graphette 0.1.1

Step-by-step graph algorithm visualization with NetworkX and Matplotlib

Graph Algorithm Visualizer

Step-by-step graph algorithm visualization with NetworkX and Matplotlib.

Usage

pip install graphette
python -m graphette.  # run included BFS example

Custom Algorithms

To make a custom visualization, create a generator that yields snapshot() at each step:

from algo import snapshot, adjacency
from visualize import visualize, animate


def dfs(edges, start):
    adj = adjacency(edges)
    stack, visited, step = [start], set(), 0

    while stack:
        cur = stack.pop()
        if cur in visited: continue
        visited.add(cur)
        step += 1

        yield snapshot(step, edges=edges, event="visit",
                       state={'visited': list(visited)},
                       node_color={'orange': ('current', cur)},
                       nodes_color={'lightgreen': ('visited', visited)})

        stack.extend(n for n in adj[cur] if n not in visited)


states = dfs(edges, 'A')
visualize(states)  # interactive (← → to navigate)
animate(states, 'out.gif')  # supports all matplotlib.animation formats

Snapshot Parameters

Parameter	Description
step, event	Step number and label
nodes, edges	Current state of the graph
state	Algorithm state dict to display
node_color	{color: (label, node)}
nodes_color	{color: (label, node_set)}
edges_color	{color: (label, edge_set)}

Requirements

matplotlib, networkx, toolz