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wemm/lib/python3.10/site-packages/botocore/data/forecastquery/2018-06-26/endpoint-rule-set-1.json.gz

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2dc67e787a865b46d80969022aad9aaa54c4caac3e024949f04c45f4009fbfaf
+size 1151

wemm/lib/python3.10/site-packages/networkx/algorithms/flow/capacityscaling.py

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+"""
+Capacity scaling minimum cost flow algorithm.
+"""
+
+__all__ = ["capacity_scaling"]
+
+from itertools import chain
+from math import log
+
+import networkx as nx
+
+from ...utils import BinaryHeap, arbitrary_element, not_implemented_for
+
+
+def _detect_unboundedness(R):
+    """Detect infinite-capacity negative cycles."""
+    G = nx.DiGraph()
+    G.add_nodes_from(R)
+
+    # Value simulating infinity.
+    inf = R.graph["inf"]
+    # True infinity.
+    f_inf = float("inf")
+    for u in R:
+        for v, e in R[u].items():
+            # Compute the minimum weight of infinite-capacity (u, v) edges.
+            w = f_inf
+            for k, e in e.items():
+                if e["capacity"] == inf:
+                    w = min(w, e["weight"])
+            if w != f_inf:
+                G.add_edge(u, v, weight=w)
+
+    if nx.negative_edge_cycle(G):
+        raise nx.NetworkXUnbounded(
+            "Negative cost cycle of infinite capacity found. "
+            "Min cost flow may be unbounded below."
+        )
+
+
+@not_implemented_for("undirected")
+def _build_residual_network(G, demand, capacity, weight):
+    """Build a residual network and initialize a zero flow."""
+    if sum(G.nodes[u].get(demand, 0) for u in G) != 0:
+        raise nx.NetworkXUnfeasible("Sum of the demands should be 0.")
+
+    R = nx.MultiDiGraph()
+    R.add_nodes_from(
+        (u, {"excess": -G.nodes[u].get(demand, 0), "potential": 0}) for u in G
+    )
+
+    inf = float("inf")
+    # Detect selfloops with infinite capacities and negative weights.
+    for u, v, e in nx.selfloop_edges(G, data=True):
+        if e.get(weight, 0) < 0 and e.get(capacity, inf) == inf:
+            raise nx.NetworkXUnbounded(
+                "Negative cost cycle of infinite capacity found. "
+                "Min cost flow may be unbounded below."
+            )
+
+    # Extract edges with positive capacities. Self loops excluded.
+    if G.is_multigraph():
+        edge_list = [
+            (u, v, k, e)
+            for u, v, k, e in G.edges(data=True, keys=True)
+            if u != v and e.get(capacity, inf) > 0
+        ]
+    else:
+        edge_list = [
+            (u, v, 0, e)
+            for u, v, e in G.edges(data=True)
+            if u != v and e.get(capacity, inf) > 0
+        ]
+    # Simulate infinity with the larger of the sum of absolute node imbalances
+    # the sum of finite edge capacities or any positive value if both sums are
+    # zero. This allows the infinite-capacity edges to be distinguished for
+    # unboundedness detection and directly participate in residual capacity
+    # calculation.
+    inf = (
+        max(
+            sum(abs(R.nodes[u]["excess"]) for u in R),
+            2
+            * sum(
+                e[capacity]
+                for u, v, k, e in edge_list
+                if capacity in e and e[capacity] != inf
+            ),
+        )
+        or 1
+    )
+    for u, v, k, e in edge_list:
+        r = min(e.get(capacity, inf), inf)
+        w = e.get(weight, 0)
+        # Add both (u, v) and (v, u) into the residual network marked with the
+        # original key. (key[1] == True) indicates the (u, v) is in the
+        # original network.
+        R.add_edge(u, v, key=(k, True), capacity=r, weight=w, flow=0)
+        R.add_edge(v, u, key=(k, False), capacity=0, weight=-w, flow=0)
+
+    # Record the value simulating infinity.
+    R.graph["inf"] = inf
+
+    _detect_unboundedness(R)
+
+    return R
+
+
+def _build_flow_dict(G, R, capacity, weight):
+    """Build a flow dictionary from a residual network."""
+    inf = float("inf")
+    flow_dict = {}
+    if G.is_multigraph():
+        for u in G:
+            flow_dict[u] = {}
+            for v, es in G[u].items():
+                flow_dict[u][v] = {
+                    # Always saturate negative selfloops.
+                    k: (
+                        0
+                        if (
+                            u != v or e.get(capacity, inf) <= 0 or e.get(weight, 0) >= 0
+                        )
+                        else e[capacity]
+                    )
+                    for k, e in es.items()
+                }
+            for v, es in R[u].items():
+                if v in flow_dict[u]:
+                    flow_dict[u][v].update(
+                        (k[0], e["flow"]) for k, e in es.items() if e["flow"] > 0
+                    )
+    else:
+        for u in G:
+            flow_dict[u] = {
+                # Always saturate negative selfloops.
+                v: (
+                    0
+                    if (u != v or e.get(capacity, inf) <= 0 or e.get(weight, 0) >= 0)
+                    else e[capacity]
+                )
+                for v, e in G[u].items()
+            }
+            flow_dict[u].update(
+                (v, e["flow"])
+                for v, es in R[u].items()
+                for e in es.values()
+                if e["flow"] > 0
+            )
+    return flow_dict
+
+
+@nx._dispatchable(
+    node_attrs="demand", edge_attrs={"capacity": float("inf"), "weight": 0}
+)
+def capacity_scaling(
+    G, demand="demand", capacity="capacity", weight="weight", heap=BinaryHeap
+):
+    r"""Find a minimum cost flow satisfying all demands in digraph G.
+
+    This is a capacity scaling successive shortest augmenting path algorithm.
+
+    G is a digraph with edge costs and capacities and in which nodes
+    have demand, i.e., they want to send or receive some amount of
+    flow. A negative demand means that the node wants to send flow, a
+    positive demand means that the node want to receive flow. A flow on
+    the digraph G satisfies all demand if the net flow into each node
+    is equal to the demand of that node.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+        DiGraph or MultiDiGraph on which a minimum cost flow satisfying all
+        demands is to be found.
+
+    demand : string
+        Nodes of the graph G are expected to have an attribute demand
+        that indicates how much flow a node wants to send (negative
+        demand) or receive (positive demand). Note that the sum of the
+        demands should be 0 otherwise the problem in not feasible. If
+        this attribute is not present, a node is considered to have 0
+        demand. Default value: 'demand'.
+
+    capacity : string
+        Edges of the graph G are expected to have an attribute capacity
+        that indicates how much flow the edge can support. If this
+        attribute is not present, the edge is considered to have
+        infinite capacity. Default value: 'capacity'.
+
+    weight : string
+        Edges of the graph G are expected to have an attribute weight
+        that indicates the cost incurred by sending one unit of flow on
+        that edge. If not present, the weight is considered to be 0.
+        Default value: 'weight'.
+
+    heap : class
+        Type of heap to be used in the algorithm. It should be a subclass of
+        :class:`MinHeap` or implement a compatible interface.
+
+        If a stock heap implementation is to be used, :class:`BinaryHeap` is
+        recommended over :class:`PairingHeap` for Python implementations without
+        optimized attribute accesses (e.g., CPython) despite a slower
+        asymptotic running time. For Python implementations with optimized
+        attribute accesses (e.g., PyPy), :class:`PairingHeap` provides better
+        performance. Default value: :class:`BinaryHeap`.
+
+    Returns
+    -------
+    flowCost : integer
+        Cost of a minimum cost flow satisfying all demands.
+
+    flowDict : dictionary
+        If G is a digraph, a dict-of-dicts keyed by nodes such that
+        flowDict[u][v] is the flow on edge (u, v).
+        If G is a MultiDiGraph, a dict-of-dicts-of-dicts keyed by nodes
+        so that flowDict[u][v][key] is the flow on edge (u, v, key).
+
+    Raises
+    ------
+    NetworkXError
+        This exception is raised if the input graph is not directed,
+        not connected.
+
+    NetworkXUnfeasible
+        This exception is raised in the following situations:
+
+            * The sum of the demands is not zero. Then, there is no
+              flow satisfying all demands.
+            * There is no flow satisfying all demand.
+
+    NetworkXUnbounded
+        This exception is raised if the digraph G has a cycle of
+        negative cost and infinite capacity. Then, the cost of a flow
+        satisfying all demands is unbounded below.
+
+    Notes
+    -----
+    This algorithm does not work if edge weights are floating-point numbers.
+
+    See also
+    --------
+    :meth:`network_simplex`
+
+    Examples
+    --------
+    A simple example of a min cost flow problem.
+
+    >>> G = nx.DiGraph()
+    >>> G.add_node("a", demand=-5)
+    >>> G.add_node("d", demand=5)
+    >>> G.add_edge("a", "b", weight=3, capacity=4)
+    >>> G.add_edge("a", "c", weight=6, capacity=10)
+    >>> G.add_edge("b", "d", weight=1, capacity=9)
+    >>> G.add_edge("c", "d", weight=2, capacity=5)
+    >>> flowCost, flowDict = nx.capacity_scaling(G)
+    >>> flowCost
+    24
+    >>> flowDict
+    {'a': {'b': 4, 'c': 1}, 'd': {}, 'b': {'d': 4}, 'c': {'d': 1}}
+
+    It is possible to change the name of the attributes used for the
+    algorithm.
+
+    >>> G = nx.DiGraph()
+    >>> G.add_node("p", spam=-4)
+    >>> G.add_node("q", spam=2)
+    >>> G.add_node("a", spam=-2)
+    >>> G.add_node("d", spam=-1)
+    >>> G.add_node("t", spam=2)
+    >>> G.add_node("w", spam=3)
+    >>> G.add_edge("p", "q", cost=7, vacancies=5)
+    >>> G.add_edge("p", "a", cost=1, vacancies=4)
+    >>> G.add_edge("q", "d", cost=2, vacancies=3)
+    >>> G.add_edge("t", "q", cost=1, vacancies=2)
+    >>> G.add_edge("a", "t", cost=2, vacancies=4)
+    >>> G.add_edge("d", "w", cost=3, vacancies=4)
+    >>> G.add_edge("t", "w", cost=4, vacancies=1)
+    >>> flowCost, flowDict = nx.capacity_scaling(
+    ...     G, demand="spam", capacity="vacancies", weight="cost"
+    ... )
+    >>> flowCost
+    37
+    >>> flowDict
+    {'p': {'q': 2, 'a': 2}, 'q': {'d': 1}, 'a': {'t': 4}, 'd': {'w': 2}, 't': {'q': 1, 'w': 1}, 'w': {}}
+    """
+    R = _build_residual_network(G, demand, capacity, weight)
+
+    inf = float("inf")
+    # Account cost of negative selfloops.
+    flow_cost = sum(
+        0
+        if e.get(capacity, inf) <= 0 or e.get(weight, 0) >= 0
+        else e[capacity] * e[weight]
+        for u, v, e in nx.selfloop_edges(G, data=True)
+    )
+
+    # Determine the maximum edge capacity.
+    wmax = max(chain([-inf], (e["capacity"] for u, v, e in R.edges(data=True))))
+    if wmax == -inf:
+        # Residual network has no edges.
+        return flow_cost, _build_flow_dict(G, R, capacity, weight)
+
+    R_nodes = R.nodes
+    R_succ = R.succ
+
+    delta = 2 ** int(log(wmax, 2))
+    while delta >= 1:
+        # Saturate Δ-residual edges with negative reduced costs to achieve
+        # Δ-optimality.
+        for u in R:
+            p_u = R_nodes[u]["potential"]
+            for v, es in R_succ[u].items():
+                for k, e in es.items():
+                    flow = e["capacity"] - e["flow"]
+                    if e["weight"] - p_u + R_nodes[v]["potential"] < 0:
+                        flow = e["capacity"] - e["flow"]
+                        if flow >= delta:
+                            e["flow"] += flow
+                            R_succ[v][u][(k[0], not k[1])]["flow"] -= flow
+                            R_nodes[u]["excess"] -= flow
+                            R_nodes[v]["excess"] += flow
+        # Determine the Δ-active nodes.
+        S = set()
+        T = set()
+        S_add = S.add
+        S_remove = S.remove
+        T_add = T.add
+        T_remove = T.remove
+        for u in R:
+            excess = R_nodes[u]["excess"]
+            if excess >= delta:
+                S_add(u)
+            elif excess <= -delta:
+                T_add(u)
+        # Repeatedly augment flow from S to T along shortest paths until
+        # Δ-feasibility is achieved.
+        while S and T:
+            s = arbitrary_element(S)
+            t = None
+            # Search for a shortest path in terms of reduce costs from s to
+            # any t in T in the Δ-residual network.
+            d = {}
+            pred = {s: None}
+            h = heap()
+            h_insert = h.insert
+            h_get = h.get
+            h_insert(s, 0)
+            while h:
+                u, d_u = h.pop()
+                d[u] = d_u
+                if u in T:
+                    # Path found.
+                    t = u
+                    break
+                p_u = R_nodes[u]["potential"]
+                for v, es in R_succ[u].items():
+                    if v in d:
+                        continue
+                    wmin = inf
+                    # Find the minimum-weighted (u, v) Δ-residual edge.
+                    for k, e in es.items():
+                        if e["capacity"] - e["flow"] >= delta:
+                            w = e["weight"]
+                            if w < wmin:
+                                wmin = w
+                                kmin = k
+                                emin = e
+                    if wmin == inf:
+                        continue
+                    # Update the distance label of v.
+                    d_v = d_u + wmin - p_u + R_nodes[v]["potential"]
+                    if h_insert(v, d_v):
+                        pred[v] = (u, kmin, emin)
+            if t is not None:
+                # Augment Δ units of flow from s to t.
+                while u != s:
+                    v = u
+                    u, k, e = pred[v]
+                    e["flow"] += delta
+                    R_succ[v][u][(k[0], not k[1])]["flow"] -= delta
+                # Account node excess and deficit.
+                R_nodes[s]["excess"] -= delta
+                R_nodes[t]["excess"] += delta
+                if R_nodes[s]["excess"] < delta:
+                    S_remove(s)
+                if R_nodes[t]["excess"] > -delta:
+                    T_remove(t)
+                # Update node potentials.
+                d_t = d[t]
+                for u, d_u in d.items():
+                    R_nodes[u]["potential"] -= d_u - d_t
+            else:
+                # Path not found.
+                S_remove(s)
+        delta //= 2
+
+    if any(R.nodes[u]["excess"] != 0 for u in R):
+        raise nx.NetworkXUnfeasible("No flow satisfying all demands.")
+
+    # Calculate the flow cost.
+    for u in R:
+        for v, es in R_succ[u].items():
+            for e in es.values():
+                flow = e["flow"]
+                if flow > 0:
+                    flow_cost += flow * e["weight"]
+
+    return flow_cost, _build_flow_dict(G, R, capacity, weight)

wemm/lib/python3.10/site-packages/networkx/algorithms/traversal/edgebfs.py

@@ -0,0 +1,178 @@
+"""
+=============================
+Breadth First Search on Edges
+=============================
+
+Algorithms for a breadth-first traversal of edges in a graph.
+
+"""
+
+from collections import deque
+
+import networkx as nx
+
+FORWARD = "forward"
+REVERSE = "reverse"
+
+__all__ = ["edge_bfs"]
+
+
+@nx._dispatchable
+def edge_bfs(G, source=None, orientation=None):
+    """A directed, breadth-first-search of edges in `G`, beginning at `source`.
+
+    Yield the edges of G in a breadth-first-search order continuing until
+    all edges are generated.
+
+    Parameters
+    ----------
+    G : graph
+        A directed/undirected graph/multigraph.
+
+    source : node, list of nodes
+        The node from which the traversal begins. If None, then a source
+        is chosen arbitrarily and repeatedly until all edges from each node in
+        the graph are searched.
+
+    orientation : None | 'original' | 'reverse' | 'ignore' (default: None)
+        For directed graphs and directed multigraphs, edge traversals need not
+        respect the original orientation of the edges.
+        When set to 'reverse' every edge is traversed in the reverse direction.
+        When set to 'ignore', every edge is treated as undirected.
+        When set to 'original', every edge is treated as directed.
+        In all three cases, the yielded edge tuples add a last entry to
+        indicate the direction in which that edge was traversed.
+        If orientation is None, the yielded edge has no direction indicated.
+        The direction is respected, but not reported.
+
+    Yields
+    ------
+    edge : directed edge
+        A directed edge indicating the path taken by the breadth-first-search.
+        For graphs, `edge` is of the form `(u, v)` where `u` and `v`
+        are the tail and head of the edge as determined by the traversal.
+        For multigraphs, `edge` is of the form `(u, v, key)`, where `key` is
+        the key of the edge. When the graph is directed, then `u` and `v`
+        are always in the order of the actual directed edge.
+        If orientation is not None then the edge tuple is extended to include
+        the direction of traversal ('forward' or 'reverse') on that edge.
+
+    Examples
+    --------
+    >>> nodes = [0, 1, 2, 3]
+    >>> edges = [(0, 1), (1, 0), (1, 0), (2, 0), (2, 1), (3, 1)]
+
+    >>> list(nx.edge_bfs(nx.Graph(edges), nodes))
+    [(0, 1), (0, 2), (1, 2), (1, 3)]
+
+    >>> list(nx.edge_bfs(nx.DiGraph(edges), nodes))
+    [(0, 1), (1, 0), (2, 0), (2, 1), (3, 1)]
+
+    >>> list(nx.edge_bfs(nx.MultiGraph(edges), nodes))
+    [(0, 1, 0), (0, 1, 1), (0, 1, 2), (0, 2, 0), (1, 2, 0), (1, 3, 0)]
+
+    >>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes))
+    [(0, 1, 0), (1, 0, 0), (1, 0, 1), (2, 0, 0), (2, 1, 0), (3, 1, 0)]
+
+    >>> list(nx.edge_bfs(nx.DiGraph(edges), nodes, orientation="ignore"))
+    [(0, 1, 'forward'), (1, 0, 'reverse'), (2, 0, 'reverse'), (2, 1, 'reverse'), (3, 1, 'reverse')]
+
+    >>> list(nx.edge_bfs(nx.MultiDiGraph(edges), nodes, orientation="ignore"))
+    [(0, 1, 0, 'forward'), (1, 0, 0, 'reverse'), (1, 0, 1, 'reverse'), (2, 0, 0, 'reverse'), (2, 1, 0, 'reverse'), (3, 1, 0, 'reverse')]
+
+    Notes
+    -----
+    The goal of this function is to visit edges. It differs from the more
+    familiar breadth-first-search of nodes, as provided by
+    :func:`networkx.algorithms.traversal.breadth_first_search.bfs_edges`, in
+    that it does not stop once every node has been visited. In a directed graph
+    with edges [(0, 1), (1, 2), (2, 1)], the edge (2, 1) would not be visited
+    if not for the functionality provided by this function.
+
+    The naming of this function is very similar to bfs_edges. The difference
+    is that 'edge_bfs' yields edges even if they extend back to an already
+    explored node while 'bfs_edges' yields the edges of the tree that results
+    from a breadth-first-search (BFS) so no edges are reported if they extend
+    to already explored nodes. That means 'edge_bfs' reports all edges while
+    'bfs_edges' only report those traversed by a node-based BFS. Yet another
+    description is that 'bfs_edges' reports the edges traversed during BFS
+    while 'edge_bfs' reports all edges in the order they are explored.
+
+    See Also
+    --------
+    bfs_edges
+    bfs_tree
+    edge_dfs
+
+    """
+    nodes = list(G.nbunch_iter(source))
+    if not nodes:
+        return
+
+    directed = G.is_directed()
+    kwds = {"data": False}
+    if G.is_multigraph() is True:
+        kwds["keys"] = True
+
+    # set up edge lookup
+    if orientation is None:
+
+        def edges_from(node):
+            return iter(G.edges(node, **kwds))
+
+    elif not directed or orientation == "original":
+
+        def edges_from(node):
+            for e in G.edges(node, **kwds):
+                yield e + (FORWARD,)
+
+    elif orientation == "reverse":
+
+        def edges_from(node):
+            for e in G.in_edges(node, **kwds):
+                yield e + (REVERSE,)
+
+    elif orientation == "ignore":
+
+        def edges_from(node):
+            for e in G.edges(node, **kwds):
+                yield e + (FORWARD,)
+            for e in G.in_edges(node, **kwds):
+                yield e + (REVERSE,)
+
+    else:
+        raise nx.NetworkXError("invalid orientation argument.")
+
+    if directed:
+        neighbors = G.successors
+
+        def edge_id(edge):
+            # remove direction indicator
+            return edge[:-1] if orientation is not None else edge
+
+    else:
+        neighbors = G.neighbors
+
+        def edge_id(edge):
+            return (frozenset(edge[:2]),) + edge[2:]
+
+    check_reverse = directed and orientation in ("reverse", "ignore")
+
+    # start BFS
+    visited_nodes = set(nodes)
+    visited_edges = set()
+    queue = deque([(n, edges_from(n)) for n in nodes])
+    while queue:
+        parent, children_edges = queue.popleft()
+        for edge in children_edges:
+            if check_reverse and edge[-1] == REVERSE:
+                child = edge[0]
+            else:
+                child = edge[1]
+            if child not in visited_nodes:
+                visited_nodes.add(child)
+                queue.append((child, edges_from(child)))
+            edgeid = edge_id(edge)
+            if edgeid not in visited_edges:
+                visited_edges.add(edgeid)
+                yield edge

wemm/lib/python3.10/site-packages/networkx/generators/cographs.py

@@ -0,0 +1,68 @@
+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

wemm/lib/python3.10/site-packages/networkx/generators/duplication.py

@@ -0,0 +1,174 @@
+"""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
+from networkx.utils.misc import check_create_using
+
+__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, *, create_using=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>`.
+
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    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>
+
+    """
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    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, create_using)
+    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, *, create_using=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>`.
+    create_using : Graph constructor, optional (default=nx.Graph)
+        Graph type to create. If graph instance, then cleared before populated.
+        Multigraph and directed types are not supported and raise a ``NetworkXError``.
+
+    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)
+
+    create_using = check_create_using(create_using, directed=False, multigraph=False)
+    G = nx.empty_graph(create_using=create_using)
+
+    # 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

wemm/lib/python3.10/site-packages/networkx/generators/ego.py

@@ -0,0 +1,66 @@
+"""
+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

wemm/lib/python3.10/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

wemm/lib/python3.10/site-packages/networkx/generators/interval_graph.py

@@ -0,0 +1,70 @@
+"""
+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

wemm/lib/python3.10/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

wemm/lib/python3.10/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

wemm/lib/python3.10/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

wemm/lib/python3.10/site-packages/networkx/generators/social.py

@@ -0,0 +1,554 @@
+"""
+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.
+
+    The dataset is derived from the 'Club After Split From Data' column of Table 3 in [1]_.
+    This was in turn derived from the 'Club After Fission' column of Table 1 in the
+    same paper. Note that the nodes are 0-indexed in NetworkX, but 1-indexed in the
+    paper (the 'Individual Number in Matrix C' column of Table 3 starts at 1). This
+    means, for example, that ``G.nodes[9]["club"]`` returns 'Officer', which
+    corresponds to row 10 of Table 3 in the paper.
+
+    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

wemm/lib/python3.10/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

wemm/lib/python3.10/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

wemm/lib/python3.10/site-packages/networkx/generators/tests/test_classic.py

@@ -0,0 +1,640 @@
+"""
+====================
+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 nx.average_shortest_path_length(G) == 1.0
+        assert sorted(nx.triangles(G).values()) == [1, 1, 1]
+        assert nx.is_planar(G)
+        G = nx.dorogovtsev_goltsev_mendes_graph(2)
+        assert nx.number_of_nodes(G) == 6
+        assert nx.number_of_edges(G) == 9
+        assert nx.average_clustering(G) == 0.75
+        assert nx.average_shortest_path_length(G) == 1.4
+        assert nx.is_planar(G)
+        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
+
+        with pytest.raises(nx.NetworkXError, match=r"n must be greater than"):
+            nx.dorogovtsev_goltsev_mendes_graph(-1)
+        with pytest.raises(nx.NetworkXError, match=r"directed graph not supported"):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.DiGraph)
+        with pytest.raises(nx.NetworkXError, match=r"multigraph not supported"):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.MultiGraph)
+        with pytest.raises(nx.NetworkXError):
+            nx.dorogovtsev_goltsev_mendes_graph(7, create_using=nx.MultiDiGraph)
+
+    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

wemm/lib/python3.10/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])

wemm/lib/python3.10/site-packages/networkx/generators/tests/test_expanders.py

@@ -0,0 +1,162 @@
+"""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)

wemm/lib/python3.10/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