t1c-viz 0.0.2

Interactive Visualizer for T1C-IR Graphs

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://[e-mail]:type1compute/t1cir.git

Or with Jupyter support:

uv add "t1c-viz[jupyter]@git+ssh://[e-mail]: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