networkx

问题 I was wondering is there a way to obtain all the different trees using Networkx? It's a known fact that the number of trees with n nodes is n^(n-2) (using Cayley's Formula) so if there is 3 nodes, then it has 3 tree graphs, if it has 4 nodes then it has 16 trees and so on. What I want is to code all the trees using the prufer sequence, I know that Networkx has a function to create random trees, but there is a chance I can get duplicates, all I can think of is use Numpy so I can find all the

问题 I have the following code that produces an image but the labels are cut off. I edited the first value in each df column on purpose as my real data has really large labels. I tried the plt.tight_layout but it doesnt work. I also tried the solution from the following link, but was not successful. It only shifted the image to the left. I also tried increasing the width of the figsize and while it i make it large enough the whole label is shown, the image looks really tiny and stretched out. I

问题 I am making a modified SIR model with an added vaccination parameter V. InitIALLY all the nodes in the graph are susceptible and there are a few initially infected people. The initial infected people neighbours are first vaccinated with prob w (which means they cant be infected) and then they are infected with prob b. The total number of vaccinated people is controlled by Vl which is a fraction of the total population. Here is my code- import networkx as nx import random import scipy from

问题 I'm working with dictionaries and I have the following error '>' not supported between instances of 'dict' and 'dict' I know that there are some problems with dictionaries in Python 2.7 and 3.x version. print("number of nodes %d" % G.number_of_nodes()) print("number of edges %d" % G.number_of_edges()) print("Graph is connected?: %s" % nx.is_connected(G)) print("Number of connected components: %s" % nx.number_connected_components(G)) print("Size of connected componnents: %s" % [len(cc) for cc

问题 I am looking for a way to pass a color, as assinged in NetworkX's node construction, to a Bokeh plot. There are some great ways to implement color in the Bokeh plot after it is generated, such as this, but this solution requires that I apply the transformation to the entire data set based on an attribute. I wanted to do something even simpler and assign a color and size based on what I assign those to be in NetworkX. I normally plot node set 1 as red, then node set 2 as blue in NetworkX, then

问题 I created a networkX weighted graph (refer to this question for the code) and I wanted to color code the edges based on node color. If two nodes are of the same color and connected I want their edge color to be one color, say red. Moreover, if the nodes aren't the same color but connected I want them to be a different color, say blue. Is there a way I could achieve this? The code for the edges is as follows: for edge in G.edges(): source, target = edge rad = 0.35 arrowprops=dict(lw=G.edges[

问题 To start, I created a Network using 2014 flight data as explained here:https://ipython-books.github.io/142-drawing-flight-routes-with-networkx/ I'm trying to create smaller graphs using airline specific flight paths to test connectivity within each business, starting with American Airlines, I did the following: AASys = routes[ routes['source'].isin(airports_us.index) & routes['dest'].isin(airports_us.index) & routes['airline'].str.contains('AA')] AASys AAedges = AASys[['source', 'dest']]

问题 In a game, we have a universe described as a strongly-connected graph full of sectors and edges. Occasionally there are small pockets, players call them 'bubbles', where a small group of nodes all access the rest of the network through a single node. In the graph below, sectors 769, 195, 733, 918, and 451 are all only reachable via node 855. If you can guard 855 effectively, then those other sectors are safe. Other nodes on the chart have more edges (purple lines) and aren't 'bubbles' in the

问题 My algorithm outputs the set of vertices describing objects in 3D space (x, y, z). In this case, there are two objects: verts = [[0.1 1. 1. ] [1. 1. 0.1] [1. 0.1 1. ] [1. 1. 1.9] [1. 1.9 1. ] [1.9 1. 1. ] [7.1 8. 8. ] [8. 8. 7.1] [8. 7.1 8. ] [8. 8. 8.9] [8. 8.9 8. ] [8.9 8. 8. ]] There are two tetrahedrons, one confined between centered on (1, 1, 1), the other on (8, 8, 8). My goal is to use breadth-first search to identify that the objects are separate, and then classify each. I have not

问题 My algorithm outputs the set of vertices describing objects in 3D space (x, y, z). In this case, there are two objects: verts = [[0.1 1. 1. ] [1. 1. 0.1] [1. 0.1 1. ] [1. 1. 1.9] [1. 1.9 1. ] [1.9 1. 1. ] [7.1 8. 8. ] [8. 8. 7.1] [8. 7.1 8. ] [8. 8. 8.9] [8. 8.9 8. ] [8.9 8. 8. ]] There are two tetrahedrons, one confined between centered on (1, 1, 1), the other on (8, 8, 8). My goal is to use breadth-first search to identify that the objects are separate, and then classify each. I have not