fix example bugs, close #22

docs/cookbook/groups.rst

@@ -93,13 +93,14 @@ Save as PDF
 
 .. code:: python
 
-	import networkx as nx
-	import netwulf as wulf
-
-	G = nx.barabasi_albert_graph(100, 2)
-
-	network, config = wulf.visualize(G, plot_in_cell_below=False)
-
-	fig, ax = plt.subplots(figsize=(10, 10))
-	wulf.draw_netwulf(network, fig, ax)
-	plt.savefig("myfigure.pdf")
+    import networkx as nx
+    import netwulf as wulf
+    import matplotlib.pyplot as plt
+    
+    G = nx.barabasi_albert_graph(100, 2)
+    
+    network, config = wulf.visualize(G, plot_in_cell_below=False)
+    
+    fig, ax = plt.subplots(figsize=(10, 10))
+    wulf.draw_netwulf(network, fig, ax)
+    plt.savefig("myfigure.pdf")

docs/python_api/network_manipulation.rst

@@ -25,14 +25,14 @@ will be grouped and colored in the visualization. Here's an example
         G[u][v]['foo'] = np.random.rand()
         G[u][v]['bar'] = np.random.rand()
 
-    # assign node attributes according to some generic grouping 
-    grp = {u: 'ABCDE'[u%5]  for u in G.nodes() }
-    nx.set_node_attributes(G, grp, 'wum')
-
     # filter the Graph to visualize one where the weight is determined by 'foo'
     new_G = wulf.get_filtered_network(G,edge_weight_key='foo')
     wulf.visualize(new_G)
 
+    # assign node attributes according to some generic grouping 
+    grp = {u: 'ABCDE'[u%5]  for u in G.nodes() }
+    nx.set_node_attributes(G, grp, 'wum')
+
     # filter the Graph to visualize one where the weight is determined by 'bar'
     # and the node group (coloring) is determined by the node attribute 'wum'
     new_G = wulf.get_filtered_network(G,edge_weight_key='bar',node_group_key='wum')