scigraphs-engine
Graph visualization as a plain Python package. Coordinates, an edge list and whatever attributes they carry go in; geometry or pixels come out, with 26 filter channels, structural sparsification, edge-style tessellation and hierarchical bundling in between.
from scigraphs_engine import Graph
g = Graph.from_arrays(coords, edges)
g = g.filter(core__gte=0.5).simplify(backbone="disparity", alpha=0.05)
g.style(edges="bundled").render(size=(1920, 1080)).save("out.png")
Nothing in this package imports bpy, gpu, bmesh or mathutils. The
compute half needs only numpy, and Graph.geometry() runs with no graphics
library installed at all, returning MeshSpecs that name their shader as a
string, so any renderer can draw them. The GPU half is an optional extra.
Install
pip install scigraphs-engine # numpy only
pip install "scigraphs-engine[igraph]" # coreness, PageRank, betweenness, bridges
pip install "scigraphs-engine[scipy]" # components, hop distance, crowding
pip install "scigraphs-engine[wgpu]" # render() to pixels
pip install "scigraphs-engine[all]" # everything
Requires Python 3.10 or newer. A missing extra makes individual channels
unavailable rather than breaking anything: available_channels() returns all 26
with a reason for each one that cannot run.
Documentation
https://github.com/SciBlend/SciGraphs/tree/main/docs
python engine/scigraphs_engine/tests/run.py # -v for full output
License
MIT, see LICENSE. The SciGraphs Blender add-on is GPLv3 and is one caller of
this package, not its owner. It reaches the package through a shim that prefers
an installed scigraphs_engine over any copy shipped alongside it.
1 maintainer
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