Fixed easy linter issues.

dgl_ptm/dgl_ptm/network/network_creation.py

@@ -1,26 +1,24 @@
 #!/usr/bin/env python
-# coding: utf-8
+
 
 import dgl
 import networkx as nx
-import random
 import torch
 
 # network_creation - Creates the network between the initialized nodes using edges from DGL
 
 def network_creation(num_agents, method, **kwargs):
-    '''
-        network_creation - Creates the graph network for the model using the barabasi albert model from networkx  
-
-        Args:
-            num_agents: Number of agent nodes
-            method: Current implemented methods include:
-                barabasi_albert model: This method takes the following possible keyword arguments,
-                    seed: random seed for networkx barabasi_albert_graph function
-                    new_node_edges: number of edges to create for each new node
-        Return:
-            agent_graph: Created agent_graph as per the chosen method
-    '''
+    """network_creation - Creates the graph network for the model using the barabasi albert model from networkx
+
+    Args:
+        num_agents: Number of agent nodes
+        method: Current implemented methods include:
+            barabasi_albert model: This method takes the following possible keyword arguments,
+                seed: random seed for networkx barabasi_albert_graph function
+                new_node_edges: number of edges to create for each new node
+    Return:
+        agent_graph: Created agent_graph as per the chosen method
+    """
     if (method == 'barabasi-albert'):
         if 'seed' in kwargs.keys():
             seed  = kwargs['seed']
@@ -40,19 +38,17 @@ def network_creation(num_agents, method, **kwargs):
     return agent_graph
 
 def barabasi_albert_graph(num_agents, new_node_edges=1, seed=1):
-    '''
-        Creates a network graph for user-defined number of agents using the barabasi 
-        albert model function from networkx.
-
-        Args:
-            num_agents = Number of agent nodes
-            new_node_edges = Number of edges to create for each new node
-            seed = random seed for function
+    """Creates a network graph for user-defined number of agents using the barabasi
+    albert model function from networkx.
 
-        Return:
-            agent_graph: Created agent_graph as per the chosen method
-    '''
+    Args:
+        num_agents = Number of agent nodes
+        new_node_edges = Number of edges to create for each new node
+        seed = random seed for function
 
+    Return:
+        agent_graph: Created agent_graph as per the chosen method
+    """
     #Create graph using networkx function for barabasi albert graph 
     networkx_graph = nx.barabasi_albert_graph(n=num_agents, m=new_node_edges, seed=seed)
     barabasi_albert_coo = nx.to_scipy_sparse_array(networkx_graph,format='coo')