Merge branch 'u/tmonteil/upgrade_networkx_to_2_2_and_add_self_tests' …

…of trac.sagemath.org:sage into HEAD

build/pkgs/networkx/checksums.ini

@@ -1,4 +1,4 @@
 tarball=networkx-VERSION.zip
-sha1=a481bea7b41c0d6af191c7d2bcde56d75da20b3e
-md5=4a2c2a62dfc46ba7d594daca7c834995
-cksum=1070647523
+sha1=a68f8faa3726c220b93d68cfa28bed24874ba3fc
+md5=82608a3686fb3e61f20cf13bfd3c1b4a
+cksum=539980282

build/pkgs/networkx/package-version.txt

@@ -1 +1 @@
-2.1
+2.2

build/pkgs/networkx/spkg-check

@@ -0,0 +1,22 @@
+if [ -z "$SAGE_LOCAL" ]; then
+    echo >&2 "SAGE_LOCAL undefined ... exiting"
+    echo >&2 "Maybe run 'sage --sh'?"
+    exit 1
+fi
+
+cd src
+
+if ! command -v nosetests ; then 
+    echo >&2 'Testing networkx requires the package nose to be installed'
+    exit 1
+fi
+
+echo "Testing networkx..."
+
+nosetests networkx -v
+
+if [ $? -ne 0 ]; then
+    echo >&2 "Error running self tests."
+    exit 1
+fi
+

src/sage/graphs/digraph_generators.py

@@ -1108,7 +1108,7 @@ def RandomDirectedGN(self, n, kernel=lambda x:x, seed=None):
 
         -  ``kernel`` - the attachment kernel
 
-        -  ``seed`` - for the random number generator
+        -  ``seed`` - a ``random.Random`` seed for the random number generator
 
 
         EXAMPLES::
@@ -1124,7 +1124,7 @@ def RandomDirectedGN(self, n, kernel=lambda x:x, seed=None):
           Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = current_randstate().python_random()
         import networkx
         return DiGraph(networkx.gn_graph(n, kernel, seed=seed))
 
@@ -1144,7 +1144,7 @@ def RandomDirectedGNC(self, n, seed=None):
 
         -  ``n`` - number of vertices.
 
-        -  ``seed`` - for the random number generator
+        -  ``seed`` - a ``random.Random`` seed for the random number generator
 
 
         EXAMPLES::
@@ -1160,7 +1160,7 @@ def RandomDirectedGNC(self, n, seed=None):
           Copying, Phys. Rev. E vol. 71 (2005), p. 036118.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = current_randstate().python_random()
         import networkx
         return DiGraph(networkx.gnc_graph(n, seed=seed))
 
@@ -1355,7 +1355,7 @@ def RandomDirectedGNR(self, n, p, seed=None):
 
         -  ``p`` - redirection probability
 
-        -  ``seed`` - for the random number generator.
+        -  ``seed`` - a ``random.Random`` seed for the random number generator.
 
 
         EXAMPLES::
@@ -1371,7 +1371,7 @@ def RandomDirectedGNR(self, n, p, seed=None):
           Random Networks, Phys. Rev. E vol. 63 (2001), p. 066123.
         """
         if seed is None:
-            seed = current_randstate().long_seed()
+            seed = current_randstate().python_random()
         import networkx
         return DiGraph(networkx.gnc_graph(n, seed=seed))
 

src/sage/graphs/generators/random.py

@@ -30,7 +30,7 @@ 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 for 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
@@ -106,7 +106,7 @@ 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 = current_randstate().python_random()
     if p == 1:
         from sage.graphs.generators.basic import CompleteGraph
         return CompleteGraph(n)
@@ -140,7 +140,7 @@ 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 for random number generator (default ``None``).
 
     EXAMPLES:
 
@@ -175,7 +175,7 @@ def RandomBarabasiAlbert(n, m, seed=None):
 
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = current_randstate().python_random()
     import networkx
     return Graph(networkx.barabasi_albert_graph(n,m,seed=seed))
 
@@ -625,7 +625,7 @@ 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 for random number generator (default ``None``).
 
     EXAMPLES: We show the edge list of a random graph on 5 nodes with
     10 edges.
@@ -658,7 +658,7 @@ def RandomGNM(n, m, dense=False, seed=None):
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = current_randstate().python_random()
     import networkx
     if dense:
         return Graph(networkx.dense_gnm_random_graph(n, m, seed=seed))
@@ -688,7 +688,7 @@ 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 for 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`::
@@ -708,7 +708,7 @@ def RandomNewmanWattsStrogatz(n, k, p, seed=None):
       99, 2566-2572.
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = current_randstate().python_random()
     import networkx
     return Graph(networkx.newman_watts_strogatz_graph(n, k, p, seed=seed))
 
@@ -728,7 +728,7 @@ 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 for 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
@@ -764,7 +764,7 @@ 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 = current_randstate().python_random()
     import networkx
     return Graph(networkx.powerlaw_cluster_graph(n, m, p, seed=seed))
 
@@ -838,7 +838,7 @@ 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 for 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`::
@@ -852,7 +852,7 @@ def RandomLobster(n, p, q, seed=None):
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = current_randstate().python_random()
     import networkx
     return Graph(networkx.random_lobster(n, p, q, seed=seed))
 
@@ -943,7 +943,7 @@ 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 for 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.
@@ -960,7 +960,7 @@ 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 = current_randstate().python_random()
     import networkx
     try:
         return Graph(networkx.random_powerlaw_tree(n, gamma, seed=seed, tries=tries))
@@ -981,7 +981,7 @@ def RandomRegular(d, n, seed=None):
 
     - ``d`` - degree
 
-    - ``seed`` -- integer seed for random number generator (default ``None``).
+    - ``seed`` -- a ``random.Random`` seed for random number generator (default ``None``).
 
 
     EXAMPLES: We show the edge list of a random graph with 8 nodes each
@@ -1009,7 +1009,7 @@ 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 = current_randstate().python_random()
     import networkx
     try:
         N = networkx.random_regular_graph(d, n, seed=seed)
@@ -1034,7 +1034,7 @@ 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 for random number generator (default ``None``).
 
     EXAMPLES::
 
@@ -1044,7 +1044,7 @@ def RandomShell(constructor, seed=None):
         sage: G.show()  # long time
     """
     if seed is None:
-        seed = current_randstate().long_seed()
+        seed = current_randstate().python_random()
     import networkx
     return Graph(networkx.random_shell_graph(constructor, seed=seed))