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https://github.com/ericmjl/nxviz
Visualization Package for NetworkX
https://github.com/ericmjl/nxviz
network network-visualization networkx visualization
Last synced: 3 months ago
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Visualization Package for NetworkX
- Host: GitHub
- URL: https://github.com/ericmjl/nxviz
- Owner: ericmjl
- License: mit
- Created: 2016-07-15T14:55:02.000Z (almost 8 years ago)
- Default Branch: master
- Last Pushed: 2024-02-12T23:58:09.000Z (5 months ago)
- Last Synced: 2024-04-07T08:58:51.524Z (3 months ago)
- Topics: network, network-visualization, networkx, visualization
- Language: Python
- Homepage: https://ericmjl.github.io/nxviz
- Size: 3.57 MB
- Stars: 448
- Watchers: 23
- Forks: 85
- Open Issues: 41
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-network-analysis - nxviz - Visualization package for NetworkX. (Software / Python)
README
# nxviz: Composable and rational network visualizations in matplotlib
`nxviz` is a package for building _rational_ network visualizations
using matplotlib as a backend.
Inspired heavily by the principles espoused in the grammar of graphics,
nxviz provides ways to _compose_ a graph visualization together
by adhering to the following recipe:
1. Prioritize node placement, mapping data to position and visual properties,
2. Draw in edges, mapping data to visual properties,
3. Add in annotations and highlights on the graph.
`nxviz` is simultaneously a data visualization research project,
art project,
and declarative data visualization tool.
We hope you enjoy using it to build beautiful graph visualizations.
## Installation
### Official Releases
`nxviz` is available on PyPI:
```bash
pip install nxviz
```
It's also available on conda-forge:
```bash
conda install -c conda-forge nxviz
```
### Pre-releases
Pre-releases are done by installing directly from git:
```bash
pip install git+https://github.com/ericmjl/nxviz.git
```
## Quickstart
To make a Circos plot:
```python
# We assume you have a graph G that is a NetworkX graph object.
# In this example, all nodes possess the "group" and "value" node attributes
# where "group" is categorical and "value" is continuous,
# and all edges have the "edge_value" node attribute as well.
import nxviz as nv
ax = nv.circos(
G,
group_by="group",
sort_by="value",
node_color_by="group",
edge_alpha_by="edge_value"
)
nv.annotate.circos_group(G, group_by="group")
```
For more examples, including other plots that can be made,
please see the examples gallery on the docs.