nirviz 0.1.0

Visualisation tool for the Neuromorphic Intermediate Representation

Neuromorphic Intermediate Representation Visualisation Tool

Turn your NIR definitions into a nice graph, the original publication serving as a template.

Customise your node colour preferences in style.yml, and quickly generate graphs from your neuromorphic networks.

This work is in progress.

Running Example (Jupyter Notebook)

By running the following code (from a notebook),

import nir
import nirviz
import numpy as np


a = np.random.randn(2)
ir = nir.NIRGraph(
    nodes={
        "input": nir.Input(input_type=np.array([2])),
        "affine1": nir.Affine(weight=np.zeros((2,2)), bias=False),
        "cu1": nir.CubaLIF(tau_mem=a, tau_syn=a, r=a, v_leak=a, v_threshold=a, v_reset=a),
        "affine_rec": nir.Affine(weight=np.zeros((2,2)), bias=False),
        "affine2": nir.Affine(weight=np.zeros((2,2)), bias=False),
        "cu2": nir.CubaLIF(tau_mem=a, tau_syn=a, r=a, v_leak=a, v_threshold=a, v_reset=a),
        "output": nir.Output(output_type=np.array([2]))
    },
    edges=[("input", "affine1"), ("affine1", "cu1"), ("affine_rec", "cu1"),  ("cu1", "affine_rec"), ("cu1", "affine2"), ("affine2", "cu2"), ("cu2", "output")])

viz = nirviz.visualize(ir)
viz.show()

You would get the following visualisation

Similar to Figure 3 of the publication.

Running example (CLI)

To convert a saved NIR graph (e.g. srnn.nir) to a PNG or SVG, you can use one of the following commands:

python -m nirviz srnn.nir              # SVG -> stdout
python -m nirviz srnn.nir img/srnn.png # PNG -> file
python -m nirviz srnn.nir img/srnn.svg # SVG -> file