https://github.com/heitorbaldo/DigplexQ

DigplexQ is a Python package to perform computations with digraph-based complexes.
https://github.com/heitorbaldo/DigplexQ

directed-flag-complexes directed-q-analysis path-complexes q-analysis tda

Last synced: about 1 year ago
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DigplexQ is a Python package to perform computations with digraph-based complexes.

• Host: GitHub
• URL: https://github.com/heitorbaldo/DigplexQ
• Owner: heitorbaldo
• License: mit
• Created: 2023-05-22T15:04:39.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2024-03-31T21:31:09.000Z (about 2 years ago)
• Last Synced: 2024-10-03T01:07:03.716Z (over 1 year ago)
• Topics: directed-flag-complexes, directed-q-analysis, path-complexes, q-analysis, tda
• Language: Jupyter Notebook
• Homepage:
• Size: 5.23 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          



[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)]()

[![PyPI version](https://img.shields.io/pypi/v/digplexq)](https://pypi.org/project/digplexq/)

------

DigplexQ is a Python package to perform computations with digraph-based complexes (directed flag complexes and path complexes). It is an "adjacency matrix-centered" package since it was designed so that 

the user can perform all computations just by entering an adjacency matrix as input.

* Free software: MIT license

* Documentation: TODO

Installation

--------

```bash

pip3 install digplexq

```

Examples

--------

```python

from digplexq.directed_q_analysis import *

from digplexq.digraph_based_complexes import *

from digplexq.structure_based_simplicial_measures import *

from digplexq.random_digraphs import *

from digplexq.utils import *

M = directed_erdos_renyi_GnM_model(20, 40, weight=False)

M = remove_double_edges(M) #remove double edges.

#Directed flag complex:

DFC = DirectedFlagComplex(M, "by_dimension_with_nodes")

#Maximal directed simplices:

maxsimp = MaximalSimplices(DFC)

#q-Adjacency matrix:

fast_q_adjacency_matrix(M, q=1)

#in-q-degree centrality

in_q_degree_centrality(M, q=1, results="nodes")

```

More examples are available in the ![Jupyter Notebook](https://github.com/heitorbaldo/DigplexQ/blob/main/Tutorial_DigplexQ.ipynb).

Dependencies

--------

* [NumPy](https://github.com/numpy/numpy)

* [SciPy](https://scipy.org/)

* [NetworkX](https://github.com/networkx/networkx)

* [gtda](https://giotto-ai.github.io/gtda-docs/0.5.1/library.html)

* [persim](https://persim.scikit-tda.org/en/latest/)

* [hodgelaplacians](https://github.com/tsitsvero/hodgelaplacians)