--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.9/site-packages/networkx/relabel.py	Mon Mar 22 18:12:50 2021 +0000
@@ -0,0 +1,282 @@
+import networkx as nx
+
+__all__ = ["convert_node_labels_to_integers", "relabel_nodes"]
+
+
+def relabel_nodes(G, mapping, copy=True):
+    """Relabel the nodes of the graph G.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    mapping : dictionary
+       A dictionary with the old labels as keys and new labels as values.
+       A partial mapping is allowed. Mapping 2 nodes to a single node is allowed.
+
+    copy : bool (optional, default=True)
+       If True return a copy, or if False relabel the nodes in place.
+
+    Examples
+    --------
+    To create a new graph with nodes relabeled according to a given
+    dictionary:
+
+    >>> G = nx.path_graph(3)
+    >>> sorted(G)
+    [0, 1, 2]
+    >>> mapping = {0: "a", 1: "b", 2: "c"}
+    >>> H = nx.relabel_nodes(G, mapping)
+    >>> sorted(H)
+    ['a', 'b', 'c']
+
+    Nodes can be relabeled with any hashable object, including numbers
+    and strings:
+
+    >>> import string
+    >>> G = nx.path_graph(26)  # nodes are integers 0 through 25
+    >>> sorted(G)[:3]
+    [0, 1, 2]
+    >>> mapping = dict(zip(G, string.ascii_lowercase))
+    >>> G = nx.relabel_nodes(G, mapping)  # nodes are characters a through z
+    >>> sorted(G)[:3]
+    ['a', 'b', 'c']
+    >>> mapping = dict(zip(G, range(1, 27)))
+    >>> G = nx.relabel_nodes(G, mapping)  # nodes are integers 1 through 26
+    >>> sorted(G)[:3]
+    [1, 2, 3]
+
+    To perform a partial in-place relabeling, provide a dictionary
+    mapping only a subset of the nodes, and set the `copy` keyword
+    argument to False:
+
+    >>> G = nx.path_graph(3)  # nodes 0-1-2
+    >>> mapping = {0: "a", 1: "b"}  # 0->'a' and 1->'b'
+    >>> G = nx.relabel_nodes(G, mapping, copy=False)
+    >>> sorted(G, key=str)
+    [2, 'a', 'b']
+
+    A mapping can also be given as a function:
+
+    >>> G = nx.path_graph(3)
+    >>> H = nx.relabel_nodes(G, lambda x: x ** 2)
+    >>> list(H)
+    [0, 1, 4]
+
+    In a multigraph, relabeling two or more nodes to the same new node
+    will retain all edges, but may change the edge keys in the process:
+
+    >>> G = nx.MultiGraph()
+    >>> G.add_edge(0, 1, value="a")  # returns the key for this edge
+    0
+    >>> G.add_edge(0, 2, value="b")
+    0
+    >>> G.add_edge(0, 3, value="c")
+    0
+    >>> mapping = {1: 4, 2: 4, 3: 4}
+    >>> H = nx.relabel_nodes(G, mapping, copy=True)
+    >>> print(H[0])
+    {4: {0: {'value': 'a'}, 1: {'value': 'b'}, 2: {'value': 'c'}}}
+
+    This works for in-place relabeling too:
+
+    >>> G = nx.relabel_nodes(G, mapping, copy=False)
+    >>> print(G[0])
+    {4: {0: {'value': 'a'}, 1: {'value': 'b'}, 2: {'value': 'c'}}}
+
+    Notes
+    -----
+    Only the nodes specified in the mapping will be relabeled.
+
+    The keyword setting copy=False modifies the graph in place.
+    Relabel_nodes avoids naming collisions by building a
+    directed graph from ``mapping`` which specifies the order of
+    relabelings. Naming collisions, such as a->b, b->c, are ordered
+    such that "b" gets renamed to "c" before "a" gets renamed "b".
+    In cases of circular mappings (e.g. a->b, b->a), modifying the
+    graph is not possible in-place and an exception is raised.
+    In that case, use copy=True.
+
+    If a relabel operation on a multigraph would cause two or more
+    edges to have the same source, target and key, the second edge must
+    be assigned a new key to retain all edges. The new key is set
+    to the lowest non-negative integer not already used as a key
+    for edges between these two nodes. Note that this means non-numeric
+    keys may be replaced by numeric keys.
+
+    See Also
+    --------
+    convert_node_labels_to_integers
+    """
+    # you can pass a function f(old_label)->new_label
+    # but we'll just make a dictionary here regardless
+    if not hasattr(mapping, "__getitem__"):
+        m = {n: mapping(n) for n in G}
+    else:
+        m = mapping
+    if copy:
+        return _relabel_copy(G, m)
+    else:
+        return _relabel_inplace(G, m)
+
+
+def _relabel_inplace(G, mapping):
+    old_labels = set(mapping.keys())
+    new_labels = set(mapping.values())
+    if len(old_labels & new_labels) > 0:
+        # labels sets overlap
+        # can we topological sort and still do the relabeling?
+        D = nx.DiGraph(list(mapping.items()))
+        D.remove_edges_from(nx.selfloop_edges(D))
+        try:
+            nodes = reversed(list(nx.topological_sort(D)))
+        except nx.NetworkXUnfeasible as e:
+            raise nx.NetworkXUnfeasible(
+                "The node label sets are overlapping and no ordering can "
+                "resolve the mapping. Use copy=True."
+            ) from e
+    else:
+        # non-overlapping label sets
+        nodes = old_labels
+
+    multigraph = G.is_multigraph()
+    directed = G.is_directed()
+
+    for old in nodes:
+        try:
+            new = mapping[old]
+        except KeyError:
+            continue
+        if new == old:
+            continue
+        try:
+            G.add_node(new, **G.nodes[old])
+        except KeyError as e:
+            raise KeyError(f"Node {old} is not in the graph") from e
+        if multigraph:
+            new_edges = [
+                (new, new if old == target else target, key, data)
+                for (_, target, key, data) in G.edges(old, data=True, keys=True)
+            ]
+            if directed:
+                new_edges += [
+                    (new if old == source else source, new, key, data)
+                    for (source, _, key, data) in G.in_edges(old, data=True, keys=True)
+                ]
+            # Ensure new edges won't overwrite existing ones
+            seen = set()
+            for i, (source, target, key, data) in enumerate(new_edges):
+                if target in G[source] and key in G[source][target]:
+                    new_key = 0 if not isinstance(key, (int, float)) else key
+                    while new_key in G[source][target] or (target, new_key) in seen:
+                        new_key += 1
+                    new_edges[i] = (source, target, new_key, data)
+                    seen.add((target, new_key))
+        else:
+            new_edges = [
+                (new, new if old == target else target, data)
+                for (_, target, data) in G.edges(old, data=True)
+            ]
+            if directed:
+                new_edges += [
+                    (new if old == source else source, new, data)
+                    for (source, _, data) in G.in_edges(old, data=True)
+                ]
+        G.remove_node(old)
+        G.add_edges_from(new_edges)
+    return G
+
+
+def _relabel_copy(G, mapping):
+    H = G.__class__()
+    H.add_nodes_from(mapping.get(n, n) for n in G)
+    H._node.update((mapping.get(n, n), d.copy()) for n, d in G.nodes.items())
+    if G.is_multigraph():
+        new_edges = [
+            (mapping.get(n1, n1), mapping.get(n2, n2), k, d.copy())
+            for (n1, n2, k, d) in G.edges(keys=True, data=True)
+        ]
+
+        # check for conflicting edge-keys
+        undirected = not G.is_directed()
+        seen_edges = set()
+        for i, (source, target, key, data) in enumerate(new_edges):
+            while (source, target, key) in seen_edges:
+                if not isinstance(key, (int, float)):
+                    key = 0
+                key += 1
+            seen_edges.add((source, target, key))
+            if undirected:
+                seen_edges.add((target, source, key))
+            new_edges[i] = (source, target, key, data)
+
+        H.add_edges_from(new_edges)
+    else:
+        H.add_edges_from(
+            (mapping.get(n1, n1), mapping.get(n2, n2), d.copy())
+            for (n1, n2, d) in G.edges(data=True)
+        )
+    H.graph.update(G.graph)
+    return H
+
+
+def convert_node_labels_to_integers(
+    G, first_label=0, ordering="default", label_attribute=None
+):
+    """Returns a copy of the graph G with the nodes relabeled using
+    consecutive integers.
+
+    Parameters
+    ----------
+    G : graph
+       A NetworkX graph
+
+    first_label : int, optional (default=0)
+       An integer specifying the starting offset in numbering nodes.
+       The new integer labels are numbered first_label, ..., n-1+first_label.
+
+    ordering : string
+       "default" : inherit node ordering from G.nodes()
+       "sorted"  : inherit node ordering from sorted(G.nodes())
+       "increasing degree" : nodes are sorted by increasing degree
+       "decreasing degree" : nodes are sorted by decreasing degree
+
+    label_attribute : string, optional (default=None)
+       Name of node attribute to store old label.  If None no attribute
+       is created.
+
+    Notes
+    -----
+    Node and edge attribute data are copied to the new (relabeled) graph.
+
+    There is no guarantee that the relabeling of nodes to integers will
+    give the same two integers for two (even identical graphs).
+    Use the `ordering` argument to try to preserve the order.
+
+    See Also
+    --------
+    relabel_nodes
+    """
+    N = G.number_of_nodes() + first_label
+    if ordering == "default":
+        mapping = dict(zip(G.nodes(), range(first_label, N)))
+    elif ordering == "sorted":
+        nlist = sorted(G.nodes())
+        mapping = dict(zip(nlist, range(first_label, N)))
+    elif ordering == "increasing degree":
+        dv_pairs = [(d, n) for (n, d) in G.degree()]
+        dv_pairs.sort()  # in-place sort from lowest to highest degree
+        mapping = dict(zip([n for d, n in dv_pairs], range(first_label, N)))
+    elif ordering == "decreasing degree":
+        dv_pairs = [(d, n) for (n, d) in G.degree()]
+        dv_pairs.sort()  # in-place sort from lowest to highest degree
+        dv_pairs.reverse()
+        mapping = dict(zip([n for d, n in dv_pairs], range(first_label, N)))
+    else:
+        raise nx.NetworkXError(f"Unknown node ordering: {ordering}")
+    H = relabel_nodes(G, mapping)
+    # create node attribute with the old label
+    if label_attribute is not None:
+        nx.set_node_attributes(H, {v: k for k, v in mapping.items()}, label_attribute)
+    return H