rm CondaPkg environment

.CondaPkg/env/Lib/site-packages/networkx/generators/classic.py

@@ -403,9 +403,38 @@ def cycle_graph(n, create_using=None):
 def dorogovtsev_goltsev_mendes_graph(n, create_using=None):
     """Returns the hierarchically constructed Dorogovtsev-Goltsev-Mendes graph.
 
-    n is the generation.
-    See: arXiv:/cond-mat/0112143 by Dorogovtsev, Goltsev and Mendes.
+    The Dorogovtsev-Goltsev-Mendes [1]_ procedure produces a scale-free graph
+    deterministically with the following properties for a given `n`:
+    - Total number of nodes = ``3 * (3**n + 1) / 2``
+    - Total number of edges = ``3 ** (n + 1)``
 
+    Parameters
+    ----------
+    n : integer
+       The generation number.
+
+    create_using : NetworkX Graph, optional
+       Graph type to be returned. Directed graphs and multi graphs are not
+       supported.
+
+    Returns
+    -------
+    G : NetworkX Graph
+
+    Examples
+    --------
+    >>> G = nx.dorogovtsev_goltsev_mendes_graph(3)
+    >>> G.number_of_nodes()
+    15
+    >>> G.number_of_edges()
+    27
+    >>> nx.is_planar(G)
+    True
+
+    References
+    ----------
+    .. [1] Dorogotsev S.N., Goltsev A.V., and Mendes J.F.F "Pseudofractal
+       Scale-free Web". arXiv:cond-mat/0112143
     """
     G = empty_graph(0, create_using)
     if G.is_directed():
@@ -507,7 +536,7 @@ def empty_graph(n=0, create_using=None, default=Graph):
     """
     if create_using is None:
         G = default()
-    elif type(create_using) is type:
+    elif isinstance(create_using, type):
         G = create_using()
     elif not hasattr(create_using, "adj"):
         raise TypeError("create_using is not a valid NetworkX graph type or instance")
@@ -802,7 +831,7 @@ def complete_multipartite_graph(*subset_sizes):
     # add nodes with subset attribute
     # while checking that ints are not mixed with iterables
     try:
-        for (i, subset) in enumerate(subsets):
+        for i, subset in enumerate(subsets):
             G.add_nodes_from(subset, subset=i)
     except TypeError as err:
         raise NetworkXError("Arguments must be all ints or all iterables") from err

.CondaPkg/env/Lib/site-packages/networkx/generators/community.py

@@ -159,7 +159,7 @@ def relaxed_caveman_graph(l, k, p, seed=None):
     """
     G = nx.caveman_graph(l, k)
     nodes = list(G)
-    for (u, v) in G.edges():
+    for u, v in G.edges():
         if seed.random() < p:  # rewire the edge
             x = seed.choice(nodes)
             if G.has_edge(u, x):

.CondaPkg/env/Lib/site-packages/networkx/generators/directed.py

@@ -94,7 +94,7 @@ def gnr_graph(n, p, create_using=None, seed=None):
 
     The GNR graph is built by adding nodes one at a time with a link to one
     previously added node.  The previous target node is chosen uniformly at
-    random.  With probabiliy `p` the link is instead "redirected" to the
+    random.  With probability `p` the link is instead "redirected" to the
     successor node of the target.
 
     The graph is always a (directed) tree.

.CondaPkg/env/Lib/site-packages/networkx/generators/expanders.py

@@ -70,8 +70,8 @@ def margulis_gabber_galil_graph(n, create_using=None):
         msg = "`create_using` must be an undirected multigraph."
         raise nx.NetworkXError(msg)
 
-    for (x, y) in itertools.product(range(n), repeat=2):
-        for (u, v) in (
+    for x, y in itertools.product(range(n), repeat=2):
+        for u, v in (
             ((x + 2 * y) % n, y),
             ((x + (2 * y + 1)) % n, y),
             (x, (y + 2 * x) % n),
@@ -146,22 +146,22 @@ def chordal_cycle_graph(p, create_using=None):
 
 
 def paley_graph(p, create_using=None):
-    """Returns the Paley (p-1)/2-regular graph on p nodes.
+    r"""Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes.
 
-    The returned graph is a graph on Z/pZ with edges between x and y
-    if and only if x-y is a nonzero square in Z/pZ.
+    The returned graph is a graph on $\mathbb{Z}/p\mathbb{Z}$ with edges between $x$ and $y$
+    if and only if $x-y$ is a nonzero square in $\mathbb{Z}/p\mathbb{Z}$.
 
-    If p = 1 mod 4, -1 is a square in Z/pZ and therefore x-y is a square if and
-    only if y-x is also a square, i.e the edges in the Paley graph are symmetric.
+    If $p \equiv 1  \pmod 4$, $-1$ is a square in $\mathbb{Z}/p\mathbb{Z}$ and therefore $x-y$ is a square if and
+    only if $y-x$ is also a square, i.e the edges in the Paley graph are symmetric.
 
-    If p = 3 mod 4, -1 is not a square in Z/pZ and therefore either x-y or y-x
-    is a square in Z/pZ but not both.
+    If $p \equiv 3 \pmod 4$, $-1$ is not a square in $\mathbb{Z}/p\mathbb{Z}$ and therefore either $x-y$ or $y-x$
+    is a square in $\mathbb{Z}/p\mathbb{Z}$ but not both.
 
     Note that a more general definition of Paley graphs extends this construction
-    to graphs over q=p^n vertices, by using the finite field F_q instead of Z/pZ.
+    to graphs over $q=p^n$ vertices, by using the finite field $F_q$ instead of $\mathbb{Z}/p\mathbb{Z}$.
     This construction requires to compute squares in general finite fields and is
-    not what is implemented here (i.e paley_graph(25) does not return the true
-    Paley graph associated with 5^2).
+    not what is implemented here (i.e `paley_graph(25)` does not return the true
+    Paley graph associated with $5^2$).
 
     Parameters
     ----------

.CondaPkg/env/Lib/site-packages/networkx/generators/internet_as_graphs.py

@@ -376,7 +376,7 @@ class AS_graph_generator:
         self.nodes = {"T": set(), "M": set(), "CP": set(), "C": set()}
 
         self.t_graph()
-        self.nodes["T"] = set(list(self.G.nodes()))
+        self.nodes["T"] = set(self.G.nodes())
 
         i = len(self.nodes["T"])
         for _ in range(self.n_m):

.CondaPkg/env/Lib/site-packages/networkx/generators/joint_degree_seq.py

@@ -227,13 +227,11 @@ def joint_degree_graph(joint_degrees, seed=None):
     # for each pair
     for k in joint_degrees:
         for l in joint_degrees[k]:
-
             # n_edges_add is the number of edges to add for the
             # degree pair (k,l)
             n_edges_add = joint_degrees[k][l]
 
             if (n_edges_add > 0) and (k >= l):
-
                 # number of nodes with degree k and l
                 k_size = degree_count[k]
                 l_size = degree_count[l]
@@ -253,14 +251,12 @@ def joint_degree_graph(joint_degrees, seed=None):
                     n_edges_add = joint_degrees[k][l] // 2
 
                 while n_edges_add > 0:
-
                     # randomly pick nodes v and w that have degrees k and l
                     v = k_nodes[seed.randrange(k_size)]
                     w = l_nodes[seed.randrange(l_size)]
 
                     # if nodes v and w are disconnected then attempt to connect
                     if not G.has_edge(v, w) and (v != w):
-
                         # if node v has no free stubs then do neighbor switch
                         if h_node_residual[v] == 0:
                             _neighbor_switch(G, v, k_unsat, h_node_residual)
@@ -351,13 +347,7 @@ def is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
                 if val + forbidden.get((k, l), 0) > V[(k, 1)] * V[(l, 0)]:
                     return False
 
-    for s in S:
-        if not S[s] / s[0] == V[s]:  # condition 2
-            return False
-
-    # if all conditions abive have been satisfied then the input nkk is
-    # realizable as a simple graph.
-    return True
+    return all(S[s] / s[0] == V[s] for s in S)
 
 
 def _directed_neighbor_switch(

.CondaPkg/env/Lib/site-packages/networkx/generators/line.py

@@ -243,7 +243,7 @@ def inverse_line_graph(G):
 
     Notes
     -----
-    This is an implementation of the Roussopoulos algorithm.
+    This is an implementation of the Roussopoulos algorithm[1]_.
 
     If G consists of multiple components, then the algorithm doesn't work.
     You should invert every component separately:
@@ -259,8 +259,9 @@ def inverse_line_graph(G):
 
     References
     ----------
-    * Roussopolous, N, "A max {m, n} algorithm for determining the graph H from
-      its line graph G", Information Processing Letters 2, (1973), 108--112.
+    .. [1] Roussopoulos, N.D. , "A max {m, n} algorithm for determining the graph H from
+       its line graph G", Information Processing Letters 2, (1973), 108--112, ISSN 0020-0190,
+       `DOI link <https://doi.org/10.1016/0020-0190(73)90029-X>`_
 
     """
     if G.number_of_nodes() == 0:
@@ -357,10 +358,7 @@ def _odd_triangle(G, T):
         for v in G[t]:
             if v not in T:
                 T_neighbors[v] += 1
-    for v in T_neighbors:
-        if T_neighbors[v] in [1, 3]:
-            return True
-    return False
+    return any(T_neighbors[v] in [1, 3] for v in T_neighbors)
 
 
 def _find_partition(G, starting_cell):

.CondaPkg/env/Lib/site-packages/networkx/generators/random_graphs.py

@@ -343,7 +343,7 @@ def newman_watts_strogatz_graph(n, k, p, seed=None):
     # for each edge u-v, with probability p, randomly select existing
     # node w and add new edge u-w
     e = list(G.edges())
-    for (u, v) in e:
+    for u, v in e:
         if seed.random() < p:
             w = seed.choice(nlist)
             # no self-loops and reject if edge u-w exists
@@ -487,6 +487,8 @@ def connected_watts_strogatz_graph(n, k, p, tries=100, seed=None):
 def random_regular_graph(d, n, seed=None):
     r"""Returns a random $d$-regular graph on $n$ nodes.
 
+    A regular graph is a graph where each node has the same number of neighbors.
+
     The resulting graph has no self-loops or parallel edges.
 
     Parameters
@@ -518,7 +520,7 @@ def random_regular_graph(d, n, seed=None):
     .. [1] A. Steger and N. Wormald,
        Generating random regular graphs quickly,
        Probability and Computing 8 (1999), 377-396, 1999.
-       http://citeseer.ist.psu.edu/steger99generating.html
+       https://doi.org/10.1017/S0963548399003867
 
     .. [2] Jeong Han Kim and Van H. Vu,
        Generating random regular graphs,
@@ -1015,7 +1017,7 @@ def powerlaw_cluster_graph(n, m, p, seed=None):
                 neighborhood = [
                     nbr
                     for nbr in G.neighbors(target)
-                    if not G.has_edge(source, nbr) and not nbr == source
+                    if not G.has_edge(source, nbr) and nbr != source
                 ]
                 if neighborhood:  # if there is a neighbor without a link
                     nbr = seed.choice(neighborhood)
@@ -1065,7 +1067,7 @@ def random_lobster(n, p1, p2, seed=None):
         If `p1` or `p2` parameters are >= 1 because the while loops would never finish.
     """
     p1, p2 = abs(p1), abs(p2)
-    if any([p >= 1 for p in [p1, p2]]):
+    if any(p >= 1 for p in [p1, p2]):
         raise nx.NetworkXError("Probability values for `p1` and `p2` must both be < 1.")
 
     # a necessary ingredient in any self-respecting graph library
@@ -1114,7 +1116,7 @@ def random_shell_graph(constructor, seed=None):
     intra_edges = []
     nnodes = 0
     # create gnm graphs for each shell
-    for (n, m, d) in constructor:
+    for n, m, d in constructor:
         inter_edges = int(m * d)
         intra_edges.append(m - inter_edges)
         g = nx.convert_node_labels_to_integers(

.CondaPkg/env/Lib/site-packages/networkx/generators/small.py

@@ -103,7 +103,7 @@ def LCF_graph(n, shift_list, repeats, create_using=None):
     if G.is_directed():
         raise NetworkXError("Directed Graph not supported")
     G.name = "LCF_graph"
-    nodes = sorted(list(G))
+    nodes = sorted(G)
 
     n_extra_edges = repeats * len(shift_list)
     # edges are added n_extra_edges times

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_classic.py

@@ -6,6 +6,7 @@ Generators - Classic
 Unit tests for various classic graph generators in generators/classic.py
 """
 import itertools
+import typing
 
 import pytest
 
@@ -288,6 +289,15 @@ class TestGeneratorClassic:
         assert not H.is_directed()
         assert G is not H
 
+        # test for subclasses that also use typing.Protocol. See gh-6243
+        class Mixin(typing.Protocol):
+            pass
+
+        class MyGraph(Mixin, nx.DiGraph):
+            pass
+
+        G = nx.empty_graph(create_using=MyGraph)
+
     def test_empty_graph(self):
         G = nx.empty_graph()
         assert nx.number_of_nodes(G) == 0
@@ -543,7 +553,7 @@ class TestGeneratorClassic:
                 assert v not in G[u]
                 assert G.nodes[u] == G.nodes[v]
         # Across blocks, all vertices should be adjacent.
-        for (block1, block2) in itertools.combinations(blocks, 2):
+        for block1, block2 in itertools.combinations(blocks, 2):
             for u, v in itertools.product(block1, block2):
                 assert v in G[u]
                 assert G.nodes[u] != G.nodes[v]

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_degree_seq.py

@@ -87,19 +87,19 @@ class TestConfigurationModel:
             nx.configuration_model([1, 2])
 
 
-def test_directed_configuation_raise_unequal():
+def test_directed_configuration_raise_unequal():
     with pytest.raises(nx.NetworkXError):
         zin = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1]
         zout = [5, 3, 3, 3, 3, 2, 2, 2, 1, 2]
         nx.directed_configuration_model(zin, zout)
 
 
-def test_directed_configuation_model():
+def test_directed_configuration_model():
     G = nx.directed_configuration_model([], [], seed=0)
     assert len(G) == 0
 
 
-def test_simple_directed_configuation_model():
+def test_simple_directed_configuration_model():
     G = nx.directed_configuration_model([1, 1], [1, 1], seed=0)
     assert len(G) == 2
 
@@ -169,11 +169,11 @@ def test_directed_havel_hakimi():
     p = 1.0 / r
     for i in range(r):
         G1 = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
-        din1 = list(d for n, d in G1.in_degree())
-        dout1 = list(d for n, d in G1.out_degree())
+        din1 = [d for n, d in G1.in_degree()]
+        dout1 = [d for n, d in G1.out_degree()]
         G2 = nx.directed_havel_hakimi_graph(din1, dout1)
-        din2 = list(d for n, d in G2.in_degree())
-        dout2 = list(d for n, d in G2.out_degree())
+        din2 = [d for n, d in G2.in_degree()]
+        dout2 = [d for n, d in G2.out_degree()]
         assert sorted(din1) == sorted(din2)
         assert sorted(dout1) == sorted(dout2)
 

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_directed.py

@@ -57,6 +57,41 @@ class TestGeneratorsDirected:
         pytest.raises(ValueError, scale_free_graph, 100, beta=-0.3)
         pytest.raises(ValueError, scale_free_graph, 100, gamma=-0.3)
 
+    def test_parameters(self):
+        G = nx.DiGraph()
+        G.add_node(0)
+
+        def kernel(x):
+            return x
+
+        assert nx.is_isomorphic(gn_graph(1), G)
+        assert nx.is_isomorphic(gn_graph(1, kernel=kernel), G)
+        assert nx.is_isomorphic(gnc_graph(1), G)
+        assert nx.is_isomorphic(gnr_graph(1, 0.5), G)
+
+
+def test_scale_free_graph_create_using_with_initial_graph():
+    G = nx.MultiGraph()
+    with pytest.raises(
+        ValueError,
+        match="Cannot set both create_using and initial_graph. Set create_using=None.",
+    ):
+        scale_free_graph(10, create_using=nx.Graph, initial_graph=G)
+
+
+def test_scale_free_graph_negative_delta():
+    with pytest.raises(ValueError, match="delta_in must be >= 0."):
+        scale_free_graph(10, create_using=None, delta_in=-1)
+    with pytest.raises(ValueError, match="delta_out must be >= 0."):
+        scale_free_graph(10, create_using=None, delta_out=-1)
+
+
+def test_non_numeric_ordering():
+    G = MultiDiGraph([("a", "b"), ("b", "c"), ("c", "a")])
+    s = scale_free_graph(3, initial_graph=G)
+    assert len(s) == 3
+    assert len(s.edges) == 3
+
 
 @pytest.mark.parametrize("ig", (nx.Graph(), nx.DiGraph([(0, 1)])))
 def test_scale_free_graph_initial_graph_kwarg(ig):
@@ -88,6 +123,10 @@ class TestRandomKOutGraph:
         G = random_k_out_graph(n, k, alpha, self_loops=False)
         assert nx.number_of_selfloops(G) == 0
 
+    def test_negative_alpha(self):
+        with pytest.raises(ValueError, match="alpha must be positive"):
+            random_k_out_graph(10, 3, -1)
+
 
 class TestUniformRandomKOutGraph:
     """Unit tests for the
@@ -119,6 +158,11 @@ class TestUniformRandomKOutGraph:
         G = random_uniform_k_out_graph(n, k, with_replacement=True)
         assert G.is_multigraph()
         assert all(d == k for v, d in G.out_degree())
+        n = 10
+        k = 9
+        G = random_uniform_k_out_graph(n, k, with_replacement=False, self_loops=False)
+        assert nx.number_of_selfloops(G) == 0
+        assert all(d == k for v, d in G.out_degree())
 
     def test_without_replacement(self):
         n = 10

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_expanders.py

@@ -60,7 +60,7 @@ def test_paley_graph(p):
     # If p = 1 mod 4, -1 is a square mod 4 and therefore the
     # edge in the Paley graph are symmetric.
     if p % 4 == 1:
-        for (u, v) in G.edges:
+        for u, v in G.edges:
             assert (v, u) in G.edges
 
 

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_geometric.py

@@ -101,6 +101,7 @@ class TestSoftRandomGeometricGraph:
         generator.
 
         """
+
         # Use the L1 metric.
         def dist(x, y):
             return sum(abs(a - b) for a, b in zip(x, y))
@@ -287,6 +288,7 @@ class TestThresholdedRandomGeometricGraph:
         generator.
 
         """
+
         # Use the L1 metric.
         def dist(x, y):
             return sum(abs(a - b) for a, b in zip(x, y))

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_harary_graph.py

@@ -16,7 +16,7 @@ class TestHararyGraph:
     def test_hnm_harary_graph(self):
         # When d is even and r = 0, the hnm_harary_graph(n,m) is
         # the circulant_graph(n, list(range(1,d/2+1)))
-        for (n, m) in [(5, 5), (6, 12), (7, 14)]:
+        for n, m in [(5, 5), (6, 12), (7, 14)]:
             G1 = hnm_harary_graph(n, m)
             d = 2 * m // n
             G2 = nx.circulant_graph(n, list(range(1, d // 2 + 1)))
@@ -25,7 +25,7 @@ class TestHararyGraph:
         # When d is even and r > 0, the hnm_harary_graph(n,m) is
         # the circulant_graph(n, list(range(1,d/2+1)))
         # with r edges added arbitrarily
-        for (n, m) in [(5, 7), (6, 13), (7, 16)]:
+        for n, m in [(5, 7), (6, 13), (7, 16)]:
             G1 = hnm_harary_graph(n, m)
             d = 2 * m // n
             G2 = nx.circulant_graph(n, list(range(1, d // 2 + 1)))
@@ -34,7 +34,7 @@ class TestHararyGraph:
 
         # When d is odd and n is even and r = 0, the hnm_harary_graph(n,m)
         # is the circulant_graph(n, list(range(1,(d+1)/2) plus [n//2])
-        for (n, m) in [(6, 9), (8, 12), (10, 15)]:
+        for n, m in [(6, 9), (8, 12), (10, 15)]:
             G1 = hnm_harary_graph(n, m)
             d = 2 * m // n
             L = list(range(1, (d + 1) // 2))
@@ -45,7 +45,7 @@ class TestHararyGraph:
         # When d is odd and n is even and r > 0, the hnm_harary_graph(n,m)
         # is the circulant_graph(n, list(range(1,(d+1)/2) plus [n//2])
         # with r edges added arbitrarily
-        for (n, m) in [(6, 10), (8, 13), (10, 17)]:
+        for n, m in [(6, 10), (8, 13), (10, 17)]:
             G1 = hnm_harary_graph(n, m)
             d = 2 * m // n
             L = list(range(1, (d + 1) // 2))
@@ -57,7 +57,7 @@ class TestHararyGraph:
         # When d is odd and n is odd, the hnm_harary_graph(n,m) is
         # the circulant_graph(n, list(range(1,(d+1)/2))
         # with m - n*(d-1)/2 edges added arbitrarily
-        for (n, m) in [(5, 4), (7, 12), (9, 14)]:
+        for n, m in [(5, 4), (7, 12), (9, 14)]:
             G1 = hnm_harary_graph(n, m)
             d = 2 * m // n
             L = list(range(1, (d + 1) // 2))
@@ -87,21 +87,21 @@ class TestHararyGraph:
     def test_hkn_harary_graph(self):
         # When k == 1, the hkn_harary_graph(k,n) is
         # the path_graph(n)
-        for (k, n) in [(1, 6), (1, 7)]:
+        for k, n in [(1, 6), (1, 7)]:
             G1 = hkn_harary_graph(k, n)
             G2 = nx.path_graph(n)
             assert is_isomorphic(G1, G2)
 
         # When k is even, the hkn_harary_graph(k,n) is
         # the circulant_graph(n, list(range(1,k/2+1)))
-        for (k, n) in [(2, 6), (2, 7), (4, 6), (4, 7)]:
+        for k, n in [(2, 6), (2, 7), (4, 6), (4, 7)]:
             G1 = hkn_harary_graph(k, n)
             G2 = nx.circulant_graph(n, list(range(1, k // 2 + 1)))
             assert is_isomorphic(G1, G2)
 
         # When k is odd and n is even, the hkn_harary_graph(k,n) is
         # the circulant_graph(n, list(range(1,(k+1)/2)) plus [n/2])
-        for (k, n) in [(3, 6), (5, 8), (7, 10)]:
+        for k, n in [(3, 6), (5, 8), (7, 10)]:
             G1 = hkn_harary_graph(k, n)
             L = list(range(1, (k + 1) // 2))
             L.append(n // 2)
@@ -111,7 +111,7 @@ class TestHararyGraph:
         # When k is odd and n is odd, the hkn_harary_graph(k,n) is
         # the circulant_graph(n, list(range(1,(k+1)/2))) with
         # n//2+1 edges added between node i and node i+n//2+1
-        for (k, n) in [(3, 5), (5, 9), (7, 11)]:
+        for k, n in [(3, 5), (5, 9), (7, 11)]:
             G1 = hkn_harary_graph(k, n)
             G2 = nx.circulant_graph(n, list(range(1, (k + 1) // 2)))
             eSet1 = set(G1.edges)

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_joint_degree_seq.py

@@ -80,7 +80,6 @@ def test_joint_degree_graph(ntimes=10):
 
 
 def test_is_valid_directed_joint_degree():
-
     in_degrees = [0, 1, 1, 2]
     out_degrees = [1, 1, 1, 1]
     nkk = {1: {1: 2, 2: 2}}
@@ -106,7 +105,6 @@ def test_is_valid_directed_joint_degree():
 
 def test_directed_joint_degree_graph(n=15, m=100, ntimes=1000):
     for _ in range(ntimes):
-
         # generate gnm random graph and calculate its joint degree.
         g = gnm_random_graph(n, m, None, directed=True)
 

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_lattice.py

@@ -159,7 +159,7 @@ class TestTriangularLatticeGraph:
             G = nx.triangular_lattice_graph(m, n)
             N = (n + 1) // 2
             assert len(G) == (m + 1) * (1 + N) - (n % 2) * ((m + 1) // 2)
-        for (i, j) in G.nodes():
+        for i, j in G.nodes():
             nbrs = G[(i, j)]
             if i < N:
                 assert (i + 1, j) in nbrs

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_line.py

@@ -1,7 +1,7 @@
 import pytest
 
 import networkx as nx
-import networkx.generators.line as line
+from networkx.generators import line
 from networkx.utils import edges_equal
 
 

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_random_graphs.py

@@ -65,7 +65,7 @@ def test_gnp_generators_edge_probability(generator, p, directed):
     edge_counts = [[0] * n for _ in range(n)]
     for i in range(runs):
         G = generator(n, p, directed=directed)
-        for (v, w) in G.edges:
+        for v, w in G.edges:
             edge_counts[v][w] += 1
             if not directed:
                 edge_counts[w][v] += 1
@@ -207,7 +207,6 @@ class TestGeneratorsRandom:
         initial_graph = nx.complete_graph(10)
 
         for seed in seeds:
-
             # This should be BA with m = m1
             BA1 = nx.barabasi_albert_graph(100, m1, seed)
             DBA1 = nx.dual_barabasi_albert_graph(100, m1, m2, 1, seed)

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_small.py

@@ -45,21 +45,21 @@ class TestGeneratorsSmall:
         G = nx.chvatal_graph()
         assert sorted(G) == list(range(12))
         assert G.number_of_edges() == 24
-        assert list(d for n, d in G.degree()) == 12 * [4]
+        assert [d for n, d in G.degree()] == 12 * [4]
         assert nx.diameter(G) == 2
         assert nx.radius(G) == 2
 
         G = nx.cubical_graph()
         assert sorted(G) == list(range(8))
         assert G.number_of_edges() == 12
-        assert list(d for n, d in G.degree()) == 8 * [3]
+        assert [d for n, d in G.degree()] == 8 * [3]
         assert nx.diameter(G) == 3
         assert nx.radius(G) == 3
 
         G = nx.desargues_graph()
         assert sorted(G) == list(range(20))
         assert G.number_of_edges() == 30
-        assert list(d for n, d in G.degree()) == 20 * [3]
+        assert [d for n, d in G.degree()] == 20 * [3]
 
         G = nx.diamond_graph()
         assert sorted(G) == list(range(4))
@@ -70,28 +70,28 @@ class TestGeneratorsSmall:
         G = nx.dodecahedral_graph()
         assert sorted(G) == list(range(20))
         assert G.number_of_edges() == 30
-        assert list(d for n, d in G.degree()) == 20 * [3]
+        assert [d for n, d in G.degree()] == 20 * [3]
         assert nx.diameter(G) == 5
         assert nx.radius(G) == 5
 
         G = nx.frucht_graph()
         assert sorted(G) == list(range(12))
         assert G.number_of_edges() == 18
-        assert list(d for n, d in G.degree()) == 12 * [3]
+        assert [d for n, d in G.degree()] == 12 * [3]
         assert nx.diameter(G) == 4
         assert nx.radius(G) == 3
 
         G = nx.heawood_graph()
         assert sorted(G) == list(range(14))
         assert G.number_of_edges() == 21
-        assert list(d for n, d in G.degree()) == 14 * [3]
+        assert [d for n, d in G.degree()] == 14 * [3]
         assert nx.diameter(G) == 3
         assert nx.radius(G) == 3
 
         G = nx.hoffman_singleton_graph()
         assert sorted(G) == list(range(50))
         assert G.number_of_edges() == 175
-        assert list(d for n, d in G.degree()) == 50 * [7]
+        assert [d for n, d in G.degree()] == 50 * [7]
         assert nx.diameter(G) == 2
         assert nx.radius(G) == 2
 
@@ -112,7 +112,7 @@ class TestGeneratorsSmall:
         G = nx.icosahedral_graph()
         assert sorted(G) == list(range(12))
         assert G.number_of_edges() == 30
-        assert list(d for n, d in G.degree()) == [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
+        assert [d for n, d in G.degree()] == [5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5]
         assert nx.diameter(G) == 3
         assert nx.radius(G) == 3
 
@@ -124,26 +124,26 @@ class TestGeneratorsSmall:
         G = nx.moebius_kantor_graph()
         assert sorted(G) == list(range(16))
         assert G.number_of_edges() == 24
-        assert list(d for n, d in G.degree()) == 16 * [3]
+        assert [d for n, d in G.degree()] == 16 * [3]
         assert nx.diameter(G) == 4
 
         G = nx.octahedral_graph()
         assert sorted(G) == list(range(6))
         assert G.number_of_edges() == 12
-        assert list(d for n, d in G.degree()) == 6 * [4]
+        assert [d for n, d in G.degree()] == 6 * [4]
         assert nx.diameter(G) == 2
         assert nx.radius(G) == 2
 
         G = nx.pappus_graph()
         assert sorted(G) == list(range(18))
         assert G.number_of_edges() == 27
-        assert list(d for n, d in G.degree()) == 18 * [3]
+        assert [d for n, d in G.degree()] == 18 * [3]
         assert nx.diameter(G) == 4
 
         G = nx.petersen_graph()
         assert sorted(G) == list(range(10))
         assert G.number_of_edges() == 15
-        assert list(d for n, d in G.degree()) == 10 * [3]
+        assert [d for n, d in G.degree()] == 10 * [3]
         assert nx.diameter(G) == 2
         assert nx.radius(G) == 2
 
@@ -155,24 +155,24 @@ class TestGeneratorsSmall:
         G = nx.tetrahedral_graph()
         assert sorted(G) == list(range(4))
         assert G.number_of_edges() == 6
-        assert list(d for n, d in G.degree()) == [3, 3, 3, 3]
+        assert [d for n, d in G.degree()] == [3, 3, 3, 3]
         assert nx.diameter(G) == 1
         assert nx.radius(G) == 1
 
         G = nx.truncated_cube_graph()
         assert sorted(G) == list(range(24))
         assert G.number_of_edges() == 36
-        assert list(d for n, d in G.degree()) == 24 * [3]
+        assert [d for n, d in G.degree()] == 24 * [3]
 
         G = nx.truncated_tetrahedron_graph()
         assert sorted(G) == list(range(12))
         assert G.number_of_edges() == 18
-        assert list(d for n, d in G.degree()) == 12 * [3]
+        assert [d for n, d in G.degree()] == 12 * [3]
 
         G = nx.tutte_graph()
         assert sorted(G) == list(range(46))
         assert G.number_of_edges() == 69
-        assert list(d for n, d in G.degree()) == 46 * [3]
+        assert [d for n, d in G.degree()] == 46 * [3]
 
         # Test create_using with directed or multigraphs on small graphs
         pytest.raises(nx.NetworkXError, nx.tutte_graph, create_using=nx.DiGraph)

.CondaPkg/env/Lib/site-packages/networkx/generators/tests/test_stochastic.py

@@ -43,7 +43,7 @@ class TestStochasticGraph:
         G = nx.MultiDiGraph()
         G.add_edges_from([(0, 1), (0, 1), (0, 2), (0, 2)])
         S = nx.stochastic_graph(G)
-        d = dict(weight=0.25)
+        d = {"weight": 0.25}
         assert sorted(S.edges(data=True)) == [
             (0, 1, d),
             (0, 1, d),

.CondaPkg/env/Lib/site-packages/networkx/generators/trees.py

@@ -323,29 +323,29 @@ def random_tree(n, seed=None, create_using=None):
     Examples
     --------
     >>> tree = nx.random_tree(n=10, seed=0)
-    >>> print(nx.forest_str(tree, sources=[0]))
+    >>> nx.write_network_text(tree, sources=[0])
     ╙── 0
         ├── 3
         └── 4
             ├── 6
-            │   ├── 1
-            │   ├── 2
-            │   └── 7
-            │       └── 8
-            │           └── 5
+            │   ├── 1
+            │   ├── 2
+            │   └── 7
+            │       └── 8
+            │           └── 5
             └── 9
 
     >>> tree = nx.random_tree(n=10, seed=0, create_using=nx.DiGraph)
-    >>> print(nx.forest_str(tree))
+    >>> nx.write_network_text(tree)
     ╙── 0
         ├─╼ 3
         └─╼ 4
             ├─╼ 6
-            │   ├─╼ 1
-            │   ├─╼ 2
-            │   └─╼ 7
-            │       └─╼ 8
-            │           └─╼ 5
+            │   ├─╼ 1
+            │   ├─╼ 2
+            │   └─╼ 7
+            │       └─╼ 8
+            │           └─╼ 5
             └─╼ 9
     """
     if n == 0: