path: root/sagemath-networkx-2.2.patch

diff --git a/src/sage/graphs/digraph_generators.py b/src/sage/graphs/digraph_generators.py
index 204c013..af4d8ea 100644
--- a/src/sage/graphs/digraph_generators.py
+++ b/src/sage/graphs/digraph_generators.py
@@ -63,6 +63,7 @@ Functions and methods
 from __future__ import print_function, division
 from six.moves import range
 
+import sys
 from sage.misc.randstate import current_randstate
 from sage.graphs.digraph import DiGraph
 
@@ -1104,18 +1105,19 @@ class DiGraphGenerators():
         INPUT:
 
 
-        -  ``n`` - number of vertices.
+        - ``n`` - number of vertices.
 
-        -  ``kernel`` - the attachment kernel
+        - ``kernel`` - the attachment kernel.
 
-        -  ``seed`` - for the random number generator
+        - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+          number generator (default: ``None``).
 
 
         EXAMPLES::
 
             sage: D = digraphs.RandomDirectedGN(25)
             sage: D.edges(labels=False)
-            [(1, 0), (2, 0), (3, 1), (4, 0), (5, 0), (6, 1), (7, 0), (8, 3), (9, 0), (10, 8), (11, 3), (12, 9), (13, 8), (14, 0), (15, 11), (16, 11), (17, 5), (18, 11), (19, 6), (20, 5), (21, 14), (22, 5), (23, 18), (24, 11)]
+            [(1, 0), (2, 1), (3, 0), (4, 2), (5, 0), (6, 2), (7, 3), (8, 2), (9, 3), (10, 4), (11, 5), (12, 9), (13, 2), (14, 2), (15, 5), (16, 2), (17, 15), (18, 1), (19, 5), (20, 2), (21, 5), (22, 1), (23, 5), (24, 14)]
             sage: D.show()  # long time
 
         REFERENCE:
@@ -1124,7 +1126,7 @@ class DiGraphGenerators():
           Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = int(current_randstate().long_seed() % sys.maxint)
         import networkx
         return DiGraph(networkx.gn_graph(n, kernel, seed=seed))
 
@@ -1142,16 +1144,17 @@ class DiGraphGenerators():
         INPUT:
 
 
-        -  ``n`` - number of vertices.
+        - ``n`` - number of vertices.
 
-        -  ``seed`` - for the random number generator
+        - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+          number generator (default: ``None``).
 
 
         EXAMPLES::
 
             sage: D = digraphs.RandomDirectedGNC(25)
             sage: D.edges(labels=False)
-            [(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
+            [(1, 0), (2, 0), (3, 0), (3, 2), (4, 0), (5, 0), (5, 2), (5, 3), (6, 0), (6, 4), (7, 0), (7, 2), (7, 3), (8, 0), (8, 2), (8, 3), (8, 5), (9, 0), (9, 4), (9, 6), (10, 0), (10, 2), (10, 3), (10, 7), (11, 0), (11, 4), (11, 6), (11, 9), (12, 0), (12, 4), (13, 0), (13, 4), (14, 0), (14, 2), (14, 3), (14, 5), (14, 8), (15, 0), (15, 2), (15, 3), (16, 0), (16, 2), (16, 3), (16, 15), (17, 0), (17, 2), (18, 0), (18, 2), (18, 3), (18, 7), (18, 10), (19, 0), (19, 2), (19, 3), (19, 5), (20, 0), (20, 2), (20, 3), (20, 7), (20, 10), (21, 0), (21, 2), (22, 0), (22, 4), (22, 6), (22, 9), (22, 11), (23, 0), (23, 2), (23, 17), (24, 0), (24, 4), (24, 6), (24, 9)]
             sage: D.show()  # long time
 
         REFERENCE:
@@ -1160,7 +1163,7 @@ class DiGraphGenerators():
           Copying, Phys. Rev. E vol. 71 (2005), p. 036118.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = int(current_randstate().long_seed() % sys.maxint)
         import networkx
         return DiGraph(networkx.gnc_graph(n, seed=seed))
 
@@ -1351,18 +1354,19 @@ class DiGraphGenerators():
         INPUT:
 
 
-        -  ``n`` - number of vertices.
+        - ``n`` - number of vertices.
 
-        -  ``p`` - redirection probability
+        - ``p`` - redirection probability.
 
-        -  ``seed`` - for the random number generator.
+        - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+          number generator (default: ``None``).
 
 
         EXAMPLES::
 
             sage: D = digraphs.RandomDirectedGNR(25, .2)
             sage: D.edges(labels=False)
-            [(1, 0), (2, 0), (2, 1), (3, 0), (4, 0), (4, 1), (5, 0), (5, 1), (5, 2), (6, 0), (6, 1), (7, 0), (7, 1), (7, 4), (8, 0), (9, 0), (9, 8), (10, 0), (10, 1), (10, 2), (10, 5), (11, 0), (11, 8), (11, 9), (12, 0), (12, 8), (12, 9), (13, 0), (13, 1), (14, 0), (14, 8), (14, 9), (14, 12), (15, 0), (15, 8), (15, 9), (15, 12), (16, 0), (16, 1), (16, 4), (16, 7), (17, 0), (17, 8), (17, 9), (17, 12), (18, 0), (18, 8), (19, 0), (19, 1), (19, 4), (19, 7), (20, 0), (20, 1), (20, 4), (20, 7), (20, 16), (21, 0), (21, 8), (22, 0), (22, 1), (22, 4), (22, 7), (22, 19), (23, 0), (23, 8), (23, 9), (23, 12), (23, 14), (24, 0), (24, 8), (24, 9), (24, 12), (24, 15)]
+            [(1, 0), (2, 0), (3, 0), (3, 2), (4, 0), (5, 0), (5, 2), (5, 3), (6, 0), (6, 4), (7, 0), (7, 2), (7, 3), (8, 0), (8, 2), (8, 3), (8, 5), (9, 0), (9, 4), (9, 6), (10, 0), (10, 2), (10, 3), (10, 7), (11, 0), (11, 4), (11, 6), (11, 9), (12, 0), (12, 4), (13, 0), (13, 4), (14, 0), (14, 2), (14, 3), (14, 5), (14, 8), (15, 0), (15, 2), (15, 3), (16, 0), (16, 2), (16, 3), (16, 15), (17, 0), (17, 2), (18, 0), (18, 2), (18, 3), (18, 7), (18, 10), (19, 0), (19, 2), (19, 3), (19, 5), (20, 0), (20, 2), (20, 3), (20, 7), (20, 10), (21, 0), (21, 2), (22, 0), (22, 4), (22, 6), (22, 9), (22, 11), (23, 0), (23, 2), (23, 17), (24, 0), (24, 4), (24, 6), (24, 9)]
             sage: D.show()  # long time
 
         REFERENCE:
@@ -1371,7 +1375,7 @@ class DiGraphGenerators():
           Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = int(current_randstate().long_seed() % sys.maxint)
         import networkx
         return DiGraph(networkx.gnc_graph(n, seed=seed))
 
diff --git a/src/sage/graphs/generators/degree_sequence.py b/src/sage/graphs/generators/degree_sequence.py
index bcea8db..0d85654 100644
--- a/src/sage/graphs/generators/degree_sequence.py
+++ b/src/sage/graphs/generators/degree_sequence.py
@@ -16,6 +16,8 @@ The methods defined here appear in :mod:`sage.graphs.graph_generators`.
 # (at your option) any later version.
 #                  https://www.gnu.org/licenses/
 # ****************************************************************************
+
+import sys
 from sage.graphs.graph import Graph
 from sage.misc.randstate import current_randstate
 
@@ -142,10 +144,11 @@ def DegreeSequenceConfigurationModel(deg_sequence, seed=None):
 
     INPUT:
 
-    -  ``deg_sequence`` - a list of integers with each
-       entry corresponding to the expected degree of a different vertex.
+    - ``deg_sequence`` - a list of integers with each entry corresponding to the
+      expected degree of a different vertex.
 
-    -  ``seed`` - for the random number generator.
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
 
 
     EXAMPLES::
@@ -172,7 +175,7 @@ def DegreeSequenceConfigurationModel(deg_sequence, seed=None):
       networks, SIAM Review vol. 45, no. 2 (2003), pp. 167-256.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.configuration_model([int(i) for i in deg_sequence], seed=seed), loops=True, multiedges=True, sparse=True)
 
@@ -210,17 +213,18 @@ def DegreeSequenceExpected(deg_sequence, seed=None):
 
     INPUT:
 
-    -  ``deg_sequence`` - a list of integers with each
-       entry corresponding to the expected degree of a different vertex.
+    - ``deg_sequence`` - a list of integers with each entry corresponding to the
+      expected degree of a different vertex.
 
-    -  ``seed`` - for the random number generator.
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
 
 
     EXAMPLES::
 
         sage: G = graphs.DegreeSequenceExpected([1,2,3,2,3])
         sage: G.edges(labels=False)
-        [(0, 2), (0, 3), (1, 1), (1, 4), (2, 3), (2, 4), (3, 4), (4, 4)]
+        [(0, 3), (1, 4), (2, 2), (2, 3), (2, 4), (4, 4)]
         sage: G.show()  # long time
 
     REFERENCE:
@@ -230,6 +234,6 @@ def DegreeSequenceExpected(deg_sequence, seed=None):
       Ann. Combinatorics (6), 2002 pp. 125-145.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.expected_degree_graph([int(i) for i in deg_sequence], seed=seed), loops=True)
diff --git a/src/sage/graphs/generators/random.py b/src/sage/graphs/generators/random.py
index 06a5203..5cbf5e9 100644
--- a/src/sage/graphs/generators/random.py
+++ b/src/sage/graphs/generators/random.py
@@ -14,6 +14,7 @@ The methods defined here appear in :mod:`sage.graphs.graph_generators`.
 #                         http://www.gnu.org/licenses/
 ###########################################################################
 from six.moves import range
+import sys
 # import from Sage library
 from sage.graphs.graph import Graph
 from sage.misc.randstate import current_randstate
@@ -30,7 +31,8 @@ def RandomGNP(n, p, seed=None, fast=True, algorithm='Sage'):
 
     - ``p`` -- probability of an edge
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
 
     - ``fast`` -- boolean set to True (default) to use the algorithm with
       time complexity in `O(n+m)` proposed in [BatBra2005]_. It is designed
@@ -98,7 +100,7 @@ def RandomGNP(n, p, seed=None, fast=True, algorithm='Sage'):
         sage: graphs.RandomGNP(50,.2, algorithm="Sage").size()
         243
         sage: graphs.RandomGNP(50,.2, algorithm="networkx").size()
-        258
+        245
     """
     if n < 0:
         raise ValueError("The number of nodes must be positive or null.")
@@ -106,7 +108,7 @@ def RandomGNP(n, p, seed=None, fast=True, algorithm='Sage'):
         raise ValueError("The probability p must be in [0..1].")
 
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     if p == 1:
         from sage.graphs.generators.basic import CompleteGraph
         return CompleteGraph(n)
@@ -140,7 +142,9 @@ def RandomBarabasiAlbert(n, m, seed=None):
 
     - ``m`` - number of edges to attach from each new node
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES:
 
@@ -149,7 +153,7 @@ def RandomBarabasiAlbert(n, m, seed=None):
     ::
 
         sage: graphs.RandomBarabasiAlbert(6,2).edges(labels=False)
-        [(0, 2), (0, 3), (0, 4), (1, 2), (2, 3), (2, 4), (2, 5), (3, 5)]
+        [(0, 2), (0, 3), (1, 2), (1, 4), (1, 5), (2, 3), (2, 4), (3, 5)]
 
     We plot a random graph on 12 nodes with m = 3.
 
@@ -175,7 +179,7 @@ def RandomBarabasiAlbert(n, m, seed=None):
 
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.barabasi_albert_graph(n,m,seed=seed))
 
@@ -625,7 +629,9 @@ def RandomGNM(n, m, dense=False, seed=None):
     - ``dense`` - whether to use NetworkX's
       dense_gnm_random_graph or gnm_random_graph
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES: We show the edge list of a random graph on 5 nodes with
     10 edges.
@@ -658,7 +664,7 @@ def RandomGNM(n, m, dense=False, seed=None):
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     if dense:
         return Graph(networkx.dense_gnm_random_graph(n, m, seed=seed))
@@ -688,13 +694,15 @@ def RandomNewmanWattsStrogatz(n, k, p, seed=None):
     - ``p`` - the probability of adding a new edge for
       each edge
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES: We show the edge list of a random graph on 7 nodes with 2
     "nearest neighbors" and probability `p = 0.2`::
 
         sage: graphs.RandomNewmanWattsStrogatz(7, 2, 0.2).edges(labels=False)
-        [(0, 1), (0, 2), (0, 3), (0, 6), (1, 2), (2, 3), (2, 4), (3, 4), (3, 6), (4, 5), (5, 6)]
+        [(0, 1), (0, 4), (0, 6), (1, 2), (1, 4), (2, 3), (3, 4), (4, 5), (5, 6)]
 
     ::
 
@@ -708,7 +716,7 @@ def RandomNewmanWattsStrogatz(n, k, p, seed=None):
       99, 2566-2572.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.newman_watts_strogatz_graph(n, k, p, seed=seed))
 
@@ -728,7 +736,9 @@ def RandomHolmeKim(n, m, p, seed=None):
     - ``p`` - probability of adding a triangle after
       adding a random edge.
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     From the NetworkX documentation: The average clustering has a hard
     time getting above a certain cutoff that depends on m. This cutoff
@@ -750,8 +760,7 @@ def RandomHolmeKim(n, m, p, seed=None):
     ::
 
         sage: graphs.RandomHolmeKim(8, 2, 0.5).edges(labels=False)
-        [(0, 2), (0, 5), (1, 2), (1, 3), (2, 3), (2, 4), (2, 6), (2, 7),
-         (3, 4), (3, 6), (3, 7), (4, 5)]
+        [(0, 2), (0, 3), (0, 4), (0, 5), (0, 7), (1, 2), (1, 3), (1, 6), (2, 4), (2, 6), (3, 5), (4, 7)]
 
     ::
 
@@ -764,7 +773,7 @@ def RandomHolmeKim(n, m, p, seed=None):
       with tunable clustering, Phys. Rev. E (2002). vol 65, no 2, 026107.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.powerlaw_cluster_graph(n, m, p, seed=seed))
 
@@ -838,13 +847,15 @@ def RandomLobster(n, p, q, seed=None):
     - ``q`` - probability of adding an edge (claw) to the
       arms
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES: We show the edge list of a random graph with 3 backbone
     nodes and probabilities `p = 0.7` and `q = 0.3`::
 
         sage: graphs.RandomLobster(3, 0.7, 0.3).edges(labels=False)
-        [(0, 1), (1, 2)]
+        [(0, 1), (0, 5), (1, 2), (1, 6), (2, 3), (2, 7), (3, 4), (3, 8)]
 
     ::
 
@@ -852,7 +863,7 @@ def RandomLobster(n, p, q, seed=None):
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.random_lobster(n, p, q, seed=seed))
 
@@ -924,7 +935,7 @@ def RandomTree(n):
 
     return g
 
-def RandomTreePowerlaw(n, gamma=3, tries=100, seed=None):
+def RandomTreePowerlaw(n, gamma=3, tries=1000, seed=None):
     """
     Returns a tree with a power law degree distribution. Returns False
     on failure.
@@ -943,15 +954,17 @@ def RandomTreePowerlaw(n, gamma=3, tries=100, seed=None):
     - ``tries`` - number of attempts to adjust sequence to
       make a tree
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES: We show the edge list of a random graph with 10 nodes and
     a power law exponent of 2.
 
     ::
 
-        sage: graphs.RandomTreePowerlaw(10, 2).edges(labels=False)
-        [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (6, 7), (6, 8), (6, 9)]
+        sage: graphs.RandomTreePowerlaw(10, 3).edges(labels=False)
+        [(0, 1), (1, 2), (2, 3), (3, 4), (4, 5), (5, 6), (5, 8), (6, 7), (6, 9)]
 
     ::
 
@@ -960,7 +973,7 @@ def RandomTreePowerlaw(n, gamma=3, tries=100, seed=None):
         ....:     G.show()  # random output, long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     try:
         return Graph(networkx.random_powerlaw_tree(n, gamma, seed=seed, tries=tries))
@@ -981,7 +994,8 @@ def RandomRegular(d, n, seed=None):
 
     - ``d`` - degree
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
 
 
     EXAMPLES: We show the edge list of a random graph with 8 nodes each
@@ -990,7 +1004,7 @@ def RandomRegular(d, n, seed=None):
     ::
 
         sage: graphs.RandomRegular(3, 8).edges(labels=False)
-        [(0, 1), (0, 4), (0, 7), (1, 5), (1, 7), (2, 3), (2, 5), (2, 6), (3, 4), (3, 6), (4, 5), (6, 7)]
+        [(0, 3), (0, 5), (0, 6), (1, 2), (1, 3), (1, 7), (2, 4), (2, 6), (3, 6), (4, 5), (4, 7), (5, 7)]
 
     ::
 
@@ -1009,7 +1023,7 @@ def RandomRegular(d, n, seed=None):
       regular graphs quickly. Prob. and Comp. 8 (1999), pp 377-396.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     try:
         N = networkx.random_regular_graph(d, n, seed=seed)
@@ -1034,17 +1048,19 @@ def RandomShell(constructor, seed=None):
     - ``d`` - the ratio of inter (next) shell edges to
       intra shell edges
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` - a ``random.Random`` seed or a Python ``int`` for the random
+      number generator (default: ``None``).
+
 
     EXAMPLES::
 
         sage: G = graphs.RandomShell([(10,20,0.8),(20,40,0.8)])
         sage: G.edges(labels=False)
-        [(0, 3), (0, 7), (0, 8), (1, 2), (1, 5), (1, 8), (1, 9), (3, 6), (3, 11), (4, 6), (4, 7), (4, 8), (4, 21), (5, 8), (5, 9), (6, 9), (6, 10), (7, 8), (7, 9), (8, 18), (10, 11), (10, 13), (10, 19), (10, 22), (10, 26), (11, 18), (11, 26), (11, 28), (12, 13), (12, 14), (12, 28), (12, 29), (13, 16), (13, 21), (13, 29), (14, 18), (16, 20), (17, 18), (17, 26), (17, 28), (18, 19), (18, 22), (18, 27), (18, 28), (19, 23), (19, 25), (19, 28), (20, 22), (24, 26), (24, 27), (25, 27), (25, 29)]
+        [(0, 7), (0, 8), (0, 9), (1, 3), (1, 4), (1, 5), (1, 7), (1, 9), (1, 27), (2, 5), (2, 9), (2, 15), (2, 21), (3, 6), (3, 8), (3, 9), (4, 6), (4, 7), (6, 7), (8, 21), (10, 26), (12, 17), (12, 18), (12, 20), (12, 25), (12, 26), (13, 14), (13, 19), (14, 16), (14, 18), (14, 19), (14, 22), (14, 24), (15, 21), (16, 17), (16, 25), (16, 26), (16, 28), (17, 19), (17, 29), (18, 24), (18, 26), (19, 28), (20, 27), (20, 29), (22, 24), (22, 27), (22, 29), (23, 24), (23, 26), (24, 27), (26, 29)]
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = int(current_randstate().long_seed() % sys.maxint)
     import networkx
     return Graph(networkx.random_shell_graph(constructor, seed=seed))