Merge branch 'master' into pyup-update-networkx-1.11-to-2.0

ndlib/models/dynamic/DynSIModel.py

@@ -51,7 +51,7 @@ def iteration(self, node_status=True):
 
         # streaming
         if self.stream_execution:
-            u, v = self.graph.edges()[0]
+            u, v = list(self.graph.edges)[0]
             u_status = self.status[u]
             v_status = self.status[v]
 

ndlib/models/dynamic/DynSIRModel.py

@@ -58,7 +58,7 @@ def iteration(self, node_status=True):
 
         # streamong
         if self.stream_execution:
-            u, v = self.graph.edges()[0]
+            u, v = list(self.graph.edges)[0]
             u_status = self.status[u]
             v_status = self.status[v]
 

ndlib/models/dynamic/DynSISModel.py

@@ -57,7 +57,7 @@ def iteration(self, node_status=True):
 
         # streaming
         if self.stream_execution:
-            u, v = self.graph.edges()[0]
+            u, v = list(self.graph.edges)[0]
             u_status = self.status[u]
             v_status = self.status[v]
 

ndlib/models/epidemics/IndependentCascadesModel.py

@@ -65,7 +65,7 @@ def iteration(self, node_status=True):
             if actual_status[u] != 1:
                 continue
 
-            neighbors = self.graph.neighbors(u)  # neighbors and successors (in DiGraph) produce the same result
+            neighbors = list(self.graph.neighbors(u))  # neighbors and successors (in DiGraph) produce the same result
 
             # Standard threshold
             if len(neighbors) > 0:

ndlib/models/epidemics/KerteszThresholdModel.py

@@ -76,7 +76,7 @@ def iteration(self, node_status=True):
                 i = 0
                 while i < number_node_blocked:
                     # select a random node
-                    node = self.graph.nodes()[np.random.randint(0, self.graph.number_of_nodes())]
+                    node = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
 
                     # node not infected
                     if actual_status[node] == 0:
@@ -107,7 +107,7 @@ def iteration(self, node_status=True):
                         actual_status[node] = 1
                         continue
 
-                neighbors = self.graph.neighbors(node)
+                neighbors = list(self.graph.neighbors(node))
                 if len(neighbors) == 0:
                     continue
 

ndlib/models/epidemics/ProfileThresholdModel.py

@@ -96,9 +96,9 @@ def iteration(self, node_status=True):
                     actual_status[u] = 1
                     continue
 
-            neighbors = self.graph.neighbors(u)
+            neighbors = list(self.graph.neighbors(u))
             if isinstance(self.graph, nx.DiGraph):
-                neighbors = self.graph.predecessors(u)
+                neighbors = list(self.graph.predecessors(u))
 
             infected = 0
             for v in neighbors:

ndlib/models/epidemics/ThresholdModel.py

@@ -64,9 +64,9 @@ def iteration(self, node_status=True):
             if actual_status[u] == 1:
                 continue
 
-            neighbors = self.graph.neighbors(u)
+            neighbors = list(self.graph.neighbors(u))
             if isinstance(self.graph, nx.DiGraph):
-                neighbors = self.graph.predecessors(u)
+                neighbors = list(self.graph.predecessors(u))
 
             infected = 0
             for v in neighbors:

ndlib/models/opinions/CognitiveOpDynModel.py

@@ -176,10 +176,10 @@ def iteration(self, node_status=True):
         # then interact with peers
         for i in range(0, self.graph.number_of_nodes()):
             # select a random node
-            n1 = self.graph.nodes()[np.random.randint(0, self.graph.number_of_nodes())]
+            n1 = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
 
             # select all of the nodes neighbours (no digraph possible)
-            neighbours = self.graph.neighbors(n1)
+            neighbours = list(self.graph.neighbors(n1))
             if len(neighbours) == 0:
                 continue
 

ndlib/models/opinions/MajorityRuleModel.py

@@ -61,7 +61,7 @@ def iteration(self, node_status=True):
                         "node_count": node_count.copy(), "status_delta": status_delta.copy()}
 
         # select q random nodes
-        discussion_group = [self.graph.nodes()[i]
+        discussion_group = [list(self.graph.nodes)[i]
                             for i in np.random.randint(0, self.graph.number_of_nodes(), self.params['model']['q'])]
 
         # compute majority

ndlib/models/opinions/QVoterModel.py

@@ -62,14 +62,14 @@ def iteration(self, node_status=True):
                         "node_count": node_count.copy(), "status_delta": status_delta.copy()}
 
                 # select a random listener
-        listener = self.graph.nodes()[np.random.randint(0, self.graph.number_of_nodes())]
+        listener = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
 
         # select all of the listener's neighbours
-        neighbours = self.graph.neighbors(listener)
+        neighbours = list(self.graph.neighbors(listener))
         if isinstance(self.graph, nx.DiGraph):
             # consider only the predecessors
             # assumed if a->b then b can be influenced by a
-            neighbours = self.graph.predecessors(listener)
+            neighbours = list(self.graph.predecessors(listener))
 
         # select q random neighbours (with repetitions)
         influence_group_state = [self.status[neighbours[i]]

ndlib/models/opinions/SznajdModel.py

@@ -52,24 +52,24 @@ def iteration(self, node_status=True):
         status_delta = {st: 0 for st in self.available_statuses.values()}
 
         # select a random node
-        speaker1 = self.graph.nodes()[np.random.randint(0, self.graph.number_of_nodes())]
+        speaker1 = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
 
         # select a random neighbour
-        neighbours = self.graph.neighbors(speaker1)
+        neighbours = list(self.graph.neighbors(speaker1))
         if isinstance(self.graph, nx.DiGraph):
             # add also the predecessors
-            neighbours += self.graph.predecessors(speaker1)
+            neighbours += list(self.graph.predecessors(speaker1))
 
         speaker2 = neighbours[np.random.randint(0, len(neighbours))]
 
         if self.status[speaker1] == self.status[speaker2]:
             # select listeners (all neighbours of two speakers)
-            neighbours = self.graph.neighbors(speaker1) + self.graph.neighbors(speaker2)
+            neighbours = list(self.graph.neighbors(speaker1)) + list(self.graph.neighbors(speaker2))
 
             if isinstance(self.graph, nx.DiGraph):
                 # assumed if a->b then b can be influenced by a
                 # but not the other way around - the link between the speakers doesn't matter
-                neighbours = self.graph.successors(speaker1) + self.graph.successors(speaker2)
+                neighbours = list(self.graph.successors(speaker1)) + list(self.graph.successors(speaker2))
 
             # update status of listeners
             for listener in neighbours:

ndlib/models/opinions/VoterModel.py

@@ -50,14 +50,14 @@ def iteration(self, node_status=True):
                         "node_count": node_count.copy(), "status_delta": status_delta.copy()}
 
         # select a random node
-        listener = self.graph.nodes()[np.random.randint(0, self.graph.number_of_nodes())]
+        listener = list(self.graph.nodes)[np.random.randint(0, self.graph.number_of_nodes())]
 
         # select a random neighbour
-        neighbours = self.graph.neighbors(listener)
+        neighbours = list(self.graph.neighbors(listener))
         if isinstance(self.graph, nx.DiGraph):
             # difficult to have a digraph but assumed if a->b then b can be influenced by a
             # but not the other way around
-            neighbours = self.graph.predecessors(listener)
+            neighbours = list(self.graph.predecessors(listener))
 
         speaker = neighbours[np.random.randint(0, len(neighbours))]
 

ndlib/test/test_ndlib.py

@@ -412,19 +412,19 @@ def test_config(self):
         config = mc.Configuration()
         config.add_model_parameter('percentage_infected', 0.1)
         config.add_model_initial_configuration("Infected", [1, 2, 3])
-        config.add_node_set_configuration("partial", {1:1, 2:2})
+        config.add_node_set_configuration("partial", {1: 1, 2: 2})
         try:
             model.set_initial_status(config)
         except:
             pass
 
-        config.add_edge_set_configuration("partial", {e: 1 for e in g.edges()[:10]})
+        config.add_edge_set_configuration("partial", {e: 1 for e in list(g.edges)[:10]})
         try:
             model.set_initial_status(config)
         except:
             pass
-        config.add_node_set_configuration("partial", {n: 1 for n in g.nodes()})
-        config.add_edge_set_configuration("partial", {e: 1 for e in g.edges()})
+        config.add_node_set_configuration("partial", {n: 1 for n in g.nodes})
+        config.add_edge_set_configuration("partial", {e: 1 for e in g.edges})
         model.set_initial_status(config)
 
         g = nx.complete_graph(100)

ndlib/test/test_parallel.py

@@ -45,5 +45,6 @@ def test_multi_initial_set(self):
                             nprocesses=4)
         self.assertIsNotNone(trends)
 
+
 if __name__ == '__main__':
     unittest.main()

requirements.txt

@@ -1,8 +1,8 @@
 mock==2.0.0
 sphinx_rtd_theme==0.2.4
-numpy==1.13.1
+numpy==1.13.3
 future==0.16.0
 networkx==2.0
-dynetx==0.1.6
+dynetx==0.2.0
 bokeh==0.12.9
 matplotlib==2.0.2

setup.py

@@ -13,7 +13,7 @@
 #    long_description = f.read()
 
 setup(name='ndlib',
-      version='3.0.0',
+      version='3.0.1',
       license='GNU General Public License v3 or later (GPLv3+)',
       description='Network Diffusion Library',
       url='https://github.com/GiulioRossetti/ndlib',