text: # -*- encoding: utf-8 -*-
  # test_random_graphs.py - unit tests for random graph generators
  #
  # Copyright 2010-2019 NetworkX developers.
  #
  # This file is part of NetworkX.
  #
  # NetworkX is distributed under a BSD license; see LICENSE.txt for more
  # information.
  """Unit tests for the :mod:`networkx.generators.random_graphs` module.
  
  """
  import pytest
  
  from networkx.exception import NetworkXError
  from networkx.generators.random_graphs import barabasi_albert_graph
  from networkx.generators.random_graphs import dual_barabasi_albert_graph
  from networkx.generators.random_graphs import extended_barabasi_albert_graph
  from networkx.generators.random_graphs import binomial_graph
  from networkx.generators.random_graphs import connected_watts_strogatz_graph
  from networkx.generators.random_graphs import dense_gnm_random_graph
  from networkx.generators.random_graphs import erdos_renyi_graph
  from networkx.generators.random_graphs import fast_gnp_random_graph
  from networkx.generators.random_graphs import gnm_random_graph
  from networkx.generators.random_graphs import gnp_random_graph
  from networkx.generators.random_graphs import newman_watts_strogatz_graph
  from networkx.generators.random_graphs import powerlaw_cluster_graph
  from networkx.generators.random_graphs import random_kernel_graph
  from networkx.generators.random_graphs import random_lobster
  from networkx.generators.random_graphs import random_powerlaw_tree
  from networkx.generators.random_graphs import random_powerlaw_tree_sequence
  from networkx.generators.random_graphs import random_regular_graph
  from networkx.generators.random_graphs import random_shell_graph
  from networkx.generators.random_graphs import watts_strogatz_graph
  
  
  class TestGeneratorsRandom(object):
  
      def smoke_test_random_graph(self):
          seed = 42
          G = gnp_random_graph(100, 0.25, seed)
          G = gnp_random_graph(100, 0.25, seed, directed=True)
          G = binomial_graph(100, 0.25, seed)
          G = erdos_renyi_graph(100, 0.25, seed)
          G = fast_gnp_random_graph(100, 0.25, seed)
          G = fast_gnp_random_graph(100, 0.25, seed, directed=True)
          G = gnm_random_graph(100, 20, seed)
          G = gnm_random_graph(100, 20, seed, directed=True)
          G = dense_gnm_random_graph(100, 20, seed)
  
          G = watts_strogatz_graph(10, 2, 0.25, seed)
          assert len(G) == 10
          assert G.number_of_edges() == 10
  
          G = connected_watts_strogatz_graph(10, 2, 0.1, tries=10, seed=seed)
          assert len(G) == 10
          assert G.number_of_edges() == 10
          pytest.raises(NetworkXError, connected_watts_strogatz_graph, \
                        10, 2, 0.1, tries=0)
  
          G = watts_strogatz_graph(10, 4, 0.25, seed)
          assert len(G) == 10
          assert G.number_of_edges() == 20
  
          G = newman_watts_strogatz_graph(10, 2, 0.0, seed)
          assert len(G) == 10
          assert G.number_of_edges() == 10
  
          G = newman_watts_strogatz_graph(10, 4, 0.25, seed)
          assert len(G) == 10
          assert G.number_of_edges() >= 20
  
          G = barabasi_albert_graph(100, 1, seed)
          G = barabasi_albert_graph(100, 3, seed)
          assert G.number_of_edges() == (97 * 3)
  
          G = extended_barabasi_albert_graph(100, 1, 0, 0, seed)
          assert G.number_of_edges() == 99
          G = extended_barabasi_albert_graph(100, 3, 0, 0, seed)
          assert G.number_of_edges() == 97 * 3
          G = extended_barabasi_albert_graph(100, 1, 0, 0.5, seed)
          assert G.number_of_edges() == 99
          G = extended_barabasi_albert_graph(100, 2, 0.5, 0, seed)
          assert G.number_of_edges() > 100 * 3
          assert G.number_of_edges() < 100 * 4
  
          G = extended_barabasi_albert_graph(100, 2, 0.3, 0.3, seed)
          assert G.number_of_edges() > 100 * 2
          assert G.number_of_edges() < 100 * 4
  
          G = powerlaw_cluster_graph(100, 1, 1.0, seed)
          G = powerlaw_cluster_graph(100, 3, 0.0, seed)
          assert G.number_of_edges() == (97 * 3)
  
          G = random_regular_graph(10, 20, seed)
  
          pytest.raises(NetworkXError, random_regular_graph, 3, 21)
          pytest.raises(NetworkXError, random_regular_graph, 33, 21)
  
          constructor = [(10, 20, 0.8), (20, 40, 0.8)]
          G = random_shell_graph(constructor, seed)
  
          G = random_lobster(10, 0.1, 0.5, seed)
  
          # difficult to find seed that requires few tries
          seq = random_powerlaw_tree_sequence(10, 3, seed=14, tries=1)
          G = random_powerlaw_tree(10, 3, seed=14, tries=1)
  
      def test_dual_barabasi_albert(self, m1=1, m2=4, p=0.5):
          """
          Tests that the dual BA random graph generated behaves consistently.
  
          Tests the exceptions are raised as expected.
  
          The graphs generation are repeated several times to prevent lucky shots
  
          """
          seed = 42
          repeats = 2
  
          while repeats:
              repeats -= 1
  
              # This should be BA with m = m1
              BA1 = barabasi_albert_graph(100, m1, seed)
              DBA1 = dual_barabasi_albert_graph(100, m1, m2, 1, seed)
              assert BA1.size() == DBA1.size()
  
              # This should be BA with m = m2
              BA2 = barabasi_albert_graph(100, m2, seed)
              DBA2 = dual_barabasi_albert_graph(100, m1, m2, 0, seed)
              assert BA2.size() == DBA2.size()
  
          # Testing exceptions
          dbag = dual_barabasi_albert_graph
          pytest.raises(NetworkXError, dbag, m1, m1, m2, 0)
          pytest.raises(NetworkXError, dbag, m2, m1, m2, 0)
          pytest.raises(NetworkXError, dbag, 100, m1, m2, -0.5)
          pytest.raises(NetworkXError, dbag, 100, m1, m2, 1.5)
  
      def test_extended_barabasi_albert(self, m=2):
          """
          Tests that the extended BA random graph generated behaves consistently.
  
          Tests the exceptions are raised as expected.
  
          The graphs generation are repeated several times to prevent lucky-shots
  
          """
          seed = 42
          repeats = 2
          BA_model = barabasi_albert_graph(100, m, seed)
          BA_model_edges = BA_model.number_of_edges()
  
          while repeats:
              repeats -= 1
  
              # This behaves just like BA, the number of edges must be the same
              G1 = extended_barabasi_albert_graph(100, m, 0, 0, seed)
              assert G1.size() == BA_model_edges
  
              # More than twice more edges should have been added
              G1 = extended_barabasi_albert_graph(100, m, 0.8, 0, seed)
              assert G1.size() > BA_model_edges * 2
  
              # Only edge rewiring, so the number of edges less than original
              G2 = extended_barabasi_albert_graph(100, m, 0, 0.8, seed)
              assert G2.size() == BA_model_edges
  
              # Mixed scenario: less edges than G1 and more edges than G2
              G3 = extended_barabasi_albert_graph(100, m, 0.3, 0.3, seed)
              assert G3.size() > G2.size()
              assert G3.size() < G1.size()
  
          # Testing exceptions
          ebag = extended_barabasi_albert_graph
          pytest.raises(NetworkXError, ebag, m, m, 0, 0)
          pytest.raises(NetworkXError, ebag, 1, 0.5, 0, 0)
          pytest.raises(NetworkXError, ebag, 100, 2, 0.5, 0.5)
  
      def test_random_zero_regular_graph(self):
          """Tests that a 0-regular graph has the correct number of nodes and
          edges.
  
          """
          seed = 42
          G = random_regular_graph(0, 10, seed)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 0
  
      def test_gnp(self):
          for generator in [gnp_random_graph, binomial_graph, erdos_renyi_graph,
                            fast_gnp_random_graph]:
              G = generator(10, -1.1)
              assert len(G) == 10
              assert sum(1 for _ in G.edges()) == 0
  
              G = generator(10, 0.1)
              assert len(G) == 10
  
              G = generator(10, 0.1, seed=42)
              assert len(G) == 10
  
              G = generator(10, 1.1)
              assert len(G) == 10
              assert sum(1 for _ in G.edges()) == 45
  
              G = generator(10, -1.1, directed=True)
              assert G.is_directed()
              assert len(G) == 10
              assert sum(1 for _ in G.edges()) == 0
  
              G = generator(10, 0.1, directed=True)
              assert G.is_directed()
              assert len(G) == 10
  
              G = generator(10, 1.1, directed=True)
              assert G.is_directed()
              assert len(G) == 10
              assert sum(1 for _ in G.edges()) == 90
  
              # assert that random graphs generate all edges for p close to 1
              edges = 0
              runs = 100
              for i in range(runs):
                  edges += sum(1 for _ in generator(10, 0.99999, directed=True).edges())
              assert abs(edges / float(runs) - 90) <= runs * 2.0 / 100
  
      def test_gnm(self):
          G = gnm_random_graph(10, 3)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 3
  
          G = gnm_random_graph(10, 3, seed=42)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 3
  
          G = gnm_random_graph(10, 100)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 45
  
          G = gnm_random_graph(10, 100, directed=True)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 90
  
          G = gnm_random_graph(10, -1.1)
          assert len(G) == 10
          assert sum(1 for _ in G.edges()) == 0
  
      def test_watts_strogatz_big_k(self):
          # Test to make sure than n <= k
          pytest.raises(NetworkXError, watts_strogatz_graph, 10, 11, 0.25)
          pytest.raises(NetworkXError, newman_watts_strogatz_graph, 10, 11, 0.25)
  
          # could create an infinite loop, now doesn't
          # infinite loop used to occur when a node has degree n-1 and needs to rewire
          watts_strogatz_graph(10, 9, 0.25, seed=0)
          newman_watts_strogatz_graph(10, 9, 0.5, seed=0)
  
          # Test k==n scenario
          watts_strogatz_graph(10, 10, 0.25, seed=0)
          newman_watts_strogatz_graph(10, 10, 0.25, seed=0)
  
      def test_random_kernel_graph(self):
          def integral(u, w, z):
              return c * (z - w)
  
          def root(u, w, r):
              return r / c + w
          c = 1
          graph = random_kernel_graph(1000, integral, root)
          graph = random_kernel_graph(1000, integral, root, seed=42)
          assert len(graph) == 1000
  