Weighted Graphs (1)

Default visualisation for a weighted graph.

import matplotlib.pyplot as plt

from netgraph import Graph

weighted_cube = [
    (0, 1, -0.1),
    (1, 2, -0.2),
    (2, 3, -0.4),
    (3, 2,  0.0),
    (3, 0, -0.2),
    (4, 5,  0.7),
    (5, 6,  0.9),
    (6, 5, -0.2),
    (6, 7,  0.5),
    (7, 4,  0.1),
    (0, 4,  0.5),
    (1, 5, -0.3),
    (5, 1, -0.4),
    (2, 6,  0.8),
    (3, 7,  0.4)
]

cmap = 'RdGy'
Graph(weighted_cube, edge_cmap=cmap, edge_width=2., arrows=True)
plt.show()

By default, different edge weights are represented by different colors. The default colormap is 'RdGy' but any diverging matplotlib colormap can be used:

• 'PiYG'

• 'PRGn'

• 'BrBG'

• 'PuOr'

• 'RdGy' (default)

• 'RdBu'

• 'RdYlBu'

• 'RdYlGn'

• 'Spectral'

• 'coolwarm'

• 'bwr'

• 'seismic'

If edge weights are strictly positive, weights are mapped to the left hand side of the color map with vmin=0 and vmax=np.max(weights). If edge weights are positive and negative, then weights are mapped to colors such that a weight of zero corresponds to the center of the color map; the boundaries are set to +/- the maximum absolute weight.

Custom diverging colormaps can be created using matploltib’s LinearSegmentedColormap:

from matplotlib.colors import LinearSegmentedColormap
cmap = LinearSegmentedColormap.from_list('my_name', ['red', 'white', 'blue'])