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https://github.com/discopy/discopy
The Python toolkit for computing with string diagrams.
https://github.com/discopy/discopy
category-theory diagrams nlp quantum-computing
Last synced: about 1 month ago
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The Python toolkit for computing with string diagrams.
- Host: GitHub
- URL: https://github.com/discopy/discopy
- Owner: discopy
- License: bsd-3-clause
- Created: 2019-10-11T09:02:00.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2024-04-13T09:25:44.000Z (5 months ago)
- Last Synced: 2024-04-14T04:55:02.321Z (5 months ago)
- Topics: category-theory, diagrams, nlp, quantum-computing
- Language: Python
- Homepage: https://discopy.org
- Size: 32.4 MB
- Stars: 326
- Watchers: 27
- Forks: 68
- Open Issues: 41
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# DisCoPy
[](https://github.com/discopy/discopy/actions/workflows/build.yml)
[](https://docs.discopy.org/)
[](https://badge.fury.io/py/discopy)
[](https://doi.org/10.4204/EPTCS.333.13)
DisCoPy is a Python toolkit for computing with [string diagrams](https://en.wikipedia.org/wiki/String_diagram).
* **Organisation:**
* **Documentation:**
* **Source code:**
* **Paper (for applied category theorists):**
* **Paper (for quantum computer scientists):**
DisCoPy began as an implementation of [DisCoCat](https://en.wikipedia.org/wiki/DisCoCat) and [QNLP](https://en.wikipedia.org/wiki/Quantum_natural_language_processing). This has now become its own library: [lambeq](https://cqcl.github.io/lambeq).
## Features
* an `Arrow` data structure for free [dagger categories](https://en.wikipedia.org/wiki/Dagger_category) with formal sums, unary operators and symbolic variables from [SymPy](https://www.sympy.org/en/index.html)
* a `Diagram` data structure for planar string diagrams in any ([pre](https://ncatlab.org/nlab/show/premonoidal+category))[monoidal category](https://en.wikipedia.org/wiki/Monoidal_category) in the [hierarchy of graphical languages](https://en.wikipedia.org/wiki/String_diagram#Hierarchy_of_graphical_languages) (with braids, twists, spiders, etc.)
* a `Hypergraph` data structure for string diagrams in hypergraph categories and its restrictions to symmetric, traced, compact and Markov categories
* methods for diagram composition, drawing, rewriting and `Functor` evaluation into:
- Python code, i.e. wires as types and boxes as functions
- [tensor networks](https://en.wikipedia.org/wiki/Tensor_network), i.e. wires as dimensions and boxes as arrays from [NumPy](https://numpy.org), [PyTorch](https://pytorch.org/), [TensorFlow](https://www.tensorflow.org/), [TensorNetwork](https://github.com/google/TensorNetwork) and [JAX](https://github.com/google/jax)
* an implementation of [categorical quantum mechanics](https://en.wikipedia.org/wiki/Categorical_quantum_mechanics) interfacing with:
- [tket](https://github.com/CQCL/tket) for circuit compilation
- [PyZX](https://github.com/Quantomatic/pyzx) for optimisation with the [ZX calculus](https://zxcalculus.com/)
- [PennyLane](https://pennylane.ai/) for automatic differentiation
* an implementation of formal grammars ([context-free](https://en.wikipedia.org/wiki/Context-free_grammar), [categorial](https://en.wikipedia.org/wiki/Categorial_grammar), [pregroup](https://en.wikipedia.org/wiki/Pregroup_grammar) or [dependency](https://en.wikipedia.org/wiki/Dependency_grammar)) with interfaces to [lambeq](https://cqcl.github.io/lambeq), [spaCy](https://spacy.io/) and [NLTK](https://www.nltk.org/)
## Example: Cooking
This example is inspired from Pawel Sobocinski's blog post [Crema di Mascarpone and Diagrammatic Reasoning](https://graphicallinearalgebra.net/2015/05/06/crema-di-mascarpone-rules-of-the-game-part-2-and-diagrammatic-reasoning/).
```python
from discopy.symmetric import Ty as Ingredient, Box as Step, Diagram as Recipe
egg, white, yolk = Ingredient("egg"), Ingredient("white"), Ingredient("yolk")
crack = Step("crack", egg, white @ yolk)
merge = lambda x: Step("merge", x @ x, x)
# DisCoPy allows string diagrams to be defined as Python functions
@Recipe.from_callable(egg @ egg, white @ yolk)
def crack_two_eggs(left_egg, right_egg):
left_white, left_yolk = crack(left_egg)
right_white, right_yolk = crack(right_egg)
return (merge(white)(left_white, right_white),
merge(yolk)(left_yolk, right_yolk))
# ... or in point-free style using parallel (@) and sequential (>>) composition
assert crack_two_eggs == crack @ crack\
>> white @ Recipe.swap(yolk, white) @ yolk\
>> merge(white) @ merge(yolk)
crack_two_eggs.draw()
```
## Quickstart
```shell
pip install discopy
```
If you want to see DisCoPy in action, check out the [QNLP tutorial](https://docs.discopy.org/en/main/notebooks/qnlp.html)!
## Contribute
We're keen to welcome new contributors!
First, read the [contributing guidelines](https://github.com/discopy/discopy/blob/main/CONTRIBUTING.md).
Then get in touch on [Discord](https://discopy.org/discord)
or [open an issue](https://github.com/discopy/discopy/issues/new).
## How to cite
If you used DisCoPy in the context of 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](https://doi.org/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](https://arxiv.org/abs/2205.05190)
If you use any of the recent features (e.g. `Hypergraph`) you should also mention:
* A. Toumi, R. Yeung, B. Poór & G. de Felice, _DisCoPy: the Hierarchy of Graphical Languages in Python_ [arXiv:2311.10608](https://arxiv.org/abs/2311.10608)