CODEWITHSUNDEEP
pythonmatplotlibplotgraphbellman-ford
Mukesh Jha
Mukesh Jha

Reputation: 175

Generate a directed Graph using Python Library any python library

I am implementing Bellman ford's algorithm from GeeksForGeeks in Python. I want to generate the Graph(The Diagramatic form and not in dictionary type-which is easy) using some library like pyplot or networkx or something similar. I want the graph UI to contain the nodes,edges and the respective cost.

from collections import defaultdict 

#Class to represent a graph 
class Graph: 

    def __init__(self,vertices): 
        self.V= vertices #No. of vertices 
        self.graph = [] # default dictionary to store graph 

    # function to add an edge to graph 
    def addEdge(self,u,v,w): 
        self.graph.append([u, v, w]) 

    # utility function used to print the solution 
    def printArr(self, dist): 
        print("Vertex   Distance from Source") 
        for i in range(self.V): 
            print("%d \t\t %d" % (i, dist[i])) 

    # The main function that finds shortest distances from src to 
    # all other vertices using Bellman-Ford algorithm.  The function 
    # also detects negative weight cycle 
    def BellmanFord(self, src): 

        # Step 1: Initialize distances from src to all other vertices 
        # as INFINITE 
        dist = [float("Inf")] * self.V 
        dist[src] = 0 


        # Step 2: Relax all edges |V| - 1 times. A simple shortest  
        # path from src to any other vertex can have at-most |V| - 1  
        # edges 
        for i in range(self.V - 1): 
            # Update dist value and parent index of the adjacent vertices of 
            # the picked vertex. Consider only those vertices which are still in 
            # queue 
            for u, v, w in self.graph: 
                if dist[u] != float("Inf") and dist[u] + w < dist[v]: 
                        dist[v] = dist[u] + w 

        # Step 3: check for negative-weight cycles.  The above step  
        # guarantees shortest distances if graph doesn't contain  
        # negative weight cycle.  If we get a shorter path, then there 
        # is a cycle. 

        for u, v, w in self.graph: 
                if dist[u] != float("Inf") and dist[u] + w < dist[v]: 
                        print "Graph contains negative weight cycle"
                        return

        # print all distance 
        self.printArr(dist) 

g = Graph(5) 
g.addEdge(0, 1, -1) 
g.addEdge(0, 2, 4) 
g.addEdge(1, 2, 3) 
g.addEdge(1, 3, 2) 
g.addEdge(1, 4, 2) 
g.addEdge(3, 2, 5) 
g.addEdge(3, 1, 1) 
g.addEdge(4, 3, -3)

The graph that I want either in terminal or in separate file is(based on above code):

enter image description here

Upvotes: 3

Views: 8002

Answers (2)

Mukesh Jha
Mukesh Jha

Reputation: 175

The link of documentation by ekiim is highly useful. This is the code that I did for plotting graph:

import networkx as nx  
import matplotlib.pyplot as plt
G=nx.DiGraph()
G.add_node(0),G.add_node(1),G.add_node(2),G.add_node(3),G.add_node(4)
G.add_edge(0, 1),G.add_edge(1, 2),G.add_edge(0, 2),G.add_edge(1, 4),G.add_edge(1, 3),G.add_edge(3, 2),G.add_edge(3,1),G.add_edge(4,3)
nx.draw(G, with_labels=True, font_weight='bold')
plt.show()

This code prints the directed graph without cost. I tried printing with cost but the output was highly distorted with costs jumbled up. Some costs were written in blank spaces while only one or two were present on the edges. Hence if someone knows to implement that it would be highly useful.

Upvotes: 5

ekiim
ekiim

Reputation: 842

If you check this tutorial for networkx, you'll see how easy, is to create a directed graph, plus, plotting it.

Pretty much, It's the same for a directed or simple graph, (API wise), and the plotting, is also simple enough and uses Matplotlib to generate it.

You could make a Tk app, that allows you to input, manually the nodes, and Edges, and store them in ListBoxes, and plot a graph, in function of that, this won't be drag and drop, but still, it helps you visualize the graph on the fly.

and this Matplotlib tutorial, will give you the idea how to embed it in a TK app.

Upvotes: 2

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