networkx

问题 I am running a loop that computes a networkx.classes.multidigraph.MultiDiGraph for each row (neighbourhood) of a list of GeoDataFrames (cities). It then computes some statistics for each row and writes the file out to disk. The problem is that the loop is extremely long to compute because the graph is computed for each row. The way I want to quicken the loop is by computing the graph for the whole GeoDataFrame and then clipping the graph into each row (each row has a polygon). You can do this

问题 I am running a loop that computes a networkx.classes.multidigraph.MultiDiGraph for each row (neighbourhood) of a list of GeoDataFrames (cities). It then computes some statistics for each row and writes the file out to disk. The problem is that the loop is extremely long to compute because the graph is computed for each row. The way I want to quicken the loop is by computing the graph for the whole GeoDataFrame and then clipping the graph into each row (each row has a polygon). You can do this

问题 My networkx graph consists of objects that have property named "coords" (x,y) : import networkx as nx import matplotlib.pyplot as plt class device(): def __init__(self, name): self.name = name self.coords = (0,0) def __repr__(self): return self.name device1 = device('1') device1.coords = (20, 5) device2 = device('2') device2.coords = (-4, 10.5) device3 = device('3') device3.coords = (17, -5) G = nx.Graph() G.add_nodes_from([device1, device2, device3]) nx.draw(G, with_labels = True) plt.show()

问题 My networkx graph consists of objects that have property named "coords" (x,y) : import networkx as nx import matplotlib.pyplot as plt class device(): def __init__(self, name): self.name = name self.coords = (0,0) def __repr__(self): return self.name device1 = device('1') device1.coords = (20, 5) device2 = device('2') device2.coords = (-4, 10.5) device3 = device('3') device3.coords = (17, -5) G = nx.Graph() G.add_nodes_from([device1, device2, device3]) nx.draw(G, with_labels = True) plt.show()

问题 I'm developing a python application, and i want to list all possible connected subgraph of any size and starting from every node using NetworkX. I just tried using combinations() from itertools library to find all possible combination of nodes but it is very too slow because it searchs also for not connected nodes: for r in range(0,NumberOfNodes) for SG in (G.subgraph(s) for s in combinations(G,r): if (nx.is_connected(SG)): nx.draw(SG,with_labels=True) plt.show() The actual output is correct.

问题 I'm developing a python application, and i want to list all possible connected subgraph of any size and starting from every node using NetworkX. I just tried using combinations() from itertools library to find all possible combination of nodes but it is very too slow because it searchs also for not connected nodes: for r in range(0,NumberOfNodes) for SG in (G.subgraph(s) for s in combinations(G,r): if (nx.is_connected(SG)): nx.draw(SG,with_labels=True) plt.show() The actual output is correct.

问题 As a warning, I'm still a bit inexperienced in python I'm trying to perform the transitive reduction of directed graph using the networkx library. I've figured out an algorithm but I'm having trouble implementing it. After a quick search, I found algorithms similar to mine in other stack exchange questions but no demonstrations of how to actually code the algorithm. Here's my algorthm: For X in Nodes For Y in Nodes For z in Nodes if (x,y) != (y,z) and (x,y) != (x,z) if edges xy and yz are in

问题 I have written below code to login to HP ARuba Switches and Make changes.. But it is not working.. Gives Error after login. What Can I change in below code to make it work with Aruba OS. It should throw error if anything goes wrong and save it in one file based on error.. And if any other device comes other than Aruba , should throw error and continue. Model - 2930F-48G-PoE Any help will be highly appreciated. from getpass import getpass from netmiko import ConnectHandler from netmiko.ssh

问题 I am currently using networkx library for Python with BFS and DFS. I need to get a tree and then explore it to get a path from a start node to an end node. For the BFS part I am using bfs_successors and it returns an iterator of successors in breadth-first-search from source. For the DFS part I am using: dfs_successors and it returns a dictionary of successors in depth-first-search from source. I need to get a list of nodes from source to end from both the algorithms. Each node is (x, y) and

问题 I am trying to run a neural gas network with an older script that doesn't work well with networkx 2 so I modified some things. However I am getting the error : Dictionary changed size during iteration and I don't get how to fix this because networkx is not my speciality. Any help? The code that is causing the problem right now: def prune_connections(self, a_max): for u, v, attributes in self.network.edges(data=True): if attributes['age'] > a_max: self.network.remove_edge(u, v) for u in self