[scroll down a bit to see what kind of output the code produces] edit (7 Nov 2019) I’ve put a more refined version of this into a package I’ve been writing: https://epidemicsonnetworks.readthedocs.io/en/latest/_modules/EoN/auxiliary.html#hierarchy_pos. The main difference between the code here and the version there is that the code here gives all children of a given node … Read more
You could try either smaller nodes/fonts or larger canvas. Here is a way to do both: import networkx as nx import matplotlib.pyplot as plt G = nx.cycle_graph(80) pos = nx.circular_layout(G) # default plt.figure(1) nx.draw(G,pos) # smaller nodes and fonts plt.figure(2) nx.draw(G,pos,node_size=60,font_size=8) # larger figure size plt.figure(3,figsize=(12,12)) nx.draw(G,pos) plt.show()
You’ll have to call nx.draw_networkx_edge_labels(), which will allow you to… draw networkX edge labels 🙂 EDIT: full modified source #!/usr/bin/python import networkx as nx import matplotlib.pyplot as plt G=nx.Graph() i=1 G.add_node(i,pos=(i,i)) G.add_node(2,pos=(2,2)) G.add_node(3,pos=(1,0)) G.add_edge(1,2,weight=0.5) G.add_edge(1,3,weight=9.8) pos=nx.get_node_attributes(G,’pos’) nx.draw(G,pos) labels = nx.get_edge_attributes(G,’weight’) nx.draw_networkx_edge_labels(G,pos,edge_labels=labels) plt.savefig(<wherever>)
It looks like the easiest way to do this is to tell gunicorn to preload your application using the preload_app option. This assumes that you can load the data structure as a module-level variable: from flask import Flask from your.application import CustomDataStructure CUSTOM_DATA_STRUCTURE = CustomDataStructure(‘/data/lives/here’) # @app.routes, etc. Alternatively, you could use a memory-mapped file … Read more
The solution to this problem lies in the NetworkX version 1.8 which is yet to be released, but is available via git hub. Do the following to increase the distance between nodes: pos = nx.spring_layout(G, k=0.15, iterations=20) # k controls the distance between the nodes and varies between 0 and 1 # iterations is the … Read more
In networkx, it’s worth checking out the graph drawing algorithms provided by graphviz via nx.graphviz_layout. I’ve had good success with neato but the other possible inputs are dot – “hierarchical” or layered drawings of directed graphs. This is the default tool to use if edges have directionality. neato – “spring model” layouts. This is the … Read more
The order of the edges passed to the drawing functions are important. If you don’t specify (using the edges keyword) you’ll get the default order of G.edges(). It is safest to explicitly give the parameter like this: import networkx as nx G = nx.Graph() G.add_edge(1,2,color=”r”,weight=2) G.add_edge(2,3,color=”b”,weight=4) G.add_edge(3,4,color=”g”,weight=6) pos = nx.circular_layout(G) edges = G.edges() colors = … Read more
If the graph is undirected, you can use G.edges(node) In networkx 2.x this is an EdgeDataView object. In networkx 1.x this is a list – if you want a generator in 1.x rather than getting the whole list, G.edges_iter(node) works (this no longer exists in 2.x). If the graph is directed the command above will … Read more
All you need is to specify a color map which maps a color to each node and send it to nx.draw function. To clarify, for a 20 node I want to color the first 10 in blue and the rest in green. The code will be as follows: G = nx.erdos_renyi_graph(20, 0.1) color_map = [] … Read more
tl/dr: just add with_labels=True to the nx.draw call. The page you were looking at is somewhat complex because it shows how to set lots of different things as the labels, how to give different nodes different colors, and how to provide carefully control node positions. So there’s a lot going on. However, it appears you … Read more