Network manipulation

Filtering

Sometimes you have multiple edge or node attributes which can be used to style your network. Using netwulf.tools.get_filtered_network you can get a filtered network, where edge_weight_key is the name of the edge attribute which will be used as the weight in the visualization and node_group_key is the node attributed following which the nodes will be grouped and colored in the visualization. Here’s an example

import networkx as nx
import netwulf as wulf

import numpy as np

G = nx.barabasi_albert_graph(100,1)

for u, v in G.edges():
    # assign two random edge values to the network
    G[u][v]['foo'] = np.random.rand()
    G[u][v]['bar'] = np.random.rand()

# filter the Graph to visualize one where the weight is determined by 'foo'
new_G = wulf.get_filtered_network(G,edge_weight_key='foo')
wulf.visualize(new_G)

# assign node attributes according to some generic grouping
grp = {u: 'ABCDE'[u%5]  for u in G.nodes() }
nx.set_node_attributes(G, grp, 'wum')

# filter the Graph to visualize one where the weight is determined by 'bar'
# and the node group (coloring) is determined by the node attribute 'wum'
new_G = wulf.get_filtered_network(G,edge_weight_key='bar',node_group_key='wum')
wulf.visualize(new_G)

Binding positions

If the network has node attributes 'x' and 'y', those will be used as default values in the visualization. In order to reproduce a visualization or continue where you left off last time, you can bind the positions to the network

wulf.bind_positions_to_network(G, stylized_network)

There’s no return value and the positions are directly written to the Graph-object G.