TeNPy (short for Tensor Network Python) is a Python library for the simulation of strongly correlated quantum systems with tensor networks.
The philosophy of this library is to get a new balance of a good readability and usability for new-comers, and at the same time powerful algorithms and fast development of new algorithms for experts. For good readability, we include an extensive documentation next to the code, both in Python doc strings and separately as user guides, as well as simple example codes and even toy codes, which just demonstrate various algorithms (like TEBD and DMRG) in ~100 lines per file.
Follow the instructions in the file doc/INSTALL.rst, online here.
The latest version of the source code can be obtained from [TeNPySource].
The documentation is available online at https://tenpy.github.io, see [TeNPyDoc]. The documentation is roughly split in two parts: on one hand the full "reference" containing the documentation of all functions, classes, methods, etc., and on the other hand the "user guide" containing some introductions and additional explanations.
The documentation is based on Python's docstrings, and some additional *.rst files located in the folder doc/ of the repository.
All documentation is formated as reStructuredText,
which means it is quite readable in the source plain text, but can also be converted to other formats.
If you like it simple, you can just use intective python help(), Python IDEs of your choice or jupyter notebooks, or just read the source.
Moreover, the documentation is nightly converted into HTML using Sphinx, and made available online at https://tenpy.github.io/.
The big advantages of the (online) HTML documentation are a lot of cross-links between different functions, and even a search function.
If you prefer yet another format, you can try to build the documentation yourself, as described in :doc:`doc/contributing.rst <contributing>`.
We have set up a community forum at https://tenpy.johannes-hauschild.de/, see [TeNPyForum]), where you can post questions and hopefully find answers. Once you got some experience with TeNPy, you might also be able to contribute to the community and answer some questions yourself ;-) We also use this forum for official annoucements, for example when we release a new version.
When you use TeNPy for a work published in an academic journal, you can cite the [TeNPyNotes] to acknowledge the work put into the development of TeNPy. Although the license of TeNPy does not require you to cite us, we kindly ask you to do so, as it will support the devolopment of TeNPy.
You might want to check the github issues, if someone else already reported the same problem. To report a new bug, just open a new issue on github. If you already know how to fix it, you can just create a pull request :) If you are not sure whether your problem is a bug or a feature, you can also ask for help in the [TeNPyForum].
The code is licensed under GPL-v3.0 given in the file LICENSE of the repository,
in the online documentation readable at https://tenpy.github.io/license.html.
| [TeNPySource] | Source code available for download from GitHub, https://github.com/tenpy/tenpy |
| [TeNPyDoc] | Online documentation, https://tenpy.github.io/ |
| [TeNPyForum] | (1, 2) Community forum for discussions, FAQ and announcements, https://tenpy.johannes-hauschild.de |
| [TeNPyNotes] | Lecture notes introducing TeNPy, J. Hauschild, F. Pollmann, arXiv:1805.00055 |