Visualise Changes in Edge Weights

Here, we demonstrate how to visualise changes in edge weights over time. We change both, the colour and the width of the edges depending on the weight.

Once Loop Reflect

import numpy as np
import matplotlib.pyplot as plt

from matplotlib.animation import FuncAnimation
from netgraph import Graph

# Simulate a dynamic network with
# - 5 frames / network states,
# - with 10 nodes at each time point,
# - an expected edge density of 25%, and
# - edge weights drawn from a Gaussian distribution.
total_frames = 5
total_nodes = 10
adjacency_matrix = np.random.rand(total_nodes, total_nodes) < 0.25
weight_matrix = np.random.randn(total_frames, total_nodes, total_nodes)

# Normalise the weights, such that they are on the interval [0, 1].
# They can then be passed directly to matplotlib colormaps (which expect floats on that interval).
vmin, vmax = -2, 2
weight_matrix[weight_matrix<vmin] = vmin
weight_matrix[weight_matrix>vmax] = vmax
weight_matrix -= vmin
weight_matrix /= vmax - vmin

cmap = plt.cm.RdGy

fig, ax = plt.subplots()
g = Graph(adjacency_matrix, edge_cmap=cmap, arrows=True, ax=ax)

def update(ii):
    artists = []
    for jj, kk in zip(*np.where(adjacency_matrix)):
        w = weight_matrix[ii, jj, kk]
        artist = g.edge_artists[(jj, kk)]
        artist.set_facecolor(cmap(w))
        artist.update_width(0.03 * np.abs(w-0.5)) # np.abs(w-0.5) so that large negative edges are also wide
        artists.append(artist)
    return artists

animation = FuncAnimation(fig, update, frames=total_frames, interval=200, blit=True)