https://github.com/pavlin-policar/fdeb

Force-Directed Edge Bundling (FDEB)
https://github.com/pavlin-policar/fdeb

Last synced: 15 days ago
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Force-Directed Edge Bundling (FDEB)

• Host: GitHub
• URL: https://github.com/pavlin-policar/fdeb
• Owner: pavlin-policar
• License: bsd-3-clause
• Created: 2023-04-21T10:01:34.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-04-21T17:15:59.000Z (over 1 year ago)
• Last Synced: 2024-10-07T17:04:31.546Z (about 1 month ago)
• Language: Python
• Size: 1020 KB
• Stars: 7
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Force-Directed Edge Bundling (FDEB)

[![BSD 3-Clause License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)

![Example](assets/europe_flights.png)

## Installation

`fdeb` can be easily installed through pip using

```

pip install fdeb

```

## Usage

```python

edges = ...  # (N, 2, 2) -> N edges, each with two endpoints in 2D

from fdeb import fdeb

optimized_edges = fdeb(edges)

```

## Notes

This package currently implements a numpy-only version of the FDEB algorithm, which has asymptotic complexity O(n^2), making it too slow and memory-hungry for large numbers of graphs.

A numba version of this algorithm is also available at https://github.com/verasativa/python.ForceBundle, however, it also implements the O(n^2) algorithm, so it should (asymptotically) run equally slowly as this package, but is more memory efficient, and perhaps a bit faster on larger graphs.

## A longer example

```python

import numpy as np

import networkx as nx

from fdeb import fdeb

import matplotlib.pyplot as plt

import matplotlib.collections as collections

# Setup embedding and graph

g = nx.karate_club_graph()

x = np.array(list(nx.spring_layout(g).values()))

adj = nx.to_scipy_sparse_array(g).tocoo()

# Extract edges from embedding and adjacency matrix

edges = np.stack([x[adj.row], x[adj.col]], axis=1)

# Compute FDEB

edges_fdeb = fdeb(edges)

# Plot results

fig, ax = plt.subplots(ncols=2, figsize=(8, 4), dpi=150)

collection = collections.LineCollection(edges, color="k", alpha=0.05)

ax[0].add_collection(collection)

ax[0].scatter(x[:, 0], x[:, 1], c="tab:red", s=3, zorder=3)

collection = collections.LineCollection(edges_fdeb, color="k", alpha=0.05)

ax[1].add_collection(collection)

ax[1].scatter(x[:, 0], x[:, 1], c="tab:red", s=3, zorder=3)

```