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amc

An automatic angular-momentum reduction package for tensor networks

https://github.com/radnut/amc

Science Score: 33.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ○
    codemeta.json file
  • ○
    .zenodo.json file
  • ✓
    DOI references
    Found 6 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: arxiv.org, zenodo.org
  • ✓
    Committers with academic emails
    2 of 3 committers (66.7%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (16.9%) to scientific vocabulary

Keywords from Contributors

transformation
Last synced: 10 months ago · JSON representation

Repository

An automatic angular-momentum reduction package for tensor networks

Basic Info
  • Host: GitHub
  • Owner: radnut
  • License: gpl-3.0
  • Language: Python
  • Default Branch: master
  • Size: 268 KB
Statistics
  • Stars: 10
  • Watchers: 2
  • Forks: 3
  • Open Issues: 0
  • Releases: 0
Created over 6 years ago · Last pushed almost 6 years ago
Metadata Files
Readme License

README.rst 

AMC
===

.. image:: https://img.shields.io/readthedocs/amc
   :alt: Read the Docs
   :target: https://amc.readthedocs.io/en/latest/
.. image:: https://img.shields.io/pypi/v/amc
   :alt: PyPI version
   :target: https://pypi.org/project/amc/
.. image:: https://img.shields.io/pypi/l/amc
   :alt: PyPI license
   :target: https://choosealicense.com/licenses/gpl-3.0/
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3663058.svg
   :alt: DOI:10.5281/zenodo.3663058
   :target: https://doi.org/10.5281/zenodo.3663058

In quantum many-body theory, one often encounters problems with rotational
symmetry. While methods are most conveniently derived in schemes that do not
exploit the symmetry, a symmetry-adapted formulation can lead to orders of
magnitude savings in computation time. However, actually reducing the formulas
of a many-body method to symmetry-adapted form is tedious and error-prone.

The AMC package aims to help practitioners by automating the reduction
process. The unreduced (m-scheme) equations can be entered via an easy-to-use
language. The package then uses Yutsis graph techniques to reduce the
resulting network of angular-momentum variables to irreducible Wigner 6j and
9j symbols, and outputs the reduced equations as a LaTeX file. Moreover, the
package is based on abstract representations of the unreduced and reduced
equations in the form of syntax trees, which enable other uses such as
automatic generation of code that evaluates the reduced equations.

Installation
------------
Install amc using the `pip `_ package manager.

.. code-block:: bash

    pip install amc

Usage
-----
Prepare a file with the properties of the tensors and the equations to reduce.
For example, second-order many-body perturbation theory can be reduced in this
way:

.. code-block:: none

    # mbpt.amc

    declare E2 {
        mode=0,
        latex="E^{(2)}_{0}",
    }

    # Hamiltonian
    declare H {
        mode=4,
        latex="H",
    }

    E2 = 1/4 * sum_abij(H_abij * H_ijab);

Then run the ``amc`` program on the input

.. code-block:: bash

    amc -o mbpt.tex mbpt.amc

The result is

.. math::

    E^{(2)}_{0} = \frac{1}{4} \sum_{a b i j {J}_{0}} \hat{J}_{0}^{2}
    H_{a b i j}^{{J}_{0} {J}_{0} 0} H_{i j a b}^{{J}_{0} {J}_{0} 0}

See the `User's Guide `__ for
details.

Citing
------
Releases of this code are deposited to the Zenodo repository. If you use it in
research work please cite the version used. Go to `the Zenodo record
`__ to find bibliographic information
for each release.

If you use this code in research work please also cite the following publication

    A. Tichai, R. Wirth, J. Ripoche, T. Duguet. *Symmetry reduction of tensor
    networks in many-body theory I. Automated symbolic evaluation of SU(2)
    algebra*. `arXiv:2002.05011 `__ [nucl-th]

Contributing
------------
Pull requests are welcome. For major changes, please open an issue first to
discuss what you would like to change.

License
-------
`GPLv3 `__

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: over 2 years ago

All Time
  • Total Commits: 125
  • Total Committers: 3
  • Avg Commits per committer: 41.667
  • Development Distribution Score (DDS): 0.272
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Roland Wirth r****h@p****e 91
Julien Ripoche j****e@u****r 28
Alexander Tichai 6
Committer Domains (Top 20 + Academic)
u-psud.fr: 1 physik.tu-darmstadt.de: 1

Issues and Pull Requests

Last synced: about 1 year ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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Issue Authors
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Packages

  • Total packages: 1
  • Total downloads:
    • pypi 87 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 1
  • Total versions: 5
  • Total maintainers: 1
pypi.org: amc

Automatic angular-momentum reduction

  • Versions: 5
  • Dependent Packages: 0
  • Dependent Repositories: 1
  • Downloads: 87 Last month
Rankings
Dependent packages count: 10.0%
Average: 18.8%
Forks count: 19.1%
Stargazers count: 20.3%
Dependent repos count: 21.7%
Downloads: 23.0%
Maintainers (1)
rwirth
Last synced: 11 months ago

Dependencies

docs/requirements.txt pypi
  • numpydoc *
  • sphinx *