main package, with tt.tensor and tt.matrix classes, basic arithmetic, norms, scalar products, rounding full -> tt and tt -> full conversion routines, and so on
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tt.amr : Contains the AMR/DMRG fast matrix-by-vector product (subroutine tt.amr.mvk4) and AMR/DMRG approximate linear system solver (subroutine tt.amr.amr_solve). The matrices and vectors should be given in the TT-format
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tt.eigb : Contains a test version of the ALS block eigenvalue solver in the TT-format (subroutine tt.eigb.eigb)
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tt.kls : OUTDATED and will be fixed in a few weeks (with the KSL scheme). Please check again soon.
Right now the examples are located in the top of the directory, which is not correct, of course. When I will figure out, how it should be done in a right way (via unittests or something?) I will fix that. Right now you just take a look at files starting with test_ to see what happens. Also, the files hh_hermite.py and hh_hermite2.py contain experiments for the molecular Schrodinger equation.