Python implementation of the Tensor Train (TT) -Toolbox. It contains several important packages for working with the TT-format in Python. It is able to do TT-interpolation, solve linear systems, eigenproblems, solve dynamical problems. Several computational routines are done in Fortran (which can be used separatedly), and are wrapped with the f2py tool.
It is recommended that you use Anaconda Python distribution which has MKL library built-in. Anaconda Python is used for the development of ttpy.
Install dependencies (numpy and cython)
conda install numpy cython
pip install ttpy
Installing from source code
To install the development version, you need to install from the source. First, clone the repository with all submodules:
git clone --recursive git://github.com/oseledets/ttpy.git
python setup.py install
To update to the latest version (with all submodules) run
git pull git submodule update --init --recursive *
What those packages do
They have the following functionality
tt: The main package, with tt.vector and tt.matrix classes, basic arithmetic, norms, scalar products, rounding full -> tt and tt -> full conversion routines, and many others
tt.amen: AMEN solver for linear systems (Python wrapper for Fortran code written by S. V. Dolgov and D. V. Savostyanov) it can be also used for fast matrix-by-vector products.
tt.eigb: Block eigenvalue solver in the TT-format
tt.ksl: Solution of the linear dynamic problems in the TT-format, using the projector-splitting KSL scheme. A Python wrapper for a Fortran code (I. V. Oseledets)
tt.cross: Has a working implementation of the black-box cross method.
Documentation and examples
The package provides Sphinx-generated documentation. To build HTML version, just do
cd tt/doc make html
A few examples are available right now under examples directory
For any questions, please create an issue on Github.
This project is now following the git flow approach. Namely:
masteris only for stable versions and releases;
developis main working branch;
- contributor should create new branch for certain feature and then merge with
developbranch as feature was done;
- each release on
masterbranch should correspond to package on PyPI;
- A maintainer checks all the pull request
A pull request should satisfy the following requirements:
- style and quality description of pull request;
- new changes should be tested and shouldn't break anything;
- pull request for one fix or one feature(could be several commits);
- try to keep the code style of the project;
Current maintainer is Ivan Oseledets.