update

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

@@ -0,0 +1,33 @@
+"""
+A package for generating various graphs in networkx.
+
+"""
+from networkx.generators.atlas import *
+from networkx.generators.classic import *
+from networkx.generators.cographs import *
+from networkx.generators.community import *
+from networkx.generators.degree_seq import *
+from networkx.generators.directed import *
+from networkx.generators.duplication import *
+from networkx.generators.ego import *
+from networkx.generators.expanders import *
+from networkx.generators.geometric import *
+from networkx.generators.harary_graph import *
+from networkx.generators.internet_as_graphs import *
+from networkx.generators.intersection import *
+from networkx.generators.interval_graph import *
+from networkx.generators.joint_degree_seq import *
+from networkx.generators.lattice import *
+from networkx.generators.line import *
+from networkx.generators.mycielski import *
+from networkx.generators.nonisomorphic_trees import *
+from networkx.generators.random_clustered import *
+from networkx.generators.random_graphs import *
+from networkx.generators.small import *
+from networkx.generators.social import *
+from networkx.generators.spectral_graph_forge import *
+from networkx.generators.stochastic import *
+from networkx.generators.sudoku import *
+from networkx.generators.time_series import *
+from networkx.generators.trees import *
+from networkx.generators.triads import *

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

@@ -0,0 +1,179 @@
+"""
+Generators for the small graph atlas.
+"""
+import gzip
+import importlib.resources
+import os
+import os.path
+from itertools import islice
+
+import networkx as nx
+
+__all__ = ["graph_atlas", "graph_atlas_g"]
+
+#: The total number of graphs in the atlas.
+#:
+#: The graphs are labeled starting from 0 and extending to (but not
+#: including) this number.
+NUM_GRAPHS = 1253
+
+#: The path to the data file containing the graph edge lists.
+#:
+#: This is the absolute path of the gzipped text file containing the
+#: edge list for each graph in the atlas. The file contains one entry
+#: per graph in the atlas, in sequential order, starting from graph
+#: number 0 and extending through graph number 1252 (see
+#: :data:`NUM_GRAPHS`). Each entry looks like
+#:
+#: .. sourcecode:: text
+#:
+#:    GRAPH 6
+#:    NODES 3
+#:    0 1
+#:    0 2
+#:
+#: where the first two lines are the graph's index in the atlas and the
+#: number of nodes in the graph, and the remaining lines are the edge
+#: list.
+#:
+#: This file was generated from a Python list of graphs via code like
+#: the following::
+#:
+#:     import gzip
+#:     from networkx.generators.atlas import graph_atlas_g
+#:     from networkx.readwrite.edgelist import write_edgelist
+#:
+#:     with gzip.open('atlas.dat.gz', 'wb') as f:
+#:         for i, G in enumerate(graph_atlas_g()):
+#:             f.write(bytes(f'GRAPH {i}\n', encoding='utf-8'))
+#:             f.write(bytes(f'NODES {len(G)}\n', encoding='utf-8'))
+#:             write_edgelist(G, f, data=False)
+#:
+
+# Path to the atlas file
+ATLAS_FILE = importlib.resources.files("networkx.generators") / "atlas.dat.gz"
+
+
+def _generate_graphs():
+    """Sequentially read the file containing the edge list data for the
+    graphs in the atlas and generate the graphs one at a time.
+
+    This function reads the file given in :data:`.ATLAS_FILE`.
+
+    """
+    with gzip.open(ATLAS_FILE, "rb") as f:
+        line = f.readline()
+        while line and line.startswith(b"GRAPH"):
+            # The first two lines of each entry tell us the index of the
+            # graph in the list and the number of nodes in the graph.
+            # They look like this:
+            #
+            #     GRAPH 3
+            #     NODES 2
+            #
+            graph_index = int(line[6:].rstrip())
+            line = f.readline()
+            num_nodes = int(line[6:].rstrip())
+            # The remaining lines contain the edge list, until the next
+            # GRAPH line (or until the end of the file).
+            edgelist = []
+            line = f.readline()
+            while line and not line.startswith(b"GRAPH"):
+                edgelist.append(line.rstrip())
+                line = f.readline()
+            G = nx.Graph()
+            G.name = f"G{graph_index}"
+            G.add_nodes_from(range(num_nodes))
+            G.add_edges_from(tuple(map(int, e.split())) for e in edgelist)
+            yield G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas(i):
+    """Returns graph number `i` from the Graph Atlas.
+
+    For more information, see :func:`.graph_atlas_g`.
+
+    Parameters
+    ----------
+    i : int
+        The index of the graph from the atlas to get. The graph at index
+        0 is assumed to be the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    See also
+    --------
+    graph_atlas_g
+
+    Notes
+    -----
+    The time required by this function increases linearly with the
+    argument `i`, since it reads a large file sequentially in order to
+    generate the graph [1]_.
+
+    References
+    ----------
+    .. [1] Ronald C. Read and Robin J. Wilson, *An Atlas of Graphs*.
+           Oxford University Press, 1998.
+
+    """
+    if not (0 <= i < NUM_GRAPHS):
+        raise ValueError(f"index must be between 0 and {NUM_GRAPHS}")
+    return next(islice(_generate_graphs(), i, None))
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def graph_atlas_g():
+    """Returns the list of all graphs with up to seven nodes named in the
+    Graph Atlas.
+
+    The graphs are listed in increasing order by
+
+    1. number of nodes,
+    2. number of edges,
+    3. degree sequence (for example 111223 < 112222),
+    4. number of automorphisms,
+
+    in that order, with three exceptions as described in the *Notes*
+    section below. This causes the list to correspond with the index of
+    the graphs in the Graph Atlas [atlas]_, with the first graph,
+    ``G[0]``, being the null graph.
+
+    Returns
+    -------
+    list
+        A list of :class:`~networkx.Graph` objects, the one at index *i*
+        corresponding to the graph *i* in the Graph Atlas.
+
+    See also
+    --------
+    graph_atlas
+
+    Notes
+    -----
+    This function may be expensive in both time and space, since it
+    reads a large file sequentially in order to populate the list.
+
+    Although the NetworkX atlas functions match the order of graphs
+    given in the "Atlas of Graphs" book, there are (at least) three
+    errors in the ordering described in the book. The following three
+    pairs of nodes violate the lexicographically nondecreasing sorted
+    degree sequence rule:
+
+    - graphs 55 and 56 with degree sequences 001111 and 000112,
+    - graphs 1007 and 1008 with degree sequences 3333444 and 3333336,
+    - graphs 1012 and 1213 with degree sequences 1244555 and 1244456.
+
+    References
+    ----------
+    .. [atlas] Ronald C. Read and Robin J. Wilson,
+               *An Atlas of Graphs*.
+               Oxford University Press, 1998.
+
+    """
+    return list(_generate_graphs())

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

@@ -0,0 +1,67 @@
+r"""Generators for cographs
+
+A cograph is a graph containing no path on four vertices.
+Cographs or $P_4$-free graphs can be obtained from a single vertex
+by disjoint union and complementation operations.
+
+References
+----------
+.. [0] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+    "Complement reducible graphs",
+    Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+    ISSN 0166-218X.
+"""
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_cograph"]
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_cograph(n, seed=None):
+    r"""Returns a random cograph with $2 ^ n$ nodes.
+
+    A cograph is a graph containing no path on four vertices.
+    Cographs or $P_4$-free graphs can be obtained from a single vertex
+    by disjoint union and complementation operations.
+
+    This generator starts off from a single vertex and performs disjoint
+    union and full join operations on itself.
+    The decision on which operation will take place is random.
+
+    Parameters
+    ----------
+    n : int
+        The order of the cograph.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : A random graph containing no path on four vertices.
+
+    See Also
+    --------
+    full_join
+    union
+
+    References
+    ----------
+    .. [1] D.G. Corneil, H. Lerchs, L.Stewart Burlingham,
+       "Complement reducible graphs",
+       Discrete Applied Mathematics, Volume 3, Issue 3, 1981, Pages 163-174,
+       ISSN 0166-218X.
+    """
+    R = nx.empty_graph(1)
+
+    for i in range(n):
+        RR = nx.relabel_nodes(R.copy(), lambda x: x + len(R))
+
+        if seed.randint(0, 1) == 0:
+            R = nx.full_join(R, RR)
+        else:
+            R = nx.disjoint_union(R, RR)
+
+    return R

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

@@ -0,0 +1,868 @@
+"""Generate graphs with a given degree sequence or expected degree sequence.
+"""
+
+import heapq
+import math
+from itertools import chain, combinations, zip_longest
+from operator import itemgetter
+
+import networkx as nx
+from networkx.utils import py_random_state, random_weighted_sample
+
+__all__ = [
+    "configuration_model",
+    "directed_configuration_model",
+    "expected_degree_graph",
+    "havel_hakimi_graph",
+    "directed_havel_hakimi_graph",
+    "degree_sequence_tree",
+    "random_degree_sequence_graph",
+]
+
+chaini = chain.from_iterable
+
+
+def _to_stublist(degree_sequence):
+    """Returns a list of degree-repeated node numbers.
+
+    ``degree_sequence`` is a list of nonnegative integers representing
+    the degrees of nodes in a graph.
+
+    This function returns a list of node numbers with multiplicities
+    according to the given degree sequence. For example, if the first
+    element of ``degree_sequence`` is ``3``, then the first node number,
+    ``0``, will appear at the head of the returned list three times. The
+    node numbers are assumed to be the numbers zero through
+    ``len(degree_sequence) - 1``.
+
+    Examples
+    --------
+
+    >>> degree_sequence = [1, 2, 3]
+    >>> _to_stublist(degree_sequence)
+    [0, 1, 1, 2, 2, 2]
+
+    If a zero appears in the sequence, that means the node exists but
+    has degree zero, so that number will be skipped in the returned
+    list::
+
+    >>> degree_sequence = [2, 0, 1]
+    >>> _to_stublist(degree_sequence)
+    [0, 0, 2]
+
+    """
+    return list(chaini([n] * d for n, d in enumerate(degree_sequence)))
+
+
+def _configuration_model(
+    deg_sequence, create_using, directed=False, in_deg_sequence=None, seed=None
+):
+    """Helper function for generating either undirected or directed
+    configuration model graphs.
+
+    ``deg_sequence`` is a list of nonnegative integers representing the
+    degree of the node whose label is the index of the list element.
+
+    ``create_using`` see :func:`~networkx.empty_graph`.
+
+    ``directed`` and ``in_deg_sequence`` are required if you want the
+    returned graph to be generated using the directed configuration
+    model algorithm. If ``directed`` is ``False``, then ``deg_sequence``
+    is interpreted as the degree sequence of an undirected graph and
+    ``in_deg_sequence`` is ignored. Otherwise, if ``directed`` is
+    ``True``, then ``deg_sequence`` is interpreted as the out-degree
+    sequence and ``in_deg_sequence`` as the in-degree sequence of a
+    directed graph.
+
+    .. note::
+
+       ``deg_sequence`` and ``in_deg_sequence`` need not be the same
+       length.
+
+    ``seed`` is a random.Random or numpy.random.RandomState instance
+
+    This function returns a graph, directed if and only if ``directed``
+    is ``True``, generated according to the configuration model
+    algorithm. For more information on the algorithm, see the
+    :func:`configuration_model` or :func:`directed_configuration_model`
+    functions.
+
+    """
+    n = len(deg_sequence)
+    G = nx.empty_graph(n, create_using)
+    # If empty, return the null graph immediately.
+    if n == 0:
+        return G
+    # Build a list of available degree-repeated nodes.  For example,
+    # for degree sequence [3, 2, 1, 1, 1], the "stub list" is
+    # initially [0, 0, 0, 1, 1, 2, 3, 4], that is, node 0 has degree
+    # 3 and thus is repeated 3 times, etc.
+    #
+    # Also, shuffle the stub list in order to get a random sequence of
+    # node pairs.
+    if directed:
+        pairs = zip_longest(deg_sequence, in_deg_sequence, fillvalue=0)
+        # Unzip the list of pairs into a pair of lists.
+        out_deg, in_deg = zip(*pairs)
+
+        out_stublist = _to_stublist(out_deg)
+        in_stublist = _to_stublist(in_deg)
+
+        seed.shuffle(out_stublist)
+        seed.shuffle(in_stublist)
+    else:
+        stublist = _to_stublist(deg_sequence)
+        # Choose a random balanced bipartition of the stublist, which
+        # gives a random pairing of nodes. In this implementation, we
+        # shuffle the list and then split it in half.
+        n = len(stublist)
+        half = n // 2
+        seed.shuffle(stublist)
+        out_stublist, in_stublist = stublist[:half], stublist[half:]
+    G.add_edges_from(zip(out_stublist, in_stublist))
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def configuration_model(deg_sequence, create_using=None, seed=None):
+    """Returns a random graph with the given degree sequence.
+
+    The configuration model generates a random pseudograph (graph with
+    parallel edges and self loops) by randomly assigning edges to
+    match the given degree sequence.
+
+    Parameters
+    ----------
+    deg_sequence :  list of nonnegative integers
+        Each list entry corresponds to the degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequence does not have an even sum.
+
+    See Also
+    --------
+    is_graphical
+
+    Notes
+    -----
+    As described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequence does not have an even sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.
+
+    The density of self-loops and parallel edges tends to decrease as
+    the number of nodes increases. However, typically the number of
+    self-loops will approach a Poisson distribution with a nonzero mean,
+    and similarly for the number of parallel edges.  Consider a node
+    with *k* stubs. The probability of being joined to another stub of
+    the same node is basically (*k* - *1*) / *N*, where *k* is the
+    degree and *N* is the number of nodes. So the probability of a
+    self-loop scales like *c* / *N* for some constant *c*. As *N* grows,
+    this means we expect *c* self-loops. Similarly for parallel edges.
+
+    References
+    ----------
+    .. [1] M.E.J. Newman, "The structure and function of complex networks",
+       SIAM REVIEW 45-2, pp 167-256, 2003.
+
+    Examples
+    --------
+    You can create a degree sequence following a particular distribution
+    by using the one of the distribution functions in
+    :mod:`~networkx.utils.random_sequence` (or one of your own). For
+    example, to create an undirected multigraph on one hundred nodes
+    with degree sequence chosen from the power law distribution:
+
+    >>> sequence = nx.random_powerlaw_tree_sequence(100, tries=5000)
+    >>> G = nx.configuration_model(sequence)
+    >>> len(G)
+    100
+    >>> actual_degrees = [d for v, d in G.degree()]
+    >>> actual_degrees == sequence
+    True
+
+    The returned graph is a multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> G = nx.Graph(G)
+
+    Similarly, to remove self-loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    if sum(deg_sequence) % 2 != 0:
+        msg = "Invalid degree sequence: sum of degrees must be even, not odd"
+        raise nx.NetworkXError(msg)
+
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXNotImplemented("not implemented for directed graphs")
+
+    G = _configuration_model(deg_sequence, G, seed=seed)
+
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_configuration_model(
+    in_degree_sequence, out_degree_sequence, create_using=None, seed=None
+):
+    """Returns a directed_random graph with the given degree sequences.
+
+    The configuration model generates a random directed pseudograph
+    (graph with parallel edges and self loops) by randomly assigning
+    edges to match the given degree sequences.
+
+    Parameters
+    ----------
+    in_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the in-degree of a node.
+    out_degree_sequence :  list of nonnegative integers
+       Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiDiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiDiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences do not have the same sum.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Newman [1]_.
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the degree
+    sequences does not have the same sum.
+
+    This configuration model construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Newman, M. E. J. and Strogatz, S. H. and Watts, D. J.
+       Random graphs with arbitrary degree distributions and their applications
+       Phys. Rev. E, 64, 026118 (2001)
+
+    Examples
+    --------
+    One can modify the in- and out-degree sequences from an existing
+    directed graph in order to create a new directed graph. For example,
+    here we modify the directed path graph:
+
+    >>> D = nx.DiGraph([(0, 1), (1, 2), (2, 3)])
+    >>> din = list(d for n, d in D.in_degree())
+    >>> dout = list(d for n, d in D.out_degree())
+    >>> din.append(1)
+    >>> dout[0] = 2
+    >>> # We now expect an edge from node 0 to a new node, node 3.
+    ... D = nx.directed_configuration_model(din, dout)
+
+    The returned graph is a directed multigraph, which may have parallel
+    edges. To remove any parallel edges from the returned graph:
+
+    >>> D = nx.DiGraph(D)
+
+    Similarly, to remove self-loops:
+
+    >>> D.remove_edges_from(nx.selfloop_edges(D))
+
+    """
+    if sum(in_degree_sequence) != sum(out_degree_sequence):
+        msg = "Invalid degree sequences: sequences must have equal sums"
+        raise nx.NetworkXError(msg)
+
+    if create_using is None:
+        create_using = nx.MultiDiGraph
+
+    G = _configuration_model(
+        out_degree_sequence,
+        create_using,
+        directed=True,
+        in_deg_sequence=in_degree_sequence,
+        seed=seed,
+    )
+
+    name = "directed configuration_model {} nodes {} edges"
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def expected_degree_graph(w, seed=None, selfloops=True):
+    r"""Returns a random graph with given expected degrees.
+
+    Given a sequence of expected degrees $W=(w_0,w_1,\ldots,w_{n-1})$
+    of length $n$ this algorithm assigns an edge between node $u$ and
+    node $v$ with probability
+
+    .. math::
+
+       p_{uv} = \frac{w_u w_v}{\sum_k w_k} .
+
+    Parameters
+    ----------
+    w : list
+        The list of expected degrees.
+    selfloops: bool (default=True)
+        Set to False to remove the possibility of self-loop edges.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    Graph
+
+    Examples
+    --------
+    >>> z = [10 for i in range(100)]
+    >>> G = nx.expected_degree_graph(z)
+
+    Notes
+    -----
+    The nodes have integer labels corresponding to index of expected degrees
+    input sequence.
+
+    The complexity of this algorithm is $\mathcal{O}(n+m)$ where $n$ is the
+    number of nodes and $m$ is the expected number of edges.
+
+    The model in [1]_ includes the possibility of self-loop edges.
+    Set selfloops=False to produce a graph without self loops.
+
+    For finite graphs this model doesn't produce exactly the given
+    expected degree sequence.  Instead the expected degrees are as
+    follows.
+
+    For the case without self loops (selfloops=False),
+
+    .. math::
+
+       E[deg(u)] = \sum_{v \ne u} p_{uv}
+                = w_u \left( 1 - \frac{w_u}{\sum_k w_k} \right) .
+
+
+    NetworkX uses the standard convention that a self-loop edge counts 2
+    in the degree of a node, so with self loops (selfloops=True),
+
+    .. math::
+
+       E[deg(u)] =  \sum_{v \ne u} p_{uv}  + 2 p_{uu}
+                = w_u \left( 1 + \frac{w_u}{\sum_k w_k} \right) .
+
+    References
+    ----------
+    .. [1] Fan Chung and L. Lu, Connected components in random graphs with
+       given expected degree sequences, Ann. Combinatorics, 6,
+       pp. 125-145, 2002.
+    .. [2] Joel Miller and Aric Hagberg,
+       Efficient generation of networks with given expected degrees,
+       in Algorithms and Models for the Web-Graph (WAW 2011),
+       Alan Frieze, Paul Horn, and Paweł Prałat (Eds), LNCS 6732,
+       pp. 115-126, 2011.
+    """
+    n = len(w)
+    G = nx.empty_graph(n)
+
+    # If there are no nodes are no edges in the graph, return the empty graph.
+    if n == 0 or max(w) == 0:
+        return G
+
+    rho = 1 / sum(w)
+    # Sort the weights in decreasing order. The original order of the
+    # weights dictates the order of the (integer) node labels, so we
+    # need to remember the permutation applied in the sorting.
+    order = sorted(enumerate(w), key=itemgetter(1), reverse=True)
+    mapping = {c: u for c, (u, v) in enumerate(order)}
+    seq = [v for u, v in order]
+    last = n
+    if not selfloops:
+        last -= 1
+    for u in range(last):
+        v = u
+        if not selfloops:
+            v += 1
+        factor = seq[u] * rho
+        p = min(seq[v] * factor, 1)
+        while v < n and p > 0:
+            if p != 1:
+                r = seed.random()
+                v += math.floor(math.log(r, 1 - p))
+            if v < n:
+                q = min(seq[v] * factor, 1)
+                if seed.random() < q / p:
+                    G.add_edge(mapping[u], mapping[v])
+                v += 1
+                p = q
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def havel_hakimi_graph(deg_sequence, create_using=None):
+    """Returns a simple graph with given degree sequence constructed
+    using the Havel-Hakimi algorithm.
+
+    Parameters
+    ----------
+    deg_sequence: list of integers
+        Each integer corresponds to the degree of a node (need not be sorted).
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Directed graphs are not allowed.
+
+    Raises
+    ------
+    NetworkXException
+        For a non-graphical degree sequence (i.e. one
+        not realizable by some simple graph).
+
+    Notes
+    -----
+    The Havel-Hakimi algorithm constructs a simple graph by
+    successively connecting the node of highest degree to other nodes
+    of highest degree, resorting remaining nodes by degree, and
+    repeating the process. The resulting graph has a high
+    degree-associativity.  Nodes are labeled 1,.., len(deg_sequence),
+    corresponding to their position in deg_sequence.
+
+    The basic algorithm is from Hakimi [1]_ and was generalized by
+    Kleitman and Wang [2]_.
+
+    References
+    ----------
+    .. [1] Hakimi S., On Realizability of a Set of Integers as
+       Degrees of the Vertices of a Linear Graph. I,
+       Journal of SIAM, 10(3), pp. 496-506 (1962)
+    .. [2] Kleitman D.J. and Wang D.L.
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors  Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    if not nx.is_graphical(deg_sequence):
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    p = len(deg_sequence)
+    G = nx.empty_graph(p, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed graphs are not supported")
+    num_degs = [[] for i in range(p)]
+    dmax, dsum, n = 0, 0, 0
+    for d in deg_sequence:
+        # Process only the non-zero integers
+        if d > 0:
+            num_degs[d].append(n)
+            dmax, dsum, n = max(dmax, d), dsum + d, n + 1
+    # Return graph if no edges
+    if n == 0:
+        return G
+
+    modstubs = [(0, 0)] * (dmax + 1)
+    # Successively reduce degree sequence by removing the maximum degree
+    while n > 0:
+        # Retrieve the maximum degree in the sequence
+        while len(num_degs[dmax]) == 0:
+            dmax -= 1
+        # If there are not enough stubs to connect to, then the sequence is
+        # not graphical
+        if dmax > n - 1:
+            raise nx.NetworkXError("Non-graphical integer sequence")
+
+        # Remove largest stub in list
+        source = num_degs[dmax].pop()
+        n -= 1
+        # Reduce the next dmax largest stubs
+        mslen = 0
+        k = dmax
+        for i in range(dmax):
+            while len(num_degs[k]) == 0:
+                k -= 1
+            target = num_degs[k].pop()
+            G.add_edge(source, target)
+            n -= 1
+            if k > 1:
+                modstubs[mslen] = (k - 1, target)
+                mslen += 1
+        # Add back to the list any nonzero stubs that were removed
+        for i in range(mslen):
+            (stubval, stubtarget) = modstubs[i]
+            num_degs[stubval].append(stubtarget)
+            n += 1
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_havel_hakimi_graph(in_deg_sequence, out_deg_sequence, create_using=None):
+    """Returns a directed graph with the given degree sequences.
+
+    Parameters
+    ----------
+    in_deg_sequence :  list of integers
+        Each list entry corresponds to the in-degree of a node.
+    out_deg_sequence : list of integers
+        Each list entry corresponds to the out-degree of a node.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : DiGraph
+        A graph with the specified degree sequences.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in deg_sequence
+
+    Raises
+    ------
+    NetworkXError
+        If the degree sequences are not digraphical.
+
+    See Also
+    --------
+    configuration_model
+
+    Notes
+    -----
+    Algorithm as described by Kleitman and Wang [1]_.
+
+    References
+    ----------
+    .. [1] D.J. Kleitman and D.L. Wang
+       Algorithms for Constructing Graphs and Digraphs with Given Valences
+       and Factors Discrete Mathematics, 6(1), pp. 79-88 (1973)
+    """
+    in_deg_sequence = nx.utils.make_list_of_ints(in_deg_sequence)
+    out_deg_sequence = nx.utils.make_list_of_ints(out_deg_sequence)
+
+    # Process the sequences and form two heaps to store degree pairs with
+    # either zero or nonzero out degrees
+    sumin, sumout = 0, 0
+    nin, nout = len(in_deg_sequence), len(out_deg_sequence)
+    maxn = max(nin, nout)
+    G = nx.empty_graph(maxn, create_using, default=nx.DiGraph)
+    if maxn == 0:
+        return G
+    maxin = 0
+    stubheap, zeroheap = [], []
+    for n in range(maxn):
+        in_deg, out_deg = 0, 0
+        if n < nout:
+            out_deg = out_deg_sequence[n]
+        if n < nin:
+            in_deg = in_deg_sequence[n]
+        if in_deg < 0 or out_deg < 0:
+            raise nx.NetworkXError(
+                "Invalid degree sequences. Sequence values must be positive."
+            )
+        sumin, sumout, maxin = sumin + in_deg, sumout + out_deg, max(maxin, in_deg)
+        if in_deg > 0:
+            stubheap.append((-1 * out_deg, -1 * in_deg, n))
+        elif out_deg > 0:
+            zeroheap.append((-1 * out_deg, n))
+    if sumin != sumout:
+        raise nx.NetworkXError(
+            "Invalid degree sequences. Sequences must have equal sums."
+        )
+    heapq.heapify(stubheap)
+    heapq.heapify(zeroheap)
+
+    modstubs = [(0, 0, 0)] * (maxin + 1)
+    # Successively reduce degree sequence by removing the maximum
+    while stubheap:
+        # Remove first value in the sequence with a non-zero in degree
+        (freeout, freein, target) = heapq.heappop(stubheap)
+        freein *= -1
+        if freein > len(stubheap) + len(zeroheap):
+            raise nx.NetworkXError("Non-digraphical integer sequence")
+
+        # Attach arcs from the nodes with the most stubs
+        mslen = 0
+        for i in range(freein):
+            if zeroheap and (not stubheap or stubheap[0][0] > zeroheap[0][0]):
+                (stubout, stubsource) = heapq.heappop(zeroheap)
+                stubin = 0
+            else:
+                (stubout, stubin, stubsource) = heapq.heappop(stubheap)
+            if stubout == 0:
+                raise nx.NetworkXError("Non-digraphical integer sequence")
+            G.add_edge(stubsource, target)
+            # Check if source is now totally connected
+            if stubout + 1 < 0 or stubin < 0:
+                modstubs[mslen] = (stubout + 1, stubin, stubsource)
+                mslen += 1
+
+        # Add the nodes back to the heaps that still have available stubs
+        for i in range(mslen):
+            stub = modstubs[i]
+            if stub[1] < 0:
+                heapq.heappush(stubheap, stub)
+            else:
+                heapq.heappush(zeroheap, (stub[0], stub[2]))
+        if freeout < 0:
+            heapq.heappush(zeroheap, (freeout, target))
+
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def degree_sequence_tree(deg_sequence, create_using=None):
+    """Make a tree for the given degree sequence.
+
+    A tree has #nodes-#edges=1 so
+    the degree sequence must have
+    len(deg_sequence)-sum(deg_sequence)/2=1
+    """
+    # The sum of the degree sequence must be even (for any undirected graph).
+    degree_sum = sum(deg_sequence)
+    if degree_sum % 2 != 0:
+        msg = "Invalid degree sequence: sum of degrees must be even, not odd"
+        raise nx.NetworkXError(msg)
+    if len(deg_sequence) - degree_sum // 2 != 1:
+        msg = (
+            "Invalid degree sequence: tree must have number of nodes equal"
+            " to one less than the number of edges"
+        )
+        raise nx.NetworkXError(msg)
+    G = nx.empty_graph(0, create_using)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    # Sort all degrees greater than 1 in decreasing order.
+    #
+    # TODO Does this need to be sorted in reverse order?
+    deg = sorted((s for s in deg_sequence if s > 1), reverse=True)
+
+    # make path graph as backbone
+    n = len(deg) + 2
+    nx.add_path(G, range(n))
+    last = n
+
+    # add the leaves
+    for source in range(1, n - 1):
+        nedges = deg.pop() - 2
+        for target in range(last, last + nedges):
+            G.add_edge(source, target)
+        last += nedges
+
+    # in case we added one too many
+    if len(G) > len(deg_sequence):
+        G.remove_node(0)
+    return G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_degree_sequence_graph(sequence, seed=None, tries=10):
+    r"""Returns a simple random graph with the given degree sequence.
+
+    If the maximum degree $d_m$ in the sequence is $O(m^{1/4})$ then the
+    algorithm produces almost uniform random graphs in $O(m d_m)$ time
+    where $m$ is the number of edges.
+
+    Parameters
+    ----------
+    sequence :  list of integers
+        Sequence of degrees
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    tries : int, optional
+        Maximum number of tries to create a graph
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified degree sequence.
+        Nodes are labeled starting at 0 with an index
+        corresponding to the position in the sequence.
+
+    Raises
+    ------
+    NetworkXUnfeasible
+        If the degree sequence is not graphical.
+    NetworkXError
+        If a graph is not produced in specified number of tries
+
+    See Also
+    --------
+    is_graphical, configuration_model
+
+    Notes
+    -----
+    The generator algorithm [1]_ is not guaranteed to produce a graph.
+
+    References
+    ----------
+    .. [1] Moshen Bayati, Jeong Han Kim, and Amin Saberi,
+       A sequential algorithm for generating random graphs.
+       Algorithmica, Volume 58, Number 4, 860-910,
+       DOI: 10.1007/s00453-009-9340-1
+
+    Examples
+    --------
+    >>> sequence = [1, 2, 2, 3]
+    >>> G = nx.random_degree_sequence_graph(sequence, seed=42)
+    >>> sorted(d for n, d in G.degree())
+    [1, 2, 2, 3]
+    """
+    DSRG = DegreeSequenceRandomGraph(sequence, seed)
+    for try_n in range(tries):
+        try:
+            return DSRG.generate()
+        except nx.NetworkXUnfeasible:
+            pass
+    raise nx.NetworkXError(f"failed to generate graph in {tries} tries")
+
+
+class DegreeSequenceRandomGraph:
+    # class to generate random graphs with a given degree sequence
+    # use random_degree_sequence_graph()
+    def __init__(self, degree, rng):
+        if not nx.is_graphical(degree):
+            raise nx.NetworkXUnfeasible("degree sequence is not graphical")
+        self.rng = rng
+        self.degree = list(degree)
+        # node labels are integers 0,...,n-1
+        self.m = sum(self.degree) / 2.0  # number of edges
+        try:
+            self.dmax = max(self.degree)  # maximum degree
+        except ValueError:
+            self.dmax = 0
+
+    def generate(self):
+        # remaining_degree is mapping from int->remaining degree
+        self.remaining_degree = dict(enumerate(self.degree))
+        # add all nodes to make sure we get isolated nodes
+        self.graph = nx.Graph()
+        self.graph.add_nodes_from(self.remaining_degree)
+        # remove zero degree nodes
+        for n, d in list(self.remaining_degree.items()):
+            if d == 0:
+                del self.remaining_degree[n]
+        if len(self.remaining_degree) > 0:
+            # build graph in three phases according to how many unmatched edges
+            self.phase1()
+            self.phase2()
+            self.phase3()
+        return self.graph
+
+    def update_remaining(self, u, v, aux_graph=None):
+        # decrement remaining nodes, modify auxiliary graph if in phase3
+        if aux_graph is not None:
+            # remove edges from auxiliary graph
+            aux_graph.remove_edge(u, v)
+        if self.remaining_degree[u] == 1:
+            del self.remaining_degree[u]
+            if aux_graph is not None:
+                aux_graph.remove_node(u)
+        else:
+            self.remaining_degree[u] -= 1
+        if self.remaining_degree[v] == 1:
+            del self.remaining_degree[v]
+            if aux_graph is not None:
+                aux_graph.remove_node(v)
+        else:
+            self.remaining_degree[v] -= 1
+
+    def p(self, u, v):
+        # degree probability
+        return 1 - self.degree[u] * self.degree[v] / (4.0 * self.m)
+
+    def q(self, u, v):
+        # remaining degree probability
+        norm = max(self.remaining_degree.values()) ** 2
+        return self.remaining_degree[u] * self.remaining_degree[v] / norm
+
+    def suitable_edge(self):
+        """Returns True if and only if an arbitrary remaining node can
+        potentially be joined with some other remaining node.
+
+        """
+        nodes = iter(self.remaining_degree)
+        u = next(nodes)
+        return any(v not in self.graph[u] for v in nodes)
+
+    def phase1(self):
+        # choose node pairs from (degree) weighted distribution
+        rem_deg = self.remaining_degree
+        while sum(rem_deg.values()) >= 2 * self.dmax**2:
+            u, v = sorted(random_weighted_sample(rem_deg, 2, self.rng))
+            if self.graph.has_edge(u, v):
+                continue
+            if self.rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase2(self):
+        # choose remaining nodes uniformly at random and use rejection sampling
+        remaining_deg = self.remaining_degree
+        rng = self.rng
+        while len(remaining_deg) >= 2 * self.dmax:
+            while True:
+                u, v = sorted(rng.sample(list(remaining_deg.keys()), 2))
+                if self.graph.has_edge(u, v):
+                    continue
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v)
+
+    def phase3(self):
+        # build potential remaining edges and choose with rejection sampling
+        potential_edges = combinations(self.remaining_degree, 2)
+        # build auxiliary graph of potential edges not already in graph
+        H = nx.Graph(
+            [(u, v) for (u, v) in potential_edges if not self.graph.has_edge(u, v)]
+        )
+        rng = self.rng
+        while self.remaining_degree:
+            if not self.suitable_edge():
+                raise nx.NetworkXUnfeasible("no suitable edges left")
+            while True:
+                u, v = sorted(rng.choice(list(H.edges())))
+                if rng.random() < self.q(u, v):
+                    break
+            if rng.random() < self.p(u, v):  # accept edge
+                self.graph.add_edge(u, v)
+                self.update_remaining(u, v, aux_graph=H)

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

@@ -0,0 +1,501 @@
+"""
+Generators for some directed graphs, including growing network (GN) graphs and
+scale-free graphs.
+
+"""
+
+import numbers
+from collections import Counter
+
+import networkx as nx
+from networkx.generators.classic import empty_graph
+from networkx.utils import discrete_sequence, py_random_state, weighted_choice
+
+__all__ = [
+    "gn_graph",
+    "gnc_graph",
+    "gnr_graph",
+    "random_k_out_graph",
+    "scale_free_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gn_graph(n, kernel=None, create_using=None, seed=None):
+    """Returns the growing network (GN) digraph with `n` nodes.
+
+    The GN graph is built by adding nodes one at a time with a link to one
+    previously added node.  The target node for the link is chosen with
+    probability based on degree.  The default attachment kernel is a linear
+    function of the degree of a node.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    kernel : function
+        The attachment kernel.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GN graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gn_graph(10)  # the GN graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    To specify an attachment kernel, use the `kernel` keyword argument::
+
+    >>> D = nx.gn_graph(10, kernel=lambda x: x**1.5)  # A_k = k^1.5
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if kernel is None:
+
+        def kernel(x):
+            return x
+
+    if n == 1:
+        return G
+
+    G.add_edge(1, 0)  # get started
+    ds = [1, 1]  # degree sequence
+
+    for source in range(2, n):
+        # compute distribution from kernel and degree
+        dist = [kernel(d) for d in ds]
+        # choose target from discrete distribution
+        target = discrete_sequence(1, distribution=dist, seed=seed)[0]
+        G.add_edge(source, target)
+        ds.append(1)  # the source has only one link (degree one)
+        ds[target] += 1  # add one to the target link degree
+    return G
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnr_graph(n, p, create_using=None, seed=None):
+    """Returns the growing network with redirection (GNR) digraph with `n`
+    nodes and redirection probability `p`.
+
+    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 probability `p` the link is instead "redirected" to the
+    successor node of the target.
+
+    The graph is always a (directed) tree.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    p : float
+        The redirection probability.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Examples
+    --------
+    To create the undirected GNR graph, use the :meth:`~DiGraph.to_directed`
+    method::
+
+    >>> D = nx.gnr_graph(10, 0.5)  # the GNR graph
+    >>> G = D.to_undirected()  # the undirected version
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Organization of Growing Random Networks,
+           Phys. Rev. E, 63, 066123, 2001.
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        if seed.random() < p and target != 0:
+            target = next(G.successors(target))
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def gnc_graph(n, create_using=None, seed=None):
+    """Returns the growing network with copying (GNC) digraph with `n` nodes.
+
+    The GNC graph is built by adding nodes one at a time with a link to one
+    previously added node (chosen uniformly at random) and to all of that
+    node's successors.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes for the generated graph.
+    create_using : NetworkX graph constructor, optional (default DiGraph)
+        Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    References
+    ----------
+    .. [1] P. L. Krapivsky and S. Redner,
+           Network Growth by Copying,
+           Phys. Rev. E, 71, 036118, 2005k.},
+    """
+    G = empty_graph(1, create_using, default=nx.DiGraph)
+    if not G.is_directed():
+        raise nx.NetworkXError("create_using must indicate a Directed Graph")
+
+    if n == 1:
+        return G
+
+    for source in range(1, n):
+        target = seed.randrange(0, source)
+        for succ in G.successors(target):
+            G.add_edge(source, succ)
+        G.add_edge(source, target)
+    return G
+
+
+@py_random_state(6)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def scale_free_graph(
+    n,
+    alpha=0.41,
+    beta=0.54,
+    gamma=0.05,
+    delta_in=0.2,
+    delta_out=0,
+    seed=None,
+    initial_graph=None,
+):
+    """Returns a scale-free directed graph.
+
+    Parameters
+    ----------
+    n : integer
+        Number of nodes in graph
+    alpha : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the in-degree distribution.
+    beta : float
+        Probability for adding an edge between two existing nodes.
+        One existing node is chosen randomly according the in-degree
+        distribution and the other chosen randomly according to the out-degree
+        distribution.
+    gamma : float
+        Probability for adding a new node connected to an existing node
+        chosen randomly according to the out-degree distribution.
+    delta_in : float
+        Bias for choosing nodes from in-degree distribution.
+    delta_out : float
+        Bias for choosing nodes from out-degree distribution.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+    initial_graph : MultiDiGraph instance, optional
+        Build the scale-free graph starting from this initial MultiDiGraph,
+        if provided.
+
+    Returns
+    -------
+    MultiDiGraph
+
+    Examples
+    --------
+    Create a scale-free graph on one hundred nodes::
+
+    >>> G = nx.scale_free_graph(100)
+
+    Notes
+    -----
+    The sum of `alpha`, `beta`, and `gamma` must be 1.
+
+    References
+    ----------
+    .. [1] B. Bollobás, C. Borgs, J. Chayes, and O. Riordan,
+           Directed scale-free graphs,
+           Proceedings of the fourteenth annual ACM-SIAM Symposium on
+           Discrete Algorithms, 132--139, 2003.
+    """
+
+    def _choose_node(candidates, node_list, delta):
+        if delta > 0:
+            bias_sum = len(node_list) * delta
+            p_delta = bias_sum / (bias_sum + len(candidates))
+            if seed.random() < p_delta:
+                return seed.choice(node_list)
+        return seed.choice(candidates)
+
+    if initial_graph is not None and hasattr(initial_graph, "_adj"):
+        if not isinstance(initial_graph, nx.MultiDiGraph):
+            raise nx.NetworkXError("initial_graph must be a MultiDiGraph.")
+        G = initial_graph
+    else:
+        # Start with 3-cycle
+        G = nx.MultiDiGraph([(0, 1), (1, 2), (2, 0)])
+
+    if alpha <= 0:
+        raise ValueError("alpha must be > 0.")
+    if beta <= 0:
+        raise ValueError("beta must be > 0.")
+    if gamma <= 0:
+        raise ValueError("gamma must be > 0.")
+
+    if abs(alpha + beta + gamma - 1.0) >= 1e-9:
+        raise ValueError("alpha+beta+gamma must equal 1.")
+
+    if delta_in < 0:
+        raise ValueError("delta_in must be >= 0.")
+
+    if delta_out < 0:
+        raise ValueError("delta_out must be >= 0.")
+
+    # pre-populate degree states
+    vs = sum((count * [idx] for idx, count in G.out_degree()), [])
+    ws = sum((count * [idx] for idx, count in G.in_degree()), [])
+
+    # pre-populate node state
+    node_list = list(G.nodes())
+
+    # see if there already are number-based nodes
+    numeric_nodes = [n for n in node_list if isinstance(n, numbers.Number)]
+    if len(numeric_nodes) > 0:
+        # set cursor for new nodes appropriately
+        cursor = max(int(n.real) for n in numeric_nodes) + 1
+    else:
+        # or start at zero
+        cursor = 0
+
+    while len(G) < n:
+        r = seed.random()
+
+        # random choice in alpha,beta,gamma ranges
+        if r < alpha:
+            # alpha
+            # add new node v
+            v = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(v)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        elif r < alpha + beta:
+            # beta
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # choose w according to in-degree and delta_in
+            w = _choose_node(ws, node_list, delta_in)
+
+        else:
+            # gamma
+            # choose v according to out-degree and delta_out
+            v = _choose_node(vs, node_list, delta_out)
+            # add new node w
+            w = cursor
+            cursor += 1
+            # also add to node state
+            node_list.append(w)
+
+        # add edge to graph
+        G.add_edge(v, w)
+
+        # update degree states
+        vs.append(v)
+        ws.append(w)
+
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_uniform_k_out_graph(n, k, self_loops=True, with_replacement=True, seed=None):
+    """Returns a random `k`-out graph with uniform attachment.
+
+    A random `k`-out graph with uniform attachment is a multidigraph
+    generated by the following algorithm. For each node *u*, choose
+    `k` nodes *v* uniformly at random (with replacement). Add a
+    directed edge joining *u* to *v*.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    with_replacement : bool
+        If True, neighbors are chosen with replacement and the
+        returned graph will be a directed multigraph. Otherwise,
+        neighbors are chosen without replacement and the returned graph
+        will be a directed graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    NetworkX graph
+        A `k`-out-regular directed graph generated according to the
+        above algorithm. It will be a multigraph if and only if
+        `with_replacement` is True.
+
+    Raises
+    ------
+    ValueError
+        If `with_replacement` is False and `k` is greater than
+        `n`.
+
+    See also
+    --------
+    random_k_out_graph
+
+    Notes
+    -----
+    The return digraph or multidigraph may not be strongly connected, or
+    even weakly connected.
+
+    If `with_replacement` is True, this function is similar to
+    :func:`random_k_out_graph`, if that function had parameter `alpha`
+    set to positive infinity.
+
+    """
+    if with_replacement:
+        create_using = nx.MultiDiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return (seed.choice(list(nodes)) for i in range(k))
+
+    else:
+        create_using = nx.DiGraph()
+
+        def sample(v, nodes):
+            if not self_loops:
+                nodes = nodes - {v}
+            return seed.sample(list(nodes), k)
+
+    G = nx.empty_graph(n, create_using)
+    nodes = set(G)
+    for u in G:
+        G.add_edges_from((u, v) for v in sample(u, nodes))
+    return G
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_k_out_graph(n, k, alpha, self_loops=True, seed=None):
+    """Returns a random `k`-out graph with preferential attachment.
+
+    A random `k`-out graph with preferential attachment is a
+    multidigraph generated by the following algorithm.
+
+    1. Begin with an empty digraph, and initially set each node to have
+       weight `alpha`.
+    2. Choose a node `u` with out-degree less than `k` uniformly at
+       random.
+    3. Choose a node `v` from with probability proportional to its
+       weight.
+    4. Add a directed edge from `u` to `v`, and increase the weight
+       of `v` by one.
+    5. If each node has out-degree `k`, halt, otherwise repeat from
+       step 2.
+
+    For more information on this model of random graph, see [1].
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the returned graph.
+
+    k : int
+        The out-degree of each node in the returned graph.
+
+    alpha : float
+        A positive :class:`float` representing the initial weight of
+        each vertex. A higher number means that in step 3 above, nodes
+        will be chosen more like a true uniformly random sample, and a
+        lower number means that nodes are more likely to be chosen as
+        their in-degree increases. If this parameter is not positive, a
+        :exc:`ValueError` is raised.
+
+    self_loops : bool
+        If True, self-loops are allowed when generating the graph.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    :class:`~networkx.classes.MultiDiGraph`
+        A `k`-out-regular multidigraph generated according to the above
+        algorithm.
+
+    Raises
+    ------
+    ValueError
+        If `alpha` is not positive.
+
+    Notes
+    -----
+    The returned multidigraph may not be strongly connected, or even
+    weakly connected.
+
+    References
+    ----------
+    [1]: Peterson, Nicholas R., and Boris Pittel.
+         "Distance between two random `k`-out digraphs, with and without
+         preferential attachment."
+         arXiv preprint arXiv:1311.5961 (2013).
+         <https://arxiv.org/abs/1311.5961>
+
+    """
+    if alpha < 0:
+        raise ValueError("alpha must be positive")
+    G = nx.empty_graph(n, create_using=nx.MultiDiGraph)
+    weights = Counter({v: alpha for v in G})
+    for i in range(k * n):
+        u = seed.choice([v for v, d in G.out_degree() if d < k])
+        # If self-loops are not allowed, make the source node `u` have
+        # weight zero.
+        if not self_loops:
+            adjustment = Counter({u: weights[u]})
+        else:
+            adjustment = Counter()
+        v = weighted_choice(weights - adjustment, seed=seed)
+        G.add_edge(u, v)
+        weights[v] += 1
+    return G

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

@@ -0,0 +1,163 @@
+"""Functions for generating graphs based on the "duplication" method.
+
+These graph generators start with a small initial graph then duplicate
+nodes and (partially) duplicate their edges. These functions are
+generally inspired by biological networks.
+
+"""
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.utils import py_random_state
+
+__all__ = ["partial_duplication_graph", "duplication_divergence_graph"]
+
+
+@py_random_state(4)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def partial_duplication_graph(N, n, p, q, seed=None):
+    """Returns a random graph using the partial duplication model.
+
+    Parameters
+    ----------
+    N : int
+        The total number of nodes in the final graph.
+
+    n : int
+        The number of nodes in the initial clique.
+
+    p : float
+        The probability of joining each neighbor of a node to the
+        duplicate node. Must be a number in the between zero and one,
+        inclusive.
+
+    q : float
+        The probability of joining the source node to the duplicate
+        node. Must be a number in the between zero and one, inclusive.
+
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Notes
+    -----
+    A graph of nodes is grown by creating a fully connected graph
+    of size `n`. The following procedure is then repeated until
+    a total of `N` nodes have been reached.
+
+    1. A random node, *u*, is picked and a new node, *v*, is created.
+    2. For each neighbor of *u* an edge from the neighbor to *v* is created
+       with probability `p`.
+    3. An edge from *u* to *v* is created with probability `q`.
+
+    This algorithm appears in [1].
+
+    This implementation allows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] Knudsen Michael, and Carsten Wiuf. "A Markov chain approach to
+           randomly grown graphs." Journal of Applied Mathematics 2008.
+           <https://doi.org/10.1155/2008/190836>
+
+    """
+    if p < 0 or p > 1 or q < 0 or q > 1:
+        msg = "partial duplication graph must have 0 <= p, q <= 1."
+        raise NetworkXError(msg)
+    if n > N:
+        raise NetworkXError("partial duplication graph must have n <= N.")
+
+    G = nx.complete_graph(n)
+    for new_node in range(n, N):
+        # Pick a random vertex, u, already in the graph.
+        src_node = seed.randint(0, new_node - 1)
+
+        # Add a new vertex, v, to the graph.
+        G.add_node(new_node)
+
+        # For each neighbor of u...
+        for nbr_node in list(nx.all_neighbors(G, src_node)):
+            # Add the neighbor to v with probability p.
+            if seed.random() < p:
+                G.add_edge(new_node, nbr_node)
+
+        # Join v and u with probability q.
+        if seed.random() < q:
+            G.add_edge(new_node, src_node)
+    return G
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def duplication_divergence_graph(n, p, seed=None):
+    """Returns an undirected graph using the duplication-divergence model.
+
+    A graph of `n` nodes is created by duplicating the initial nodes
+    and retaining edges incident to the original nodes with a retention
+    probability `p`.
+
+    Parameters
+    ----------
+    n : int
+        The desired number of nodes in the graph.
+    p : float
+        The probability for retaining the edge of the replicated node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : Graph
+
+    Raises
+    ------
+    NetworkXError
+        If `p` is not a valid probability.
+        If `n` is less than 2.
+
+    Notes
+    -----
+    This algorithm appears in [1].
+
+    This implementation disallows the possibility of generating
+    disconnected graphs.
+
+    References
+    ----------
+    .. [1] I. Ispolatov, P. L. Krapivsky, A. Yuryev,
+       "Duplication-divergence model of protein interaction network",
+       Phys. Rev. E, 71, 061911, 2005.
+
+    """
+    if p > 1 or p < 0:
+        msg = f"NetworkXError p={p} is not in [0,1]."
+        raise nx.NetworkXError(msg)
+    if n < 2:
+        msg = "n must be greater than or equal to 2"
+        raise nx.NetworkXError(msg)
+
+    G = nx.Graph()
+
+    # Initialize the graph with two connected nodes.
+    G.add_edge(0, 1)
+    i = 2
+    while i < n:
+        # Choose a random node from current graph to duplicate.
+        random_node = seed.choice(list(G))
+        # Make the replica.
+        G.add_node(i)
+        # flag indicates whether at least one edge is connected on the replica.
+        flag = False
+        for nbr in G.neighbors(random_node):
+            if seed.random() < p:
+                # Link retention step.
+                G.add_edge(i, nbr)
+                flag = True
+        if not flag:
+            # Delete replica if no edges retained.
+            G.remove_node(i)
+        else:
+            # Successful duplication.
+            i += 1
+    return G

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

@@ -0,0 +1,65 @@
+"""
+Ego graph.
+"""
+__all__ = ["ego_graph"]
+
+import networkx as nx
+
+
+@nx._dispatchable(preserve_all_attrs=True, returns_graph=True)
+def ego_graph(G, n, radius=1, center=True, undirected=False, distance=None):
+    """Returns induced subgraph of neighbors centered at node n within
+    a given radius.
+
+    Parameters
+    ----------
+    G : graph
+      A NetworkX Graph or DiGraph
+
+    n : node
+      A single node
+
+    radius : number, optional
+      Include all neighbors of distance<=radius from n.
+
+    center : bool, optional
+      If False, do not include center node in graph
+
+    undirected : bool, optional
+      If True use both in- and out-neighbors of directed graphs.
+
+    distance : key, optional
+      Use specified edge data key as distance.  For example, setting
+      distance='weight' will use the edge weight to measure the
+      distance from the node n.
+
+    Notes
+    -----
+    For directed graphs D this produces the "out" neighborhood
+    or successors.  If you want the neighborhood of predecessors
+    first reverse the graph with D.reverse().  If you want both
+    directions use the keyword argument undirected=True.
+
+    Node, edge, and graph attributes are copied to the returned subgraph.
+    """
+    if undirected:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(
+                G.to_undirected(), n, cutoff=radius, weight=distance
+            )
+        else:
+            sp = dict(
+                nx.single_source_shortest_path_length(
+                    G.to_undirected(), n, cutoff=radius
+                )
+            )
+    else:
+        if distance is not None:
+            sp, _ = nx.single_source_dijkstra(G, n, cutoff=radius, weight=distance)
+        else:
+            sp = dict(nx.single_source_shortest_path_length(G, n, cutoff=radius))
+
+    H = G.subgraph(sp).copy()
+    if not center:
+        H.remove_node(n)
+    return H

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

@@ -0,0 +1,475 @@
+"""Provides explicit constructions of expander graphs.
+
+"""
+import itertools
+
+import networkx as nx
+
+__all__ = [
+    "margulis_gabber_galil_graph",
+    "chordal_cycle_graph",
+    "paley_graph",
+    "maybe_regular_expander",
+    "is_regular_expander",
+    "random_regular_expander_graph",
+]
+
+
+# Other discrete torus expanders can be constructed by using the following edge
+# sets. For more information, see Chapter 4, "Expander Graphs", in
+# "Pseudorandomness", by Salil Vadhan.
+#
+# For a directed expander, add edges from (x, y) to:
+#
+#     (x, y),
+#     ((x + 1) % n, y),
+#     (x, (y + 1) % n),
+#     (x, (x + y) % n),
+#     (-y % n, x)
+#
+# For an undirected expander, add the reverse edges.
+#
+# Also appearing in the paper of Gabber and Galil:
+#
+#     (x, y),
+#     (x, (x + y) % n),
+#     (x, (x + y + 1) % n),
+#     ((x + y) % n, y),
+#     ((x + y + 1) % n, y)
+#
+# and:
+#
+#     (x, y),
+#     ((x + 2*y) % n, y),
+#     ((x + (2*y + 1)) % n, y),
+#     ((x + (2*y + 2)) % n, y),
+#     (x, (y + 2*x) % n),
+#     (x, (y + (2*x + 1)) % n),
+#     (x, (y + (2*x + 2)) % n),
+#
+@nx._dispatchable(graphs=None, returns_graph=True)
+def margulis_gabber_galil_graph(n, create_using=None):
+    r"""Returns the Margulis-Gabber-Galil undirected MultiGraph on `n^2` nodes.
+
+    The undirected MultiGraph is regular with degree `8`. Nodes are integer
+    pairs. The second-largest eigenvalue of the adjacency matrix of the graph
+    is at most `5 \sqrt{2}`, regardless of `n`.
+
+    Parameters
+    ----------
+    n : int
+        Determines the number of nodes in the graph: `n^2`.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is directed or not a multigraph.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        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 (
+            ((x + 2 * y) % n, y),
+            ((x + (2 * y + 1)) % n, y),
+            (x, (y + 2 * x) % n),
+            (x, (y + (2 * x + 1)) % n),
+        ):
+            G.add_edge((x, y), (u, v))
+    G.graph["name"] = f"margulis_gabber_galil_graph({n})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chordal_cycle_graph(p, create_using=None):
+    """Returns the chordal cycle graph on `p` nodes.
+
+    The returned graph is a cycle graph on `p` nodes with chords joining each
+    vertex `x` to its inverse modulo `p`. This graph is a (mildly explicit)
+    3-regular expander [1]_.
+
+    `p` *must* be a prime number.
+
+    Parameters
+    ----------
+    p : a prime number
+
+        The number of vertices in the graph. This also indicates where the
+        chordal edges in the cycle will be created.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected multigraph.
+
+    Raises
+    ------
+    NetworkXError
+
+        If `create_using` indicates directed or not a multigraph.
+
+    References
+    ----------
+
+    .. [1] Theorem 4.4.2 in A. Lubotzky. "Discrete groups, expanding graphs and
+           invariant measures", volume 125 of Progress in Mathematics.
+           Birkhäuser Verlag, Basel, 1994.
+
+    """
+    G = nx.empty_graph(0, create_using, default=nx.MultiGraph)
+    if G.is_directed() or not G.is_multigraph():
+        msg = "`create_using` must be an undirected multigraph."
+        raise nx.NetworkXError(msg)
+
+    for x in range(p):
+        left = (x - 1) % p
+        right = (x + 1) % p
+        # Here we apply Fermat's Little Theorem to compute the multiplicative
+        # inverse of x in Z/pZ. By Fermat's Little Theorem,
+        #
+        #     x^p = x (mod p)
+        #
+        # Therefore,
+        #
+        #     x * x^(p - 2) = 1 (mod p)
+        #
+        # The number 0 is a special case: we just let its inverse be itself.
+        chord = pow(x, p - 2, p) if x > 0 else 0
+        for y in (left, right, chord):
+            G.add_edge(x, y)
+    G.graph["name"] = f"chordal_cycle_graph({p})"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def paley_graph(p, create_using=None):
+    r"""Returns the Paley $\frac{(p-1)}{2}$ -regular graph on $p$ nodes.
+
+    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 $p \equiv 1  \pmod 4$, $-1$ 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 $p \equiv 3 \pmod 4$, $-1$ 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 $\mathbb{Z}/p\mathbb{Z}$.
+    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 $5^2$).
+
+    Parameters
+    ----------
+    p : int, an odd prime number.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : graph
+        The constructed directed graph.
+
+    Raises
+    ------
+    NetworkXError
+        If the graph is a multigraph.
+
+    References
+    ----------
+    Chapter 13 in B. Bollobas, Random Graphs. Second edition.
+    Cambridge Studies in Advanced Mathematics, 73.
+    Cambridge University Press, Cambridge (2001).
+    """
+    G = nx.empty_graph(0, create_using, default=nx.DiGraph)
+    if G.is_multigraph():
+        msg = "`create_using` cannot be a multigraph."
+        raise nx.NetworkXError(msg)
+
+    # Compute the squares in Z/pZ.
+    # Make it a set to uniquify (there are exactly (p-1)/2 squares in Z/pZ
+    # when is prime).
+    square_set = {(x**2) % p for x in range(1, p) if (x**2) % p != 0}
+
+    for x in range(p):
+        for x2 in square_set:
+            G.add_edge(x, (x + x2) % p)
+    G.graph["name"] = f"paley({p})"
+    return G
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def maybe_regular_expander(n, d, *, create_using=None, max_tries=100, seed=None):
+    r"""Utility for creating a random regular expander.
+
+    Returns a random $d$-regular graph on $n$ nodes which is an expander
+    graph with very good probability.
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    create_using : Graph Instance or Constructor
+      Indicator of type of graph to return.
+      If a Graph-type instance, then clear and use it.
+      If a constructor, call it to create an empty graph.
+      Use the Graph constructor by default.
+    max_tries : int. (default: 100)
+      The number of allowed loops when generating each independent cycle
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Notes
+    -----
+    The nodes are numbered from $0$ to $n - 1$.
+
+    The graph is generated by taking $d / 2$ random independent cycles.
+
+    Joel Friedman proved that in this model the resulting
+    graph is an expander with probability
+    $1 - O(n^{-\tau})$ where $\tau = \lceil (\sqrt{d - 1}) / 2 \rceil - 1$. [1]_
+
+    Examples
+    --------
+    >>> G = nx.maybe_regular_expander(n=200, d=6, seed=8020)
+
+    Returns
+    -------
+    G : graph
+        The constructed undirected graph.
+
+    Raises
+    ------
+    NetworkXError
+        If $d % 2 != 0$ as the degree must be even.
+        If $n - 1$ is less than $ 2d $ as the graph is complete at most.
+        If max_tries is reached
+
+    See Also
+    --------
+    is_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Joel Friedman,
+       A Proof of Alon’s Second Eigenvalue Conjecture and Related Problems, 2004
+       https://arxiv.org/abs/cs/0405020
+
+    """
+
+    import numpy as np
+
+    if n < 1:
+        raise nx.NetworkXError("n must be a positive integer")
+
+    if not (d >= 2):
+        raise nx.NetworkXError("d must be greater than or equal to 2")
+
+    if not (d % 2 == 0):
+        raise nx.NetworkXError("d must be even")
+
+    if not (n - 1 >= d):
+        raise nx.NetworkXError(
+            f"Need n-1>= d to have room for {d//2} independent cycles with {n} nodes"
+        )
+
+    G = nx.empty_graph(n, create_using)
+
+    if n < 2:
+        return G
+
+    cycles = []
+    edges = set()
+
+    # Create d / 2 cycles
+    for i in range(d // 2):
+        iterations = max_tries
+        # Make sure the cycles are independent to have a regular graph
+        while len(edges) != (i + 1) * n:
+            iterations -= 1
+            # Faster than random.permutation(n) since there are only
+            # (n-1)! distinct cycles against n! permutations of size n
+            cycle = seed.permutation(n - 1).tolist()
+            cycle.append(n - 1)
+
+            new_edges = {
+                (u, v)
+                for u, v in nx.utils.pairwise(cycle, cyclic=True)
+                if (u, v) not in edges and (v, u) not in edges
+            }
+            # If the new cycle has no edges in common with previous cycles
+            # then add it to the list otherwise try again
+            if len(new_edges) == n:
+                cycles.append(cycle)
+                edges.update(new_edges)
+
+            if iterations == 0:
+                raise nx.NetworkXError("Too many iterations in maybe_regular_expander")
+
+    G.add_edges_from(edges)
+
+    return G
+
+
+@nx.utils.not_implemented_for("directed")
+@nx.utils.not_implemented_for("multigraph")
+@nx._dispatchable(preserve_edge_attrs={"G": {"weight": 1}})
+def is_regular_expander(G, *, epsilon=0):
+    r"""Determines whether the graph G is a regular expander. [1]_
+
+    An expander graph is a sparse graph with strong connectivity properties.
+
+    More precisely, this helper checks whether the graph is a
+    regular $(n, d, \lambda)$-expander with $\lambda$ close to
+    the Alon-Boppana bound and given by
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$. [2]_
+
+    In the case where $\epsilon = 0$ then if the graph successfully passes the test
+    it is a Ramanujan graph. [3]_
+
+    A Ramanujan graph has spectral gap almost as large as possible, which makes them
+    excellent expanders.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+    epsilon : int, float, default=0
+
+    Returns
+    -------
+    bool
+        Whether the given graph is a regular $(n, d, \lambda)$-expander
+        where $\lambda = 2 \sqrt{d - 1} + \epsilon$.
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    See Also
+    --------
+    maybe_regular_expander
+    random_regular_expander_graph
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+
+    import numpy as np
+    from scipy.sparse.linalg import eigsh
+
+    if epsilon < 0:
+        raise nx.NetworkXError("epsilon must be non negative")
+
+    if not nx.is_regular(G):
+        return False
+
+    _, d = nx.utils.arbitrary_element(G.degree)
+
+    A = nx.adjacency_matrix(G, dtype=float)
+    lams = eigsh(A, which="LM", k=2, return_eigenvectors=False)
+
+    # lambda2 is the second biggest eigenvalue
+    lambda2 = min(lams)
+
+    # Use bool() to convert numpy scalar to Python Boolean
+    return bool(abs(lambda2) < 2 ** np.sqrt(d - 1) + epsilon)
+
+
+@nx.utils.decorators.np_random_state("seed")
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_regular_expander_graph(
+    n, d, *, epsilon=0, create_using=None, max_tries=100, seed=None
+):
+    r"""Returns a random regular expander graph on $n$ nodes with degree $d$.
+
+    An expander graph is a sparse graph with strong connectivity properties. [1]_
+
+    More precisely the returned graph is a $(n, d, \lambda)$-expander with
+    $\lambda = 2 \sqrt{d - 1} + \epsilon$, close to the Alon-Boppana bound. [2]_
+
+    In the case where $\epsilon = 0$ it returns a Ramanujan graph.
+    A Ramanujan graph has spectral gap almost as large as possible,
+    which makes them excellent expanders. [3]_
+
+    Parameters
+    ----------
+    n : int
+      The number of nodes.
+    d : int
+      The degree of each node.
+    epsilon : int, float, default=0
+    max_tries : int, (default: 100)
+      The number of allowed loops, also used in the maybe_regular_expander utility
+    seed : (default: None)
+      Seed used to set random number generation state. See :ref`Randomness<randomness>`.
+
+    Raises
+    ------
+    NetworkXError
+        If max_tries is reached
+
+    Examples
+    --------
+    >>> G = nx.random_regular_expander_graph(20, 4)
+    >>> nx.is_regular_expander(G)
+    True
+
+    Notes
+    -----
+    This loops over `maybe_regular_expander` and can be slow when
+    $n$ is too big or $\epsilon$ too small.
+
+    See Also
+    --------
+    maybe_regular_expander
+    is_regular_expander
+
+    References
+    ----------
+    .. [1] Expander graph, https://en.wikipedia.org/wiki/Expander_graph
+    .. [2] Alon-Boppana bound, https://en.wikipedia.org/wiki/Alon%E2%80%93Boppana_bound
+    .. [3] Ramanujan graphs, https://en.wikipedia.org/wiki/Ramanujan_graph
+
+    """
+    G = maybe_regular_expander(
+        n, d, create_using=create_using, max_tries=max_tries, seed=seed
+    )
+    iterations = max_tries
+
+    while not is_regular_expander(G, epsilon=epsilon):
+        iterations -= 1
+        G = maybe_regular_expander(
+            n=n, d=d, create_using=create_using, max_tries=max_tries, seed=seed
+        )
+
+        if iterations == 0:
+            raise nx.NetworkXError(
+                "Too many iterations in random_regular_expander_graph"
+            )
+
+    return G

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

@@ -0,0 +1,199 @@
+"""Generators for Harary graphs
+
+This module gives two generators for the Harary graph, which was
+introduced by the famous mathematician Frank Harary in his 1962 work [H]_.
+The first generator gives the Harary graph that maximizes the node
+connectivity with given number of nodes and given number of edges.
+The second generator gives the Harary graph that minimizes
+the number of edges in the graph with given node connectivity and
+number of nodes.
+
+References
+----------
+.. [H] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["hnm_harary_graph", "hkn_harary_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hnm_harary_graph(n, m, create_using=None):
+    """Returns the Harary graph with given numbers of nodes and edges.
+
+    The Harary graph $H_{n,m}$ is the graph that maximizes node connectivity
+    with $n$ nodes and $m$ edges.
+
+    This maximum node connectivity is known to be floor($2m/n$). [1]_
+
+    Parameters
+    ----------
+    n: integer
+       The number of nodes the generated graph is to contain
+
+    m: integer
+       The number of edges the generated graph is to contain
+
+    create_using : NetworkX graph constructor, optional Graph type
+     to create (default=nx.Graph). If graph instance, then cleared
+     before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{n,m}$.
+
+    See Also
+    --------
+    hkn_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(m)$ time.
+    It is implemented by following the Reference [2]_.
+
+    References
+    ----------
+    .. [1] F. T. Boesch, A. Satyanarayana, and C. L. Suffel,
+       "A Survey of Some Network Reliability Analysis and Synthesis Results,"
+       Networks, pp. 99-107, 2009.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+       Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if n < 1:
+        raise NetworkXError("The number of nodes must be >= 1!")
+    if m < n - 1:
+        raise NetworkXError("The number of edges must be >= n - 1 !")
+    if m > n * (n - 1) // 2:
+        raise NetworkXError("The number of edges must be <= n(n-1)/2")
+
+    # Construct an empty graph with n nodes first
+    H = nx.empty_graph(n, create_using)
+    # Get the floor of average node degree
+    d = 2 * m // n
+
+    # Test the parity of n and d
+    if (n % 2 == 0) or (d % 2 == 0):
+        # Start with a regular graph of d degrees
+        offset = d // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        if d & 1:
+            # in case d is odd; n must be even in this case
+            half = n // 2
+            for i in range(half):
+                # add edges diagonally
+                H.add_edge(i, i + half)
+        # Get the remainder of 2*m modulo n
+        r = 2 * m % n
+        if r > 0:
+            # add remaining edges at offset+1
+            for i in range(r // 2):
+                H.add_edge(i, i + offset + 1)
+    else:
+        # Start with a regular graph of (d - 1) degrees
+        offset = (d - 1) // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        half = n // 2
+        for i in range(m - n * offset):
+            # add the remaining m - n*offset edges between i and i+half
+            H.add_edge(i, (i + half) % n)
+
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hkn_harary_graph(k, n, create_using=None):
+    """Returns the Harary graph with given node connectivity and node number.
+
+    The Harary graph $H_{k,n}$ is the graph that minimizes the number of
+    edges needed with given node connectivity $k$ and node number $n$.
+
+    This smallest number of edges is known to be ceil($kn/2$) [1]_.
+
+    Parameters
+    ----------
+    k: integer
+       The node connectivity of the generated graph
+
+    n: integer
+       The number of nodes the generated graph is to contain
+
+    create_using : NetworkX graph constructor, optional Graph type
+     to create (default=nx.Graph). If graph instance, then cleared
+     before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The Harary graph $H_{k,n}$.
+
+    See Also
+    --------
+    hnm_harary_graph
+
+    Notes
+    -----
+    This algorithm runs in $O(kn)$ time.
+    It is implemented by following the Reference [2]_.
+
+    References
+    ----------
+    .. [1] Weisstein, Eric W. "Harary Graph." From MathWorld--A Wolfram Web
+     Resource. http://mathworld.wolfram.com/HararyGraph.html.
+
+    .. [2] Harary, F. "The Maximum Connectivity of a Graph."
+      Proc. Nat. Acad. Sci. USA 48, 1142-1146, 1962.
+    """
+
+    if k < 1:
+        raise NetworkXError("The node connectivity must be >= 1!")
+    if n < k + 1:
+        raise NetworkXError("The number of nodes must be >= k+1 !")
+
+    # in case of connectivity 1, simply return the path graph
+    if k == 1:
+        H = nx.path_graph(n, create_using)
+        return H
+
+    # Construct an empty graph with n nodes first
+    H = nx.empty_graph(n, create_using)
+
+    # Test the parity of k and n
+    if (k % 2 == 0) or (n % 2 == 0):
+        # Construct a regular graph with k degrees
+        offset = k // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        if k & 1:
+            # odd degree; n must be even in this case
+            half = n // 2
+            for i in range(half):
+                # add edges diagonally
+                H.add_edge(i, i + half)
+    else:
+        # Construct a regular graph with (k - 1) degrees
+        offset = (k - 1) // 2
+        for i in range(n):
+            for j in range(1, offset + 1):
+                H.add_edge(i, (i - j) % n)
+                H.add_edge(i, (i + j) % n)
+        half = n // 2
+        for i in range(half + 1):
+            # add half+1 edges between i and i+half
+            H.add_edge(i, (i + half) % n)
+
+    return H

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

@@ -0,0 +1,441 @@
+"""Generates graphs resembling the Internet Autonomous System network"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_internet_as_graph"]
+
+
+def uniform_int_from_avg(a, m, seed):
+    """Pick a random integer with uniform probability.
+
+    Returns a random integer uniformly taken from a distribution with
+    minimum value 'a' and average value 'm', X~U(a,b), E[X]=m, X in N where
+    b = 2*m - a.
+
+    Notes
+    -----
+    p = (b-floor(b))/2
+    X = X1 + X2; X1~U(a,floor(b)), X2~B(p)
+    E[X] = E[X1] + E[X2] = (floor(b)+a)/2 + (b-floor(b))/2 = (b+a)/2 = m
+    """
+
+    from math import floor
+
+    assert m >= a
+    b = 2 * m - a
+    p = (b - floor(b)) / 2
+    X1 = round(seed.random() * (floor(b) - a) + a)
+    if seed.random() < p:
+        X2 = 1
+    else:
+        X2 = 0
+    return X1 + X2
+
+
+def choose_pref_attach(degs, seed):
+    """Pick a random value, with a probability given by its weight.
+
+    Returns a random choice among degs keys, each of which has a
+    probability proportional to the corresponding dictionary value.
+
+    Parameters
+    ----------
+    degs: dictionary
+        It contains the possible values (keys) and the corresponding
+        probabilities (values)
+    seed: random state
+
+    Returns
+    -------
+    v: object
+        A key of degs or None if degs is empty
+    """
+
+    if len(degs) == 0:
+        return None
+    s = sum(degs.values())
+    if s == 0:
+        return seed.choice(list(degs.keys()))
+    v = seed.random() * s
+
+    nodes = list(degs.keys())
+    i = 0
+    acc = degs[nodes[i]]
+    while v > acc:
+        i += 1
+        acc += degs[nodes[i]]
+    return nodes[i]
+
+
+class AS_graph_generator:
+    """Generates random internet AS graphs."""
+
+    def __init__(self, n, seed):
+        """Initializes variables. Immediate numbers are taken from [1].
+
+        Parameters
+        ----------
+        n: integer
+            Number of graph nodes
+        seed: random state
+            Indicator of random number generation state.
+            See :ref:`Randomness<randomness>`.
+
+        Returns
+        -------
+        GG: AS_graph_generator object
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.seed = seed
+        self.n_t = min(n, round(self.seed.random() * 2 + 4))  # num of T nodes
+        self.n_m = round(0.15 * n)  # number of M nodes
+        self.n_cp = round(0.05 * n)  # number of CP nodes
+        self.n_c = max(0, n - self.n_t - self.n_m - self.n_cp)  # number of C nodes
+
+        self.d_m = 2 + (2.5 * n) / 10000  # average multihoming degree for M nodes
+        self.d_cp = 2 + (1.5 * n) / 10000  # avg multihoming degree for CP nodes
+        self.d_c = 1 + (5 * n) / 100000  # average multihoming degree for C nodes
+
+        self.p_m_m = 1 + (2 * n) / 10000  # avg num of peer edges between M and M
+        self.p_cp_m = 0.2 + (2 * n) / 10000  # avg num of peer edges between CP, M
+        self.p_cp_cp = 0.05 + (2 * n) / 100000  # avg num of peer edges btwn CP, CP
+
+        self.t_m = 0.375  # probability M's provider is T
+        self.t_cp = 0.375  # probability CP's provider is T
+        self.t_c = 0.125  # probability C's provider is T
+
+    def t_graph(self):
+        """Generates the core mesh network of tier one nodes of a AS graph.
+
+        Returns
+        -------
+        G: Networkx Graph
+            Core network
+        """
+
+        self.G = nx.Graph()
+        for i in range(self.n_t):
+            self.G.add_node(i, type="T")
+            for r in self.regions:
+                self.regions[r].add(i)
+            for j in self.G.nodes():
+                if i != j:
+                    self.add_edge(i, j, "peer")
+            self.customers[i] = set()
+            self.providers[i] = set()
+        return self.G
+
+    def add_edge(self, i, j, kind):
+        if kind == "transit":
+            customer = str(i)
+        else:
+            customer = "none"
+        self.G.add_edge(i, j, type=kind, customer=customer)
+
+    def choose_peer_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its peer degree.
+
+        Pick a node from node_list with preferential attachment
+        computed only on their peer degree
+        """
+
+        d = {}
+        for n in node_list:
+            d[n] = self.G.nodes[n]["peers"]
+        return choose_pref_attach(d, self.seed)
+
+    def choose_node_pref_attach(self, node_list):
+        """Pick a node with a probability weighted by its degree.
+
+        Pick a node from node_list with preferential attachment
+        computed on their degree
+        """
+
+        degs = dict(self.G.degree(node_list))
+        return choose_pref_attach(degs, self.seed)
+
+    def add_customer(self, i, j):
+        """Keep the dictionaries 'customers' and 'providers' consistent."""
+
+        self.customers[j].add(i)
+        self.providers[i].add(j)
+        for z in self.providers[j]:
+            self.customers[z].add(i)
+            self.providers[i].add(z)
+
+    def add_node(self, i, kind, reg2prob, avg_deg, t_edge_prob):
+        """Add a node and its customer transit edges to the graph.
+
+        Parameters
+        ----------
+        i: object
+            Identifier of the new node
+        kind: string
+            Type of the new node. Options are: 'M' for middle node, 'CP' for
+            content provider and 'C' for customer.
+        reg2prob: float
+            Probability the new node can be in two different regions.
+        avg_deg: float
+            Average number of transit nodes of which node i is customer.
+        t_edge_prob: float
+            Probability node i establish a customer transit edge with a tier
+            one (T) node
+
+        Returns
+        -------
+        i: object
+            Identifier of the new node
+        """
+
+        regs = 1  # regions in which node resides
+        if self.seed.random() < reg2prob:  # node is in two regions
+            regs = 2
+        node_options = set()
+
+        self.G.add_node(i, type=kind, peers=0)
+        self.customers[i] = set()
+        self.providers[i] = set()
+        self.nodes[kind].add(i)
+        for r in self.seed.sample(list(self.regions), regs):
+            node_options = node_options.union(self.regions[r])
+            self.regions[r].add(i)
+
+        edge_num = uniform_int_from_avg(1, avg_deg, self.seed)
+
+        t_options = node_options.intersection(self.nodes["T"])
+        m_options = node_options.intersection(self.nodes["M"])
+        if i in m_options:
+            m_options.remove(i)
+        d = 0
+        while d < edge_num and (len(t_options) > 0 or len(m_options) > 0):
+            if len(m_options) == 0 or (
+                len(t_options) > 0 and self.seed.random() < t_edge_prob
+            ):  # add edge to a T node
+                j = self.choose_node_pref_attach(t_options)
+                t_options.remove(j)
+            else:
+                j = self.choose_node_pref_attach(m_options)
+                m_options.remove(j)
+            self.add_edge(i, j, "transit")
+            self.add_customer(i, j)
+            d += 1
+
+        return i
+
+    def add_m_peering_link(self, m, to_kind):
+        """Add a peering link between two middle tier (M) nodes.
+
+        Target node j is drawn considering a preferential attachment based on
+        other M node peering degree.
+
+        Parameters
+        ----------
+        m: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be always M)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        # candidates are of type 'M' and are not customers of m
+        node_options = self.nodes["M"].difference(self.customers[m])
+        # candidates are not providers of m
+        node_options = node_options.difference(self.providers[m])
+        # remove self
+        if m in node_options:
+            node_options.remove(m)
+
+        # remove candidates we are already connected to
+        for j in self.G.neighbors(m):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.choose_peer_pref_attach(node_options)
+            self.add_edge(m, j, "peer")
+            self.G.nodes[m]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def add_cp_peering_link(self, cp, to_kind):
+        """Add a peering link to a content provider (CP) node.
+
+        Target node j can be CP or M and it is drawn uniformly among the nodes
+        belonging to the same region as cp.
+
+        Parameters
+        ----------
+        cp: object
+            Node identifier
+        to_kind: string
+            type for target node j (must be M or CP)
+
+        Returns
+        -------
+        success: boolean
+        """
+
+        node_options = set()
+        for r in self.regions:  # options include nodes in the same region(s)
+            if cp in self.regions[r]:
+                node_options = node_options.union(self.regions[r])
+
+        # options are restricted to the indicated kind ('M' or 'CP')
+        node_options = self.nodes[to_kind].intersection(node_options)
+
+        # remove self
+        if cp in node_options:
+            node_options.remove(cp)
+
+        # remove nodes that are cp's providers
+        node_options = node_options.difference(self.providers[cp])
+
+        # remove nodes we are already connected to
+        for j in self.G.neighbors(cp):
+            if j in node_options:
+                node_options.remove(j)
+
+        if len(node_options) > 0:
+            j = self.seed.sample(list(node_options), 1)[0]
+            self.add_edge(cp, j, "peer")
+            self.G.nodes[cp]["peers"] += 1
+            self.G.nodes[j]["peers"] += 1
+            return True
+        else:
+            return False
+
+    def graph_regions(self, rn):
+        """Initializes AS network regions.
+
+        Parameters
+        ----------
+        rn: integer
+            Number of regions
+        """
+
+        self.regions = {}
+        for i in range(rn):
+            self.regions["REG" + str(i)] = set()
+
+    def add_peering_links(self, from_kind, to_kind):
+        """Utility function to add peering links among node groups."""
+        peer_link_method = None
+        if from_kind == "M":
+            peer_link_method = self.add_m_peering_link
+            m = self.p_m_m
+        if from_kind == "CP":
+            peer_link_method = self.add_cp_peering_link
+            if to_kind == "M":
+                m = self.p_cp_m
+            else:
+                m = self.p_cp_cp
+
+        for i in self.nodes[from_kind]:
+            num = uniform_int_from_avg(0, m, self.seed)
+            for _ in range(num):
+                peer_link_method(i, to_kind)
+
+    def generate(self):
+        """Generates a random AS network graph as described in [1].
+
+        Returns
+        -------
+        G: Graph object
+
+        Notes
+        -----
+        The process steps are the following: first we create the core network
+        of tier one nodes, then we add the middle tier (M), the content
+        provider (CP) and the customer (C) nodes along with their transit edges
+        (link i,j means i is customer of j). Finally we add peering links
+        between M nodes, between M and CP nodes and between CP node couples.
+        For a detailed description of the algorithm, please refer to [1].
+
+        References
+        ----------
+        [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+        BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+        in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+        """
+
+        self.graph_regions(5)
+        self.customers = {}
+        self.providers = {}
+        self.nodes = {"T": set(), "M": set(), "CP": set(), "C": set()}
+
+        self.t_graph()
+        self.nodes["T"] = set(self.G.nodes())
+
+        i = len(self.nodes["T"])
+        for _ in range(self.n_m):
+            self.nodes["M"].add(self.add_node(i, "M", 0.2, self.d_m, self.t_m))
+            i += 1
+        for _ in range(self.n_cp):
+            self.nodes["CP"].add(self.add_node(i, "CP", 0.05, self.d_cp, self.t_cp))
+            i += 1
+        for _ in range(self.n_c):
+            self.nodes["C"].add(self.add_node(i, "C", 0, self.d_c, self.t_c))
+            i += 1
+
+        self.add_peering_links("M", "M")
+        self.add_peering_links("CP", "M")
+        self.add_peering_links("CP", "CP")
+
+        return self.G
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_internet_as_graph(n, seed=None):
+    """Generates a random undirected graph resembling the Internet AS network
+
+    Parameters
+    ----------
+    n: integer in [1000, 10000]
+        Number of graph nodes
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G: Networkx Graph object
+        A randomly generated undirected graph
+
+    Notes
+    -----
+    This algorithm returns an undirected graph resembling the Internet
+    Autonomous System (AS) network, it uses the approach by Elmokashfi et al.
+    [1]_ and it grants the properties described in the related paper [1]_.
+
+    Each node models an autonomous system, with an attribute 'type' specifying
+    its kind; tier-1 (T), mid-level (M), customer (C) or content-provider (CP).
+    Each edge models an ADV communication link (hence, bidirectional) with
+    attributes:
+
+      - type: transit|peer, the kind of commercial agreement between nodes;
+      - customer: <node id>, the identifier of the node acting as customer
+        ('none' if type is peer).
+
+    References
+    ----------
+    .. [1] A. Elmokashfi, A. Kvalbein and C. Dovrolis, "On the Scalability of
+       BGP: The Role of Topology Growth," in IEEE Journal on Selected Areas
+       in Communications, vol. 28, no. 8, pp. 1250-1261, October 2010.
+    """
+
+    GG = AS_graph_generator(n, seed)
+    G = GG.generate()
+    return G

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

@@ -0,0 +1,124 @@
+"""
+Generators for random intersection graphs.
+"""
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "uniform_random_intersection_graph",
+    "k_random_intersection_graph",
+    "general_random_intersection_graph",
+]
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def uniform_random_intersection_graph(n, m, p, seed=None):
+    """Returns a uniform random intersection graph.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : float
+        Probability of connecting nodes between bipartite sets
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph
+
+    References
+    ----------
+    .. [1] K.B. Singer-Cohen, Random Intersection Graphs, 1995,
+       PhD thesis, Johns Hopkins University
+    .. [2] Fill, J. A., Scheinerman, E. R., and Singer-Cohen, K. B.,
+       Random intersection graphs when m = !(n):
+       An equivalence theorem relating the evolution of the g(n, m, p)
+       and g(n, p) models. Random Struct. Algorithms 16, 2 (2000), 156–176.
+    """
+    from networkx.algorithms import bipartite
+
+    G = bipartite.random_graph(n, m, p, seed)
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def k_random_intersection_graph(n, m, k, seed=None):
+    """Returns a intersection graph with randomly chosen attribute sets for
+    each node that are of equal size (k).
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    k : float
+        Size of attribute set to assign to each node.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Godehardt, E., and Jaworski, J.
+       Two models of random intersection graphs and their applications.
+       Electronic Notes in Discrete Mathematics 10 (2001), 129--132.
+    """
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for v in range(n):
+        targets = seed.sample(mset, k)
+        G.add_edges_from(zip([v] * len(targets), targets))
+    return nx.projected_graph(G, range(n))
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def general_random_intersection_graph(n, m, p, seed=None):
+    """Returns a random intersection graph with independent probabilities
+    for connections between node and attribute sets.
+
+    Parameters
+    ----------
+    n : int
+        The number of nodes in the first bipartite set (nodes)
+    m : int
+        The number of nodes in the second bipartite set (attributes)
+    p : list of floats of length m
+        Probabilities for connecting nodes to each attribute
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    See Also
+    --------
+    gnp_random_graph, uniform_random_intersection_graph
+
+    References
+    ----------
+    .. [1] Nikoletseas, S. E., Raptopoulos, C., and Spirakis, P. G.
+       The existence and efficient construction of large independent sets
+       in general random intersection graphs. In ICALP (2004), J. D´ıaz,
+       J. Karhum¨aki, A. Lepist¨o, and D. Sannella, Eds., vol. 3142
+       of Lecture Notes in Computer Science, Springer, pp. 1029–1040.
+    """
+    if len(p) != m:
+        raise ValueError("Probability list p must have m elements.")
+    G = nx.empty_graph(n + m)
+    mset = range(n, n + m)
+    for u in range(n):
+        for v, q in zip(mset, p):
+            if seed.random() < q:
+                G.add_edge(u, v)
+    return nx.projected_graph(G, range(n))

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

@@ -0,0 +1,69 @@
+"""
+Generators for interval graph.
+"""
+from collections.abc import Sequence
+
+import networkx as nx
+
+__all__ = ["interval_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def interval_graph(intervals):
+    """Generates an interval graph for a list of intervals given.
+
+    In graph theory, an interval graph is an undirected graph formed from a set
+    of closed intervals on the real line, with a vertex for each interval
+    and an edge between vertices whose intervals intersect.
+    It is the intersection graph of the intervals.
+
+    More information can be found at:
+    https://en.wikipedia.org/wiki/Interval_graph
+
+    Parameters
+    ----------
+    intervals : a sequence of intervals, say (l, r) where l is the left end,
+    and r is the right end of the closed interval.
+
+    Returns
+    -------
+    G : networkx graph
+
+    Examples
+    --------
+    >>> intervals = [(-2, 3), [1, 4], (2, 3), (4, 6)]
+    >>> G = nx.interval_graph(intervals)
+    >>> sorted(G.edges)
+    [((-2, 3), (1, 4)), ((-2, 3), (2, 3)), ((1, 4), (2, 3)), ((1, 4), (4, 6))]
+
+    Raises
+    ------
+    :exc:`TypeError`
+        if `intervals` contains None or an element which is not
+        collections.abc.Sequence or not a length of 2.
+    :exc:`ValueError`
+        if `intervals` contains an interval such that min1 > max1
+        where min1,max1 = interval
+    """
+    intervals = list(intervals)
+    for interval in intervals:
+        if not (isinstance(interval, Sequence) and len(interval) == 2):
+            raise TypeError(
+                "Each interval must have length 2, and be a "
+                "collections.abc.Sequence such as tuple or list."
+            )
+        if interval[0] > interval[1]:
+            raise ValueError(f"Interval must have lower value first. Got {interval}")
+
+    graph = nx.Graph()
+
+    tupled_intervals = [tuple(interval) for interval in intervals]
+    graph.add_nodes_from(tupled_intervals)
+
+    while tupled_intervals:
+        min1, max1 = interval1 = tupled_intervals.pop()
+        for interval2 in tupled_intervals:
+            min2, max2 = interval2
+            if max1 >= min2 and max2 >= min1:
+                graph.add_edge(interval1, interval2)
+    return graph

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

@@ -0,0 +1,664 @@
+"""Generate graphs with a given joint degree and directed joint degree"""
+
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = [
+    "is_valid_joint_degree",
+    "is_valid_directed_joint_degree",
+    "joint_degree_graph",
+    "directed_joint_degree_graph",
+]
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_joint_degree(joint_degrees):
+    """Checks whether the given joint degree dictionary is realizable.
+
+    A *joint degree dictionary* is a dictionary of dictionaries, in
+    which entry ``joint_degrees[k][l]`` is an integer representing the
+    number of edges joining nodes of degree *k* with nodes of degree
+    *l*. Such a dictionary is realizable as a simple graph if and only
+    if the following conditions are satisfied.
+
+    - each entry must be an integer,
+    - the total number of nodes of degree *k*, computed by
+      ``sum(joint_degrees[k].values()) / k``, must be an integer,
+    - the total number of edges joining nodes of degree *k* with
+      nodes of degree *l* cannot exceed the total number of possible edges,
+    - each diagonal entry ``joint_degrees[k][k]`` must be even (this is
+      a convention assumed by the :func:`joint_degree_graph` function).
+
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]``
+        is the number of edges joining nodes of degree *k* with nodes of
+        degree *l*.
+
+    Returns
+    -------
+    bool
+        Whether the given joint degree dictionary is realizable as a
+        simple graph.
+
+    References
+    ----------
+    .. [1] M. Gjoka, M. Kurant, A. Markopoulou, "2.5K Graphs: from Sampling
+       to Generation", IEEE Infocom, 2013.
+    .. [2] I. Stanton, A. Pinar, "Constructing and sampling graphs with a
+       prescribed joint degree distribution", Journal of Experimental
+       Algorithmics, 2012.
+    """
+
+    degree_count = {}
+    for k in joint_degrees:
+        if k > 0:
+            k_size = sum(joint_degrees[k].values()) / k
+            if not k_size.is_integer():
+                return False
+            degree_count[k] = k_size
+
+    for k in joint_degrees:
+        for l in joint_degrees[k]:
+            if not float(joint_degrees[k][l]).is_integer():
+                return False
+
+            if (k != l) and (joint_degrees[k][l] > degree_count[k] * degree_count[l]):
+                return False
+            elif k == l:
+                if joint_degrees[k][k] > degree_count[k] * (degree_count[k] - 1):
+                    return False
+                if joint_degrees[k][k] % 2 != 0:
+                    return False
+
+    # if all above conditions have been satisfied then the input
+    # joint degree is realizable as a simple graph.
+    return True
+
+
+def _neighbor_switch(G, w, unsat, h_node_residual, avoid_node_id=None):
+    """Releases one free stub for ``w``, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        Graph in which the neighbor switch will take place.
+    w : integer
+        Node id for which we will execute this neighbor switch.
+    unsat : set of integers
+        Set of unsaturated node ids that have the same degree as w.
+    h_node_residual: dictionary of integers
+        Keeps track of the remaining stubs  for a given node.
+    avoid_node_id: integer
+        Node id to avoid when selecting w_prime.
+
+    Notes
+    -----
+    First, it selects *w_prime*, an  unsaturated node that has the same degree
+    as ``w``. Second, it selects *switch_node*, a neighbor node of ``w`` that
+    is not  connected to *w_prime*. Then it executes an edge swap i.e. removes
+    (``w``,*switch_node*) and adds (*w_prime*,*switch_node*). Gjoka et. al. [1]
+    prove that such an edge swap is always possible.
+
+    References
+    ----------
+    .. [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+       Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+    """
+
+    if (avoid_node_id is None) or (h_node_residual[avoid_node_id] > 1):
+        # select unsaturated node w_prime that has the same degree as w
+        w_prime = next(iter(unsat))
+    else:
+        # assume that the node pair (v,w) has been selected for connection. if
+        # - neighbor_switch is called for node w,
+        # - nodes v and w have the same degree,
+        # - node v=avoid_node_id has only one stub left,
+        # then prevent v=avoid_node_id from being selected as w_prime.
+
+        iter_var = iter(unsat)
+        while True:
+            w_prime = next(iter_var)
+            if w_prime != avoid_node_id:
+                break
+
+    # select switch_node, a neighbor of w, that is not connected to w_prime
+    w_prime_neighbs = G[w_prime]  # slightly faster declaring this variable
+    for v in G[w]:
+        if (v not in w_prime_neighbs) and (v != w_prime):
+            switch_node = v
+            break
+
+    # remove edge (w,switch_node), add edge (w_prime,switch_node) and update
+    # data structures
+    G.remove_edge(w, switch_node)
+    G.add_edge(w_prime, switch_node)
+    h_node_residual[w] += 1
+    h_node_residual[w_prime] -= 1
+    if h_node_residual[w_prime] == 0:
+        unsat.remove(w_prime)
+
+
+@py_random_state(1)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def joint_degree_graph(joint_degrees, seed=None):
+    """Generates a random simple graph with the given joint degree dictionary.
+
+    Parameters
+    ----------
+    joint_degrees :  dictionary of dictionary of integers
+        A joint degree dictionary in which entry ``joint_degrees[k][l]`` is the
+        number of edges joining nodes of degree *k* with nodes of degree *l*.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : Graph
+        A graph with the specified joint degree dictionary.
+
+    Raises
+    ------
+    NetworkXError
+        If *joint_degrees* dictionary is not realizable.
+
+    Notes
+    -----
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes *v* and *w*, of degree *k* and *l* correspondingly,  for which
+    ``joint_degrees[k][l]`` has not reached its target yet. It then adds
+    edge (*v*, *w*) and increases the number of edges in graph G by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between such disconnected nodes *v* and *w*,
+    even if one or both nodes do not have free stubs. That is made possible by
+    executing a "neighbor switch", an edge rewiring move that releases
+    a free stub while keeping the joint degree of G the same.
+
+    The algorithm continues for E (number of edges) iterations of
+    the "while loop", at the which point all entries of the given
+    ``joint_degrees[k][l]`` have reached their target values and the
+    construction is complete.
+
+    References
+    ----------
+    ..  [1] M. Gjoka, B. Tillman, A. Markopoulou, "Construction of Simple
+        Graphs with a Target Joint Degree Matrix and Beyond", IEEE Infocom, '15
+
+    Examples
+    --------
+    >>> joint_degrees = {
+    ...     1: {4: 1},
+    ...     2: {2: 2, 3: 2, 4: 2},
+    ...     3: {2: 2, 4: 1},
+    ...     4: {1: 1, 2: 2, 3: 1},
+    ... }
+    >>> G = nx.joint_degree_graph(joint_degrees)
+    >>>
+    """
+
+    if not is_valid_joint_degree(joint_degrees):
+        msg = "Input joint degree dict not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # compute degree count from joint_degrees
+    degree_count = {k: sum(l.values()) // k for k, l in joint_degrees.items() if k > 0}
+
+    # start with empty N-node graph
+    N = sum(degree_count.values())
+    G = nx.empty_graph(N)
+
+    # for a given degree group, keep the list of all node ids
+    h_degree_nodelist = {}
+
+    # for a given node, keep track of the remaining stubs
+    h_node_residual = {}
+
+    # populate h_degree_nodelist and h_node_residual
+    nodeid = 0
+    for degree, num_nodes in degree_count.items():
+        h_degree_nodelist[degree] = range(nodeid, nodeid + num_nodes)
+        for v in h_degree_nodelist[degree]:
+            h_node_residual[v] = degree
+        nodeid += int(num_nodes)
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # 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]
+
+                # k_nodes and l_nodes consist of all nodes of degree k and l
+                k_nodes = h_degree_nodelist[k]
+                l_nodes = h_degree_nodelist[l]
+
+                # k_unsat and l_unsat consist of nodes of degree k and l that
+                # are unsaturated (nodes that have at least 1 available stub)
+                k_unsat = {v for v in k_nodes if h_node_residual[v] > 0}
+
+                if k != l:
+                    l_unsat = {w for w in l_nodes if h_node_residual[w] > 0}
+                else:
+                    l_unsat = k_unsat
+                    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)
+
+                        # if node w has no free stubs then do neighbor switch
+                        if h_node_residual[w] == 0:
+                            if k != l:
+                                _neighbor_switch(G, w, l_unsat, h_node_residual)
+                            else:
+                                _neighbor_switch(
+                                    G, w, l_unsat, h_node_residual, avoid_node_id=v
+                                )
+
+                        # add edge (v, w) and update data structures
+                        G.add_edge(v, w)
+                        h_node_residual[v] -= 1
+                        h_node_residual[w] -= 1
+                        n_edges_add -= 1
+
+                        if h_node_residual[v] == 0:
+                            k_unsat.discard(v)
+                        if h_node_residual[w] == 0:
+                            l_unsat.discard(w)
+    return G
+
+
+@nx._dispatchable(graphs=None)
+def is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+    """Checks whether the given directed joint degree input is realizable
+
+    Parameters
+    ----------
+    in_degrees :  list of integers
+        in degree sequence contains the in degrees of nodes.
+    out_degrees : list of integers
+        out degree sequence contains the out degrees of nodes.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary. for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+
+    Returns
+    -------
+    boolean
+        returns true if given input is realizable, else returns false.
+
+    Notes
+    -----
+    Here is the list of conditions that the inputs (in/out degree sequences,
+    nkk) need to satisfy for simple directed graph realizability:
+
+    - Condition 0: in_degrees and out_degrees have the same length
+    - Condition 1: nkk[k][l]  is integer for all k,l
+    - Condition 2: sum(nkk[k])/k = number of nodes with partition id k, is an
+                   integer and matching degree sequence
+    - Condition 3: number of edges and non-chords between k and l cannot exceed
+                   maximum possible number of edges
+
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    V = {}  # number of nodes with in/out degree.
+    forbidden = {}
+    if len(in_degrees) != len(out_degrees):
+        return False
+
+    for idx in range(len(in_degrees)):
+        i = in_degrees[idx]
+        o = out_degrees[idx]
+        V[(i, 0)] = V.get((i, 0), 0) + 1
+        V[(o, 1)] = V.get((o, 1), 0) + 1
+
+        forbidden[(o, i)] = forbidden.get((o, i), 0) + 1
+
+    S = {}  # number of edges going from in/out degree nodes.
+    for k in nkk:
+        for l in nkk[k]:
+            val = nkk[k][l]
+            if not float(val).is_integer():  # condition 1
+                return False
+
+            if val > 0:
+                S[(k, 1)] = S.get((k, 1), 0) + val
+                S[(l, 0)] = S.get((l, 0), 0) + val
+                # condition 3
+                if val + forbidden.get((k, l), 0) > V[(k, 1)] * V[(l, 0)]:
+                    return False
+
+    return all(S[s] / s[0] == V[s] for s in S)
+
+
+def _directed_neighbor_switch(
+    G, w, unsat, h_node_residual_out, chords, h_partition_in, partition
+):
+    """Releases one free stub for node w, while preserving joint degree in G.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_out: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_in: dict of integers
+        for a given node, keeps track of its partition id (in degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    First, it selects node w_prime that (1) has the same degree as w and
+    (2) is unsaturated. Then, it selects node v, a neighbor of w, that is
+    not connected to w_prime and does an edge swap i.e. removes (w,v) and
+    adds (w_prime,v). If neighbor switch is not possible for w using
+    w_prime and v, then return w_prime; in [1] it's proven that
+    such unsaturated nodes can be used.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # select node t, a neighbor of w, that is not connected to w_prime
+    w_neighbs = list(G.successors(w))
+    # slightly faster declaring this variable
+    w_prime_neighbs = list(G.successors(w_prime))
+
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (w,v), add (w_prime,v)  and update data structures
+            G.remove_edge(w, v)
+            G.add_edge(w_prime, v)
+
+            if h_partition_in[v] == partition:
+                chords.add((w, v))
+                chords.discard((w_prime, v))
+
+            h_node_residual_out[w] += 1
+            h_node_residual_out[w_prime] -= 1
+            if h_node_residual_out[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use unsaturated node
+    return w_prime
+
+
+def _directed_neighbor_switch_rev(
+    G, w, unsat, h_node_residual_in, chords, h_partition_out, partition
+):
+    """The reverse of directed_neighbor_switch.
+
+    Parameters
+    ----------
+    G : networkx directed graph
+        graph within which the edge swap will take place.
+    w : integer
+        node id for which we need to perform a neighbor switch.
+    unsat: set of integers
+        set of node ids that have the same degree as w and are unsaturated.
+    h_node_residual_in: dict of integers
+        for a given node, keeps track of the remaining stubs to be added.
+    chords: set of tuples
+        keeps track of available positions to add edges.
+    h_partition_out: dict of integers
+        for a given node, keeps track of its partition id (out degree).
+    partition: integer
+        partition id to check if chords have to be updated.
+
+    Notes
+    -----
+    Same operation as directed_neighbor_switch except it handles this operation
+    for incoming edges instead of outgoing.
+    """
+    w_prime = unsat.pop()
+    unsat.add(w_prime)
+    # slightly faster declaring these as variables.
+    w_neighbs = list(G.predecessors(w))
+    w_prime_neighbs = list(G.predecessors(w_prime))
+    # select node v, a neighbor of w, that is not connected to w_prime.
+    for v in w_neighbs:
+        if (v not in w_prime_neighbs) and w_prime != v:
+            # removes (v,w), add (v,w_prime) and update data structures.
+            G.remove_edge(v, w)
+            G.add_edge(v, w_prime)
+            if h_partition_out[v] == partition:
+                chords.add((v, w))
+                chords.discard((v, w_prime))
+
+            h_node_residual_in[w] += 1
+            h_node_residual_in[w_prime] -= 1
+            if h_node_residual_in[w_prime] == 0:
+                unsat.remove(w_prime)
+            return None
+
+    # If neighbor switch didn't work, use the unsaturated node.
+    return w_prime
+
+
+@py_random_state(3)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def directed_joint_degree_graph(in_degrees, out_degrees, nkk, seed=None):
+    """Generates a random simple directed graph with the joint degree.
+
+    Parameters
+    ----------
+    degree_seq :  list of tuples (of size 3)
+        degree sequence contains tuples of nodes with node id, in degree and
+        out degree.
+    nkk  :  dictionary of dictionary of integers
+        directed joint degree dictionary, for nodes of out degree k (first
+        level of dict) and nodes of in degree l (second level of dict)
+        describes the number of edges.
+    seed : hashable object, optional
+        Seed for random number generator.
+
+    Returns
+    -------
+    G : Graph
+        A directed graph with the specified inputs.
+
+    Raises
+    ------
+    NetworkXError
+        If degree_seq and nkk are not realizable as a simple directed graph.
+
+
+    Notes
+    -----
+    Similarly to the undirected version:
+    In each iteration of the "while loop" the algorithm picks two disconnected
+    nodes v and w, of degree k and l correspondingly,  for which nkk[k][l] has
+    not reached its target yet i.e. (for given k,l): n_edges_add < nkk[k][l].
+    It then adds edge (v,w) and always increases the number of edges in graph G
+    by one.
+
+    The intelligence of the algorithm lies in the fact that  it is always
+    possible to add an edge between disconnected nodes v and w, for which
+    nkk[degree(v)][degree(w)] has not reached its target, even if one or both
+    nodes do not have free stubs. If either node v or w does not have a free
+    stub, we perform a "neighbor switch", an edge rewiring move that releases a
+    free stub while keeping nkk the same.
+
+    The difference for the directed version lies in the fact that neighbor
+    switches might not be able to rewire, but in these cases unsaturated nodes
+    can be reassigned to use instead, see [1] for detailed description and
+    proofs.
+
+    The algorithm continues for E (number of edges in the graph) iterations of
+    the "while loop", at which point all entries of the given nkk[k][l] have
+    reached their target values and the construction is complete.
+
+    References
+    ----------
+    [1] B. Tillman, A. Markopoulou, C. T. Butts & M. Gjoka,
+        "Construction of Directed 2K Graphs". In Proc. of KDD 2017.
+
+    Examples
+    --------
+    >>> in_degrees = [0, 1, 1, 2]
+    >>> out_degrees = [1, 1, 1, 1]
+    >>> nkk = {1: {1: 2, 2: 2}}
+    >>> G = nx.directed_joint_degree_graph(in_degrees, out_degrees, nkk)
+    >>>
+    """
+    if not is_valid_directed_joint_degree(in_degrees, out_degrees, nkk):
+        msg = "Input is not realizable as a simple graph"
+        raise nx.NetworkXError(msg)
+
+    # start with an empty directed graph.
+    G = nx.DiGraph()
+
+    # for a given group, keep the list of all node ids.
+    h_degree_nodelist_in = {}
+    h_degree_nodelist_out = {}
+    # for a given group, keep the list of all unsaturated node ids.
+    h_degree_nodelist_in_unsat = {}
+    h_degree_nodelist_out_unsat = {}
+    # for a given node, keep track of the remaining stubs to be added.
+    h_node_residual_out = {}
+    h_node_residual_in = {}
+    # for a given node, keep track of the partition id.
+    h_partition_out = {}
+    h_partition_in = {}
+    # keep track of non-chords between pairs of partition ids.
+    non_chords = {}
+
+    # populate data structures
+    for idx, i in enumerate(in_degrees):
+        idx = int(idx)
+        if i > 0:
+            h_degree_nodelist_in.setdefault(i, [])
+            h_degree_nodelist_in_unsat.setdefault(i, set())
+            h_degree_nodelist_in[i].append(idx)
+            h_degree_nodelist_in_unsat[i].add(idx)
+            h_node_residual_in[idx] = i
+            h_partition_in[idx] = i
+
+    for idx, o in enumerate(out_degrees):
+        o = out_degrees[idx]
+        non_chords[(o, in_degrees[idx])] = non_chords.get((o, in_degrees[idx]), 0) + 1
+        idx = int(idx)
+        if o > 0:
+            h_degree_nodelist_out.setdefault(o, [])
+            h_degree_nodelist_out_unsat.setdefault(o, set())
+            h_degree_nodelist_out[o].append(idx)
+            h_degree_nodelist_out_unsat[o].add(idx)
+            h_node_residual_out[idx] = o
+            h_partition_out[idx] = o
+
+        G.add_node(idx)
+
+    nk_in = {}
+    nk_out = {}
+    for p in h_degree_nodelist_in:
+        nk_in[p] = len(h_degree_nodelist_in[p])
+    for p in h_degree_nodelist_out:
+        nk_out[p] = len(h_degree_nodelist_out[p])
+
+    # iterate over every degree pair (k,l) and add the number of edges given
+    # for each pair.
+    for k in nkk:
+        for l in nkk[k]:
+            n_edges_add = nkk[k][l]
+
+            if n_edges_add > 0:
+                # chords contains a random set of potential edges.
+                chords = set()
+
+                k_len = nk_out[k]
+                l_len = nk_in[l]
+                chords_sample = seed.sample(
+                    range(k_len * l_len), n_edges_add + non_chords.get((k, l), 0)
+                )
+
+                num = 0
+                while len(chords) < n_edges_add:
+                    i = h_degree_nodelist_out[k][chords_sample[num] % k_len]
+                    j = h_degree_nodelist_in[l][chords_sample[num] // k_len]
+                    num += 1
+                    if i != j:
+                        chords.add((i, j))
+
+                # k_unsat and l_unsat consist of nodes of in/out degree k and l
+                # that are unsaturated i.e. those nodes that have at least one
+                # available stub
+                k_unsat = h_degree_nodelist_out_unsat[k]
+                l_unsat = h_degree_nodelist_in_unsat[l]
+
+                while n_edges_add > 0:
+                    v, w = chords.pop()
+                    chords.add((v, w))
+
+                    # if node v has no free stubs then do neighbor switch.
+                    if h_node_residual_out[v] == 0:
+                        _v = _directed_neighbor_switch(
+                            G,
+                            v,
+                            k_unsat,
+                            h_node_residual_out,
+                            chords,
+                            h_partition_in,
+                            l,
+                        )
+                        if _v is not None:
+                            v = _v
+
+                    # if node w has no free stubs then do neighbor switch.
+                    if h_node_residual_in[w] == 0:
+                        _w = _directed_neighbor_switch_rev(
+                            G,
+                            w,
+                            l_unsat,
+                            h_node_residual_in,
+                            chords,
+                            h_partition_out,
+                            k,
+                        )
+                        if _w is not None:
+                            w = _w
+
+                    # add edge (v,w) and update data structures.
+                    G.add_edge(v, w)
+                    h_node_residual_out[v] -= 1
+                    h_node_residual_in[w] -= 1
+                    n_edges_add -= 1
+                    chords.discard((v, w))
+
+                    if h_node_residual_out[v] == 0:
+                        k_unsat.discard(v)
+                    if h_node_residual_in[w] == 0:
+                        l_unsat.discard(w)
+    return G

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

@@ -0,0 +1,367 @@
+"""Functions for generating grid graphs and lattices
+
+The :func:`grid_2d_graph`, :func:`triangular_lattice_graph`, and
+:func:`hexagonal_lattice_graph` functions correspond to the three
+`regular tilings of the plane`_, the square, triangular, and hexagonal
+tilings, respectively. :func:`grid_graph` and :func:`hypercube_graph`
+are similar for arbitrary dimensions. Useful relevant discussion can
+be found about `Triangular Tiling`_, and `Square, Hex and Triangle Grids`_
+
+.. _regular tilings of the plane: https://en.wikipedia.org/wiki/List_of_regular_polytopes_and_compounds#Euclidean_tilings
+.. _Square, Hex and Triangle Grids: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+.. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+"""
+
+from itertools import repeat
+from math import sqrt
+
+import networkx as nx
+from networkx.classes import set_node_attributes
+from networkx.exception import NetworkXError
+from networkx.generators.classic import cycle_graph, empty_graph, path_graph
+from networkx.relabel import relabel_nodes
+from networkx.utils import flatten, nodes_or_number, pairwise
+
+__all__ = [
+    "grid_2d_graph",
+    "grid_graph",
+    "hypercube_graph",
+    "triangular_lattice_graph",
+    "hexagonal_lattice_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+@nodes_or_number([0, 1])
+def grid_2d_graph(m, n, periodic=False, create_using=None):
+    """Returns the two-dimensional grid graph.
+
+    The grid graph has each node connected to its four nearest neighbors.
+
+    Parameters
+    ----------
+    m, n : int or iterable container of nodes
+        If an integer, nodes are from `range(n)`.
+        If a container, elements become the coordinate of the nodes.
+
+    periodic : bool or iterable
+        If `periodic` is True, both dimensions are periodic. If False, none
+        are periodic.  If `periodic` is iterable, it should yield 2 bool
+        values indicating whether the 1st and 2nd axes, respectively, are
+        periodic.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    """
+    G = empty_graph(0, create_using)
+    row_name, rows = m
+    col_name, cols = n
+    G.add_nodes_from((i, j) for i in rows for j in cols)
+    G.add_edges_from(((i, j), (pi, j)) for pi, i in pairwise(rows) for j in cols)
+    G.add_edges_from(((i, j), (i, pj)) for i in rows for pj, j in pairwise(cols))
+
+    try:
+        periodic_r, periodic_c = periodic
+    except TypeError:
+        periodic_r = periodic_c = periodic
+
+    if periodic_r and len(rows) > 2:
+        first = rows[0]
+        last = rows[-1]
+        G.add_edges_from(((first, j), (last, j)) for j in cols)
+    if periodic_c and len(cols) > 2:
+        first = cols[0]
+        last = cols[-1]
+        G.add_edges_from(((i, first), (i, last)) for i in rows)
+    # both directions for directed
+    if G.is_directed():
+        G.add_edges_from((v, u) for u, v in G.edges())
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def grid_graph(dim, periodic=False):
+    """Returns the *n*-dimensional grid graph.
+
+    The dimension *n* is the length of the list `dim` and the size in
+    each dimension is the value of the corresponding list element.
+
+    Parameters
+    ----------
+    dim : list or tuple of numbers or iterables of nodes
+        'dim' is a tuple or list with, for each dimension, either a number
+        that is the size of that dimension or an iterable of nodes for
+        that dimension. The dimension of the grid_graph is the length
+        of `dim`.
+
+    periodic : bool or iterable
+        If `periodic` is True, all dimensions are periodic. If False all
+        dimensions are not periodic. If `periodic` is iterable, it should
+        yield `dim` bool values each of which indicates whether the
+        corresponding axis is periodic.
+
+    Returns
+    -------
+    NetworkX graph
+        The (possibly periodic) grid graph of the specified dimensions.
+
+    Examples
+    --------
+    To produce a 2 by 3 by 4 grid graph, a graph on 24 nodes:
+
+    >>> from networkx import grid_graph
+    >>> G = grid_graph(dim=(2, 3, 4))
+    >>> len(G)
+    24
+    >>> G = grid_graph(dim=(range(7, 9), range(3, 6)))
+    >>> len(G)
+    6
+    """
+    from networkx.algorithms.operators.product import cartesian_product
+
+    if not dim:
+        return empty_graph(0)
+
+    try:
+        func = (cycle_graph if p else path_graph for p in periodic)
+    except TypeError:
+        func = repeat(cycle_graph if periodic else path_graph)
+
+    G = next(func)(dim[0])
+    for current_dim in dim[1:]:
+        Gnew = next(func)(current_dim)
+        G = cartesian_product(Gnew, G)
+    # graph G is done but has labels of the form (1, (2, (3, 1))) so relabel
+    H = relabel_nodes(G, flatten)
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hypercube_graph(n):
+    """Returns the *n*-dimensional hypercube graph.
+
+    The nodes are the integers between 0 and ``2 ** n - 1``, inclusive.
+
+    For more information on the hypercube graph, see the Wikipedia
+    article `Hypercube graph`_.
+
+    .. _Hypercube graph: https://en.wikipedia.org/wiki/Hypercube_graph
+
+    Parameters
+    ----------
+    n : int
+        The dimension of the hypercube.
+        The number of nodes in the graph will be ``2 ** n``.
+
+    Returns
+    -------
+    NetworkX graph
+        The hypercube graph of dimension *n*.
+    """
+    dim = n * [2]
+    G = grid_graph(dim)
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def triangular_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    r"""Returns the $m$ by $n$ triangular lattice graph.
+
+    The `triangular lattice graph`_ is a two-dimensional `grid graph`_ in
+    which each square unit has a diagonal edge (each grid unit has a chord).
+
+    The returned graph has $m$ rows and $n$ columns of triangles. Rows and
+    columns include both triangles pointing up and down. Rows form a strip
+    of constant height. Columns form a series of diamond shapes, staggered
+    with the columns on either side. Another way to state the size is that
+    the nodes form a grid of `m+1` rows and `(n + 1) // 2` columns.
+    The odd row nodes are shifted horizontally relative to the even rows.
+
+    Directed graph types have edges pointed up or right.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    The position of each node (embedded in a euclidean plane) is stored in
+    the graph using equilateral triangles with sidelength 1.
+    The height between rows of nodes is thus $\sqrt(3)/2$.
+    Nodes lie in the first quadrant with the node $(0, 0)$ at the origin.
+
+    .. _triangular lattice graph: http://mathworld.wolfram.com/TriangularGrid.html
+    .. _grid graph: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+    .. _Triangular Tiling: https://en.wikipedia.org/wiki/Triangular_tiling
+
+    Parameters
+    ----------
+    m : int
+        The number of rows in the lattice.
+
+    n : int
+        The number of columns in the lattice.
+
+    periodic : bool (default: False)
+        If True, join the boundary vertices of the grid using periodic
+        boundary conditions. The join between boundaries is the final row
+        and column of triangles. This means there is one row and one column
+        fewer nodes for the periodic lattice. Periodic lattices require
+        `m >= 3`, `n >= 5` and are allowed but misaligned if `m` or `n` are odd
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with equilateral triangles.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* triangular lattice graph.
+    """
+    H = empty_graph(0, create_using)
+    if n == 0 or m == 0:
+        return H
+    if periodic:
+        if n < 5 or m < 3:
+            msg = f"m > 2 and n > 4 required for periodic. m={m}, n={n}"
+            raise NetworkXError(msg)
+
+    N = (n + 1) // 2  # number of nodes in row
+    rows = range(m + 1)
+    cols = range(N + 1)
+    # Make grid
+    H.add_edges_from(((i, j), (i + 1, j)) for j in rows for i in cols[:N])
+    H.add_edges_from(((i, j), (i, j + 1)) for j in rows[:m] for i in cols)
+    # add diagonals
+    H.add_edges_from(((i, j), (i + 1, j + 1)) for j in rows[1:m:2] for i in cols[:N])
+    H.add_edges_from(((i + 1, j), (i, j + 1)) for j in rows[:m:2] for i in cols[:N])
+    # identify boundary nodes if periodic
+    from networkx.algorithms.minors import contracted_nodes
+
+    if periodic is True:
+        for i in cols:
+            H = contracted_nodes(H, (i, 0), (i, m))
+        for j in rows[:m]:
+            H = contracted_nodes(H, (0, j), (N, j))
+    elif n % 2:
+        # remove extra nodes
+        H.remove_nodes_from((N, j) for j in rows[1::2])
+
+    # Add position node attributes
+    if with_positions:
+        ii = (i for i in cols for j in rows)
+        jj = (j for i in cols for j in rows)
+        xx = (0.5 * (j % 2) + i for i in cols for j in rows)
+        h = sqrt(3) / 2
+        if periodic:
+            yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+        else:
+            yy = (h * j for i in cols for j in rows)
+        pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in H}
+        set_node_attributes(H, pos, "pos")
+    return H
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hexagonal_lattice_graph(
+    m, n, periodic=False, with_positions=True, create_using=None
+):
+    """Returns an `m` by `n` hexagonal lattice graph.
+
+    The *hexagonal lattice graph* is a graph whose nodes and edges are
+    the `hexagonal tiling`_ of the plane.
+
+    The returned graph will have `m` rows and `n` columns of hexagons.
+    `Odd numbered columns`_ are shifted up relative to even numbered columns.
+
+    Positions of nodes are computed by default or `with_positions is True`.
+    Node positions creating the standard embedding in the plane
+    with sidelength 1 and are stored in the node attribute 'pos'.
+    `pos = nx.get_node_attributes(G, 'pos')` creates a dict ready for drawing.
+
+    .. _hexagonal tiling: https://en.wikipedia.org/wiki/Hexagonal_tiling
+    .. _Odd numbered columns: http://www-cs-students.stanford.edu/~amitp/game-programming/grids/
+
+    Parameters
+    ----------
+    m : int
+        The number of rows of hexagons in the lattice.
+
+    n : int
+        The number of columns of hexagons in the lattice.
+
+    periodic : bool
+        Whether to make a periodic grid by joining the boundary vertices.
+        For this to work `n` must be even and both `n > 1` and `m > 1`.
+        The periodic connections create another row and column of hexagons
+        so these graphs have fewer nodes as boundary nodes are identified.
+
+    with_positions : bool (default: True)
+        Store the coordinates of each node in the graph node attribute 'pos'.
+        The coordinates provide a lattice with vertical columns of hexagons
+        offset to interleave and cover the plane.
+        Periodic positions shift the nodes vertically in a nonlinear way so
+        the edges don't overlap so much.
+
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        If graph is directed, edges will point up or right.
+
+    Returns
+    -------
+    NetworkX graph
+        The *m* by *n* hexagonal lattice graph.
+    """
+    G = empty_graph(0, create_using)
+    if m == 0 or n == 0:
+        return G
+    if periodic and (n % 2 == 1 or m < 2 or n < 2):
+        msg = "periodic hexagonal lattice needs m > 1, n > 1 and even n"
+        raise NetworkXError(msg)
+
+    M = 2 * m  # twice as many nodes as hexagons vertically
+    rows = range(M + 2)
+    cols = range(n + 1)
+    # make lattice
+    col_edges = (((i, j), (i, j + 1)) for i in cols for j in rows[: M + 1])
+    row_edges = (((i, j), (i + 1, j)) for i in cols[:n] for j in rows if i % 2 == j % 2)
+    G.add_edges_from(col_edges)
+    G.add_edges_from(row_edges)
+    # Remove corner nodes with one edge
+    G.remove_node((0, M + 1))
+    G.remove_node((n, (M + 1) * (n % 2)))
+
+    # identify boundary nodes if periodic
+    from networkx.algorithms.minors import contracted_nodes
+
+    if periodic:
+        for i in cols[:n]:
+            G = contracted_nodes(G, (i, 0), (i, M))
+        for i in cols[1:]:
+            G = contracted_nodes(G, (i, 1), (i, M + 1))
+        for j in rows[1:M]:
+            G = contracted_nodes(G, (0, j), (n, j))
+        G.remove_node((n, M))
+
+    # calc position in embedded space
+    ii = (i for i in cols for j in rows)
+    jj = (j for i in cols for j in rows)
+    xx = (0.5 + i + i // 2 + (j % 2) * ((i % 2) - 0.5) for i in cols for j in rows)
+    h = sqrt(3) / 2
+    if periodic:
+        yy = (h * j + 0.01 * i * i for i in cols for j in rows)
+    else:
+        yy = (h * j for i in cols for j in rows)
+    # exclude nodes not in G
+    pos = {(i, j): (x, y) for i, j, x, y in zip(ii, jj, xx, yy) if (i, j) in G}
+    set_node_attributes(G, pos, "pos")
+    return G

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

@@ -0,0 +1,499 @@
+"""Functions for generating line graphs."""
+from collections import defaultdict
+from functools import partial
+from itertools import combinations
+
+import networkx as nx
+from networkx.utils import arbitrary_element
+from networkx.utils.decorators import not_implemented_for
+
+__all__ = ["line_graph", "inverse_line_graph"]
+
+
+@nx._dispatchable(returns_graph=True)
+def line_graph(G, create_using=None):
+    r"""Returns the line graph of the graph or digraph `G`.
+
+    The line graph of a graph `G` has a node for each edge in `G` and an
+    edge joining those nodes if the two edges in `G` share a common node. For
+    directed graphs, nodes are adjacent exactly when the edges they represent
+    form a directed path of length two.
+
+    The nodes of the line graph are 2-tuples of nodes in the original graph (or
+    3-tuples for multigraphs, with the key of the edge as the third element).
+
+    For information about self-loops and more discussion, see the **Notes**
+    section below.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph, DiGraph, MultiGraph, or MultiDigraph.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    L : graph
+        The line graph of G.
+
+    Examples
+    --------
+    >>> G = nx.star_graph(3)
+    >>> L = nx.line_graph(G)
+    >>> print(sorted(map(sorted, L.edges())))  # makes a 3-clique, K3
+    [[(0, 1), (0, 2)], [(0, 1), (0, 3)], [(0, 2), (0, 3)]]
+
+    Edge attributes from `G` are not copied over as node attributes in `L`, but
+    attributes can be copied manually:
+
+    >>> G = nx.path_graph(4)
+    >>> G.add_edges_from((u, v, {"tot": u + v}) for u, v in G.edges)
+    >>> G.edges(data=True)
+    EdgeDataView([(0, 1, {'tot': 1}), (1, 2, {'tot': 3}), (2, 3, {'tot': 5})])
+    >>> H = nx.line_graph(G)
+    >>> H.add_nodes_from((node, G.edges[node]) for node in H)
+    >>> H.nodes(data=True)
+    NodeDataView({(0, 1): {'tot': 1}, (2, 3): {'tot': 5}, (1, 2): {'tot': 3}})
+
+    Notes
+    -----
+    Graph, node, and edge data are not propagated to the new graph. For
+    undirected graphs, the nodes in G must be sortable, otherwise the
+    constructed line graph may not be correct.
+
+    *Self-loops in undirected graphs*
+
+    For an undirected graph `G` without multiple edges, each edge can be
+    written as a set `\{u, v\}`.  Its line graph `L` has the edges of `G` as
+    its nodes. If `x` and `y` are two nodes in `L`, then `\{x, y\}` is an edge
+    in `L` if and only if the intersection of `x` and `y` is nonempty. Thus,
+    the set of all edges is determined by the set of all pairwise intersections
+    of edges in `G`.
+
+    Trivially, every edge in G would have a nonzero intersection with itself,
+    and so every node in `L` should have a self-loop. This is not so
+    interesting, and the original context of line graphs was with simple
+    graphs, which had no self-loops or multiple edges. The line graph was also
+    meant to be a simple graph and thus, self-loops in `L` are not part of the
+    standard definition of a line graph. In a pairwise intersection matrix,
+    this is analogous to excluding the diagonal entries from the line graph
+    definition.
+
+    Self-loops and multiple edges in `G` add nodes to `L` in a natural way, and
+    do not require any fundamental changes to the definition. It might be
+    argued that the self-loops we excluded before should now be included.
+    However, the self-loops are still "trivial" in some sense and thus, are
+    usually excluded.
+
+    *Self-loops in directed graphs*
+
+    For a directed graph `G` without multiple edges, each edge can be written
+    as a tuple `(u, v)`. Its line graph `L` has the edges of `G` as its
+    nodes. If `x` and `y` are two nodes in `L`, then `(x, y)` is an edge in `L`
+    if and only if the tail of `x` matches the head of `y`, for example, if `x
+    = (a, b)` and `y = (b, c)` for some vertices `a`, `b`, and `c` in `G`.
+
+    Due to the directed nature of the edges, it is no longer the case that
+    every edge in `G` should have a self-loop in `L`. Now, the only time
+    self-loops arise is if a node in `G` itself has a self-loop.  So such
+    self-loops are no longer "trivial" but instead, represent essential
+    features of the topology of `G`. For this reason, the historical
+    development of line digraphs is such that self-loops are included. When the
+    graph `G` has multiple edges, once again only superficial changes are
+    required to the definition.
+
+    References
+    ----------
+    * Harary, Frank, and Norman, Robert Z., "Some properties of line digraphs",
+      Rend. Circ. Mat. Palermo, II. Ser. 9 (1960), 161--168.
+    * Hemminger, R. L.; Beineke, L. W. (1978), "Line graphs and line digraphs",
+      in Beineke, L. W.; Wilson, R. J., Selected Topics in Graph Theory,
+      Academic Press Inc., pp. 271--305.
+
+    """
+    if G.is_directed():
+        L = _lg_directed(G, create_using=create_using)
+    else:
+        L = _lg_undirected(G, selfloops=False, create_using=create_using)
+    return L
+
+
+def _lg_directed(G, create_using=None):
+    """Returns the line graph L of the (multi)digraph G.
+
+    Edges in G appear as nodes in L, represented as tuples of the form (u,v)
+    or (u,v,key) if G is a multidigraph. A node in L corresponding to the edge
+    (u,v) is connected to every node corresponding to an edge (v,w).
+
+    Parameters
+    ----------
+    G : digraph
+        A directed graph or directed multigraph.
+    create_using : NetworkX graph constructor, optional
+       Graph type to create. If graph instance, then cleared before populated.
+       Default is to use the same graph class as `G`.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Create a graph specific edge function.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    for from_node in get_edges():
+        # from_node is: (u,v) or (u,v,key)
+        L.add_node(from_node)
+        for to_node in get_edges(from_node[1]):
+            L.add_edge(from_node, to_node)
+
+    return L
+
+
+def _lg_undirected(G, selfloops=False, create_using=None):
+    """Returns the line graph L of the (multi)graph G.
+
+    Edges in G appear as nodes in L, represented as sorted tuples of the form
+    (u,v), or (u,v,key) if G is a multigraph. A node in L corresponding to
+    the edge {u,v} is connected to every node corresponding to an edge that
+    involves u or v.
+
+    Parameters
+    ----------
+    G : graph
+        An undirected graph or multigraph.
+    selfloops : bool
+        If `True`, then self-loops are included in the line graph. If `False`,
+        they are excluded.
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Notes
+    -----
+    The standard algorithm for line graphs of undirected graphs does not
+    produce self-loops.
+
+    """
+    L = nx.empty_graph(0, create_using, default=G.__class__)
+
+    # Graph specific functions for edges.
+    get_edges = partial(G.edges, keys=True) if G.is_multigraph() else G.edges
+
+    # Determine if we include self-loops or not.
+    shift = 0 if selfloops else 1
+
+    # Introduce numbering of nodes
+    node_index = {n: i for i, n in enumerate(G)}
+
+    # Lift canonical representation of nodes to edges in line graph
+    edge_key_function = lambda edge: (node_index[edge[0]], node_index[edge[1]])
+
+    edges = set()
+    for u in G:
+        # Label nodes as a sorted tuple of nodes in original graph.
+        # Decide on representation of {u, v} as (u, v) or (v, u) depending on node_index.
+        # -> This ensures a canonical representation and avoids comparing values of different types.
+        nodes = [tuple(sorted(x[:2], key=node_index.get)) + x[2:] for x in get_edges(u)]
+
+        if len(nodes) == 1:
+            # Then the edge will be an isolated node in L.
+            L.add_node(nodes[0])
+
+        # Add a clique of `nodes` to graph. To prevent double adding edges,
+        # especially important for multigraphs, we store the edges in
+        # canonical form in a set.
+        for i, a in enumerate(nodes):
+            edges.update(
+                [
+                    tuple(sorted((a, b), key=edge_key_function))
+                    for b in nodes[i + shift :]
+                ]
+            )
+
+    L.add_edges_from(edges)
+    return L
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def inverse_line_graph(G):
+    """Returns the inverse line graph of graph G.
+
+    If H is a graph, and G is the line graph of H, such that G = L(H).
+    Then H is the inverse line graph of G.
+
+    Not all graphs are line graphs and these do not have an inverse line graph.
+    In these cases this function raises a NetworkXError.
+
+    Parameters
+    ----------
+    G : graph
+        A NetworkX Graph
+
+    Returns
+    -------
+    H : graph
+        The inverse line graph of G.
+
+    Raises
+    ------
+    NetworkXNotImplemented
+        If G is directed or a multigraph
+
+    NetworkXError
+        If G is not a line graph
+
+    Notes
+    -----
+    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:
+
+    >>> K5 = nx.complete_graph(5)
+    >>> P4 = nx.Graph([("a", "b"), ("b", "c"), ("c", "d")])
+    >>> G = nx.union(K5, P4)
+    >>> root_graphs = []
+    >>> for comp in nx.connected_components(G):
+    ...     root_graphs.append(nx.inverse_line_graph(G.subgraph(comp)))
+    >>> len(root_graphs)
+    2
+
+    References
+    ----------
+    .. [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:
+        return nx.empty_graph(1)
+    elif G.number_of_nodes() == 1:
+        v = arbitrary_element(G)
+        a = (v, 0)
+        b = (v, 1)
+        H = nx.Graph([(a, b)])
+        return H
+    elif G.number_of_nodes() > 1 and G.number_of_edges() == 0:
+        msg = (
+            "inverse_line_graph() doesn't work on an edgeless graph. "
+            "Please use this function on each component separately."
+        )
+        raise nx.NetworkXError(msg)
+
+    if nx.number_of_selfloops(G) != 0:
+        msg = (
+            "A line graph as generated by NetworkX has no selfloops, so G has no "
+            "inverse line graph. Please remove the selfloops from G and try again."
+        )
+        raise nx.NetworkXError(msg)
+
+    starting_cell = _select_starting_cell(G)
+    P = _find_partition(G, starting_cell)
+    # count how many times each vertex appears in the partition set
+    P_count = {u: 0 for u in G.nodes}
+    for p in P:
+        for u in p:
+            P_count[u] += 1
+
+    if max(P_count.values()) > 2:
+        msg = "G is not a line graph (vertex found in more than two partition cells)"
+        raise nx.NetworkXError(msg)
+    W = tuple((u,) for u in P_count if P_count[u] == 1)
+    H = nx.Graph()
+    H.add_nodes_from(P)
+    H.add_nodes_from(W)
+    for a, b in combinations(H.nodes, 2):
+        if any(a_bit in b for a_bit in a):
+            H.add_edge(a, b)
+    return H
+
+
+def _triangles(G, e):
+    """Return list of all triangles containing edge e"""
+    u, v = e
+    if u not in G:
+        raise nx.NetworkXError(f"Vertex {u} not in graph")
+    if v not in G[u]:
+        raise nx.NetworkXError(f"Edge ({u}, {v}) not in graph")
+    triangle_list = []
+    for x in G[u]:
+        if x in G[v]:
+            triangle_list.append((u, v, x))
+    return triangle_list
+
+
+def _odd_triangle(G, T):
+    """Test whether T is an odd triangle in G
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    T : 3-tuple of vertices forming triangle in G
+
+    Returns
+    -------
+    True is T is an odd triangle
+    False otherwise
+
+    Raises
+    ------
+    NetworkXError
+        T is not a triangle in G
+
+    Notes
+    -----
+    An odd triangle is one in which there exists another vertex in G which is
+    adjacent to either exactly one or exactly all three of the vertices in the
+    triangle.
+
+    """
+    for u in T:
+        if u not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {u} not in graph")
+    for e in list(combinations(T, 2)):
+        if e[0] not in G[e[1]]:
+            raise nx.NetworkXError(f"Edge ({e[0]}, {e[1]}) not in graph")
+
+    T_nbrs = defaultdict(int)
+    for t in T:
+        for v in G[t]:
+            if v not in T:
+                T_nbrs[v] += 1
+    return any(T_nbrs[v] in [1, 3] for v in T_nbrs)
+
+
+def _find_partition(G, starting_cell):
+    """Find a partition of the vertices of G into cells of complete graphs
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_cell : tuple of vertices in G which form a cell
+
+    Returns
+    -------
+    List of tuples of vertices of G
+
+    Raises
+    ------
+    NetworkXError
+        If a cell is not a complete subgraph then G is not a line graph
+    """
+    G_partition = G.copy()
+    P = [starting_cell]  # partition set
+    G_partition.remove_edges_from(list(combinations(starting_cell, 2)))
+    # keep list of partitioned nodes which might have an edge in G_partition
+    partitioned_vertices = list(starting_cell)
+    while G_partition.number_of_edges() > 0:
+        # there are still edges left and so more cells to be made
+        u = partitioned_vertices.pop()
+        deg_u = len(G_partition[u])
+        if deg_u != 0:
+            # if u still has edges then we need to find its other cell
+            # this other cell must be a complete subgraph or else G is
+            # not a line graph
+            new_cell = [u] + list(G_partition[u])
+            for u in new_cell:
+                for v in new_cell:
+                    if (u != v) and (v not in G_partition[u]):
+                        msg = (
+                            "G is not a line graph "
+                            "(partition cell not a complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            P.append(tuple(new_cell))
+            G_partition.remove_edges_from(list(combinations(new_cell, 2)))
+            partitioned_vertices += new_cell
+    return P
+
+
+def _select_starting_cell(G, starting_edge=None):
+    """Select a cell to initiate _find_partition
+
+    Parameters
+    ----------
+    G : NetworkX Graph
+    starting_edge: an edge to build the starting cell from
+
+    Returns
+    -------
+    Tuple of vertices in G
+
+    Raises
+    ------
+    NetworkXError
+        If it is determined that G is not a line graph
+
+    Notes
+    -----
+    If starting edge not specified then pick an arbitrary edge - doesn't
+    matter which. However, this function may call itself requiring a
+    specific starting edge. Note that the r, s notation for counting
+    triangles is the same as in the Roussopoulos paper cited above.
+    """
+    if starting_edge is None:
+        e = arbitrary_element(G.edges())
+    else:
+        e = starting_edge
+        if e[0] not in G.nodes():
+            raise nx.NetworkXError(f"Vertex {e[0]} not in graph")
+        if e[1] not in G[e[0]]:
+            msg = f"starting_edge ({e[0]}, {e[1]}) is not in the Graph"
+            raise nx.NetworkXError(msg)
+    e_triangles = _triangles(G, e)
+    r = len(e_triangles)
+    if r == 0:
+        # there are no triangles containing e, so the starting cell is just e
+        starting_cell = e
+    elif r == 1:
+        # there is exactly one triangle, T, containing e. If other 2 edges
+        # of T belong only to this triangle then T is starting cell
+        T = e_triangles[0]
+        a, b, c = T
+        # ab was original edge so check the other 2 edges
+        ac_edges = len(_triangles(G, (a, c)))
+        bc_edges = len(_triangles(G, (b, c)))
+        if ac_edges == 1:
+            if bc_edges == 1:
+                starting_cell = T
+            else:
+                return _select_starting_cell(G, starting_edge=(b, c))
+        else:
+            return _select_starting_cell(G, starting_edge=(a, c))
+    else:
+        # r >= 2 so we need to count the number of odd triangles, s
+        s = 0
+        odd_triangles = []
+        for T in e_triangles:
+            if _odd_triangle(G, T):
+                s += 1
+                odd_triangles.append(T)
+        if r == 2 and s == 0:
+            # in this case either triangle works, so just use T
+            starting_cell = T
+        elif r - 1 <= s <= r:
+            # check if odd triangles containing e form complete subgraph
+            triangle_nodes = set()
+            for T in odd_triangles:
+                for x in T:
+                    triangle_nodes.add(x)
+
+            for u in triangle_nodes:
+                for v in triangle_nodes:
+                    if u != v and (v not in G[u]):
+                        msg = (
+                            "G is not a line graph (odd triangles "
+                            "do not form complete subgraph)"
+                        )
+                        raise nx.NetworkXError(msg)
+            # otherwise then we can use this as the starting cell
+            starting_cell = tuple(triangle_nodes)
+
+        else:
+            msg = (
+                "G is not a line graph (incorrect number of "
+                "odd triangles around starting edge)"
+            )
+            raise nx.NetworkXError(msg)
+    return starting_cell

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

@@ -0,0 +1,110 @@
+"""Functions related to the Mycielski Operation and the Mycielskian family
+of graphs.
+
+"""
+
+import networkx as nx
+from networkx.utils import not_implemented_for
+
+__all__ = ["mycielskian", "mycielski_graph"]
+
+
+@not_implemented_for("directed")
+@not_implemented_for("multigraph")
+@nx._dispatchable(returns_graph=True)
+def mycielskian(G, iterations=1):
+    r"""Returns the Mycielskian of a simple, undirected graph G
+
+    The Mycielskian of graph preserves a graph's triangle free
+    property while increasing the chromatic number by 1.
+
+    The Mycielski Operation on a graph, :math:`G=(V, E)`, constructs a new
+    graph with :math:`2|V| + 1` nodes and :math:`3|E| + |V|` edges.
+
+    The construction is as follows:
+
+    Let :math:`V = {0, ..., n-1}`. Construct another vertex set
+    :math:`U = {n, ..., 2n}` and a vertex, `w`.
+    Construct a new graph, `M`, with vertices :math:`U \bigcup V \bigcup w`.
+    For edges, :math:`(u, v) \in E` add edges :math:`(u, v), (u, v + n)`, and
+    :math:`(u + n, v)` to M. Finally, for all vertices :math:`u \in U`, add
+    edge :math:`(u, w)` to M.
+
+    The Mycielski Operation can be done multiple times by repeating the above
+    process iteratively.
+
+    More information can be found at https://en.wikipedia.org/wiki/Mycielskian
+
+    Parameters
+    ----------
+    G : graph
+        A simple, undirected NetworkX graph
+    iterations : int
+        The number of iterations of the Mycielski operation to
+        perform on G. Defaults to 1. Must be a non-negative integer.
+
+    Returns
+    -------
+    M : graph
+        The Mycielskian of G after the specified number of iterations.
+
+    Notes
+    -----
+    Graph, node, and edge data are not necessarily propagated to the new graph.
+
+    """
+
+    M = nx.convert_node_labels_to_integers(G)
+
+    for i in range(iterations):
+        n = M.number_of_nodes()
+        M.add_nodes_from(range(n, 2 * n))
+        old_edges = list(M.edges())
+        M.add_edges_from((u, v + n) for u, v in old_edges)
+        M.add_edges_from((u + n, v) for u, v in old_edges)
+        M.add_node(2 * n)
+        M.add_edges_from((u + n, 2 * n) for u in range(n))
+
+    return M
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def mycielski_graph(n):
+    """Generator for the n_th Mycielski Graph.
+
+    The Mycielski family of graphs is an infinite set of graphs.
+    :math:`M_1` is the singleton graph, :math:`M_2` is two vertices with an
+    edge, and, for :math:`i > 2`, :math:`M_i` is the Mycielskian of
+    :math:`M_{i-1}`.
+
+    More information can be found at
+    http://mathworld.wolfram.com/MycielskiGraph.html
+
+    Parameters
+    ----------
+    n : int
+        The desired Mycielski Graph.
+
+    Returns
+    -------
+    M : graph
+        The n_th Mycielski Graph
+
+    Notes
+    -----
+    The first graph in the Mycielski sequence is the singleton graph.
+    The Mycielskian of this graph is not the :math:`P_2` graph, but rather the
+    :math:`P_2` graph with an extra, isolated vertex. The second Mycielski
+    graph is the :math:`P_2` graph, so the first two are hard coded.
+    The remaining graphs are generated using the Mycielski operation.
+
+    """
+
+    if n < 1:
+        raise nx.NetworkXError("must satisfy n >= 1")
+
+    if n == 1:
+        return nx.empty_graph(1)
+
+    else:
+        return mycielskian(nx.path_graph(2), n - 2)

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

@@ -0,0 +1,212 @@
+"""
+Implementation of the Wright, Richmond, Odlyzko and McKay (WROM)
+algorithm for the enumeration of all non-isomorphic free trees of a
+given order.  Rooted trees are represented by level sequences, i.e.,
+lists in which the i-th element specifies the distance of vertex i to
+the root.
+
+"""
+
+__all__ = ["nonisomorphic_trees", "number_of_nonisomorphic_trees"]
+
+import networkx as nx
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def nonisomorphic_trees(order, create="graph"):
+    """Generates lists of nonisomorphic trees
+
+    Parameters
+    ----------
+    order : int
+       order of the desired tree(s)
+
+    create : one of {"graph", "matrix"} (default="graph")
+       If ``"graph"`` is selected a list of ``Graph`` instances will be returned,
+       if matrix is selected a list of adjacency matrices will be returned.
+
+       .. deprecated:: 3.3
+
+          The `create` argument is deprecated and will be removed in NetworkX
+          version 3.5. In the future, `nonisomorphic_trees` will yield graph
+          instances by default. To generate adjacency matrices, call
+          ``nx.to_numpy_array`` on the output, e.g.::
+
+             [nx.to_numpy_array(G) for G in nx.nonisomorphic_trees(N)]
+
+    Yields
+    ------
+    list
+       A list of nonisomorphic trees, in one of two formats depending on the
+       value of the `create` parameter:
+       - ``create="graph"``: yields a list of `networkx.Graph` instances
+       - ``create="matrix"``: yields a list of list-of-lists representing adjacency matrices
+    """
+
+    if order < 2:
+        raise ValueError
+    # start at the path graph rooted at its center
+    layout = list(range(order // 2 + 1)) + list(range(1, (order + 1) // 2))
+
+    while layout is not None:
+        layout = _next_tree(layout)
+        if layout is not None:
+            if create == "graph":
+                yield _layout_to_graph(layout)
+            elif create == "matrix":
+                import warnings
+
+                warnings.warn(
+                    (
+                        "\n\nThe 'create=matrix' argument of nonisomorphic_trees\n"
+                        "is deprecated and will be removed in version 3.5.\n"
+                        "Use ``nx.to_numpy_array`` to convert graphs to adjacency "
+                        "matrices, e.g.::\n\n"
+                        "   [nx.to_numpy_array(G) for G in nx.nonisomorphic_trees(N)]"
+                    ),
+                    category=DeprecationWarning,
+                    stacklevel=2,
+                )
+
+                yield _layout_to_matrix(layout)
+            layout = _next_rooted_tree(layout)
+
+
+@nx._dispatchable(graphs=None)
+def number_of_nonisomorphic_trees(order):
+    """Returns the number of nonisomorphic trees
+
+    Parameters
+    ----------
+    order : int
+      order of the desired tree(s)
+
+    Returns
+    -------
+    length : Number of nonisomorphic graphs for the given order
+
+    References
+    ----------
+
+    """
+    return sum(1 for _ in nonisomorphic_trees(order))
+
+
+def _next_rooted_tree(predecessor, p=None):
+    """One iteration of the Beyer-Hedetniemi algorithm."""
+
+    if p is None:
+        p = len(predecessor) - 1
+        while predecessor[p] == 1:
+            p -= 1
+    if p == 0:
+        return None
+
+    q = p - 1
+    while predecessor[q] != predecessor[p] - 1:
+        q -= 1
+    result = list(predecessor)
+    for i in range(p, len(result)):
+        result[i] = result[i - p + q]
+    return result
+
+
+def _next_tree(candidate):
+    """One iteration of the Wright, Richmond, Odlyzko and McKay
+    algorithm."""
+
+    # valid representation of a free tree if:
+    # there are at least two vertices at layer 1
+    # (this is always the case because we start at the path graph)
+    left, rest = _split_tree(candidate)
+
+    # and the left subtree of the root
+    # is less high than the tree with the left subtree removed
+    left_height = max(left)
+    rest_height = max(rest)
+    valid = rest_height >= left_height
+
+    if valid and rest_height == left_height:
+        # and, if left and rest are of the same height,
+        # if left does not encompass more vertices
+        if len(left) > len(rest):
+            valid = False
+        # and, if they have the same number or vertices,
+        # if left does not come after rest lexicographically
+        elif len(left) == len(rest) and left > rest:
+            valid = False
+
+    if valid:
+        return candidate
+    else:
+        # jump to the next valid free tree
+        p = len(left)
+        new_candidate = _next_rooted_tree(candidate, p)
+        if candidate[p] > 2:
+            new_left, new_rest = _split_tree(new_candidate)
+            new_left_height = max(new_left)
+            suffix = range(1, new_left_height + 2)
+            new_candidate[-len(suffix) :] = suffix
+        return new_candidate
+
+
+def _split_tree(layout):
+    """Returns a tuple of two layouts, one containing the left
+    subtree of the root vertex, and one containing the original tree
+    with the left subtree removed."""
+
+    one_found = False
+    m = None
+    for i in range(len(layout)):
+        if layout[i] == 1:
+            if one_found:
+                m = i
+                break
+            else:
+                one_found = True
+
+    if m is None:
+        m = len(layout)
+
+    left = [layout[i] - 1 for i in range(1, m)]
+    rest = [0] + [layout[i] for i in range(m, len(layout))]
+    return (left, rest)
+
+
+def _layout_to_matrix(layout):
+    """Create the adjacency matrix for the tree specified by the
+    given layout (level sequence)."""
+
+    result = [[0] * len(layout) for i in range(len(layout))]
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            result[i][j] = result[j][i] = 1
+        stack.append(i)
+    return result
+
+
+def _layout_to_graph(layout):
+    """Create a NetworkX Graph for the tree specified by the
+    given layout(level sequence)"""
+    G = nx.Graph()
+    stack = []
+    for i in range(len(layout)):
+        i_level = layout[i]
+        if stack:
+            j = stack[-1]
+            j_level = layout[j]
+            while j_level >= i_level:
+                stack.pop()
+                j = stack[-1]
+                j_level = layout[j]
+            G.add_edge(i, j)
+        stack.append(i)
+    return G

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

@@ -0,0 +1,117 @@
+"""Generate graphs with given degree and triangle sequence.
+"""
+import networkx as nx
+from networkx.utils import py_random_state
+
+__all__ = ["random_clustered_graph"]
+
+
+@py_random_state(2)
+@nx._dispatchable(graphs=None, returns_graph=True)
+def random_clustered_graph(joint_degree_sequence, create_using=None, seed=None):
+    r"""Generate a random graph with the given joint independent edge degree and
+    triangle degree sequence.
+
+    This uses a configuration model-like approach to generate a random graph
+    (with parallel edges and self-loops) by randomly assigning edges to match
+    the given joint degree sequence.
+
+    The joint degree sequence is a list of pairs of integers of the form
+    $[(d_{1,i}, d_{1,t}), \dotsc, (d_{n,i}, d_{n,t})]$. According to this list,
+    vertex $u$ is a member of $d_{u,t}$ triangles and has $d_{u, i}$ other
+    edges. The number $d_{u,t}$ is the *triangle degree* of $u$ and the number
+    $d_{u,i}$ is the *independent edge degree*.
+
+    Parameters
+    ----------
+    joint_degree_sequence : list of integer pairs
+        Each list entry corresponds to the independent edge degree and
+        triangle degree of a node.
+    create_using : NetworkX graph constructor, optional (default MultiGraph)
+       Graph type to create. If graph instance, then cleared before populated.
+    seed : integer, random_state, or None (default)
+        Indicator of random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    G : MultiGraph
+        A graph with the specified degree sequence. Nodes are labeled
+        starting at 0 with an index corresponding to the position in
+        deg_sequence.
+
+    Raises
+    ------
+    NetworkXError
+        If the independent edge degree sequence sum is not even
+        or the triangle degree sequence sum is not divisible by 3.
+
+    Notes
+    -----
+    As described by Miller [1]_ (see also Newman [2]_ for an equivalent
+    description).
+
+    A non-graphical degree sequence (not realizable by some simple
+    graph) is allowed since this function returns graphs with self
+    loops and parallel edges.  An exception is raised if the
+    independent degree sequence does not have an even sum or the
+    triangle degree sequence sum is not divisible by 3.
+
+    This configuration model-like construction process can lead to
+    duplicate edges and loops.  You can remove the self-loops and
+    parallel edges (see below) which will likely result in a graph
+    that doesn't have the exact degree sequence specified.  This
+    "finite-size effect" decreases as the size of the graph increases.
+
+    References
+    ----------
+    .. [1] Joel C. Miller. "Percolation and epidemics in random clustered
+           networks". In: Physical review. E, Statistical, nonlinear, and soft
+           matter physics 80 (2 Part 1 August 2009).
+    .. [2] M. E. J. Newman. "Random Graphs with Clustering".
+           In: Physical Review Letters 103 (5 July 2009)
+
+    Examples
+    --------
+    >>> deg = [(1, 0), (1, 0), (1, 0), (2, 0), (1, 0), (2, 1), (0, 1), (0, 1)]
+    >>> G = nx.random_clustered_graph(deg)
+
+    To remove parallel edges:
+
+    >>> G = nx.Graph(G)
+
+    To remove self loops:
+
+    >>> G.remove_edges_from(nx.selfloop_edges(G))
+
+    """
+    # In Python 3, zip() returns an iterator. Make this into a list.
+    joint_degree_sequence = list(joint_degree_sequence)
+
+    N = len(joint_degree_sequence)
+    G = nx.empty_graph(N, create_using, default=nx.MultiGraph)
+    if G.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+
+    ilist = []
+    tlist = []
+    for n in G:
+        degrees = joint_degree_sequence[n]
+        for icount in range(degrees[0]):
+            ilist.append(n)
+        for tcount in range(degrees[1]):
+            tlist.append(n)
+
+    if len(ilist) % 2 != 0 or len(tlist) % 3 != 0:
+        raise nx.NetworkXError("Invalid degree sequence")
+
+    seed.shuffle(ilist)
+    seed.shuffle(tlist)
+    while ilist:
+        G.add_edge(ilist.pop(), ilist.pop())
+    while tlist:
+        n1 = tlist.pop()
+        n2 = tlist.pop()
+        n3 = tlist.pop()
+        G.add_edges_from([(n1, n2), (n1, n3), (n2, n3)])
+    return G

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

@@ -0,0 +1,993 @@
+"""
+Various small and named graphs, together with some compact generators.
+
+"""
+
+__all__ = [
+    "LCF_graph",
+    "bull_graph",
+    "chvatal_graph",
+    "cubical_graph",
+    "desargues_graph",
+    "diamond_graph",
+    "dodecahedral_graph",
+    "frucht_graph",
+    "heawood_graph",
+    "hoffman_singleton_graph",
+    "house_graph",
+    "house_x_graph",
+    "icosahedral_graph",
+    "krackhardt_kite_graph",
+    "moebius_kantor_graph",
+    "octahedral_graph",
+    "pappus_graph",
+    "petersen_graph",
+    "sedgewick_maze_graph",
+    "tetrahedral_graph",
+    "truncated_cube_graph",
+    "truncated_tetrahedron_graph",
+    "tutte_graph",
+]
+
+from functools import wraps
+
+import networkx as nx
+from networkx.exception import NetworkXError
+from networkx.generators.classic import (
+    complete_graph,
+    cycle_graph,
+    empty_graph,
+    path_graph,
+)
+
+
+def _raise_on_directed(func):
+    """
+    A decorator which inspects the `create_using` argument and raises a
+    NetworkX exception when `create_using` is a DiGraph (class or instance) for
+    graph generators that do not support directed outputs.
+    """
+
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        if kwargs.get("create_using") is not None:
+            G = nx.empty_graph(create_using=kwargs["create_using"])
+            if G.is_directed():
+                raise NetworkXError("Directed Graph not supported")
+        return func(*args, **kwargs)
+
+    return wrapper
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def LCF_graph(n, shift_list, repeats, create_using=None):
+    """
+    Return the cubic graph specified in LCF notation.
+
+    LCF (Lederberg-Coxeter-Fruchte) notation[1]_ is a compressed
+    notation used in the generation of various cubic Hamiltonian
+    graphs of high symmetry. See, for example, `dodecahedral_graph`,
+    `desargues_graph`, `heawood_graph` and `pappus_graph`.
+
+    Nodes are drawn from ``range(n)``. Each node ``n_i`` is connected with
+    node ``n_i + shift % n`` where ``shift`` is given by cycling through
+    the input `shift_list` `repeat` s times.
+
+    Parameters
+    ----------
+    n : int
+       The starting graph is the `n`-cycle with nodes ``0, ..., n-1``.
+       The null graph is returned if `n` < 1.
+
+    shift_list : list
+       A list of integer shifts mod `n`, ``[s1, s2, .., sk]``
+
+    repeats : int
+       Integer specifying the number of times that shifts in `shift_list`
+       are successively applied to each current node in the n-cycle
+       to generate an edge between ``n_current`` and ``n_current + shift mod n``.
+
+    Returns
+    -------
+    G : Graph
+       A graph instance created from the specified LCF notation.
+
+    Examples
+    --------
+    The utility graph $K_{3,3}$
+
+    >>> G = nx.LCF_graph(6, [3, -3], 3)
+    >>> G.edges()
+    EdgeView([(0, 1), (0, 5), (0, 3), (1, 2), (1, 4), (2, 3), (2, 5), (3, 4), (4, 5)])
+
+    The Heawood graph:
+
+    >>> G = nx.LCF_graph(14, [5, -5], 7)
+    >>> nx.is_isomorphic(G, nx.heawood_graph())
+    True
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/LCF_notation
+
+    """
+    if n <= 0:
+        return empty_graph(0, create_using)
+
+    # start with the n-cycle
+    G = cycle_graph(n, create_using)
+    if G.is_directed():
+        raise NetworkXError("Directed Graph not supported")
+    G.name = "LCF_graph"
+    nodes = sorted(G)
+
+    n_extra_edges = repeats * len(shift_list)
+    # edges are added n_extra_edges times
+    # (not all of these need be new)
+    if n_extra_edges < 1:
+        return G
+
+    for i in range(n_extra_edges):
+        shift = shift_list[i % len(shift_list)]  # cycle through shift_list
+        v1 = nodes[i % n]  # cycle repeatedly through nodes
+        v2 = nodes[(i + shift) % n]
+        G.add_edge(v1, v2)
+    return G
+
+
+# -------------------------------------------------------------------------------
+#   Various small and named graphs
+# -------------------------------------------------------------------------------
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def bull_graph(create_using=None):
+    """
+    Returns the Bull Graph
+
+    The Bull Graph has 5 nodes and 5 edges. It is a planar undirected
+    graph in the form of a triangle with two disjoint pendant edges [1]_
+    The name comes from the triangle and pendant edges representing
+    respectively the body and legs of a bull.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A bull graph with 5 nodes
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Bull_graph.
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 4], 3: [1], 4: [2]},
+        create_using=create_using,
+    )
+    G.name = "Bull Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def chvatal_graph(create_using=None):
+    """
+    Returns the Chvátal Graph
+
+    The Chvátal Graph is an undirected graph with 12 nodes and 24 edges [1]_.
+    It has 370 distinct (directed) Hamiltonian cycles, giving a unique generalized
+    LCF notation of order 4, two of order 6 , and 43 of order 1 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        The Chvátal graph with 12 nodes and 24 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Chv%C3%A1tal_graph
+    .. [2] https://mathworld.wolfram.com/ChvatalGraph.html
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 6, 9],
+            1: [2, 5, 7],
+            2: [3, 6, 8],
+            3: [4, 7, 9],
+            4: [5, 8],
+            5: [10, 11],
+            6: [10, 11],
+            7: [8, 11],
+            8: [10],
+            9: [10, 11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Chvatal Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def cubical_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Cubical Graph
+
+    The skeleton of the cube (the nodes and edges) form a graph, with 8
+    nodes, and 12 edges. It is a special case of the hypercube graph.
+    It is one of 5 Platonic graphs, each a skeleton of its
+    Platonic solid [1]_.
+    Such graphs arise in parallel processing in computers.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        A cubical graph with 8 nodes and 12 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Cube#Cubical_graph
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 3, 4],
+            1: [0, 2, 7],
+            2: [1, 3, 6],
+            3: [0, 2, 5],
+            4: [0, 5, 7],
+            5: [3, 4, 6],
+            6: [2, 5, 7],
+            7: [1, 4, 6],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Cubical Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def desargues_graph(create_using=None):
+    """
+    Returns the Desargues Graph
+
+    The Desargues Graph is a non-planar, distance-transitive cubic graph
+    with 20 nodes and 30 edges [1]_.
+    It is a symmetric graph. It can be represented in LCF notation
+    as [5,-5,9,-9]^5 [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Desargues Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Desargues_graph
+    .. [2] https://mathworld.wolfram.com/DesarguesGraph.html
+    """
+    G = LCF_graph(20, [5, -5, 9, -9], 5, create_using)
+    G.name = "Desargues Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def diamond_graph(create_using=None):
+    """
+    Returns the Diamond graph
+
+    The Diamond Graph is  planar undirected graph with 4 nodes and 5 edges.
+    It is also sometimes known as the double triangle graph or kite graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Diamond Graph with 4 nodes and 5 edges
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/DiamondGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 2, 3], 2: [0, 1, 3], 3: [1, 2]}, create_using=create_using
+    )
+    G.name = "Diamond Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def dodecahedral_graph(create_using=None):
+    """
+    Returns the Platonic Dodecahedral graph.
+
+    The dodecahedral graph has 20 nodes and 30 edges. The skeleton of the
+    dodecahedron forms a graph. It is one of 5 Platonic graphs [1]_.
+    It can be described in LCF notation as:
+    ``[10, 7, 4, -4, -7, 10, -4, 7, -7, 4]^2`` [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Dodecahedral Graph with 20 nodes and 30 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Regular_dodecahedron#Dodecahedral_graph
+    .. [2] https://mathworld.wolfram.com/DodecahedralGraph.html
+
+    """
+    G = LCF_graph(20, [10, 7, 4, -4, -7, 10, -4, 7, -7, 4], 2, create_using)
+    G.name = "Dodecahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def frucht_graph(create_using=None):
+    """
+    Returns the Frucht Graph.
+
+    The Frucht Graph is the smallest cubical graph whose
+    automorphism group consists only of the identity element [1]_.
+    It has 12 nodes and 18 edges and no nontrivial symmetries.
+    It is planar and Hamiltonian [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Frucht Graph with 12 nodes and 18 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Frucht_graph
+    .. [2] https://mathworld.wolfram.com/FruchtGraph.html
+
+    """
+    G = cycle_graph(7, create_using)
+    G.add_edges_from(
+        [
+            [0, 7],
+            [1, 7],
+            [2, 8],
+            [3, 9],
+            [4, 9],
+            [5, 10],
+            [6, 10],
+            [7, 11],
+            [8, 11],
+            [8, 9],
+            [10, 11],
+        ]
+    )
+
+    G.name = "Frucht Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def heawood_graph(create_using=None):
+    """
+    Returns the Heawood Graph, a (3,6) cage.
+
+    The Heawood Graph is an undirected graph with 14 nodes and 21 edges,
+    named after Percy John Heawood [1]_.
+    It is cubic symmetric, nonplanar, Hamiltonian, and can be represented
+    in LCF notation as ``[5,-5]^7`` [2]_.
+    It is the unique (3,6)-cage: the regular cubic graph of girth 6 with
+    minimal number of vertices [3]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Heawood Graph with 14 nodes and 21 edges
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Heawood_graph
+    .. [2] https://mathworld.wolfram.com/HeawoodGraph.html
+    .. [3] https://www.win.tue.nl/~aeb/graphs/Heawood.html
+
+    """
+    G = LCF_graph(14, [5, -5], 7, create_using)
+    G.name = "Heawood Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def hoffman_singleton_graph():
+    """
+    Returns the Hoffman-Singleton Graph.
+
+    The Hoffman–Singleton graph is a symmetrical undirected graph
+    with 50 nodes and 175 edges.
+    All indices lie in ``Z % 5``: that is, the integers mod 5 [1]_.
+    It is the only regular graph of vertex degree 7, diameter 2, and girth 5.
+    It is the unique (7,5)-cage graph and Moore graph, and contains many
+    copies of the Petersen graph [2]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Hoffman–Singleton Graph with 50 nodes and 175 edges
+
+    Notes
+    -----
+    Constructed from pentagon and pentagram as follows: Take five pentagons $P_h$
+    and five pentagrams $Q_i$ . Join vertex $j$ of $P_h$ to vertex $h·i+j$ of $Q_i$ [3]_.
+
+    References
+    ----------
+    .. [1] https://blogs.ams.org/visualinsight/2016/02/01/hoffman-singleton-graph/
+    .. [2] https://mathworld.wolfram.com/Hoffman-SingletonGraph.html
+    .. [3] https://en.wikipedia.org/wiki/Hoffman%E2%80%93Singleton_graph
+
+    """
+    G = nx.Graph()
+    for i in range(5):
+        for j in range(5):
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j - 1) % 5))
+            G.add_edge(("pentagon", i, j), ("pentagon", i, (j + 1) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j - 2) % 5))
+            G.add_edge(("pentagram", i, j), ("pentagram", i, (j + 2) % 5))
+            for k in range(5):
+                G.add_edge(("pentagon", i, j), ("pentagram", k, (i * k + j) % 5))
+    G = nx.convert_node_labels_to_integers(G)
+    G.name = "Hoffman-Singleton Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_graph(create_using=None):
+    """
+    Returns the House graph (square with triangle on top)
+
+    The house graph is a simple undirected graph with
+    5 nodes and 6 edges [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph in the form of a square with a triangle on top
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2], 1: [0, 3], 2: [0, 3, 4], 3: [1, 2, 4], 4: [2, 3]},
+        create_using=create_using,
+    )
+    G.name = "House Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def house_x_graph(create_using=None):
+    """
+    Returns the House graph with a cross inside the house square.
+
+    The House X-graph is the House graph plus the two edges connecting diagonally
+    opposite vertices of the square base. It is also one of the two graphs
+    obtained by removing two edges from the pentatope graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        House graph with diagonal vertices connected
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/HouseGraph.html
+    """
+    G = house_graph(create_using)
+    G.add_edges_from([(0, 3), (1, 2)])
+    G.name = "House-with-X-inside Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def icosahedral_graph(create_using=None):
+    """
+    Returns the Platonic Icosahedral graph.
+
+    The icosahedral graph has 12 nodes and 30 edges. It is a Platonic graph
+    whose nodes have the connectivity of the icosahedron. It is undirected,
+    regular and Hamiltonian [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Icosahedral graph with 12 nodes and 30 edges.
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/IcosahedralGraph.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 5, 7, 8, 11],
+            1: [2, 5, 6, 8],
+            2: [3, 6, 8, 9],
+            3: [4, 6, 9, 10],
+            4: [5, 6, 10, 11],
+            5: [6, 11],
+            7: [8, 9, 10, 11],
+            8: [9],
+            9: [10],
+            10: [11],
+        },
+        create_using=create_using,
+    )
+    G.name = "Platonic Icosahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def krackhardt_kite_graph(create_using=None):
+    """
+    Returns the Krackhardt Kite Social Network.
+
+    A 10 actor social network introduced by David Krackhardt
+    to illustrate different centrality measures [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Krackhardt Kite graph with 10 nodes and 18 edges
+
+    Notes
+    -----
+    The traditional labeling is:
+    Andre=1, Beverley=2, Carol=3, Diane=4,
+    Ed=5, Fernando=6, Garth=7, Heather=8, Ike=9, Jane=10.
+
+    References
+    ----------
+    .. [1] Krackhardt, David. "Assessing the Political Landscape: Structure,
+       Cognition, and Power in Organizations". Administrative Science Quarterly.
+       35 (2): 342–369. doi:10.2307/2393394. JSTOR 2393394. June 1990.
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3, 5],
+            1: [0, 3, 4, 6],
+            2: [0, 3, 5],
+            3: [0, 1, 2, 4, 5, 6],
+            4: [1, 3, 6],
+            5: [0, 2, 3, 6, 7],
+            6: [1, 3, 4, 5, 7],
+            7: [5, 6, 8],
+            8: [7, 9],
+            9: [8],
+        },
+        create_using=create_using,
+    )
+    G.name = "Krackhardt Kite Social Network"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def moebius_kantor_graph(create_using=None):
+    """
+    Returns the Moebius-Kantor graph.
+
+    The Möbius-Kantor graph is the cubic symmetric graph on 16 nodes.
+    Its LCF notation is [5,-5]^8, and it is isomorphic to the generalized
+    Petersen graph [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Moebius-Kantor graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/M%C3%B6bius%E2%80%93Kantor_graph
+
+    """
+    G = LCF_graph(16, [5, -5], 8, create_using)
+    G.name = "Moebius-Kantor Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def octahedral_graph(create_using=None):
+    """
+    Returns the Platonic Octahedral graph.
+
+    The octahedral graph is the 6-node 12-edge Platonic graph having the
+    connectivity of the octahedron [1]_. If 6 couples go to a party,
+    and each person shakes hands with every person except his or her partner,
+    then this graph describes the set of handshakes that take place;
+    for this reason it is also called the cocktail party graph [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Octahedral graph
+
+    References
+    ----------
+    .. [1] https://mathworld.wolfram.com/OctahedralGraph.html
+    .. [2] https://en.wikipedia.org/wiki/Tur%C3%A1n_graph#Special_cases
+
+    """
+    G = nx.from_dict_of_lists(
+        {0: [1, 2, 3, 4], 1: [2, 3, 5], 2: [4, 5], 3: [4, 5], 4: [5]},
+        create_using=create_using,
+    )
+    G.name = "Platonic Octahedral Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def pappus_graph():
+    """
+    Returns the Pappus graph.
+
+    The Pappus graph is a cubic symmetric distance-regular graph with 18 nodes
+    and 27 edges. It is Hamiltonian and can be represented in LCF notation as
+    [5,7,-7,7,-7,-5]^3 [1]_.
+
+    Returns
+    -------
+    G : networkx Graph
+        Pappus graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Pappus_graph
+    """
+    G = LCF_graph(18, [5, 7, -7, 7, -7, -5], 3)
+    G.name = "Pappus Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def petersen_graph(create_using=None):
+    """
+    Returns the Petersen graph.
+
+    The Peterson graph is a cubic, undirected graph with 10 nodes and 15 edges [1]_.
+    Julius Petersen constructed the graph as the smallest counterexample
+    against the claim that a connected bridgeless cubic graph
+    has an edge colouring with three colours [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Petersen graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Petersen_graph
+    .. [2] https://www.win.tue.nl/~aeb/drg/graphs/Petersen.html
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 4, 5],
+            1: [0, 2, 6],
+            2: [1, 3, 7],
+            3: [2, 4, 8],
+            4: [3, 0, 9],
+            5: [0, 7, 8],
+            6: [1, 8, 9],
+            7: [2, 5, 9],
+            8: [3, 5, 6],
+            9: [4, 6, 7],
+        },
+        create_using=create_using,
+    )
+    G.name = "Petersen Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sedgewick_maze_graph(create_using=None):
+    """
+    Return a small maze with a cycle.
+
+    This is the maze used in Sedgewick, 3rd Edition, Part 5, Graph
+    Algorithms, Chapter 18, e.g. Figure 18.2 and following [1]_.
+    Nodes are numbered 0,..,7
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Small maze with a cycle
+
+    References
+    ----------
+    .. [1] Figure 18.2, Chapter 18, Graph Algorithms (3rd Ed), Sedgewick
+    """
+    G = empty_graph(0, create_using)
+    G.add_nodes_from(range(8))
+    G.add_edges_from([[0, 2], [0, 7], [0, 5]])
+    G.add_edges_from([[1, 7], [2, 6]])
+    G.add_edges_from([[3, 4], [3, 5]])
+    G.add_edges_from([[4, 5], [4, 7], [4, 6]])
+    G.name = "Sedgewick Maze"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tetrahedral_graph(create_using=None):
+    """
+    Returns the 3-regular Platonic Tetrahedral graph.
+
+    Tetrahedral graph has 4 nodes and 6 edges. It is a
+    special case of the complete graph, K4, and wheel graph, W4.
+    It is one of the 5 platonic graphs [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tetrahedral Graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tetrahedron#Tetrahedral_graph
+
+    """
+    G = complete_graph(4, create_using)
+    G.name = "Platonic Tetrahedral Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_cube_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated cube.
+
+    The truncated cube is an Archimedean solid with 14 regular
+    faces (6 octagonal and 8 triangular), 36 edges and 24 nodes [1]_.
+    The truncated cube is created by truncating (cutting off) the tips
+    of the cube one third of the way into each edge [2]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated cube
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_cube
+    .. [2] https://www.coolmath.com/reference/polyhedra-truncated-cube
+
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 4],
+            1: [11, 14],
+            2: [3, 4],
+            3: [6, 8],
+            4: [5],
+            5: [16, 18],
+            6: [7, 8],
+            7: [10, 12],
+            8: [9],
+            9: [17, 20],
+            10: [11, 12],
+            11: [14],
+            12: [13],
+            13: [21, 22],
+            14: [15],
+            15: [19, 23],
+            16: [17, 18],
+            17: [20],
+            18: [19],
+            19: [23],
+            20: [21],
+            21: [22],
+            22: [23],
+        },
+        create_using=create_using,
+    )
+    G.name = "Truncated Cube Graph"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def truncated_tetrahedron_graph(create_using=None):
+    """
+    Returns the skeleton of the truncated Platonic tetrahedron.
+
+    The truncated tetrahedron is an Archimedean solid with 4 regular hexagonal faces,
+    4 equilateral triangle faces, 12 nodes and 18 edges. It can be constructed by truncating
+    all 4 vertices of a regular tetrahedron at one third of the original edge length [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Skeleton of the truncated tetrahedron
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Truncated_tetrahedron
+
+    """
+    G = path_graph(12, create_using)
+    G.add_edges_from([(0, 2), (0, 9), (1, 6), (3, 11), (4, 11), (5, 7), (8, 10)])
+    G.name = "Truncated Tetrahedron Graph"
+    return G
+
+
+@_raise_on_directed
+@nx._dispatchable(graphs=None, returns_graph=True)
+def tutte_graph(create_using=None):
+    """
+    Returns the Tutte graph.
+
+    The Tutte graph is a cubic polyhedral, non-Hamiltonian graph. It has
+    46 nodes and 69 edges.
+    It is a counterexample to Tait's conjecture that every 3-regular polyhedron
+    has a Hamiltonian cycle.
+    It can be realized geometrically from a tetrahedron by multiply truncating
+    three of its vertices [1]_.
+
+    Parameters
+    ----------
+    create_using : NetworkX graph constructor, optional (default=nx.Graph)
+       Graph type to create. If graph instance, then cleared before populated.
+
+    Returns
+    -------
+    G : networkx Graph
+        Tutte graph
+
+    References
+    ----------
+    .. [1] https://en.wikipedia.org/wiki/Tutte_graph
+    """
+    G = nx.from_dict_of_lists(
+        {
+            0: [1, 2, 3],
+            1: [4, 26],
+            2: [10, 11],
+            3: [18, 19],
+            4: [5, 33],
+            5: [6, 29],
+            6: [7, 27],
+            7: [8, 14],
+            8: [9, 38],
+            9: [10, 37],
+            10: [39],
+            11: [12, 39],
+            12: [13, 35],
+            13: [14, 15],
+            14: [34],
+            15: [16, 22],
+            16: [17, 44],
+            17: [18, 43],
+            18: [45],
+            19: [20, 45],
+            20: [21, 41],
+            21: [22, 23],
+            22: [40],
+            23: [24, 27],
+            24: [25, 32],
+            25: [26, 31],
+            26: [33],
+            27: [28],
+            28: [29, 32],
+            29: [30],
+            30: [31, 33],
+            31: [32],
+            34: [35, 38],
+            35: [36],
+            36: [37, 39],
+            37: [38],
+            40: [41, 44],
+            41: [42],
+            42: [43, 45],
+            43: [44],
+        },
+        create_using=create_using,
+    )
+    G.name = "Tutte's Graph"
+    return G

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

@@ -0,0 +1,546 @@
+"""
+Famous social networks.
+"""
+import networkx as nx
+
+__all__ = [
+    "karate_club_graph",
+    "davis_southern_women_graph",
+    "florentine_families_graph",
+    "les_miserables_graph",
+]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def karate_club_graph():
+    """Returns Zachary's Karate Club graph.
+
+    Each node in the returned graph has a node attribute 'club' that
+    indicates the name of the club to which the member represented by that node
+    belongs, either 'Mr. Hi' or 'Officer'. Each edge has a weight based on the
+    number of contexts in which that edge's incident node members interacted.
+
+    Examples
+    --------
+    To get the name of the club to which a node belongs::
+
+        >>> G = nx.karate_club_graph()
+        >>> G.nodes[5]["club"]
+        'Mr. Hi'
+        >>> G.nodes[9]["club"]
+        'Officer'
+
+    References
+    ----------
+    .. [1] Zachary, Wayne W.
+       "An Information Flow Model for Conflict and Fission in Small Groups."
+       *Journal of Anthropological Research*, 33, 452--473, (1977).
+    """
+    # Create the set of all members, and the members of each club.
+    all_members = set(range(34))
+    club1 = {0, 1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12, 13, 16, 17, 19, 21}
+    # club2 = all_members - club1
+
+    G = nx.Graph()
+    G.add_nodes_from(all_members)
+    G.name = "Zachary's Karate Club"
+
+    zacharydat = """\
+0 4 5 3 3 3 3 2 2 0 2 3 2 3 0 0 0 2 0 2 0 2 0 0 0 0 0 0 0 0 0 2 0 0
+4 0 6 3 0 0 0 4 0 0 0 0 0 5 0 0 0 1 0 2 0 2 0 0 0 0 0 0 0 0 2 0 0 0
+5 6 0 3 0 0 0 4 5 1 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 2 2 0 0 0 3 0
+3 3 3 0 0 0 0 3 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 2 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 5 0 0 0 3 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 2 5 0 0 0 0 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 4 4 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 0 5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 4 3
+0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2
+2 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+1 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+3 5 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 4
+0 0 0 0 0 3 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 1
+2 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 0 4 0 2 0 0 5 4
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 3 0 0 0 2 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 5 2 0 0 0 0 0 0 7 0 0
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 2
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 3 0 0 0 0 0 0 0 0 4
+0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2
+0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 0 0 4 0 0 0 0 0 3 2
+0 2 0 0 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 3 3
+2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 7 0 0 2 0 0 0 4 4
+0 0 2 0 0 0 0 0 3 0 0 0 0 0 3 3 0 0 1 0 3 0 2 5 0 0 0 0 0 4 3 4 0 5
+0 0 0 0 0 0 0 0 4 2 0 0 0 3 2 4 0 0 2 1 1 0 3 4 0 0 2 4 2 2 3 4 5 0"""
+
+    for row, line in enumerate(zacharydat.split("\n")):
+        thisrow = [int(b) for b in line.split()]
+        for col, entry in enumerate(thisrow):
+            if entry >= 1:
+                G.add_edge(row, col, weight=entry)
+
+    # Add the name of each member's club as a node attribute.
+    for v in G:
+        G.nodes[v]["club"] = "Mr. Hi" if v in club1 else "Officer"
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def davis_southern_women_graph():
+    """Returns Davis Southern women social network.
+
+    This is a bipartite graph.
+
+    References
+    ----------
+    .. [1] A. Davis, Gardner, B. B., Gardner, M. R., 1941. Deep South.
+        University of Chicago Press, Chicago, IL.
+    """
+    G = nx.Graph()
+    # Top nodes
+    women = [
+        "Evelyn Jefferson",
+        "Laura Mandeville",
+        "Theresa Anderson",
+        "Brenda Rogers",
+        "Charlotte McDowd",
+        "Frances Anderson",
+        "Eleanor Nye",
+        "Pearl Oglethorpe",
+        "Ruth DeSand",
+        "Verne Sanderson",
+        "Myra Liddel",
+        "Katherina Rogers",
+        "Sylvia Avondale",
+        "Nora Fayette",
+        "Helen Lloyd",
+        "Dorothy Murchison",
+        "Olivia Carleton",
+        "Flora Price",
+    ]
+    G.add_nodes_from(women, bipartite=0)
+    # Bottom nodes
+    events = [
+        "E1",
+        "E2",
+        "E3",
+        "E4",
+        "E5",
+        "E6",
+        "E7",
+        "E8",
+        "E9",
+        "E10",
+        "E11",
+        "E12",
+        "E13",
+        "E14",
+    ]
+    G.add_nodes_from(events, bipartite=1)
+
+    G.add_edges_from(
+        [
+            ("Evelyn Jefferson", "E1"),
+            ("Evelyn Jefferson", "E2"),
+            ("Evelyn Jefferson", "E3"),
+            ("Evelyn Jefferson", "E4"),
+            ("Evelyn Jefferson", "E5"),
+            ("Evelyn Jefferson", "E6"),
+            ("Evelyn Jefferson", "E8"),
+            ("Evelyn Jefferson", "E9"),
+            ("Laura Mandeville", "E1"),
+            ("Laura Mandeville", "E2"),
+            ("Laura Mandeville", "E3"),
+            ("Laura Mandeville", "E5"),
+            ("Laura Mandeville", "E6"),
+            ("Laura Mandeville", "E7"),
+            ("Laura Mandeville", "E8"),
+            ("Theresa Anderson", "E2"),
+            ("Theresa Anderson", "E3"),
+            ("Theresa Anderson", "E4"),
+            ("Theresa Anderson", "E5"),
+            ("Theresa Anderson", "E6"),
+            ("Theresa Anderson", "E7"),
+            ("Theresa Anderson", "E8"),
+            ("Theresa Anderson", "E9"),
+            ("Brenda Rogers", "E1"),
+            ("Brenda Rogers", "E3"),
+            ("Brenda Rogers", "E4"),
+            ("Brenda Rogers", "E5"),
+            ("Brenda Rogers", "E6"),
+            ("Brenda Rogers", "E7"),
+            ("Brenda Rogers", "E8"),
+            ("Charlotte McDowd", "E3"),
+            ("Charlotte McDowd", "E4"),
+            ("Charlotte McDowd", "E5"),
+            ("Charlotte McDowd", "E7"),
+            ("Frances Anderson", "E3"),
+            ("Frances Anderson", "E5"),
+            ("Frances Anderson", "E6"),
+            ("Frances Anderson", "E8"),
+            ("Eleanor Nye", "E5"),
+            ("Eleanor Nye", "E6"),
+            ("Eleanor Nye", "E7"),
+            ("Eleanor Nye", "E8"),
+            ("Pearl Oglethorpe", "E6"),
+            ("Pearl Oglethorpe", "E8"),
+            ("Pearl Oglethorpe", "E9"),
+            ("Ruth DeSand", "E5"),
+            ("Ruth DeSand", "E7"),
+            ("Ruth DeSand", "E8"),
+            ("Ruth DeSand", "E9"),
+            ("Verne Sanderson", "E7"),
+            ("Verne Sanderson", "E8"),
+            ("Verne Sanderson", "E9"),
+            ("Verne Sanderson", "E12"),
+            ("Myra Liddel", "E8"),
+            ("Myra Liddel", "E9"),
+            ("Myra Liddel", "E10"),
+            ("Myra Liddel", "E12"),
+            ("Katherina Rogers", "E8"),
+            ("Katherina Rogers", "E9"),
+            ("Katherina Rogers", "E10"),
+            ("Katherina Rogers", "E12"),
+            ("Katherina Rogers", "E13"),
+            ("Katherina Rogers", "E14"),
+            ("Sylvia Avondale", "E7"),
+            ("Sylvia Avondale", "E8"),
+            ("Sylvia Avondale", "E9"),
+            ("Sylvia Avondale", "E10"),
+            ("Sylvia Avondale", "E12"),
+            ("Sylvia Avondale", "E13"),
+            ("Sylvia Avondale", "E14"),
+            ("Nora Fayette", "E6"),
+            ("Nora Fayette", "E7"),
+            ("Nora Fayette", "E9"),
+            ("Nora Fayette", "E10"),
+            ("Nora Fayette", "E11"),
+            ("Nora Fayette", "E12"),
+            ("Nora Fayette", "E13"),
+            ("Nora Fayette", "E14"),
+            ("Helen Lloyd", "E7"),
+            ("Helen Lloyd", "E8"),
+            ("Helen Lloyd", "E10"),
+            ("Helen Lloyd", "E11"),
+            ("Helen Lloyd", "E12"),
+            ("Dorothy Murchison", "E8"),
+            ("Dorothy Murchison", "E9"),
+            ("Olivia Carleton", "E9"),
+            ("Olivia Carleton", "E11"),
+            ("Flora Price", "E9"),
+            ("Flora Price", "E11"),
+        ]
+    )
+    G.graph["top"] = women
+    G.graph["bottom"] = events
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def florentine_families_graph():
+    """Returns Florentine families graph.
+
+    References
+    ----------
+    .. [1] Ronald L. Breiger and Philippa E. Pattison
+       Cumulated social roles: The duality of persons and their algebras,1
+       Social Networks, Volume 8, Issue 3, September 1986, Pages 215-256
+    """
+    G = nx.Graph()
+    G.add_edge("Acciaiuoli", "Medici")
+    G.add_edge("Castellani", "Peruzzi")
+    G.add_edge("Castellani", "Strozzi")
+    G.add_edge("Castellani", "Barbadori")
+    G.add_edge("Medici", "Barbadori")
+    G.add_edge("Medici", "Ridolfi")
+    G.add_edge("Medici", "Tornabuoni")
+    G.add_edge("Medici", "Albizzi")
+    G.add_edge("Medici", "Salviati")
+    G.add_edge("Salviati", "Pazzi")
+    G.add_edge("Peruzzi", "Strozzi")
+    G.add_edge("Peruzzi", "Bischeri")
+    G.add_edge("Strozzi", "Ridolfi")
+    G.add_edge("Strozzi", "Bischeri")
+    G.add_edge("Ridolfi", "Tornabuoni")
+    G.add_edge("Tornabuoni", "Guadagni")
+    G.add_edge("Albizzi", "Ginori")
+    G.add_edge("Albizzi", "Guadagni")
+    G.add_edge("Bischeri", "Guadagni")
+    G.add_edge("Guadagni", "Lamberteschi")
+    return G
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def les_miserables_graph():
+    """Returns coappearance network of characters in the novel Les Miserables.
+
+    References
+    ----------
+    .. [1] D. E. Knuth, 1993.
+       The Stanford GraphBase: a platform for combinatorial computing,
+       pp. 74-87. New York: AcM Press.
+    """
+    G = nx.Graph()
+    G.add_edge("Napoleon", "Myriel", weight=1)
+    G.add_edge("MlleBaptistine", "Myriel", weight=8)
+    G.add_edge("MmeMagloire", "Myriel", weight=10)
+    G.add_edge("MmeMagloire", "MlleBaptistine", weight=6)
+    G.add_edge("CountessDeLo", "Myriel", weight=1)
+    G.add_edge("Geborand", "Myriel", weight=1)
+    G.add_edge("Champtercier", "Myriel", weight=1)
+    G.add_edge("Cravatte", "Myriel", weight=1)
+    G.add_edge("Count", "Myriel", weight=2)
+    G.add_edge("OldMan", "Myriel", weight=1)
+    G.add_edge("Valjean", "Labarre", weight=1)
+    G.add_edge("Valjean", "MmeMagloire", weight=3)
+    G.add_edge("Valjean", "MlleBaptistine", weight=3)
+    G.add_edge("Valjean", "Myriel", weight=5)
+    G.add_edge("Marguerite", "Valjean", weight=1)
+    G.add_edge("MmeDeR", "Valjean", weight=1)
+    G.add_edge("Isabeau", "Valjean", weight=1)
+    G.add_edge("Gervais", "Valjean", weight=1)
+    G.add_edge("Listolier", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Tholomyes", weight=4)
+    G.add_edge("Fameuil", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Tholomyes", weight=4)
+    G.add_edge("Blacheville", "Listolier", weight=4)
+    G.add_edge("Blacheville", "Fameuil", weight=4)
+    G.add_edge("Favourite", "Tholomyes", weight=3)
+    G.add_edge("Favourite", "Listolier", weight=3)
+    G.add_edge("Favourite", "Fameuil", weight=3)
+    G.add_edge("Favourite", "Blacheville", weight=4)
+    G.add_edge("Dahlia", "Tholomyes", weight=3)
+    G.add_edge("Dahlia", "Listolier", weight=3)
+    G.add_edge("Dahlia", "Fameuil", weight=3)
+    G.add_edge("Dahlia", "Blacheville", weight=3)
+    G.add_edge("Dahlia", "Favourite", weight=5)
+    G.add_edge("Zephine", "Tholomyes", weight=3)
+    G.add_edge("Zephine", "Listolier", weight=3)
+    G.add_edge("Zephine", "Fameuil", weight=3)
+    G.add_edge("Zephine", "Blacheville", weight=3)
+    G.add_edge("Zephine", "Favourite", weight=4)
+    G.add_edge("Zephine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Tholomyes", weight=3)
+    G.add_edge("Fantine", "Listolier", weight=3)
+    G.add_edge("Fantine", "Fameuil", weight=3)
+    G.add_edge("Fantine", "Blacheville", weight=3)
+    G.add_edge("Fantine", "Favourite", weight=4)
+    G.add_edge("Fantine", "Dahlia", weight=4)
+    G.add_edge("Fantine", "Zephine", weight=4)
+    G.add_edge("Fantine", "Marguerite", weight=2)
+    G.add_edge("Fantine", "Valjean", weight=9)
+    G.add_edge("MmeThenardier", "Fantine", weight=2)
+    G.add_edge("MmeThenardier", "Valjean", weight=7)
+    G.add_edge("Thenardier", "MmeThenardier", weight=13)
+    G.add_edge("Thenardier", "Fantine", weight=1)
+    G.add_edge("Thenardier", "Valjean", weight=12)
+    G.add_edge("Cosette", "MmeThenardier", weight=4)
+    G.add_edge("Cosette", "Valjean", weight=31)
+    G.add_edge("Cosette", "Tholomyes", weight=1)
+    G.add_edge("Cosette", "Thenardier", weight=1)
+    G.add_edge("Javert", "Valjean", weight=17)
+    G.add_edge("Javert", "Fantine", weight=5)
+    G.add_edge("Javert", "Thenardier", weight=5)
+    G.add_edge("Javert", "MmeThenardier", weight=1)
+    G.add_edge("Javert", "Cosette", weight=1)
+    G.add_edge("Fauchelevent", "Valjean", weight=8)
+    G.add_edge("Fauchelevent", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Fantine", weight=1)
+    G.add_edge("Bamatabois", "Javert", weight=1)
+    G.add_edge("Bamatabois", "Valjean", weight=2)
+    G.add_edge("Perpetue", "Fantine", weight=1)
+    G.add_edge("Simplice", "Perpetue", weight=2)
+    G.add_edge("Simplice", "Valjean", weight=3)
+    G.add_edge("Simplice", "Fantine", weight=2)
+    G.add_edge("Simplice", "Javert", weight=1)
+    G.add_edge("Scaufflaire", "Valjean", weight=1)
+    G.add_edge("Woman1", "Valjean", weight=2)
+    G.add_edge("Woman1", "Javert", weight=1)
+    G.add_edge("Judge", "Valjean", weight=3)
+    G.add_edge("Judge", "Bamatabois", weight=2)
+    G.add_edge("Champmathieu", "Valjean", weight=3)
+    G.add_edge("Champmathieu", "Judge", weight=3)
+    G.add_edge("Champmathieu", "Bamatabois", weight=2)
+    G.add_edge("Brevet", "Judge", weight=2)
+    G.add_edge("Brevet", "Champmathieu", weight=2)
+    G.add_edge("Brevet", "Valjean", weight=2)
+    G.add_edge("Brevet", "Bamatabois", weight=1)
+    G.add_edge("Chenildieu", "Judge", weight=2)
+    G.add_edge("Chenildieu", "Champmathieu", weight=2)
+    G.add_edge("Chenildieu", "Brevet", weight=2)
+    G.add_edge("Chenildieu", "Valjean", weight=2)
+    G.add_edge("Chenildieu", "Bamatabois", weight=1)
+    G.add_edge("Cochepaille", "Judge", weight=2)
+    G.add_edge("Cochepaille", "Champmathieu", weight=2)
+    G.add_edge("Cochepaille", "Brevet", weight=2)
+    G.add_edge("Cochepaille", "Chenildieu", weight=2)
+    G.add_edge("Cochepaille", "Valjean", weight=2)
+    G.add_edge("Cochepaille", "Bamatabois", weight=1)
+    G.add_edge("Pontmercy", "Thenardier", weight=1)
+    G.add_edge("Boulatruelle", "Thenardier", weight=1)
+    G.add_edge("Eponine", "MmeThenardier", weight=2)
+    G.add_edge("Eponine", "Thenardier", weight=3)
+    G.add_edge("Anzelma", "Eponine", weight=2)
+    G.add_edge("Anzelma", "Thenardier", weight=2)
+    G.add_edge("Anzelma", "MmeThenardier", weight=1)
+    G.add_edge("Woman2", "Valjean", weight=3)
+    G.add_edge("Woman2", "Cosette", weight=1)
+    G.add_edge("Woman2", "Javert", weight=1)
+    G.add_edge("MotherInnocent", "Fauchelevent", weight=3)
+    G.add_edge("MotherInnocent", "Valjean", weight=1)
+    G.add_edge("Gribier", "Fauchelevent", weight=2)
+    G.add_edge("MmeBurgon", "Jondrette", weight=1)
+    G.add_edge("Gavroche", "MmeBurgon", weight=2)
+    G.add_edge("Gavroche", "Thenardier", weight=1)
+    G.add_edge("Gavroche", "Javert", weight=1)
+    G.add_edge("Gavroche", "Valjean", weight=1)
+    G.add_edge("Gillenormand", "Cosette", weight=3)
+    G.add_edge("Gillenormand", "Valjean", weight=2)
+    G.add_edge("Magnon", "Gillenormand", weight=1)
+    G.add_edge("Magnon", "MmeThenardier", weight=1)
+    G.add_edge("MlleGillenormand", "Gillenormand", weight=9)
+    G.add_edge("MlleGillenormand", "Cosette", weight=2)
+    G.add_edge("MlleGillenormand", "Valjean", weight=2)
+    G.add_edge("MmePontmercy", "MlleGillenormand", weight=1)
+    G.add_edge("MmePontmercy", "Pontmercy", weight=1)
+    G.add_edge("MlleVaubois", "MlleGillenormand", weight=1)
+    G.add_edge("LtGillenormand", "MlleGillenormand", weight=2)
+    G.add_edge("LtGillenormand", "Gillenormand", weight=1)
+    G.add_edge("LtGillenormand", "Cosette", weight=1)
+    G.add_edge("Marius", "MlleGillenormand", weight=6)
+    G.add_edge("Marius", "Gillenormand", weight=12)
+    G.add_edge("Marius", "Pontmercy", weight=1)
+    G.add_edge("Marius", "LtGillenormand", weight=1)
+    G.add_edge("Marius", "Cosette", weight=21)
+    G.add_edge("Marius", "Valjean", weight=19)
+    G.add_edge("Marius", "Tholomyes", weight=1)
+    G.add_edge("Marius", "Thenardier", weight=2)
+    G.add_edge("Marius", "Eponine", weight=5)
+    G.add_edge("Marius", "Gavroche", weight=4)
+    G.add_edge("BaronessT", "Gillenormand", weight=1)
+    G.add_edge("BaronessT", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Marius", weight=1)
+    G.add_edge("Mabeuf", "Eponine", weight=1)
+    G.add_edge("Mabeuf", "Gavroche", weight=1)
+    G.add_edge("Enjolras", "Marius", weight=7)
+    G.add_edge("Enjolras", "Gavroche", weight=7)
+    G.add_edge("Enjolras", "Javert", weight=6)
+    G.add_edge("Enjolras", "Mabeuf", weight=1)
+    G.add_edge("Enjolras", "Valjean", weight=4)
+    G.add_edge("Combeferre", "Enjolras", weight=15)
+    G.add_edge("Combeferre", "Marius", weight=5)
+    G.add_edge("Combeferre", "Gavroche", weight=6)
+    G.add_edge("Combeferre", "Mabeuf", weight=2)
+    G.add_edge("Prouvaire", "Gavroche", weight=1)
+    G.add_edge("Prouvaire", "Enjolras", weight=4)
+    G.add_edge("Prouvaire", "Combeferre", weight=2)
+    G.add_edge("Feuilly", "Gavroche", weight=2)
+    G.add_edge("Feuilly", "Enjolras", weight=6)
+    G.add_edge("Feuilly", "Prouvaire", weight=2)
+    G.add_edge("Feuilly", "Combeferre", weight=5)
+    G.add_edge("Feuilly", "Mabeuf", weight=1)
+    G.add_edge("Feuilly", "Marius", weight=1)
+    G.add_edge("Courfeyrac", "Marius", weight=9)
+    G.add_edge("Courfeyrac", "Enjolras", weight=17)
+    G.add_edge("Courfeyrac", "Combeferre", weight=13)
+    G.add_edge("Courfeyrac", "Gavroche", weight=7)
+    G.add_edge("Courfeyrac", "Mabeuf", weight=2)
+    G.add_edge("Courfeyrac", "Eponine", weight=1)
+    G.add_edge("Courfeyrac", "Feuilly", weight=6)
+    G.add_edge("Courfeyrac", "Prouvaire", weight=3)
+    G.add_edge("Bahorel", "Combeferre", weight=5)
+    G.add_edge("Bahorel", "Gavroche", weight=5)
+    G.add_edge("Bahorel", "Courfeyrac", weight=6)
+    G.add_edge("Bahorel", "Mabeuf", weight=2)
+    G.add_edge("Bahorel", "Enjolras", weight=4)
+    G.add_edge("Bahorel", "Feuilly", weight=3)
+    G.add_edge("Bahorel", "Prouvaire", weight=2)
+    G.add_edge("Bahorel", "Marius", weight=1)
+    G.add_edge("Bossuet", "Marius", weight=5)
+    G.add_edge("Bossuet", "Courfeyrac", weight=12)
+    G.add_edge("Bossuet", "Gavroche", weight=5)
+    G.add_edge("Bossuet", "Bahorel", weight=4)
+    G.add_edge("Bossuet", "Enjolras", weight=10)
+    G.add_edge("Bossuet", "Feuilly", weight=6)
+    G.add_edge("Bossuet", "Prouvaire", weight=2)
+    G.add_edge("Bossuet", "Combeferre", weight=9)
+    G.add_edge("Bossuet", "Mabeuf", weight=1)
+    G.add_edge("Bossuet", "Valjean", weight=1)
+    G.add_edge("Joly", "Bahorel", weight=5)
+    G.add_edge("Joly", "Bossuet", weight=7)
+    G.add_edge("Joly", "Gavroche", weight=3)
+    G.add_edge("Joly", "Courfeyrac", weight=5)
+    G.add_edge("Joly", "Enjolras", weight=5)
+    G.add_edge("Joly", "Feuilly", weight=5)
+    G.add_edge("Joly", "Prouvaire", weight=2)
+    G.add_edge("Joly", "Combeferre", weight=5)
+    G.add_edge("Joly", "Mabeuf", weight=1)
+    G.add_edge("Joly", "Marius", weight=2)
+    G.add_edge("Grantaire", "Bossuet", weight=3)
+    G.add_edge("Grantaire", "Enjolras", weight=3)
+    G.add_edge("Grantaire", "Combeferre", weight=1)
+    G.add_edge("Grantaire", "Courfeyrac", weight=2)
+    G.add_edge("Grantaire", "Joly", weight=2)
+    G.add_edge("Grantaire", "Gavroche", weight=1)
+    G.add_edge("Grantaire", "Bahorel", weight=1)
+    G.add_edge("Grantaire", "Feuilly", weight=1)
+    G.add_edge("Grantaire", "Prouvaire", weight=1)
+    G.add_edge("MotherPlutarch", "Mabeuf", weight=3)
+    G.add_edge("Gueulemer", "Thenardier", weight=5)
+    G.add_edge("Gueulemer", "Valjean", weight=1)
+    G.add_edge("Gueulemer", "MmeThenardier", weight=1)
+    G.add_edge("Gueulemer", "Javert", weight=1)
+    G.add_edge("Gueulemer", "Gavroche", weight=1)
+    G.add_edge("Gueulemer", "Eponine", weight=1)
+    G.add_edge("Babet", "Thenardier", weight=6)
+    G.add_edge("Babet", "Gueulemer", weight=6)
+    G.add_edge("Babet", "Valjean", weight=1)
+    G.add_edge("Babet", "MmeThenardier", weight=1)
+    G.add_edge("Babet", "Javert", weight=2)
+    G.add_edge("Babet", "Gavroche", weight=1)
+    G.add_edge("Babet", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Thenardier", weight=4)
+    G.add_edge("Claquesous", "Babet", weight=4)
+    G.add_edge("Claquesous", "Gueulemer", weight=4)
+    G.add_edge("Claquesous", "Valjean", weight=1)
+    G.add_edge("Claquesous", "MmeThenardier", weight=1)
+    G.add_edge("Claquesous", "Javert", weight=1)
+    G.add_edge("Claquesous", "Eponine", weight=1)
+    G.add_edge("Claquesous", "Enjolras", weight=1)
+    G.add_edge("Montparnasse", "Javert", weight=1)
+    G.add_edge("Montparnasse", "Babet", weight=2)
+    G.add_edge("Montparnasse", "Gueulemer", weight=2)
+    G.add_edge("Montparnasse", "Claquesous", weight=2)
+    G.add_edge("Montparnasse", "Valjean", weight=1)
+    G.add_edge("Montparnasse", "Gavroche", weight=1)
+    G.add_edge("Montparnasse", "Eponine", weight=1)
+    G.add_edge("Montparnasse", "Thenardier", weight=1)
+    G.add_edge("Toussaint", "Cosette", weight=2)
+    G.add_edge("Toussaint", "Javert", weight=1)
+    G.add_edge("Toussaint", "Valjean", weight=1)
+    G.add_edge("Child1", "Gavroche", weight=2)
+    G.add_edge("Child2", "Gavroche", weight=2)
+    G.add_edge("Child2", "Child1", weight=3)
+    G.add_edge("Brujon", "Babet", weight=3)
+    G.add_edge("Brujon", "Gueulemer", weight=3)
+    G.add_edge("Brujon", "Thenardier", weight=3)
+    G.add_edge("Brujon", "Gavroche", weight=1)
+    G.add_edge("Brujon", "Eponine", weight=1)
+    G.add_edge("Brujon", "Claquesous", weight=1)
+    G.add_edge("Brujon", "Montparnasse", weight=1)
+    G.add_edge("MmeHucheloup", "Bossuet", weight=1)
+    G.add_edge("MmeHucheloup", "Joly", weight=1)
+    G.add_edge("MmeHucheloup", "Grantaire", weight=1)
+    G.add_edge("MmeHucheloup", "Bahorel", weight=1)
+    G.add_edge("MmeHucheloup", "Courfeyrac", weight=1)
+    G.add_edge("MmeHucheloup", "Gavroche", weight=1)
+    G.add_edge("MmeHucheloup", "Enjolras", weight=1)
+    return G

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

@@ -0,0 +1,121 @@
+"""Generates graphs with a given eigenvector structure"""
+
+
+import networkx as nx
+from networkx.utils import np_random_state
+
+__all__ = ["spectral_graph_forge"]
+
+
+@np_random_state(3)
+@nx._dispatchable(returns_graph=True)
+def spectral_graph_forge(G, alpha, transformation="identity", seed=None):
+    """Returns a random simple graph with spectrum resembling that of `G`
+
+    This algorithm, called Spectral Graph Forge (SGF), computes the
+    eigenvectors of a given graph adjacency matrix, filters them and
+    builds a random graph with a similar eigenstructure.
+    SGF has been proved to be particularly useful for synthesizing
+    realistic social networks and it can also be used to anonymize
+    graph sensitive data.
+
+    Parameters
+    ----------
+    G : Graph
+    alpha :  float
+        Ratio representing the percentage of eigenvectors of G to consider,
+        values in [0,1].
+    transformation : string, optional
+        Represents the intended matrix linear transformation, possible values
+        are 'identity' and 'modularity'
+    seed : integer, random_state, or None (default)
+        Indicator of numpy random number generation state.
+        See :ref:`Randomness<randomness>`.
+
+    Returns
+    -------
+    H : Graph
+        A graph with a similar eigenvector structure of the input one.
+
+    Raises
+    ------
+    NetworkXError
+        If transformation has a value different from 'identity' or 'modularity'
+
+    Notes
+    -----
+    Spectral Graph Forge (SGF) generates a random simple graph resembling the
+    global properties of the given one.
+    It leverages the low-rank approximation of the associated adjacency matrix
+    driven by the *alpha* precision parameter.
+    SGF preserves the number of nodes of the input graph and their ordering.
+    This way, nodes of output graphs resemble the properties of the input one
+    and attributes can be directly mapped.
+
+    It considers the graph adjacency matrices which can optionally be
+    transformed to other symmetric real matrices (currently transformation
+    options include *identity* and *modularity*).
+    The *modularity* transformation, in the sense of Newman's modularity matrix
+    allows the focusing on community structure related properties of the graph.
+
+    SGF applies a low-rank approximation whose fixed rank is computed from the
+    ratio *alpha* of the input graph adjacency matrix dimension.
+    This step performs a filtering on the input eigenvectors similar to the low
+    pass filtering common in telecommunications.
+
+    The filtered values (after truncation) are used as input to a Bernoulli
+    sampling for constructing a random adjacency matrix.
+
+    References
+    ----------
+    ..  [1] L. Baldesi, C. T. Butts, A. Markopoulou, "Spectral Graph Forge:
+        Graph Generation Targeting Modularity", IEEE Infocom, '18.
+        https://arxiv.org/abs/1801.01715
+    ..  [2] M. Newman, "Networks: an introduction", Oxford university press,
+        2010
+
+    Examples
+    --------
+    >>> G = nx.karate_club_graph()
+    >>> H = nx.spectral_graph_forge(G, 0.3)
+    >>>
+    """
+    import numpy as np
+    import scipy as sp
+
+    available_transformations = ["identity", "modularity"]
+    alpha = np.clip(alpha, 0, 1)
+    A = nx.to_numpy_array(G)
+    n = A.shape[1]
+    level = round(n * alpha)
+
+    if transformation not in available_transformations:
+        msg = f"{transformation!r} is not a valid transformation. "
+        msg += f"Transformations: {available_transformations}"
+        raise nx.NetworkXError(msg)
+
+    K = np.ones((1, n)) @ A
+
+    B = A
+    if transformation == "modularity":
+        B -= K.T @ K / K.sum()
+
+    # Compute low-rank approximation of B
+    evals, evecs = np.linalg.eigh(B)
+    k = np.argsort(np.abs(evals))[::-1]  # indices of evals in descending order
+    evecs[:, k[np.arange(level, n)]] = 0  # set smallest eigenvectors to 0
+    B = evecs @ np.diag(evals) @ evecs.T
+
+    if transformation == "modularity":
+        B += K.T @ K / K.sum()
+
+    B = np.clip(B, 0, 1)
+    np.fill_diagonal(B, 0)
+
+    for i in range(n - 1):
+        B[i, i + 1 :] = sp.stats.bernoulli.rvs(B[i, i + 1 :], random_state=seed)
+        B[i + 1 :, i] = np.transpose(B[i, i + 1 :])
+
+    H = nx.from_numpy_array(B)
+
+    return H

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

@@ -0,0 +1,54 @@
+"""Functions for generating stochastic graphs from a given weighted directed
+graph.
+
+"""
+
+import networkx as nx
+from networkx.classes import DiGraph, MultiDiGraph
+from networkx.utils import not_implemented_for
+
+__all__ = ["stochastic_graph"]
+
+
+@not_implemented_for("undirected")
+@nx._dispatchable(
+    edge_attrs="weight", mutates_input={"not copy": 1}, returns_graph=True
+)
+def stochastic_graph(G, copy=True, weight="weight"):
+    """Returns a right-stochastic representation of directed graph `G`.
+
+    A right-stochastic graph is a weighted digraph in which for each
+    node, the sum of the weights of all the out-edges of that node is
+    1. If the graph is already weighted (for example, via a 'weight'
+    edge attribute), the reweighting takes that into account.
+
+    Parameters
+    ----------
+    G : directed graph
+        A :class:`~networkx.DiGraph` or :class:`~networkx.MultiDiGraph`.
+
+    copy : boolean, optional
+        If this is True, then this function returns a new graph with
+        the stochastic reweighting. Otherwise, the original graph is
+        modified in-place (and also returned, for convenience).
+
+    weight : edge attribute key (optional, default='weight')
+        Edge attribute key used for reading the existing weight and
+        setting the new weight.  If no attribute with this key is found
+        for an edge, then the edge weight is assumed to be 1. If an edge
+        has a weight, it must be a positive number.
+
+    """
+    if copy:
+        G = MultiDiGraph(G) if G.is_multigraph() else DiGraph(G)
+    # There is a tradeoff here: the dictionary of node degrees may
+    # require a lot of memory, whereas making a call to `G.out_degree`
+    # inside the loop may be costly in computation time.
+    degree = dict(G.out_degree(weight=weight))
+    for u, v, d in G.edges(data=True):
+        if degree[u] == 0:
+            d[weight] = 0
+        else:
+            d[weight] = d.get(weight, 1) / degree[u]
+    nx._clear_cache(G)
+    return G

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

@@ -0,0 +1,131 @@
+"""Generator for Sudoku graphs
+
+This module gives a generator for n-Sudoku graphs. It can be used to develop
+algorithms for solving or generating Sudoku puzzles.
+
+A completed Sudoku grid is a 9x9 array of integers between 1 and 9, with no
+number appearing twice in the same row, column, or 3x3 box.
+
++---------+---------+---------+
+| | 8 6 4 | | 3 7 1 | | 2 5 9 |
+| | 3 2 5 | | 8 4 9 | | 7 6 1 |
+| | 9 7 1 | | 2 6 5 | | 8 4 3 |
++---------+---------+---------+
+| | 4 3 6 | | 1 9 2 | | 5 8 7 |
+| | 1 9 8 | | 6 5 7 | | 4 3 2 |
+| | 2 5 7 | | 4 8 3 | | 9 1 6 |
++---------+---------+---------+
+| | 6 8 9 | | 7 3 4 | | 1 2 5 |
+| | 7 1 3 | | 5 2 8 | | 6 9 4 |
+| | 5 4 2 | | 9 1 6 | | 3 7 8 |
++---------+---------+---------+
+
+
+The Sudoku graph is an undirected graph with 81 vertices, corresponding to
+the cells of a Sudoku grid. It is a regular graph of degree 20. Two distinct
+vertices are adjacent if and only if the corresponding cells belong to the
+same row, column, or box. A completed Sudoku grid corresponds to a vertex
+coloring of the Sudoku graph with nine colors.
+
+More generally, the n-Sudoku graph is a graph with n^4 vertices, corresponding
+to the cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+only if they belong to the same row, column, or n by n box.
+
+References
+----------
+.. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+    polynomials. Notices of the AMS, 54(6), 708-717.
+.. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+    Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+.. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+    Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+"""
+
+import networkx as nx
+from networkx.exception import NetworkXError
+
+__all__ = ["sudoku_graph"]
+
+
+@nx._dispatchable(graphs=None, returns_graph=True)
+def sudoku_graph(n=3):
+    """Returns the n-Sudoku graph. The default value of n is 3.
+
+    The n-Sudoku graph is a graph with n^4 vertices, corresponding to the
+    cells of an n^2 by n^2 grid. Two distinct vertices are adjacent if and
+    only if they belong to the same row, column, or n-by-n box.
+
+    Parameters
+    ----------
+    n: integer
+       The order of the Sudoku graph, equal to the square root of the
+       number of rows. The default is 3.
+
+    Returns
+    -------
+    NetworkX graph
+        The n-Sudoku graph Sud(n).
+
+    Examples
+    --------
+    >>> G = nx.sudoku_graph()
+    >>> G.number_of_nodes()
+    81
+    >>> G.number_of_edges()
+    810
+    >>> sorted(G.neighbors(42))
+    [6, 15, 24, 33, 34, 35, 36, 37, 38, 39, 40, 41, 43, 44, 51, 52, 53, 60, 69, 78]
+    >>> G = nx.sudoku_graph(2)
+    >>> G.number_of_nodes()
+    16
+    >>> G.number_of_edges()
+    56
+
+    References
+    ----------
+    .. [1] Herzberg, A. M., & Murty, M. R. (2007). Sudoku squares and chromatic
+       polynomials. Notices of the AMS, 54(6), 708-717.
+    .. [2] Sander, Torsten (2009), "Sudoku graphs are integral",
+       Electronic Journal of Combinatorics, 16 (1): Note 25, 7pp, MR 2529816
+    .. [3] Wikipedia contributors. "Glossary of Sudoku." Wikipedia, The Free
+       Encyclopedia, 3 Dec. 2019. Web. 22 Dec. 2019.
+    """
+
+    if n < 0:
+        raise NetworkXError("The order must be greater than or equal to zero.")
+
+    n2 = n * n
+    n3 = n2 * n
+    n4 = n3 * n
+
+    # Construct an empty graph with n^4 nodes
+    G = nx.empty_graph(n4)
+
+    # A Sudoku graph of order 0 or 1 has no edges
+    if n < 2:
+        return G
+
+    # Add edges for cells in the same row
+    for row_no in range(n2):
+        row_start = row_no * n2
+        for j in range(1, n2):
+            for i in range(j):
+                G.add_edge(row_start + i, row_start + j)
+
+    # Add edges for cells in the same column
+    for col_no in range(n2):
+        for j in range(col_no, n4, n2):
+            for i in range(col_no, j, n2):
+                G.add_edge(i, j)
+
+    # Add edges for cells in the same box
+    for band_no in range(n):
+        for stack_no in range(n):
+            box_start = n3 * band_no + n * stack_no
+            for j in range(1, n2):
+                for i in range(j):
+                    u = box_start + (i % n) + n2 * (i // n)
+                    v = box_start + (j % n) + n2 * (j // n)
+                    G.add_edge(u, v)
+
+    return G

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

@@ -0,0 +1,75 @@
+from itertools import groupby
+
+import pytest
+
+import networkx as nx
+from networkx import graph_atlas, graph_atlas_g
+from networkx.generators.atlas import NUM_GRAPHS
+from networkx.utils import edges_equal, nodes_equal, pairwise
+
+
+class TestAtlasGraph:
+    """Unit tests for the :func:`~networkx.graph_atlas` function."""
+
+    def test_index_too_small(self):
+        with pytest.raises(ValueError):
+            graph_atlas(-1)
+
+    def test_index_too_large(self):
+        with pytest.raises(ValueError):
+            graph_atlas(NUM_GRAPHS)
+
+    def test_graph(self):
+        G = graph_atlas(6)
+        assert nodes_equal(G.nodes(), range(3))
+        assert edges_equal(G.edges(), [(0, 1), (0, 2)])
+
+
+class TestAtlasGraphG:
+    """Unit tests for the :func:`~networkx.graph_atlas_g` function."""
+
+    @classmethod
+    def setup_class(cls):
+        cls.GAG = graph_atlas_g()
+
+    def test_sizes(self):
+        G = self.GAG[0]
+        assert G.number_of_nodes() == 0
+        assert G.number_of_edges() == 0
+
+        G = self.GAG[7]
+        assert G.number_of_nodes() == 3
+        assert G.number_of_edges() == 3
+
+    def test_names(self):
+        for i, G in enumerate(self.GAG):
+            assert int(G.name[1:]) == i
+
+    def test_nondecreasing_nodes(self):
+        # check for nondecreasing number of nodes
+        for n1, n2 in pairwise(map(len, self.GAG)):
+            assert n2 <= n1 + 1
+
+    def test_nondecreasing_edges(self):
+        # check for nondecreasing number of edges (for fixed number of
+        # nodes)
+        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
+            for m1, m2 in pairwise(map(nx.number_of_edges, group)):
+                assert m2 <= m1 + 1
+
+    def test_nondecreasing_degree_sequence(self):
+        # Check for lexicographically nondecreasing degree sequences
+        # (for fixed number of nodes and edges).
+        #
+        # There are three exceptions to this rule in the order given in
+        # the "Atlas of Graphs" book, so we need to manually exclude
+        # those.
+        exceptions = [("G55", "G56"), ("G1007", "G1008"), ("G1012", "G1013")]
+        for n, group in groupby(self.GAG, key=nx.number_of_nodes):
+            for m, group in groupby(group, key=nx.number_of_edges):
+                for G1, G2 in pairwise(group):
+                    if (G1.name, G2.name) in exceptions:
+                        continue
+                    d1 = sorted(d for v, d in G1.degree())
+                    d2 = sorted(d for v, d in G2.degree())
+                    assert d1 <= d2

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

@@ -0,0 +1,635 @@
+"""
+====================
+Generators - Classic
+====================
+
+Unit tests for various classic graph generators in generators/classic.py
+"""
+import itertools
+import typing
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.isomorphism.isomorph import graph_could_be_isomorphic
+from networkx.utils import edges_equal, nodes_equal
+
+is_isomorphic = graph_could_be_isomorphic
+
+
+class TestGeneratorClassic:
+    def test_balanced_tree(self):
+        # balanced_tree(r,h) is a tree with (r**(h+1)-1)/(r-1) edges
+        for r, h in [(2, 2), (3, 3), (6, 2)]:
+            t = nx.balanced_tree(r, h)
+            order = t.order()
+            assert order == (r ** (h + 1) - 1) / (r - 1)
+            assert nx.is_connected(t)
+            assert t.size() == order - 1
+            dh = nx.degree_histogram(t)
+            assert dh[0] == 0  # no nodes of 0
+            assert dh[1] == r**h  # nodes of degree 1 are leaves
+            assert dh[r] == 1  # root is degree r
+            assert dh[r + 1] == order - r**h - 1  # everyone else is degree r+1
+            assert len(dh) == r + 2
+
+    def test_balanced_tree_star(self):
+        # balanced_tree(r,1) is the r-star
+        t = nx.balanced_tree(r=2, h=1)
+        assert is_isomorphic(t, nx.star_graph(2))
+        t = nx.balanced_tree(r=5, h=1)
+        assert is_isomorphic(t, nx.star_graph(5))
+        t = nx.balanced_tree(r=10, h=1)
+        assert is_isomorphic(t, nx.star_graph(10))
+
+    def test_balanced_tree_path(self):
+        """Tests that the balanced tree with branching factor one is the
+        path graph.
+
+        """
+        # A tree of height four has five levels.
+        T = nx.balanced_tree(1, 4)
+        P = nx.path_graph(5)
+        assert is_isomorphic(T, P)
+
+    def test_full_rary_tree(self):
+        r = 2
+        n = 9
+        t = nx.full_rary_tree(r, n)
+        assert t.order() == n
+        assert nx.is_connected(t)
+        dh = nx.degree_histogram(t)
+        assert dh[0] == 0  # no nodes of 0
+        assert dh[1] == 5  # nodes of degree 1 are leaves
+        assert dh[r] == 1  # root is degree r
+        assert dh[r + 1] == 9 - 5 - 1  # everyone else is degree r+1
+        assert len(dh) == r + 2
+
+    def test_full_rary_tree_balanced(self):
+        t = nx.full_rary_tree(2, 15)
+        th = nx.balanced_tree(2, 3)
+        assert is_isomorphic(t, th)
+
+    def test_full_rary_tree_path(self):
+        t = nx.full_rary_tree(1, 10)
+        assert is_isomorphic(t, nx.path_graph(10))
+
+    def test_full_rary_tree_empty(self):
+        t = nx.full_rary_tree(0, 10)
+        assert is_isomorphic(t, nx.empty_graph(10))
+        t = nx.full_rary_tree(3, 0)
+        assert is_isomorphic(t, nx.empty_graph(0))
+
+    def test_full_rary_tree_3_20(self):
+        t = nx.full_rary_tree(3, 20)
+        assert t.order() == 20
+
+    def test_barbell_graph(self):
+        # number of nodes = 2*m1 + m2 (2 m1-complete graphs + m2-path + 2 edges)
+        # number of edges = 2*(nx.number_of_edges(m1-complete graph) + m2 + 1
+        m1 = 3
+        m2 = 5
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        m1 = 4
+        m2 = 10
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        m1 = 3
+        m2 = 20
+        b = nx.barbell_graph(m1, m2)
+        assert nx.number_of_nodes(b) == 2 * m1 + m2
+        assert nx.number_of_edges(b) == m1 * (m1 - 1) + m2 + 1
+
+        # Raise NetworkXError if m1<2
+        m1 = 1
+        m2 = 20
+        pytest.raises(nx.NetworkXError, nx.barbell_graph, m1, m2)
+
+        # Raise NetworkXError if m2<0
+        m1 = 5
+        m2 = -2
+        pytest.raises(nx.NetworkXError, nx.barbell_graph, m1, m2)
+
+        # nx.barbell_graph(2,m) = nx.path_graph(m+4)
+        m1 = 2
+        m2 = 5
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        m1 = 2
+        m2 = 10
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        m1 = 2
+        m2 = 20
+        b = nx.barbell_graph(m1, m2)
+        assert is_isomorphic(b, nx.path_graph(m2 + 4))
+
+        pytest.raises(
+            nx.NetworkXError, nx.barbell_graph, m1, m2, create_using=nx.DiGraph()
+        )
+
+        mb = nx.barbell_graph(m1, m2, create_using=nx.MultiGraph())
+        assert edges_equal(mb.edges(), b.edges())
+
+    def test_binomial_tree(self):
+        graphs = (None, nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph)
+        for create_using in graphs:
+            for n in range(4):
+                b = nx.binomial_tree(n, create_using)
+                assert nx.number_of_nodes(b) == 2**n
+                assert nx.number_of_edges(b) == (2**n - 1)
+
+    def test_complete_graph(self):
+        # complete_graph(m) is a connected graph with
+        # m nodes and  m*(m+1)/2 edges
+        for m in [0, 1, 3, 5]:
+            g = nx.complete_graph(m)
+            assert nx.number_of_nodes(g) == m
+            assert nx.number_of_edges(g) == m * (m - 1) // 2
+
+        mg = nx.complete_graph(m, create_using=nx.MultiGraph)
+        assert edges_equal(mg.edges(), g.edges())
+
+        g = nx.complete_graph("abc")
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 3
+
+        # creates a self-loop... should it? <backward compatible says yes>
+        g = nx.complete_graph("abcb")
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 4
+
+        g = nx.complete_graph("abcb", create_using=nx.MultiGraph)
+        assert nodes_equal(g.nodes(), ["a", "b", "c"])
+        assert g.size() == 6
+
+    def test_complete_digraph(self):
+        # complete_graph(m) is a connected graph with
+        # m nodes and  m*(m+1)/2 edges
+        for m in [0, 1, 3, 5]:
+            g = nx.complete_graph(m, create_using=nx.DiGraph)
+            assert nx.number_of_nodes(g) == m
+            assert nx.number_of_edges(g) == m * (m - 1)
+
+        g = nx.complete_graph("abc", create_using=nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 6
+        assert g.is_directed()
+
+    def test_circular_ladder_graph(self):
+        G = nx.circular_ladder_graph(5)
+        pytest.raises(
+            nx.NetworkXError, nx.circular_ladder_graph, 5, create_using=nx.DiGraph
+        )
+        mG = nx.circular_ladder_graph(5, create_using=nx.MultiGraph)
+        assert edges_equal(mG.edges(), G.edges())
+
+    def test_circulant_graph(self):
+        # Ci_n(1) is the cycle graph for all n
+        Ci6_1 = nx.circulant_graph(6, [1])
+        C6 = nx.cycle_graph(6)
+        assert edges_equal(Ci6_1.edges(), C6.edges())
+
+        # Ci_n(1, 2, ..., n div 2) is the complete graph for all n
+        Ci7 = nx.circulant_graph(7, [1, 2, 3])
+        K7 = nx.complete_graph(7)
+        assert edges_equal(Ci7.edges(), K7.edges())
+
+        # Ci_6(1, 3) is K_3,3 i.e. the utility graph
+        Ci6_1_3 = nx.circulant_graph(6, [1, 3])
+        K3_3 = nx.complete_bipartite_graph(3, 3)
+        assert is_isomorphic(Ci6_1_3, K3_3)
+
+    def test_cycle_graph(self):
+        G = nx.cycle_graph(4)
+        assert edges_equal(G.edges(), [(0, 1), (0, 3), (1, 2), (2, 3)])
+        mG = nx.cycle_graph(4, create_using=nx.MultiGraph)
+        assert edges_equal(mG.edges(), [(0, 1), (0, 3), (1, 2), (2, 3)])
+        G = nx.cycle_graph(4, create_using=nx.DiGraph)
+        assert not G.has_edge(2, 1)
+        assert G.has_edge(1, 2)
+        assert G.is_directed()
+
+        G = nx.cycle_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 3
+        G = nx.cycle_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        g = nx.cycle_graph("abc", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 3
+        assert g.is_directed()
+        g = nx.cycle_graph("abcb", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 4
+
+    def test_dorogovtsev_goltsev_mendes_graph(self):
+        G = nx.dorogovtsev_goltsev_mendes_graph(0)
+        assert edges_equal(G.edges(), [(0, 1)])
+        assert nodes_equal(list(G), [0, 1])
+        G = nx.dorogovtsev_goltsev_mendes_graph(1)
+        assert edges_equal(G.edges(), [(0, 1), (0, 2), (1, 2)])
+        assert nx.average_clustering(G) == 1.0
+        assert sorted(nx.triangles(G).values()) == [1, 1, 1]
+        G = nx.dorogovtsev_goltsev_mendes_graph(10)
+        assert nx.number_of_nodes(G) == 29526
+        assert nx.number_of_edges(G) == 59049
+        assert G.degree(0) == 1024
+        assert G.degree(1) == 1024
+        assert G.degree(2) == 1024
+
+        pytest.raises(
+            nx.NetworkXError,
+            nx.dorogovtsev_goltsev_mendes_graph,
+            7,
+            create_using=nx.DiGraph,
+        )
+        pytest.raises(
+            nx.NetworkXError,
+            nx.dorogovtsev_goltsev_mendes_graph,
+            7,
+            create_using=nx.MultiGraph,
+        )
+
+    def test_create_using(self):
+        G = nx.empty_graph()
+        assert isinstance(G, nx.Graph)
+        pytest.raises(TypeError, nx.empty_graph, create_using=0.0)
+        pytest.raises(TypeError, nx.empty_graph, create_using="Graph")
+
+        G = nx.empty_graph(create_using=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+        G = nx.empty_graph(create_using=nx.DiGraph)
+        assert isinstance(G, nx.DiGraph)
+
+        G = nx.empty_graph(create_using=nx.DiGraph, default=nx.MultiGraph)
+        assert isinstance(G, nx.DiGraph)
+        G = nx.empty_graph(create_using=None, default=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+        G = nx.empty_graph(default=nx.MultiGraph)
+        assert isinstance(G, nx.MultiGraph)
+
+        G = nx.path_graph(5)
+        H = nx.empty_graph(create_using=G)
+        assert not H.is_multigraph()
+        assert not H.is_directed()
+        assert len(H) == 0
+        assert G is H
+
+        H = nx.empty_graph(create_using=nx.MultiGraph())
+        assert H.is_multigraph()
+        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
+        G = nx.empty_graph(42)
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+
+        G = nx.empty_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 0
+
+        # create empty digraph
+        G = nx.empty_graph(42, create_using=nx.DiGraph(name="duh"))
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.DiGraph)
+
+        # create empty multigraph
+        G = nx.empty_graph(42, create_using=nx.MultiGraph(name="duh"))
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.MultiGraph)
+
+        # create empty graph from another
+        pete = nx.petersen_graph()
+        G = nx.empty_graph(42, create_using=pete)
+        assert nx.number_of_nodes(G) == 42
+        assert nx.number_of_edges(G) == 0
+        assert isinstance(G, nx.Graph)
+
+    def test_ladder_graph(self):
+        for i, G in [
+            (0, nx.empty_graph(0)),
+            (1, nx.path_graph(2)),
+            (2, nx.hypercube_graph(2)),
+            (10, nx.grid_graph([2, 10])),
+        ]:
+            assert is_isomorphic(nx.ladder_graph(i), G)
+
+        pytest.raises(nx.NetworkXError, nx.ladder_graph, 2, create_using=nx.DiGraph)
+
+        g = nx.ladder_graph(2)
+        mg = nx.ladder_graph(2, create_using=nx.MultiGraph)
+        assert edges_equal(mg.edges(), g.edges())
+
+    @pytest.mark.parametrize(("m", "n"), [(3, 5), (4, 10), (3, 20)])
+    def test_lollipop_graph_right_sizes(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert nx.number_of_nodes(G) == m + n
+        assert nx.number_of_edges(G) == m * (m - 1) / 2 + n
+
+    @pytest.mark.parametrize(("m", "n"), [("ab", ""), ("abc", "defg")])
+    def test_lollipop_graph_size_node_sequence(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert nx.number_of_nodes(G) == len(m) + len(n)
+        assert nx.number_of_edges(G) == len(m) * (len(m) - 1) / 2 + len(n)
+
+    def test_lollipop_graph_exceptions(self):
+        # Raise NetworkXError if m<2
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, -1, 2)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, 1, 20)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, "", 20)
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, "a", 20)
+
+        # Raise NetworkXError if n<0
+        pytest.raises(nx.NetworkXError, nx.lollipop_graph, 5, -2)
+
+        # raise NetworkXError if create_using is directed
+        with pytest.raises(nx.NetworkXError):
+            nx.lollipop_graph(2, 20, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.lollipop_graph(2, 20, create_using=nx.MultiDiGraph)
+
+    @pytest.mark.parametrize(("m", "n"), [(2, 0), (2, 5), (2, 10), ("ab", 20)])
+    def test_lollipop_graph_same_as_path_when_m1_is_2(self, m, n):
+        G = nx.lollipop_graph(m, n)
+        assert is_isomorphic(G, nx.path_graph(n + 2))
+
+    def test_lollipop_graph_for_multigraph(self):
+        G = nx.lollipop_graph(5, 20)
+        MG = nx.lollipop_graph(5, 20, create_using=nx.MultiGraph)
+        assert edges_equal(MG.edges(), G.edges())
+
+    @pytest.mark.parametrize(
+        ("m", "n"),
+        [(4, "abc"), ("abcd", 3), ([1, 2, 3, 4], "abc"), ("abcd", [1, 2, 3])],
+    )
+    def test_lollipop_graph_mixing_input_types(self, m, n):
+        expected = nx.compose(nx.complete_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect complete graph and path graph
+        assert is_isomorphic(nx.lollipop_graph(m, n), expected)
+
+    def test_lollipop_graph_non_builtin_ints(self):
+        np = pytest.importorskip("numpy")
+        G = nx.lollipop_graph(np.int32(4), np.int64(3))
+        expected = nx.compose(nx.complete_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect complete graph and path graph
+        assert is_isomorphic(G, expected)
+
+    def test_null_graph(self):
+        assert nx.number_of_nodes(nx.null_graph()) == 0
+
+    def test_path_graph(self):
+        p = nx.path_graph(0)
+        assert is_isomorphic(p, nx.null_graph())
+
+        p = nx.path_graph(1)
+        assert is_isomorphic(p, nx.empty_graph(1))
+
+        p = nx.path_graph(10)
+        assert nx.is_connected(p)
+        assert sorted(d for n, d in p.degree()) == [1, 1, 2, 2, 2, 2, 2, 2, 2, 2]
+        assert p.order() - 1 == p.size()
+
+        dp = nx.path_graph(3, create_using=nx.DiGraph)
+        assert dp.has_edge(0, 1)
+        assert not dp.has_edge(1, 0)
+
+        mp = nx.path_graph(10, create_using=nx.MultiGraph)
+        assert edges_equal(mp.edges(), p.edges())
+
+        G = nx.path_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 2
+        G = nx.path_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        g = nx.path_graph("abc", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 2
+        assert g.is_directed()
+        g = nx.path_graph("abcb", nx.DiGraph)
+        assert len(g) == 3
+        assert g.size() == 3
+
+        G = nx.path_graph((1, 2, 3, 2, 4))
+        assert G.has_edge(2, 4)
+
+    def test_star_graph(self):
+        assert is_isomorphic(nx.star_graph(""), nx.empty_graph(0))
+        assert is_isomorphic(nx.star_graph([]), nx.empty_graph(0))
+        assert is_isomorphic(nx.star_graph(0), nx.empty_graph(1))
+        assert is_isomorphic(nx.star_graph(1), nx.path_graph(2))
+        assert is_isomorphic(nx.star_graph(2), nx.path_graph(3))
+        assert is_isomorphic(nx.star_graph(5), nx.complete_bipartite_graph(1, 5))
+
+        s = nx.star_graph(10)
+        assert sorted(d for n, d in s.degree()) == [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 10]
+
+        pytest.raises(nx.NetworkXError, nx.star_graph, 10, create_using=nx.DiGraph)
+
+        ms = nx.star_graph(10, create_using=nx.MultiGraph)
+        assert edges_equal(ms.edges(), s.edges())
+
+        G = nx.star_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 2
+
+        G = nx.star_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 2
+        G = nx.star_graph("abcb", create_using=nx.MultiGraph)
+        assert len(G) == 3
+        assert G.size() == 3
+
+        G = nx.star_graph("abcdefg")
+        assert len(G) == 7
+        assert G.size() == 6
+
+    def test_non_int_integers_for_star_graph(self):
+        np = pytest.importorskip("numpy")
+        G = nx.star_graph(np.int32(3))
+        assert len(G) == 4
+        assert G.size() == 3
+
+    @pytest.mark.parametrize(("m", "n"), [(3, 0), (3, 5), (4, 10), (3, 20)])
+    def test_tadpole_graph_right_sizes(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert nx.number_of_nodes(G) == m + n
+        assert nx.number_of_edges(G) == m + n - (m == 2)
+
+    @pytest.mark.parametrize(("m", "n"), [("ab", ""), ("ab", "c"), ("abc", "defg")])
+    def test_tadpole_graph_size_node_sequences(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert nx.number_of_nodes(G) == len(m) + len(n)
+        assert nx.number_of_edges(G) == len(m) + len(n) - (len(m) == 2)
+
+    def test_tadpole_graph_exceptions(self):
+        # Raise NetworkXError if m<2
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, -1, 3)
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 0, 3)
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 1, 3)
+
+        # Raise NetworkXError if n<0
+        pytest.raises(nx.NetworkXError, nx.tadpole_graph, 5, -2)
+
+        # Raise NetworkXError for digraphs
+        with pytest.raises(nx.NetworkXError):
+            nx.tadpole_graph(2, 20, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.tadpole_graph(2, 20, create_using=nx.MultiDiGraph)
+
+    @pytest.mark.parametrize(("m", "n"), [(2, 0), (2, 5), (2, 10), ("ab", 20)])
+    def test_tadpole_graph_same_as_path_when_m_is_2(self, m, n):
+        G = nx.tadpole_graph(m, n)
+        assert is_isomorphic(G, nx.path_graph(n + 2))
+
+    @pytest.mark.parametrize("m", [4, 7])
+    def test_tadpole_graph_same_as_cycle_when_m2_is_0(self, m):
+        G = nx.tadpole_graph(m, 0)
+        assert is_isomorphic(G, nx.cycle_graph(m))
+
+    def test_tadpole_graph_for_multigraph(self):
+        G = nx.tadpole_graph(5, 20)
+        MG = nx.tadpole_graph(5, 20, create_using=nx.MultiGraph)
+        assert edges_equal(MG.edges(), G.edges())
+
+    @pytest.mark.parametrize(
+        ("m", "n"),
+        [(4, "abc"), ("abcd", 3), ([1, 2, 3, 4], "abc"), ("abcd", [1, 2, 3])],
+    )
+    def test_tadpole_graph_mixing_input_types(self, m, n):
+        expected = nx.compose(nx.cycle_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect cycle and path
+        assert is_isomorphic(nx.tadpole_graph(m, n), expected)
+
+    def test_tadpole_graph_non_builtin_integers(self):
+        np = pytest.importorskip("numpy")
+        G = nx.tadpole_graph(np.int32(4), np.int64(3))
+        expected = nx.compose(nx.cycle_graph(4), nx.path_graph(range(100, 103)))
+        expected.add_edge(0, 100)  # Connect cycle and path
+        assert is_isomorphic(G, expected)
+
+    def test_trivial_graph(self):
+        assert nx.number_of_nodes(nx.trivial_graph()) == 1
+
+    def test_turan_graph(self):
+        assert nx.number_of_edges(nx.turan_graph(13, 4)) == 63
+        assert is_isomorphic(
+            nx.turan_graph(13, 4), nx.complete_multipartite_graph(3, 4, 3, 3)
+        )
+
+    def test_wheel_graph(self):
+        for n, G in [
+            ("", nx.null_graph()),
+            (0, nx.null_graph()),
+            (1, nx.empty_graph(1)),
+            (2, nx.path_graph(2)),
+            (3, nx.complete_graph(3)),
+            (4, nx.complete_graph(4)),
+        ]:
+            g = nx.wheel_graph(n)
+            assert is_isomorphic(g, G)
+
+        g = nx.wheel_graph(10)
+        assert sorted(d for n, d in g.degree()) == [3, 3, 3, 3, 3, 3, 3, 3, 3, 9]
+
+        pytest.raises(nx.NetworkXError, nx.wheel_graph, 10, create_using=nx.DiGraph)
+
+        mg = nx.wheel_graph(10, create_using=nx.MultiGraph())
+        assert edges_equal(mg.edges(), g.edges())
+
+        G = nx.wheel_graph("abc")
+        assert len(G) == 3
+        assert G.size() == 3
+
+        G = nx.wheel_graph("abcb")
+        assert len(G) == 3
+        assert G.size() == 4
+        G = nx.wheel_graph("abcb", nx.MultiGraph)
+        assert len(G) == 3
+        assert G.size() == 6
+
+    def test_non_int_integers_for_wheel_graph(self):
+        np = pytest.importorskip("numpy")
+        G = nx.wheel_graph(np.int32(3))
+        assert len(G) == 3
+        assert G.size() == 3
+
+    def test_complete_0_partite_graph(self):
+        """Tests that the complete 0-partite graph is the null graph."""
+        G = nx.complete_multipartite_graph()
+        H = nx.null_graph()
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_1_partite_graph(self):
+        """Tests that the complete 1-partite graph is the empty graph."""
+        G = nx.complete_multipartite_graph(3)
+        H = nx.empty_graph(3)
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_2_partite_graph(self):
+        """Tests that the complete 2-partite graph is the complete bipartite
+        graph.
+
+        """
+        G = nx.complete_multipartite_graph(2, 3)
+        H = nx.complete_bipartite_graph(2, 3)
+        assert nodes_equal(G, H)
+        assert edges_equal(G.edges(), H.edges())
+
+    def test_complete_multipartite_graph(self):
+        """Tests for generating the complete multipartite graph."""
+        G = nx.complete_multipartite_graph(2, 3, 4)
+        blocks = [(0, 1), (2, 3, 4), (5, 6, 7, 8)]
+        # Within each block, no two vertices should be adjacent.
+        for block in blocks:
+            for u, v in itertools.combinations_with_replacement(block, 2):
+                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 u, v in itertools.product(block1, block2):
+                assert v in G[u]
+                assert G.nodes[u] != G.nodes[v]
+        with pytest.raises(nx.NetworkXError, match="Negative number of nodes"):
+            nx.complete_multipartite_graph(2, -3, 4)
+
+    def test_kneser_graph(self):
+        # the petersen graph is a special case of the kneser graph when n=5 and k=2
+        assert is_isomorphic(nx.kneser_graph(5, 2), nx.petersen_graph())
+
+        # when k is 1, the kneser graph returns a complete graph with n vertices
+        for i in range(1, 7):
+            assert is_isomorphic(nx.kneser_graph(i, 1), nx.complete_graph(i))
+
+        # the kneser graph of n and n-1 is the empty graph with n vertices
+        for j in range(3, 7):
+            assert is_isomorphic(nx.kneser_graph(j, j - 1), nx.empty_graph(j))
+
+        # in general the number of edges of the kneser graph is equal to
+        # (n choose k) times (n-k choose k) divided by 2
+        assert nx.number_of_edges(nx.kneser_graph(8, 3)) == 280

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

@@ -0,0 +1,20 @@
+"""Unit tests for the :mod:`networkx.generators.cographs` module.
+
+"""
+
+import networkx as nx
+
+
+def test_random_cograph():
+    n = 3
+    G = nx.random_cograph(n)
+
+    assert len(G) == 2**n
+
+    # Every connected subgraph of G has diameter <= 2
+    if nx.is_connected(G):
+        assert nx.diameter(G) <= 2
+    else:
+        components = nx.connected_components(G)
+        for component in components:
+            assert nx.diameter(G.subgraph(component)) <= 2

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

@@ -0,0 +1,362 @@
+import pytest
+
+import networkx as nx
+
+
+def test_random_partition_graph():
+    G = nx.random_partition_graph([3, 3, 3], 1, 0, seed=42)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 9
+
+    G = nx.random_partition_graph([3, 3, 3], 0, 1)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 27
+
+    G = nx.random_partition_graph([3, 3, 3], 1, 0, directed=True)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 18
+
+    G = nx.random_partition_graph([3, 3, 3], 0, 1, directed=True)
+    C = G.graph["partition"]
+    assert C == [{0, 1, 2}, {3, 4, 5}, {6, 7, 8}]
+    assert len(G) == 9
+    assert len(list(G.edges())) == 54
+
+    G = nx.random_partition_graph([1, 2, 3, 4, 5], 0.5, 0.1)
+    C = G.graph["partition"]
+    assert C == [{0}, {1, 2}, {3, 4, 5}, {6, 7, 8, 9}, {10, 11, 12, 13, 14}]
+    assert len(G) == 15
+
+    rpg = nx.random_partition_graph
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 1.1, 0.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], -0.1, 0.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, 1.1)
+    pytest.raises(nx.NetworkXError, rpg, [1, 2, 3], 0.1, -0.1)
+
+
+def test_planted_partition_graph():
+    G = nx.planted_partition_graph(4, 3, 1, 0, seed=42)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 12
+
+    G = nx.planted_partition_graph(4, 3, 0, 1)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 54
+
+    G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42)
+    C = G.graph["partition"]
+    assert len(C) == 10
+    assert len(G) == 40
+
+    G = nx.planted_partition_graph(4, 3, 1, 0, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 24
+
+    G = nx.planted_partition_graph(4, 3, 0, 1, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 4
+    assert len(G) == 12
+    assert len(list(G.edges())) == 108
+
+    G = nx.planted_partition_graph(10, 4, 0.5, 0.1, seed=42, directed=True)
+    C = G.graph["partition"]
+    assert len(C) == 10
+    assert len(G) == 40
+
+    ppg = nx.planted_partition_graph
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 1.1, 0.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, -0.1, 0.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, 1.1)
+    pytest.raises(nx.NetworkXError, ppg, 3, 3, 0.1, -0.1)
+
+
+def test_relaxed_caveman_graph():
+    G = nx.relaxed_caveman_graph(4, 3, 0)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 1)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 0.5)
+    assert len(G) == 12
+    G = nx.relaxed_caveman_graph(4, 3, 0.5, seed=42)
+    assert len(G) == 12
+
+
+def test_connected_caveman_graph():
+    G = nx.connected_caveman_graph(4, 3)
+    assert len(G) == 12
+
+    G = nx.connected_caveman_graph(1, 5)
+    K5 = nx.complete_graph(5)
+    K5.remove_edge(3, 4)
+    assert nx.is_isomorphic(G, K5)
+
+    # need at least 2 nodes in each clique
+    pytest.raises(nx.NetworkXError, nx.connected_caveman_graph, 4, 1)
+
+
+def test_caveman_graph():
+    G = nx.caveman_graph(4, 3)
+    assert len(G) == 12
+
+    G = nx.caveman_graph(5, 1)
+    E5 = nx.empty_graph(5)
+    assert nx.is_isomorphic(G, E5)
+
+    G = nx.caveman_graph(1, 5)
+    K5 = nx.complete_graph(5)
+    assert nx.is_isomorphic(G, K5)
+
+
+def test_gaussian_random_partition_graph():
+    G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01)
+    assert len(G) == 100
+    G = nx.gaussian_random_partition_graph(100, 10, 10, 0.3, 0.01, directed=True)
+    assert len(G) == 100
+    G = nx.gaussian_random_partition_graph(
+        100, 10, 10, 0.3, 0.01, directed=False, seed=42
+    )
+    assert len(G) == 100
+    assert not isinstance(G, nx.DiGraph)
+    G = nx.gaussian_random_partition_graph(
+        100, 10, 10, 0.3, 0.01, directed=True, seed=42
+    )
+    assert len(G) == 100
+    assert isinstance(G, nx.DiGraph)
+    pytest.raises(
+        nx.NetworkXError, nx.gaussian_random_partition_graph, 100, 101, 10, 1, 0
+    )
+    # Test when clusters are likely less than 1
+    G = nx.gaussian_random_partition_graph(10, 0.5, 0.5, 0.5, 0.5, seed=1)
+    assert len(G) == 10
+
+
+def test_ring_of_cliques():
+    for i in range(2, 20, 3):
+        for j in range(2, 20, 3):
+            G = nx.ring_of_cliques(i, j)
+            assert G.number_of_nodes() == i * j
+            if i != 2 or j != 1:
+                expected_num_edges = i * (((j * (j - 1)) // 2) + 1)
+            else:
+                # the edge that already exists cannot be duplicated
+                expected_num_edges = i * (((j * (j - 1)) // 2) + 1) - 1
+            assert G.number_of_edges() == expected_num_edges
+    with pytest.raises(
+        nx.NetworkXError, match="A ring of cliques must have at least two cliques"
+    ):
+        nx.ring_of_cliques(1, 5)
+    with pytest.raises(
+        nx.NetworkXError, match="The cliques must have at least two nodes"
+    ):
+        nx.ring_of_cliques(3, 0)
+
+
+def test_windmill_graph():
+    for n in range(2, 20, 3):
+        for k in range(2, 20, 3):
+            G = nx.windmill_graph(n, k)
+            assert G.number_of_nodes() == (k - 1) * n + 1
+            assert G.number_of_edges() == n * k * (k - 1) / 2
+            assert G.degree(0) == G.number_of_nodes() - 1
+            for i in range(1, G.number_of_nodes()):
+                assert G.degree(i) == k - 1
+    with pytest.raises(
+        nx.NetworkXError, match="A windmill graph must have at least two cliques"
+    ):
+        nx.windmill_graph(1, 3)
+    with pytest.raises(
+        nx.NetworkXError, match="The cliques must have at least two nodes"
+    ):
+        nx.windmill_graph(3, 0)
+
+
+def test_stochastic_block_model():
+    sizes = [75, 75, 300]
+    probs = [[0.25, 0.05, 0.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    G = nx.stochastic_block_model(sizes, probs, seed=0)
+    C = G.graph["partition"]
+    assert len(C) == 3
+    assert len(G) == 450
+    assert G.size() == 22160
+
+    GG = nx.stochastic_block_model(sizes, probs, range(450), seed=0)
+    assert G.nodes == GG.nodes
+
+    # Test Exceptions
+    sbm = nx.stochastic_block_model
+    badnodelist = list(range(400))  # not enough nodes to match sizes
+    badprobs1 = [[0.25, 0.05, 1.02], [0.05, 0.35, 0.07], [0.02, 0.07, 0.40]]
+    badprobs2 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
+    probs_rect1 = [[0.25, 0.05, 0.02], [0.05, -0.35, 0.07]]
+    probs_rect2 = [[0.25, 0.05], [0.05, -0.35], [0.02, 0.07]]
+    asymprobs = [[0.25, 0.05, 0.01], [0.05, -0.35, 0.07], [0.02, 0.07, 0.40]]
+    pytest.raises(nx.NetworkXException, sbm, sizes, badprobs1)
+    pytest.raises(nx.NetworkXException, sbm, sizes, badprobs2)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect1, directed=True)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs_rect2, directed=True)
+    pytest.raises(nx.NetworkXException, sbm, sizes, asymprobs, directed=False)
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs, badnodelist)
+    nodelist = [0] + list(range(449))  # repeated node name in nodelist
+    pytest.raises(nx.NetworkXException, sbm, sizes, probs, nodelist)
+
+    # Extra keyword arguments test
+    GG = nx.stochastic_block_model(sizes, probs, seed=0, selfloops=True)
+    assert G.nodes == GG.nodes
+    GG = nx.stochastic_block_model(sizes, probs, selfloops=True, directed=True)
+    assert G.nodes == GG.nodes
+    GG = nx.stochastic_block_model(sizes, probs, seed=0, sparse=False)
+    assert G.nodes == GG.nodes
+
+
+def test_generator():
+    n = 250
+    tau1 = 3
+    tau2 = 1.5
+    mu = 0.1
+    G = nx.LFR_benchmark_graph(
+        n, tau1, tau2, mu, average_degree=5, min_community=20, seed=10
+    )
+    assert len(G) == 250
+    C = {frozenset(G.nodes[v]["community"]) for v in G}
+    assert nx.community.is_partition(G.nodes(), C)
+
+
+def test_invalid_tau1():
+    with pytest.raises(nx.NetworkXError, match="tau2 must be greater than one"):
+        n = 100
+        tau1 = 2
+        tau2 = 1
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_invalid_tau2():
+    with pytest.raises(nx.NetworkXError, match="tau1 must be greater than one"):
+        n = 100
+        tau1 = 1
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_mu_too_large():
+    with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_mu_too_small():
+    with pytest.raises(nx.NetworkXError, match="mu must be in the interval \\[0, 1\\]"):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = -1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2)
+
+
+def test_both_degrees_none():
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Must assign exactly one of min_degree and average_degree",
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu)
+
+
+def test_neither_degrees_none():
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Must assign exactly one of min_degree and average_degree",
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, average_degree=5)
+
+
+def test_max_iters_exceeded():
+    with pytest.raises(
+        nx.ExceededMaxIterations,
+        match="Could not assign communities; try increasing min_community",
+    ):
+        n = 10
+        tau1 = 2
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=10, seed=1)
+
+
+def test_max_deg_out_of_range():
+    with pytest.raises(
+        nx.NetworkXError, match="max_degree must be in the interval \\(0, n\\]"
+    ):
+        n = 10
+        tau1 = 2
+        tau2 = 2
+        mu = 0.1
+        nx.LFR_benchmark_graph(
+            n, tau1, tau2, mu, max_degree=n + 1, max_iters=10, seed=1
+        )
+
+
+def test_max_community():
+    n = 250
+    tau1 = 3
+    tau2 = 1.5
+    mu = 0.1
+    G = nx.LFR_benchmark_graph(
+        n,
+        tau1,
+        tau2,
+        mu,
+        average_degree=5,
+        max_degree=100,
+        min_community=50,
+        max_community=200,
+        seed=10,
+    )
+    assert len(G) == 250
+    C = {frozenset(G.nodes[v]["community"]) for v in G}
+    assert nx.community.is_partition(G.nodes(), C)
+
+
+def test_powerlaw_iterations_exceeded():
+    with pytest.raises(
+        nx.ExceededMaxIterations, match="Could not create power law sequence"
+    ):
+        n = 100
+        tau1 = 2
+        tau2 = 2
+        mu = 1
+        nx.LFR_benchmark_graph(n, tau1, tau2, mu, min_degree=2, max_iters=0)
+
+
+def test_no_scipy_zeta():
+    zeta2 = 1.6449340668482264
+    assert abs(zeta2 - nx.generators.community._hurwitz_zeta(2, 1, 0.0001)) < 0.01
+
+
+def test_generate_min_degree_itr():
+    with pytest.raises(
+        nx.ExceededMaxIterations, match="Could not match average_degree"
+    ):
+        nx.generators.community._generate_min_degree(2, 2, 1, 0.01, 0)

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

@@ -0,0 +1,230 @@
+import pytest
+
+import networkx as nx
+
+
+class TestConfigurationModel:
+    """Unit tests for the :func:`~networkx.configuration_model`
+    function.
+
+    """
+
+    def test_empty_degree_sequence(self):
+        """Tests that an empty degree sequence yields the null graph."""
+        G = nx.configuration_model([])
+        assert len(G) == 0
+
+    def test_degree_zero(self):
+        """Tests that a degree sequence of all zeros yields the empty
+        graph.
+
+        """
+        G = nx.configuration_model([0, 0, 0])
+        assert len(G) == 3
+        assert G.number_of_edges() == 0
+
+    def test_degree_sequence(self):
+        """Tests that the degree sequence of the generated graph matches
+        the input degree sequence.
+
+        """
+        deg_seq = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+        G = nx.configuration_model(deg_seq, seed=12345678)
+        assert sorted((d for n, d in G.degree()), reverse=True) == [
+            5,
+            3,
+            3,
+            3,
+            3,
+            2,
+            2,
+            2,
+            1,
+            1,
+            1,
+        ]
+        assert sorted((d for n, d in G.degree(range(len(deg_seq)))), reverse=True) == [
+            5,
+            3,
+            3,
+            3,
+            3,
+            2,
+            2,
+            2,
+            1,
+            1,
+            1,
+        ]
+
+    def test_random_seed(self):
+        """Tests that each call with the same random seed generates the
+        same graph.
+
+        """
+        deg_seq = [3] * 12
+        G1 = nx.configuration_model(deg_seq, seed=1000)
+        G2 = nx.configuration_model(deg_seq, seed=1000)
+        assert nx.is_isomorphic(G1, G2)
+        G1 = nx.configuration_model(deg_seq, seed=10)
+        G2 = nx.configuration_model(deg_seq, seed=10)
+        assert nx.is_isomorphic(G1, G2)
+
+    def test_directed_disallowed(self):
+        """Tests that attempting to create a configuration model graph
+        using a directed graph yields an exception.
+
+        """
+        with pytest.raises(nx.NetworkXNotImplemented):
+            nx.configuration_model([], create_using=nx.DiGraph())
+
+    def test_odd_degree_sum(self):
+        """Tests that a degree sequence whose sum is odd yields an
+        exception.
+
+        """
+        with pytest.raises(nx.NetworkXError):
+            nx.configuration_model([1, 2])
+
+
+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_configuration_model():
+    G = nx.directed_configuration_model([], [], seed=0)
+    assert len(G) == 0
+
+
+def test_simple_directed_configuration_model():
+    G = nx.directed_configuration_model([1, 1], [1, 1], seed=0)
+    assert len(G) == 2
+
+
+def test_expected_degree_graph_empty():
+    # empty graph has empty degree sequence
+    deg_seq = []
+    G = nx.expected_degree_graph(deg_seq)
+    assert dict(G.degree()) == {}
+
+
+def test_expected_degree_graph():
+    # test that fixed seed delivers the same graph
+    deg_seq = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert len(G1) == 12
+
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert nx.is_isomorphic(G1, G2)
+
+    G1 = nx.expected_degree_graph(deg_seq, seed=10)
+    G2 = nx.expected_degree_graph(deg_seq, seed=10)
+    assert nx.is_isomorphic(G1, G2)
+
+
+def test_expected_degree_graph_selfloops():
+    deg_seq = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000, selfloops=False)
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000, selfloops=False)
+    assert nx.is_isomorphic(G1, G2)
+    assert len(G1) == 12
+
+
+def test_expected_degree_graph_skew():
+    deg_seq = [10, 2, 2, 2, 2]
+    G1 = nx.expected_degree_graph(deg_seq, seed=1000)
+    G2 = nx.expected_degree_graph(deg_seq, seed=1000)
+    assert nx.is_isomorphic(G1, G2)
+    assert len(G1) == 5
+
+
+def test_havel_hakimi_construction():
+    G = nx.havel_hakimi_graph([])
+    assert len(G) == 0
+
+    z = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+    z = ["A", 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+
+    z = [5, 4, 3, 3, 3, 2, 2, 2]
+    G = nx.havel_hakimi_graph(z)
+    G = nx.configuration_model(z)
+    z = [6, 5, 4, 4, 2, 1, 1, 1]
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z)
+
+    z = [10, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2]
+
+    G = nx.havel_hakimi_graph(z)
+
+    pytest.raises(nx.NetworkXError, nx.havel_hakimi_graph, z, create_using=nx.DiGraph())
+
+
+def test_directed_havel_hakimi():
+    # Test range of valid directed degree sequences
+    n, r = 100, 10
+    p = 1.0 / r
+    for i in range(r):
+        G1 = nx.erdos_renyi_graph(n, p * (i + 1), None, True)
+        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 = [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)
+
+    # Test non-graphical sequence
+    dout = [1000, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1]
+    din = [103, 102, 102, 102, 102, 102, 102, 102, 102, 102]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+    # Test valid sequences
+    dout = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 0, 2]
+    G2 = nx.directed_havel_hakimi_graph(din, dout)
+    dout2 = (d for n, d in G2.out_degree())
+    din2 = (d for n, d in G2.in_degree())
+    assert sorted(dout) == sorted(dout2)
+    assert sorted(din) == sorted(din2)
+    # Test unequal sums
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+    # Test for negative values
+    din = [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, -2]
+    pytest.raises(nx.exception.NetworkXError, nx.directed_havel_hakimi_graph, din, dout)
+
+
+def test_degree_sequence_tree():
+    z = [1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    G = nx.degree_sequence_tree(z)
+    assert len(G) == len(z)
+    assert len(list(G.edges())) == sum(z) / 2
+
+    pytest.raises(
+        nx.NetworkXError, nx.degree_sequence_tree, z, create_using=nx.DiGraph()
+    )
+
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    pytest.raises(nx.NetworkXError, nx.degree_sequence_tree, z)
+
+
+def test_random_degree_sequence_graph():
+    d = [1, 2, 2, 3]
+    G = nx.random_degree_sequence_graph(d, seed=42)
+    assert d == sorted(d for n, d in G.degree())
+
+
+def test_random_degree_sequence_graph_raise():
+    z = [1, 1, 1, 1, 1, 1, 2, 2, 2, 3, 4]
+    pytest.raises(nx.NetworkXUnfeasible, nx.random_degree_sequence_graph, z)
+
+
+def test_random_degree_sequence_large():
+    G1 = nx.fast_gnp_random_graph(100, 0.1, seed=42)
+    d1 = (d for n, d in G1.degree())
+    G2 = nx.random_degree_sequence_graph(d1, seed=42)
+    d2 = (d for n, d in G2.degree())
+    assert sorted(d1) == sorted(d2)

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

@@ -0,0 +1,162 @@
+"""Generators - Directed Graphs
+----------------------------
+"""
+import pytest
+
+import networkx as nx
+from networkx.classes import Graph, MultiDiGraph
+from networkx.generators.directed import (
+    gn_graph,
+    gnc_graph,
+    gnr_graph,
+    random_k_out_graph,
+    random_uniform_k_out_graph,
+    scale_free_graph,
+)
+
+
+class TestGeneratorsDirected:
+    def test_smoke_test_random_graphs(self):
+        gn_graph(100)
+        gnr_graph(100, 0.5)
+        gnc_graph(100)
+        scale_free_graph(100)
+
+        gn_graph(100, seed=42)
+        gnr_graph(100, 0.5, seed=42)
+        gnc_graph(100, seed=42)
+        scale_free_graph(100, seed=42)
+
+    def test_create_using_keyword_arguments(self):
+        pytest.raises(nx.NetworkXError, gn_graph, 100, create_using=Graph())
+        pytest.raises(nx.NetworkXError, gnr_graph, 100, 0.5, create_using=Graph())
+        pytest.raises(nx.NetworkXError, gnc_graph, 100, create_using=Graph())
+        G = gn_graph(100, seed=1)
+        MG = gn_graph(100, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+        G = gnr_graph(100, 0.5, seed=1)
+        MG = gnr_graph(100, 0.5, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+        G = gnc_graph(100, seed=1)
+        MG = gnc_graph(100, create_using=MultiDiGraph(), seed=1)
+        assert sorted(G.edges()) == sorted(MG.edges())
+
+        G = scale_free_graph(
+            100,
+            alpha=0.3,
+            beta=0.4,
+            gamma=0.3,
+            delta_in=0.3,
+            delta_out=0.1,
+            initial_graph=nx.cycle_graph(4, create_using=MultiDiGraph),
+            seed=1,
+        )
+        pytest.raises(ValueError, scale_free_graph, 100, 0.5, 0.4, 0.3)
+        pytest.raises(ValueError, scale_free_graph, 100, alpha=-0.3)
+        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_negative_delta():
+    with pytest.raises(ValueError, match="delta_in must be >= 0."):
+        scale_free_graph(10, delta_in=-1)
+    with pytest.raises(ValueError, match="delta_out must be >= 0."):
+        scale_free_graph(10, 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):
+    with pytest.raises(nx.NetworkXError):
+        scale_free_graph(100, initial_graph=ig)
+
+
+class TestRandomKOutGraph:
+    """Unit tests for the
+    :func:`~networkx.generators.directed.random_k_out_graph` function.
+
+    """
+
+    def test_regularity(self):
+        """Tests that the generated graph is `k`-out-regular."""
+        n = 10
+        k = 3
+        alpha = 1
+        G = random_k_out_graph(n, k, alpha)
+        assert all(d == k for v, d in G.out_degree())
+        G = random_k_out_graph(n, k, alpha, seed=42)
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_no_self_loops(self):
+        """Tests for forbidding self-loops."""
+        n = 10
+        k = 3
+        alpha = 1
+        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
+    :func:`~networkx.generators.directed.random_uniform_k_out_graph`
+    function.
+
+    """
+
+    def test_regularity(self):
+        """Tests that the generated graph is `k`-out-regular."""
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k)
+        assert all(d == k for v, d in G.out_degree())
+        G = random_uniform_k_out_graph(n, k, seed=42)
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_no_self_loops(self):
+        """Tests for forbidding self-loops."""
+        n = 10
+        k = 3
+        G = random_uniform_k_out_graph(n, k, self_loops=False)
+        assert nx.number_of_selfloops(G) == 0
+        assert all(d == k for v, d in G.out_degree())
+
+    def test_with_replacement(self):
+        n = 10
+        k = 3
+        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
+        k = 3
+        G = random_uniform_k_out_graph(n, k, with_replacement=False)
+        assert not G.is_multigraph()
+        assert all(d == k for v, d in G.out_degree())

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

@@ -0,0 +1,84 @@
+"""Unit tests for the :mod:`networkx.generators.duplication` module.
+
+"""
+import pytest
+
+from networkx.exception import NetworkXError
+from networkx.generators.duplication import (
+    duplication_divergence_graph,
+    partial_duplication_graph,
+)
+
+
+class TestDuplicationDivergenceGraph:
+    """Unit tests for the
+    :func:`networkx.generators.duplication.duplication_divergence_graph`
+    function.
+
+    """
+
+    def test_final_size(self):
+        G = duplication_divergence_graph(3, p=1)
+        assert len(G) == 3
+        G = duplication_divergence_graph(3, p=1, seed=42)
+        assert len(G) == 3
+
+    def test_probability_too_large(self):
+        with pytest.raises(NetworkXError):
+            duplication_divergence_graph(3, p=2)
+
+    def test_probability_too_small(self):
+        with pytest.raises(NetworkXError):
+            duplication_divergence_graph(3, p=-1)
+
+    def test_non_extreme_probability_value(self):
+        G = duplication_divergence_graph(6, p=0.3, seed=42)
+        assert len(G) == 6
+        assert list(G.degree()) == [(0, 2), (1, 3), (2, 2), (3, 3), (4, 1), (5, 1)]
+
+    def test_minimum_desired_nodes(self):
+        with pytest.raises(
+            NetworkXError, match=".*n must be greater than or equal to 2"
+        ):
+            duplication_divergence_graph(1, p=1)
+
+
+class TestPartialDuplicationGraph:
+    """Unit tests for the
+    :func:`networkx.generators.duplication.partial_duplication_graph`
+    function.
+
+    """
+
+    def test_final_size(self):
+        N = 10
+        n = 5
+        p = 0.5
+        q = 0.5
+        G = partial_duplication_graph(N, n, p, q)
+        assert len(G) == N
+        G = partial_duplication_graph(N, n, p, q, seed=42)
+        assert len(G) == N
+
+    def test_initial_clique_size(self):
+        N = 10
+        n = 10
+        p = 0.5
+        q = 0.5
+        G = partial_duplication_graph(N, n, p, q)
+        assert len(G) == n
+
+    def test_invalid_initial_size(self):
+        with pytest.raises(NetworkXError):
+            N = 5
+            n = 10
+            p = 0.5
+            q = 0.5
+            G = partial_duplication_graph(N, n, p, q)
+
+    def test_invalid_probabilities(self):
+        N = 1
+        n = 1
+        for p, q in [(0.5, 2), (0.5, -1), (2, 0.5), (-1, 0.5)]:
+            args = (N, n, p, q)
+            pytest.raises(NetworkXError, partial_duplication_graph, *args)

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

@@ -0,0 +1,39 @@
+"""
+ego graph
+---------
+"""
+
+import networkx as nx
+from networkx.utils import edges_equal, nodes_equal
+
+
+class TestGeneratorEgo:
+    def test_ego(self):
+        G = nx.star_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(G, H)
+        G.add_edge(1, 11)
+        G.add_edge(2, 22)
+        G.add_edge(3, 33)
+        H = nx.ego_graph(G, 0)
+        assert nx.is_isomorphic(nx.star_graph(3), H)
+        G = nx.path_graph(3)
+        H = nx.ego_graph(G, 0)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, undirected=True)
+        assert edges_equal(H.edges(), [(0, 1)])
+        H = nx.ego_graph(G, 0, center=False)
+        assert edges_equal(H.edges(), [])
+
+    def test_ego_distance(self):
+        G = nx.Graph()
+        G.add_edge(0, 1, weight=2, distance=1)
+        G.add_edge(1, 2, weight=2, distance=2)
+        G.add_edge(2, 3, weight=2, distance=1)
+        assert nodes_equal(nx.ego_graph(G, 0, radius=3).nodes(), [0, 1, 2, 3])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight")
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="weight", undirected=True)
+        assert nodes_equal(eg.nodes(), [0, 1])
+        eg = nx.ego_graph(G, 0, radius=3, distance="distance")
+        assert nodes_equal(eg.nodes(), [0, 1, 2])

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

@@ -0,0 +1,164 @@
+"""Unit tests for the :mod:`networkx.generators.expanders` module.
+
+"""
+
+import pytest
+
+import networkx as nx
+
+
+@pytest.mark.parametrize("n", (2, 3, 5, 6, 10))
+def test_margulis_gabber_galil_graph_properties(n):
+    g = nx.margulis_gabber_galil_graph(n)
+    assert g.number_of_nodes() == n * n
+    for node in g:
+        assert g.degree(node) == 8
+        assert len(node) == 2
+        for i in node:
+            assert int(i) == i
+            assert 0 <= i < n
+
+
+@pytest.mark.parametrize("n", (2, 3, 5, 6, 10))
+def test_margulis_gabber_galil_graph_eigvals(n):
+    np = pytest.importorskip("numpy")
+    sp = pytest.importorskip("scipy")
+
+    g = nx.margulis_gabber_galil_graph(n)
+    # Eigenvalues are already sorted using the scipy eigvalsh,
+    # but the implementation in numpy does not guarantee order.
+    w = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(g).toarray()))
+    assert w[-2] < 5 * np.sqrt(2)
+
+
+@pytest.mark.parametrize("p", (3, 5, 7, 11))  # Primes
+def test_chordal_cycle_graph(p):
+    """Test for the :func:`networkx.chordal_cycle_graph` function."""
+    G = nx.chordal_cycle_graph(p)
+    assert len(G) == p
+    # TODO The second largest eigenvalue should be smaller than a constant,
+    # independent of the number of nodes in the graph:
+    #
+    #     eigs = sorted(sp.linalg.eigvalsh(nx.adjacency_matrix(G).toarray()))
+    #     assert_less(eigs[-2], ...)
+    #
+
+
+@pytest.mark.parametrize("p", (3, 5, 7, 11, 13))  # Primes
+def test_paley_graph(p):
+    """Test for the :func:`networkx.paley_graph` function."""
+    G = nx.paley_graph(p)
+    # G has p nodes
+    assert len(G) == p
+    # G is (p-1)/2-regular
+    in_degrees = {G.in_degree(node) for node in G.nodes}
+    out_degrees = {G.out_degree(node) for node in G.nodes}
+    assert len(in_degrees) == 1 and in_degrees.pop() == (p - 1) // 2
+    assert len(out_degrees) == 1 and out_degrees.pop() == (p - 1) // 2
+
+    # 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:
+            assert (v, u) in G.edges
+
+
+@pytest.mark.parametrize("d, n", [(2, 7), (4, 10), (4, 16)])
+def test_maybe_regular_expander(d, n):
+    pytest.importorskip("numpy")
+    G = nx.maybe_regular_expander(n, d)
+
+    assert len(G) == n, "Should have n nodes"
+    assert len(G.edges) == n * d / 2, "Should have n*d/2 edges"
+    assert nx.is_k_regular(G, d), "Should be d-regular"
+
+
+@pytest.mark.parametrize("n", (3, 5, 6, 10))
+def test_is_regular_expander(n):
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+    G = nx.complete_graph(n)
+
+    assert nx.is_regular_expander(G) == True, "Should be a regular expander"
+
+
+@pytest.mark.parametrize("d, n", [(2, 7), (4, 10), (4, 16)])
+def test_random_regular_expander(d, n):
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+    G = nx.random_regular_expander_graph(n, d)
+
+    assert len(G) == n, "Should have n nodes"
+    assert len(G.edges) == n * d / 2, "Should have n*d/2 edges"
+    assert nx.is_k_regular(G, d), "Should be d-regular"
+    assert nx.is_regular_expander(G) == True, "Should be a regular expander"
+
+
+def test_random_regular_expander_explicit_construction():
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+    G = nx.random_regular_expander_graph(d=4, n=5)
+
+    assert len(G) == 5 and len(G.edges) == 10, "Should be a complete graph"
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph, nx.MultiDiGraph))
+def test_margulis_gabber_galil_graph_badinput(graph_type):
+    with pytest.raises(
+        nx.NetworkXError, match="`create_using` must be an undirected multigraph"
+    ):
+        nx.margulis_gabber_galil_graph(3, create_using=graph_type)
+
+
+@pytest.mark.parametrize("graph_type", (nx.Graph, nx.DiGraph, nx.MultiDiGraph))
+def test_chordal_cycle_graph_badinput(graph_type):
+    with pytest.raises(
+        nx.NetworkXError, match="`create_using` must be an undirected multigraph"
+    ):
+        nx.chordal_cycle_graph(3, create_using=graph_type)
+
+
+def test_paley_graph_badinput():
+    with pytest.raises(
+        nx.NetworkXError, match="`create_using` cannot be a multigraph."
+    ):
+        nx.paley_graph(3, create_using=nx.MultiGraph)
+
+
+def test_maybe_regular_expander_badinput():
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+
+    with pytest.raises(nx.NetworkXError, match="n must be a positive integer"):
+        nx.maybe_regular_expander(n=-1, d=2)
+
+    with pytest.raises(nx.NetworkXError, match="d must be greater than or equal to 2"):
+        nx.maybe_regular_expander(n=10, d=0)
+
+    with pytest.raises(nx.NetworkXError, match="Need n-1>= d to have room"):
+        nx.maybe_regular_expander(n=5, d=6)
+
+
+def test_is_regular_expander_badinput():
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+
+    with pytest.raises(nx.NetworkXError, match="epsilon must be non negative"):
+        nx.is_regular_expander(nx.Graph(), epsilon=-1)
+
+
+def test_random_regular_expander_badinput():
+    pytest.importorskip("numpy")
+    pytest.importorskip("scipy")
+
+    with pytest.raises(nx.NetworkXError, match="n must be a positive integer"):
+        nx.random_regular_expander_graph(n=-1, d=2)
+
+    with pytest.raises(nx.NetworkXError, match="d must be greater than or equal to 2"):
+        nx.random_regular_expander_graph(n=10, d=0)
+
+    with pytest.raises(nx.NetworkXError, match="Need n-1>= d to have room"):
+        nx.random_regular_expander_graph(n=5, d=6)
+
+    with pytest.raises(nx.NetworkXError, match="epsilon must be non negative"):
+        nx.random_regular_expander_graph(n=4, d=2, epsilon=-1)

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

@@ -0,0 +1,488 @@
+import math
+import random
+from itertools import combinations
+
+import pytest
+
+import networkx as nx
+
+
+def l1dist(x, y):
+    return sum(abs(a - b) for a, b in zip(x, y))
+
+
+class TestRandomGeometricGraph:
+    """Unit tests for :func:`~networkx.random_geometric_graph`"""
+
+    def test_number_of_nodes(self):
+        G = nx.random_geometric_graph(50, 0.25, seed=42)
+        assert len(G) == 50
+        G = nx.random_geometric_graph(range(50), 0.25, seed=42)
+        assert len(G) == 50
+
+    def test_distances(self):
+        """Tests that pairs of vertices adjacent if and only if they are
+        within the prescribed radius.
+        """
+        # Use the Euclidean metric, the default according to the
+        # documentation.
+        G = nx.random_geometric_graph(50, 0.25)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+            # Nonadjacent vertices must be at greater distance.
+            else:
+                assert not math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_p(self):
+        """Tests for providing an alternate distance metric to the generator."""
+        # Use the L1 metric.
+        G = nx.random_geometric_graph(50, 0.25, p=1)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert l1dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+            # Nonadjacent vertices must be at greater distance.
+            else:
+                assert not l1dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_node_names(self):
+        """Tests using values other than sequential numbers as node IDs."""
+        import string
+
+        nodes = list(string.ascii_lowercase)
+        G = nx.random_geometric_graph(nodes, 0.25)
+        assert len(G) == len(nodes)
+
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+            # Nonadjacent vertices must be at greater distance.
+            else:
+                assert not math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_pos_name(self):
+        G = nx.random_geometric_graph(50, 0.25, seed=42, pos_name="coords")
+        assert all(len(d["coords"]) == 2 for n, d in G.nodes.items())
+
+
+class TestSoftRandomGeometricGraph:
+    """Unit tests for :func:`~networkx.soft_random_geometric_graph`"""
+
+    def test_number_of_nodes(self):
+        G = nx.soft_random_geometric_graph(50, 0.25, seed=42)
+        assert len(G) == 50
+        G = nx.soft_random_geometric_graph(range(50), 0.25, seed=42)
+        assert len(G) == 50
+
+    def test_distances(self):
+        """Tests that pairs of vertices adjacent if and only if they are
+        within the prescribed radius.
+        """
+        # Use the Euclidean metric, the default according to the
+        # documentation.
+        G = nx.soft_random_geometric_graph(50, 0.25)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_p(self):
+        """Tests for providing an alternate distance metric to the generator."""
+
+        # Use the L1 metric.
+        def dist(x, y):
+            return sum(abs(a - b) for a, b in zip(x, y))
+
+        G = nx.soft_random_geometric_graph(50, 0.25, p=1)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_node_names(self):
+        """Tests using values other than sequential numbers as node IDs."""
+        import string
+
+        nodes = list(string.ascii_lowercase)
+        G = nx.soft_random_geometric_graph(nodes, 0.25)
+        assert len(G) == len(nodes)
+
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_p_dist_default(self):
+        """Tests default p_dict = 0.5 returns graph with edge count <= RGG with
+        same n, radius, dim and positions
+        """
+        nodes = 50
+        dim = 2
+        pos = {v: [random.random() for i in range(dim)] for v in range(nodes)}
+        RGG = nx.random_geometric_graph(50, 0.25, pos=pos)
+        SRGG = nx.soft_random_geometric_graph(50, 0.25, pos=pos)
+        assert len(SRGG.edges()) <= len(RGG.edges())
+
+    def test_p_dist_zero(self):
+        """Tests if p_dict = 0 returns disconnected graph with 0 edges"""
+
+        def p_dist(dist):
+            return 0
+
+        G = nx.soft_random_geometric_graph(50, 0.25, p_dist=p_dist)
+        assert len(G.edges) == 0
+
+    def test_pos_name(self):
+        G = nx.soft_random_geometric_graph(50, 0.25, seed=42, pos_name="coords")
+        assert all(len(d["coords"]) == 2 for n, d in G.nodes.items())
+
+
+def join(G, u, v, theta, alpha, metric):
+    """Returns ``True`` if and only if the nodes whose attributes are
+    ``du`` and ``dv`` should be joined, according to the threshold
+    condition for geographical threshold graphs.
+
+    ``G`` is an undirected NetworkX graph, and ``u`` and ``v`` are nodes
+    in that graph. The nodes must have node attributes ``'pos'`` and
+    ``'weight'``.
+
+    ``metric`` is a distance metric.
+    """
+    du, dv = G.nodes[u], G.nodes[v]
+    u_pos, v_pos = du["pos"], dv["pos"]
+    u_weight, v_weight = du["weight"], dv["weight"]
+    return (u_weight + v_weight) * metric(u_pos, v_pos) ** alpha >= theta
+
+
+class TestGeographicalThresholdGraph:
+    """Unit tests for :func:`~networkx.geographical_threshold_graph`"""
+
+    def test_number_of_nodes(self):
+        G = nx.geographical_threshold_graph(50, 100, seed=42)
+        assert len(G) == 50
+        G = nx.geographical_threshold_graph(range(50), 100, seed=42)
+        assert len(G) == 50
+
+    def test_distances(self):
+        """Tests that pairs of vertices adjacent if and only if their
+        distances meet the given threshold.
+        """
+        # Use the Euclidean metric and alpha = -2
+        # the default according to the documentation.
+        G = nx.geographical_threshold_graph(50, 10)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must exceed the threshold.
+            if v in G[u]:
+                assert join(G, u, v, 10, -2, math.dist)
+            # Nonadjacent vertices must not exceed the threshold.
+            else:
+                assert not join(G, u, v, 10, -2, math.dist)
+
+    def test_metric(self):
+        """Tests for providing an alternate distance metric to the generator."""
+        # Use the L1 metric.
+        G = nx.geographical_threshold_graph(50, 10, metric=l1dist)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must exceed the threshold.
+            if v in G[u]:
+                assert join(G, u, v, 10, -2, l1dist)
+            # Nonadjacent vertices must not exceed the threshold.
+            else:
+                assert not join(G, u, v, 10, -2, l1dist)
+
+    def test_p_dist_zero(self):
+        """Tests if p_dict = 0 returns disconnected graph with 0 edges"""
+
+        def p_dist(dist):
+            return 0
+
+        G = nx.geographical_threshold_graph(50, 1, p_dist=p_dist)
+        assert len(G.edges) == 0
+
+    def test_pos_weight_name(self):
+        gtg = nx.geographical_threshold_graph
+        G = gtg(50, 100, seed=42, pos_name="coords", weight_name="wt")
+        assert all(len(d["coords"]) == 2 for n, d in G.nodes.items())
+        assert all(d["wt"] > 0 for n, d in G.nodes.items())
+
+
+class TestWaxmanGraph:
+    """Unit tests for the :func:`~networkx.waxman_graph` function."""
+
+    def test_number_of_nodes_1(self):
+        G = nx.waxman_graph(50, 0.5, 0.1, seed=42)
+        assert len(G) == 50
+        G = nx.waxman_graph(range(50), 0.5, 0.1, seed=42)
+        assert len(G) == 50
+
+    def test_number_of_nodes_2(self):
+        G = nx.waxman_graph(50, 0.5, 0.1, L=1)
+        assert len(G) == 50
+        G = nx.waxman_graph(range(50), 0.5, 0.1, L=1)
+        assert len(G) == 50
+
+    def test_metric(self):
+        """Tests for providing an alternate distance metric to the generator."""
+        # Use the L1 metric.
+        G = nx.waxman_graph(50, 0.5, 0.1, metric=l1dist)
+        assert len(G) == 50
+
+    def test_pos_name(self):
+        G = nx.waxman_graph(50, 0.5, 0.1, seed=42, pos_name="coords")
+        assert all(len(d["coords"]) == 2 for n, d in G.nodes.items())
+
+
+class TestNavigableSmallWorldGraph:
+    def test_navigable_small_world(self):
+        G = nx.navigable_small_world_graph(5, p=1, q=0, seed=42)
+        gg = nx.grid_2d_graph(5, 5).to_directed()
+        assert nx.is_isomorphic(G, gg)
+
+        G = nx.navigable_small_world_graph(5, p=1, q=0, dim=3)
+        gg = nx.grid_graph([5, 5, 5]).to_directed()
+        assert nx.is_isomorphic(G, gg)
+
+        G = nx.navigable_small_world_graph(5, p=1, q=0, dim=1)
+        gg = nx.grid_graph([5]).to_directed()
+        assert nx.is_isomorphic(G, gg)
+
+    def test_invalid_diameter_value(self):
+        with pytest.raises(nx.NetworkXException, match=".*p must be >= 1"):
+            nx.navigable_small_world_graph(5, p=0, q=0, dim=1)
+
+    def test_invalid_long_range_connections_value(self):
+        with pytest.raises(nx.NetworkXException, match=".*q must be >= 0"):
+            nx.navigable_small_world_graph(5, p=1, q=-1, dim=1)
+
+    def test_invalid_exponent_for_decaying_probability_value(self):
+        with pytest.raises(nx.NetworkXException, match=".*r must be >= 0"):
+            nx.navigable_small_world_graph(5, p=1, q=0, r=-1, dim=1)
+
+    def test_r_between_0_and_1(self):
+        """Smoke test for radius in range [0, 1]"""
+        # q=0 means no long-range connections
+        G = nx.navigable_small_world_graph(3, p=1, q=0, r=0.5, dim=2, seed=42)
+        expected = nx.grid_2d_graph(3, 3, create_using=nx.DiGraph)
+        assert nx.utils.graphs_equal(G, expected)
+
+    @pytest.mark.parametrize("seed", range(2478, 2578, 10))
+    def test_r_general_scaling(self, seed):
+        """The probability of adding a long-range edge scales with `1 / dist**r`,
+        so a navigable_small_world graph created with r < 1 should generally
+        result in more edges than a navigable_small_world graph with r >= 1
+        (for 0 < q << n).
+
+        N.B. this is probabilistic, so this test may not hold for all seeds."""
+        G1 = nx.navigable_small_world_graph(7, q=3, r=0.5, seed=seed)
+        G2 = nx.navigable_small_world_graph(7, q=3, r=1, seed=seed)
+        G3 = nx.navigable_small_world_graph(7, q=3, r=2, seed=seed)
+        assert G1.number_of_edges() > G2.number_of_edges()
+        assert G2.number_of_edges() > G3.number_of_edges()
+
+
+class TestThresholdedRandomGeometricGraph:
+    """Unit tests for :func:`~networkx.thresholded_random_geometric_graph`"""
+
+    def test_number_of_nodes(self):
+        G = nx.thresholded_random_geometric_graph(50, 0.2, 0.1, seed=42)
+        assert len(G) == 50
+        G = nx.thresholded_random_geometric_graph(range(50), 0.2, 0.1, seed=42)
+        assert len(G) == 50
+
+    def test_distances(self):
+        """Tests that pairs of vertices adjacent if and only if they are
+        within the prescribed radius.
+        """
+        # Use the Euclidean metric, the default according to the
+        # documentation.
+        G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1, seed=42)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_p(self):
+        """Tests for providing an alternate distance metric to the generator."""
+
+        # Use the L1 metric.
+        def dist(x, y):
+            return sum(abs(a - b) for a, b in zip(x, y))
+
+        G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1, p=1, seed=42)
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_node_names(self):
+        """Tests using values other than sequential numbers as node IDs."""
+        import string
+
+        nodes = list(string.ascii_lowercase)
+        G = nx.thresholded_random_geometric_graph(nodes, 0.25, 0.1, seed=42)
+        assert len(G) == len(nodes)
+
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert math.dist(G.nodes[u]["pos"], G.nodes[v]["pos"]) <= 0.25
+
+    def test_theta(self):
+        """Tests that pairs of vertices adjacent if and only if their sum
+        weights exceeds the threshold parameter theta.
+        """
+        G = nx.thresholded_random_geometric_graph(50, 0.25, 0.1, seed=42)
+
+        for u, v in combinations(G, 2):
+            # Adjacent vertices must be within the given distance.
+            if v in G[u]:
+                assert (G.nodes[u]["weight"] + G.nodes[v]["weight"]) >= 0.1
+
+    def test_pos_name(self):
+        trgg = nx.thresholded_random_geometric_graph
+        G = trgg(50, 0.25, 0.1, seed=42, pos_name="p", weight_name="wt")
+        assert all(len(d["p"]) == 2 for n, d in G.nodes.items())
+        assert all(d["wt"] > 0 for n, d in G.nodes.items())
+
+
+def test_geometric_edges_pos_attribute():
+    G = nx.Graph()
+    G.add_nodes_from(
+        [
+            (0, {"position": (0, 0)}),
+            (1, {"position": (0, 1)}),
+            (2, {"position": (1, 0)}),
+        ]
+    )
+    expected_edges = [(0, 1), (0, 2)]
+    assert expected_edges == nx.geometric_edges(G, radius=1, pos_name="position")
+
+
+def test_geometric_edges_raises_no_pos():
+    G = nx.path_graph(3)
+    msg = "all nodes. must have a '"
+    with pytest.raises(nx.NetworkXError, match=msg):
+        nx.geometric_edges(G, radius=1)
+
+
+def test_number_of_nodes_S1():
+    G = nx.geometric_soft_configuration_graph(
+        beta=1.5, n=100, gamma=2.7, mean_degree=10, seed=42
+    )
+    assert len(G) == 100
+
+
+def test_set_attributes_S1():
+    G = nx.geometric_soft_configuration_graph(
+        beta=1.5, n=100, gamma=2.7, mean_degree=10, seed=42
+    )
+    kappas = nx.get_node_attributes(G, "kappa")
+    assert len(kappas) == 100
+    thetas = nx.get_node_attributes(G, "theta")
+    assert len(thetas) == 100
+    radii = nx.get_node_attributes(G, "radius")
+    assert len(radii) == 100
+
+
+def test_mean_kappas_mean_degree_S1():
+    G = nx.geometric_soft_configuration_graph(
+        beta=2.5, n=50, gamma=2.7, mean_degree=10, seed=8023
+    )
+
+    kappas = nx.get_node_attributes(G, "kappa")
+    mean_kappas = sum(kappas.values()) / len(kappas)
+    assert math.fabs(mean_kappas - 10) < 0.5
+
+    degrees = dict(G.degree())
+    mean_degree = sum(degrees.values()) / len(degrees)
+    assert math.fabs(mean_degree - 10) < 1
+
+
+def test_dict_kappas_S1():
+    kappas = {i: 10 for i in range(1000)}
+    G = nx.geometric_soft_configuration_graph(beta=1, kappas=kappas)
+    assert len(G) == 1000
+    kappas = nx.get_node_attributes(G, "kappa")
+    assert all(kappa == 10 for kappa in kappas.values())
+
+
+def test_beta_clustering_S1():
+    G1 = nx.geometric_soft_configuration_graph(
+        beta=1.5, n=100, gamma=3.5, mean_degree=10, seed=42
+    )
+    G2 = nx.geometric_soft_configuration_graph(
+        beta=3.0, n=100, gamma=3.5, mean_degree=10, seed=42
+    )
+    assert nx.average_clustering(G1) < nx.average_clustering(G2)
+
+
+def test_wrong_parameters_S1():
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Please provide either kappas, or all 3 of: n, gamma and mean_degree.",
+    ):
+        G = nx.geometric_soft_configuration_graph(
+            beta=1.5, gamma=3.5, mean_degree=10, seed=42
+        )
+
+    with pytest.raises(
+        nx.NetworkXError,
+        match="When kappas is input, n, gamma and mean_degree must not be.",
+    ):
+        kappas = {i: 10 for i in range(1000)}
+        G = nx.geometric_soft_configuration_graph(
+            beta=1.5, kappas=kappas, gamma=2.3, seed=42
+        )
+
+    with pytest.raises(
+        nx.NetworkXError,
+        match="Please provide either kappas, or all 3 of: n, gamma and mean_degree.",
+    ):
+        G = nx.geometric_soft_configuration_graph(beta=1.5, seed=42)
+
+
+def test_negative_beta_S1():
+    with pytest.raises(
+        nx.NetworkXError, match="The parameter beta cannot be smaller or equal to 0."
+    ):
+        G = nx.geometric_soft_configuration_graph(
+            beta=-1, n=100, gamma=2.3, mean_degree=10, seed=42
+        )
+
+
+def test_non_zero_clustering_beta_lower_one_S1():
+    G = nx.geometric_soft_configuration_graph(
+        beta=0.5, n=100, gamma=3.5, mean_degree=10, seed=42
+    )
+    assert nx.average_clustering(G) > 0
+
+
+def test_mean_degree_influence_on_connectivity_S1():
+    low_mean_degree = 2
+    high_mean_degree = 20
+    G_low = nx.geometric_soft_configuration_graph(
+        beta=1.2, n=100, gamma=2.7, mean_degree=low_mean_degree, seed=42
+    )
+    G_high = nx.geometric_soft_configuration_graph(
+        beta=1.2, n=100, gamma=2.7, mean_degree=high_mean_degree, seed=42
+    )
+    assert nx.number_connected_components(G_low) > nx.number_connected_components(
+        G_high
+    )
+
+
+def test_compare_mean_kappas_different_gammas_S1():
+    G1 = nx.geometric_soft_configuration_graph(
+        beta=1.5, n=20, gamma=2.7, mean_degree=5, seed=42
+    )
+    G2 = nx.geometric_soft_configuration_graph(
+        beta=1.5, n=20, gamma=3.5, mean_degree=5, seed=42
+    )
+    kappas1 = nx.get_node_attributes(G1, "kappa")
+    mean_kappas1 = sum(kappas1.values()) / len(kappas1)
+    kappas2 = nx.get_node_attributes(G2, "kappa")
+    mean_kappas2 = sum(kappas2.values()) / len(kappas2)
+    assert math.fabs(mean_kappas1 - mean_kappas2) < 1

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

@@ -0,0 +1,134 @@
+"""Unit tests for the :mod:`networkx.generators.harary_graph` module.
+"""
+
+import pytest
+
+import networkx as nx
+from networkx.algorithms.isomorphism.isomorph import is_isomorphic
+from networkx.generators.harary_graph import hkn_harary_graph, hnm_harary_graph
+
+
+class TestHararyGraph:
+    """
+    Suppose n nodes, m >= n-1 edges, d = 2m // n, r = 2m % n
+    """
+
+    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)]:
+            G1 = hnm_harary_graph(n, m)
+            d = 2 * m // n
+            G2 = nx.circulant_graph(n, list(range(1, d // 2 + 1)))
+            assert is_isomorphic(G1, G2)
+
+        # 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)]:
+            G1 = hnm_harary_graph(n, m)
+            d = 2 * m // n
+            G2 = nx.circulant_graph(n, list(range(1, d // 2 + 1)))
+            assert set(G2.edges) < set(G1.edges)
+            assert G1.number_of_edges() == m
+
+        # 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)]:
+            G1 = hnm_harary_graph(n, m)
+            d = 2 * m // n
+            L = list(range(1, (d + 1) // 2))
+            L.append(n // 2)
+            G2 = nx.circulant_graph(n, L)
+            assert is_isomorphic(G1, G2)
+
+        # 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)]:
+            G1 = hnm_harary_graph(n, m)
+            d = 2 * m // n
+            L = list(range(1, (d + 1) // 2))
+            L.append(n // 2)
+            G2 = nx.circulant_graph(n, L)
+            assert set(G2.edges) < set(G1.edges)
+            assert G1.number_of_edges() == m
+
+        # 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)]:
+            G1 = hnm_harary_graph(n, m)
+            d = 2 * m // n
+            L = list(range(1, (d + 1) // 2))
+            G2 = nx.circulant_graph(n, L)
+            assert set(G2.edges) < set(G1.edges)
+            assert G1.number_of_edges() == m
+
+        # Raise NetworkXError if n<1
+        n = 0
+        m = 0
+        pytest.raises(nx.NetworkXError, hnm_harary_graph, n, m)
+
+        # Raise NetworkXError if m < n-1
+        n = 6
+        m = 4
+        pytest.raises(nx.NetworkXError, hnm_harary_graph, n, m)
+
+        # Raise NetworkXError if m > n(n-1)/2
+        n = 6
+        m = 16
+        pytest.raises(nx.NetworkXError, hnm_harary_graph, n, m)
+
+    """
+        Suppose connectivity k, number of nodes n
+    """
+
+    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)]:
+            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)]:
+            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)]:
+            G1 = hkn_harary_graph(k, n)
+            L = list(range(1, (k + 1) // 2))
+            L.append(n // 2)
+            G2 = nx.circulant_graph(n, L)
+            assert is_isomorphic(G1, G2)
+
+        # 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)]:
+            G1 = hkn_harary_graph(k, n)
+            G2 = nx.circulant_graph(n, list(range(1, (k + 1) // 2)))
+            eSet1 = set(G1.edges)
+            eSet2 = set(G2.edges)
+            eSet3 = set()
+            half = n // 2
+            for i in range(half + 1):
+                # add half+1 edges between i and i+half
+                eSet3.add((i, (i + half) % n))
+            assert eSet1 == eSet2 | eSet3
+
+        # Raise NetworkXError if k<1
+        k = 0
+        n = 0
+        pytest.raises(nx.NetworkXError, hkn_harary_graph, k, n)
+
+        # Raise NetworkXError if n<k+1
+        k = 6
+        n = 6
+        pytest.raises(nx.NetworkXError, hkn_harary_graph, k, n)

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

@@ -0,0 +1,176 @@
+from pytest import approx
+
+from networkx import is_connected, neighbors
+from networkx.generators.internet_as_graphs import random_internet_as_graph
+
+
+class TestInternetASTopology:
+    @classmethod
+    def setup_class(cls):
+        cls.n = 1000
+        cls.seed = 42
+        cls.G = random_internet_as_graph(cls.n, cls.seed)
+        cls.T = []
+        cls.M = []
+        cls.C = []
+        cls.CP = []
+        cls.customers = {}
+        cls.providers = {}
+
+        for i in cls.G.nodes():
+            if cls.G.nodes[i]["type"] == "T":
+                cls.T.append(i)
+            elif cls.G.nodes[i]["type"] == "M":
+                cls.M.append(i)
+            elif cls.G.nodes[i]["type"] == "C":
+                cls.C.append(i)
+            elif cls.G.nodes[i]["type"] == "CP":
+                cls.CP.append(i)
+            else:
+                raise ValueError("Inconsistent data in the graph node attributes")
+            cls.set_customers(i)
+            cls.set_providers(i)
+
+    @classmethod
+    def set_customers(cls, i):
+        if i not in cls.customers:
+            cls.customers[i] = set()
+            for j in neighbors(cls.G, i):
+                e = cls.G.edges[(i, j)]
+                if e["type"] == "transit":
+                    customer = int(e["customer"])
+                    if j == customer:
+                        cls.set_customers(j)
+                        cls.customers[i] = cls.customers[i].union(cls.customers[j])
+                        cls.customers[i].add(j)
+                    elif i != customer:
+                        raise ValueError(
+                            "Inconsistent data in the graph edge attributes"
+                        )
+
+    @classmethod
+    def set_providers(cls, i):
+        if i not in cls.providers:
+            cls.providers[i] = set()
+            for j in neighbors(cls.G, i):
+                e = cls.G.edges[(i, j)]
+                if e["type"] == "transit":
+                    customer = int(e["customer"])
+                    if i == customer:
+                        cls.set_providers(j)
+                        cls.providers[i] = cls.providers[i].union(cls.providers[j])
+                        cls.providers[i].add(j)
+                    elif j != customer:
+                        raise ValueError(
+                            "Inconsistent data in the graph edge attributes"
+                        )
+
+    def test_wrong_input(self):
+        G = random_internet_as_graph(0)
+        assert len(G.nodes()) == 0
+
+        G = random_internet_as_graph(-1)
+        assert len(G.nodes()) == 0
+
+        G = random_internet_as_graph(1)
+        assert len(G.nodes()) == 1
+
+    def test_node_numbers(self):
+        assert len(self.G.nodes()) == self.n
+        assert len(self.T) < 7
+        assert len(self.M) == round(self.n * 0.15)
+        assert len(self.CP) == round(self.n * 0.05)
+        numb = self.n - len(self.T) - len(self.M) - len(self.CP)
+        assert len(self.C) == numb
+
+    def test_connectivity(self):
+        assert is_connected(self.G)
+
+    def test_relationships(self):
+        # T nodes are not customers of anyone
+        for i in self.T:
+            assert len(self.providers[i]) == 0
+
+        # C nodes are not providers of anyone
+        for i in self.C:
+            assert len(self.customers[i]) == 0
+
+        # CP nodes are not providers of anyone
+        for i in self.CP:
+            assert len(self.customers[i]) == 0
+
+        # test whether there is a customer-provider loop
+        for i in self.G.nodes():
+            assert len(self.customers[i].intersection(self.providers[i])) == 0
+
+        # test whether there is a peering with a customer or provider
+        for i, j in self.G.edges():
+            if self.G.edges[(i, j)]["type"] == "peer":
+                assert j not in self.customers[i]
+                assert i not in self.customers[j]
+                assert j not in self.providers[i]
+                assert i not in self.providers[j]
+
+    def test_degree_values(self):
+        d_m = 0  # multihoming degree for M nodes
+        d_cp = 0  # multihoming degree for CP nodes
+        d_c = 0  # multihoming degree for C nodes
+        p_m_m = 0  # avg number of peering edges between M and M
+        p_cp_m = 0  # avg number of peering edges between CP and M
+        p_cp_cp = 0  # avg number of peering edges between CP and CP
+        t_m = 0  # probability M's provider is T
+        t_cp = 0  # probability CP's provider is T
+        t_c = 0  # probability C's provider is T
+
+        for i, j in self.G.edges():
+            e = self.G.edges[(i, j)]
+            if e["type"] == "transit":
+                cust = int(e["customer"])
+                if i == cust:
+                    prov = j
+                elif j == cust:
+                    prov = i
+                else:
+                    raise ValueError("Inconsistent data in the graph edge attributes")
+                if cust in self.M:
+                    d_m += 1
+                    if self.G.nodes[prov]["type"] == "T":
+                        t_m += 1
+                elif cust in self.C:
+                    d_c += 1
+                    if self.G.nodes[prov]["type"] == "T":
+                        t_c += 1
+                elif cust in self.CP:
+                    d_cp += 1
+                    if self.G.nodes[prov]["type"] == "T":
+                        t_cp += 1
+                else:
+                    raise ValueError("Inconsistent data in the graph edge attributes")
+            elif e["type"] == "peer":
+                if self.G.nodes[i]["type"] == "M" and self.G.nodes[j]["type"] == "M":
+                    p_m_m += 1
+                if self.G.nodes[i]["type"] == "CP" and self.G.nodes[j]["type"] == "CP":
+                    p_cp_cp += 1
+                if (
+                    self.G.nodes[i]["type"] == "M"
+                    and self.G.nodes[j]["type"] == "CP"
+                    or self.G.nodes[i]["type"] == "CP"
+                    and self.G.nodes[j]["type"] == "M"
+                ):
+                    p_cp_m += 1
+            else:
+                raise ValueError("Unexpected data in the graph edge attributes")
+
+        assert d_m / len(self.M) == approx((2 + (2.5 * self.n) / 10000), abs=1e-0)
+        assert d_cp / len(self.CP) == approx((2 + (1.5 * self.n) / 10000), abs=1e-0)
+        assert d_c / len(self.C) == approx((1 + (5 * self.n) / 100000), abs=1e-0)
+
+        assert p_m_m / len(self.M) == approx((1 + (2 * self.n) / 10000), abs=1e-0)
+        assert p_cp_m / len(self.CP) == approx((0.2 + (2 * self.n) / 10000), abs=1e-0)
+        assert p_cp_cp / len(self.CP) == approx(
+            (0.05 + (2 * self.n) / 100000), abs=1e-0
+        )
+
+        assert t_m / d_m == approx(0.375, abs=1e-1)
+        assert t_cp / d_cp == approx(0.375, abs=1e-1)
+        assert t_c / d_c == approx(0.125, abs=1e-1)