Build Graph from input data

build_graph.py

+import networkx as nx 
+import matplotlib.pyplot as plt
+
+
+def parse_graph():
+    with open('filename.txt', 'r') as file:     #Graph edges name/path
+        f = file.readlines()
+        edges = []
+        a = [int(n.split(' ')[0]) for n in f]
+        b = [int(n.split(' ')[1]) for n in f]
+        
+        nod = list(set(a + b))
+        for i in range(len(b)):
+            edges.append((a[i],b[i]))
+        return nod, edges
+    
+
+def node_labels():
+    nod, _ = parse_graph()
+    d = {}
+    with open('filename.txt', 'r') as lab:    #Graph labels name/path
+        l = lab.readlines()     #Returns a list string 
+
+        if len(l[0].split(' ')) == 2:
+                x = [int(w.split(' ')[0]) for w in l]
+                y = [int(w.split(' ')[-1].rstrip('\n')) for w in l]  
+                for j in range(len(x)):
+                    d[x[j]] = y[j]      #For label data with node          
+        elif len(l[0].split(' ')) == 1: 
+            for i, k in enumerate(l, 1):    #start counting from 1
+                d[i] = int(k.rstrip('\n'))    #For label data without node
+        else: print('Edges more than 2 nodes')
+        labels_dict = {}
+        for node in nod:
+            labels_dict[node] = d[node]
+        return labels_dict
+
+labels_dict = node_labels()
+only_labels = list(set(labels_dict.values()))
+
+
+def extract_subgraph(G, source, destination): 
+    if nx.has_path(G, source, destination):
+        nodes = nx.shortest_path(G, source, destination)
+        pattern_graph = G.subgraph(nodes)
+        nx.draw(pattern_graph, with_labels=True)
+        plt.show()
+    else:
+        print('No Path between {} and {}'.format(source, destination))
+
+
+def node_neighbors(G):
+    neighbors = {}
+    for node in G.nodes:
+        neighbors[node] = list(G.neighbors(node))
+    return neighbors
+
+        
+def node_neighbor_labels(G):
+    neighbor_labels = {}
+    for node in G.nodes:
+        nn = list(G.neighbors(node))    #nn = Node Neighbor
+        nnlabel = [labels_dict[n] for n in nn]  #Superimpose labels on node id
+        neighbor_labels[node] = nnlabel
+    return neighbor_labels
+
+
+def build():
+    nod, edges = parse_graph()
+    G = nx.Graph()
+    G.add_nodes_from(nod)
+    G.add_edges_from(edges)
+    for e in G.edges:
+        G.edges[e]['weight'] = 1 
+    return G
+