Metadata-Version: 2.1
Name: cotengra
Version: 0.6.2
Summary: Hyper optimized contraction trees for large tensor networks and einsums.
Home-page: https://github.com/jcmgray/cotengra
Author: Johnnie Gray
Author-email: johnniemcgray@gmail.com
License: Apache
Project-URL: Bug Reports, https://github.com/jcmgray/cotengra/issues
Project-URL: Source, https://github.com/jcmgray/cotengra/
Keywords: tensor network contraction graph hypergraph partition einsum
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE.md
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<p align="left"><img src="https://imgur.com/OM5XyaD.png" alt="cotengra" width="400px"></p>

[![tests](https://github.com/jcmgray/cotengra/actions/workflows/test.yml/badge.svg)](https://github.com/jcmgray/cotengra/actions/workflows/test.yml)
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[![Docs](https://readthedocs.org/projects/cotengra/badge/?version=latest)](https://cotengra.readthedocs.io)
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`cotengra` is a python library for contracting tensor networks or einsum
expressions involving large numbers of tensors - the main docs can be found
at [cotengra.readthedocs.io](https://cotengra.readthedocs.io/).
Some of the key feautures of `cotengra` include:

* drop-in ``einsum`` replacement
* an explicit **contraction tree** object that can be flexibly built, modified and visualized
* a **'hyper optimizer'** that samples trees while tuning the generating meta-paremeters
* **dynamic slicing** for massive memory savings and parallelism
* support for **hyper** edge tensor networks and thus arbitrary einsum equations
* **paths** that can be supplied to [`numpy.einsum`](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html), [`opt_einsum`](https://dgasmith.github.io/opt_einsum/), [`quimb`](https://quimb.readthedocs.io/en/latest/) among others
* **performing contractions** with tensors from many libraries via [`cotengra`](https://github.com/jcmgray/autoray),
  even if they don't provide `einsum` or `tensordot` but do have (batch) matrix
  multiplication

<p align="center"><img src="https://imgur.com/jMO138y.png" alt="cotengra" width="500px"></p>
