majidaldo/discopy

The Python toolkit for computing with string diagrams.

DisCoPy

DisCoPy is a Python toolkit for computing with string diagrams.

• Organisation: https://discopy.org
• Documentation: https://docs.discopy.org
• Source code: https://github.com/discopy/discopy
• Paper (for applied category theorists): https://doi.org/10.4204/EPTCS.333.13
• Paper (for quantum computer scientists): https://arxiv.org/abs/2205.05190

DisCoPy began as an implementation of DisCoCat and QNLP. This has now become its own library: lambeq.

Features

• a data structure for arrows in free dagger categories with formal sums, unary operators and symbolic variables from SymPy
• data structures for string diagrams in any (pre)monoidal category in the hierarchy of graphical languages (braids, twists, spiders, etc.)
• methods for diagram composition, drawing, rewriting and evaluation as:
  • Python code, i.e. wires as types and boxes as functions
  • tensor networks, i.e. wires as dimensions and boxes as arrays from NumPy, PyTorch, TensorFlow, TensorNetwork and JAX
• an implementation of categorical quantum mechanics interfacing with:
  • tket for circuit compilation
  • PyZX for optimisation with the ZX calculus
  • PennyLane for automatic differentiation
• an implementation of formal grammars (context-free, categorial, pregroup or dependency) with interfaces to lambeq, spaCy and NLTK

Architecture

Software dependencies between modules go top-to-bottom, left-to-right and forgetful functors between categories go the other way.

Install

pip install discopy

Test

If you want the bleeding edge, you can install DisCoPy locally:

git clone https://github.com/discopy/discopy.git
cd discopy
pip install .

You should check you haven't broken anything by running the test suite:

pip install ".[test]" .
coverage run -m pytest --doctest-modules --pycodestyle
coverage report -m discopy/*.py discopy/*/*.py

The documentation is built using sphinx. You can build it locally with:

pip install ".[docs]" .
sphinx-build docs docs/_build/html

Contribute

Contributions are welcome, please get in touch or open an issue.

How to cite

If you wish to cite DisCoPy in an academic publication, we suggest you cite:

• G. de Felice, A. Toumi & B. Coecke, DisCoPy: Monoidal Categories in Python, EPTCS 333, 2021, pp. 183-197, DOI: 10.4204/EPTCS.333.13

If furthermore your work is related to quantum computing, you can also cite:

• A. Toumi, G. de Felice & R. Yeung, DisCoPy for the quantum computer scientist, arXiv:2205.05190