T1C-Viz - Interactive Visualization for T1C-IR Graphs
T1C-Viz generates beautiful, interactive HTML visualizations of T1C-IR computation graphs using D3.js and ELK for automatic layout. View your neural network architectures in Jupyter notebooks or export standalone HTML files.
T1C-Viz provides an intuitive way to explore and understand T1C-IR computation graphs, making it easier to debug, analyze, and document neuromorphic neural network architectures.
Read more about T1C-IR in our documentation
Installation
uv add t1c-viz@git+ssh://git@github.com:type1compute/t1cir.git
Or with Jupyter support:
uv add "t1c-viz[jupyter]@git+ssh://git@github.com:type1compute/t1cir.git"
Quick Start
from t1c import ir, viz
# Load a graph
graph = ir.read('model.t1c')
# Display interactively (opens browser or shows in Jupyter)
viz.visualize(graph, title="My SNN Model")
Display Methods
Browser Display
Outside Jupyter, opens in default browser:
viz.visualize(graph, title="My SNN Model")
# Output: Visualization written to: /tmp/t1c-viz/My_SNN_Model_xxx.html
Jupyter Display
In Jupyter notebooks, displays as embedded iframe:
viz.visualize(graph, title="My SNN", width=960, height=640)
Export to File
Save as standalone HTML:
path = viz.export_html(graph, "model.html", title="Production Model")
print(f"Saved to: {path}")
The HTML is self-contained - no server needed.
Features
Graph Visualization (Left Panel)
- Dagre layout: Automatic hierarchical graph layout
- SNN-specific styling: LIF neurons shown with distinctive spiking icon
- Interactive: Pan, zoom, click nodes to inspect
- Parameter display: Shows tau/theta on neuron nodes, kernel sizes on convolutions
Data Explorer (Right Panel)
- HDF5 tree: Navigate graph structure
- Array visualization: Heatmaps for weights, line plots for biases
- Statistics: Min, max, mean, std for all arrays
- Bidirectional sync: Click in tree to highlight node in graph
Node Colors
| Primitive | Color | Description |
|---|---|---|
| Input | Green | Graph entry points |
| Output | Red | Graph exit points |
| Affine | Blue | Linear layers |
| SpikingAffine | Purple | Quantized affine with spike hints |
| LIF | Cyan | Leaky integrate-and-fire neurons |
| Conv2d | Orange | 2D convolution |
| SepConv2d | Deep Orange | Depthwise separable convolution |
| MaxPool2d | Brown | Max pooling |
| AvgPool2d | Blue Grey | Average pooling |
| Flatten | Grey | Reshape operations |
| Skip | Yellow | Residual/skip connections |
Development
From the repo root (requires t1c-ir installed first):
uv pip install -e .
pytest tests/ -v
1 maintainer
Metadata
1 maintainer
Release files for t1c-viz 0.0.2
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