Remove numpy dependency

Author: Gomango999

doc/source/tutorials/minimum_spanning_trees/figures/minimum_spanning_trees.png

@@ -17,13 +17,13 @@ We start by generating a grid graph with random weights
     
     import igraph as ig
     import matplotlib.pyplot as plt
-    import numpy as np
+    import random
 
     # Generate grid graph with random weights
-    np.random.seed(0)
+    random.seed(0)
 
     g = ig.Graph.Lattice([5, 5], circular=False)
-    g.es["weight"] = np.random.randint(1, 20, g.ecount()).tolist()
+    g.es["weight"] = [random.randint(1, 20) for _ in g.es]
 
 We then call |spanning_tree|_, making sure to pass in the randomly generated weights.
 

doc/source/tutorials/minimum_spanning_trees/minimum_spanning_trees.rst

@@ -1,15 +1,16 @@
 import igraph as ig
 import matplotlib.pyplot as plt
-import numpy as np
+import random
 
 g = ig.Graph.Lattice([6, 6], circular=False)
 
 # Optional: Rearrange the vertex ids to get a more interesting spanning tree
 layout = g.layout("grid")
 
-np.random.seed(0)
-permutation = np.random.permutation(36)
-g = g.permute_vertices(permutation.tolist())
+random.seed(0)
+permutation = list(range(g.vcount()))
+random.shuffle(permutation)
+g = g.permute_vertices(permutation)
 
 new_layout = g.layout("grid")
 for i in range(36):
@@ -34,5 +35,3 @@
     edge_width=g.es["width"]
 )
 plt.show()
-
-

doc/source/tutorials/spanning_trees/assets/spanning_trees.py

@@ -17,7 +17,7 @@ First we create a 6 by 6 lattice graph.
     
     import igraph as ig
     import matplotlib.pyplot as plt
-    import numpy as np
+    import random
 
     g = ig.Graph.Lattice([6, 6], circular=False)
 
@@ -31,9 +31,10 @@ As an optional step, we randomly rearrange some of the vertex IDs with |permute_
     # Optional: Rearrange the vertex ids to get a more interesting spanning tree
     layout = g.layout("grid")
 
-    np.random.seed(0)
-    permutation = np.random.permutation(36)
-    g = g.permute_vertices(permutation.tolist())
+    random.seed(0)
+    permutation = list(range(g.vcount()))
+    random.shuffle(permutation)
+    g = g.permute_vertices(permutation)
 
     # Calculate the new layout coordinates based on the permutation
     new_layout = g.layout("grid")