Custom node icons¶

Example of using custom icons to represent nodes with matplotlib.

Images for node icons courtesy of

plot custom node icons
import matplotlib.pyplot as plt
import networkx as nx
import PIL

# Image URLs for graph nodes
icons = {
    "router": "icons/router_black_144x144.png",
    "switch": "icons/switch_black_144x144.png",
    "PC": "icons/computer_black_144x144.png",

# Load images
images = {k: for k, fname in icons.items()}

# Generate the computer network graph
G = nx.Graph()

G.add_node("router", image=images["router"])
for i in range(1, 4):
    G.add_node(f"switch_{i}", image=images["switch"])
    for j in range(1, 4):
        G.add_node("PC_" + str(i) + "_" + str(j), image=images["PC"])

G.add_edge("router", "switch_1")
G.add_edge("router", "switch_2")
G.add_edge("router", "switch_3")
for u in range(1, 4):
    for v in range(1, 4):
        G.add_edge("switch_" + str(u), "PC_" + str(u) + "_" + str(v))

# Get a reproducible layout and create figure
pos = nx.spring_layout(G, seed=1734289230)
fig, ax = plt.subplots()

# Note: the min_source/target_margin kwargs only work with FancyArrowPatch objects.
# Force the use of FancyArrowPatch for edge drawing by setting `arrows=True`,
# but suppress arrowheads with `arrowstyle="-"`

# Transform from data coordinates (scaled between xlim and ylim) to display coordinates
tr_figure = ax.transData.transform
# Transform from display to figure coordinates
tr_axes = fig.transFigure.inverted().transform

# Select the size of the image (relative to the X axis)
icon_size = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.025
icon_center = icon_size / 2.0

# Add the respective image to each node
for n in G.nodes:
    xf, yf = tr_figure(pos[n])
    xa, ya = tr_axes((xf, yf))
    # get overlapped axes and plot icon
    a = plt.axes([xa - icon_center, ya - icon_center, icon_size, icon_size])

Total running time of the script: ( 0 minutes 0.337 seconds)

Gallery generated by Sphinx-Gallery