two version of R2R are here HEAD master

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diff --git a/.venv/lib/python3.12/site-packages/networkx/algorithms/shortest_paths/unweighted.py b/.venv/lib/python3.12/site-packages/networkx/algorithms/shortest_paths/unweighted.py
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+"""
+Shortest path algorithms for unweighted graphs.
+"""
+
+import warnings
+
+import networkx as nx
+
+__all__ = [
+    "bidirectional_shortest_path",
+    "single_source_shortest_path",
+    "single_source_shortest_path_length",
+    "single_target_shortest_path",
+    "single_target_shortest_path_length",
+    "all_pairs_shortest_path",
+    "all_pairs_shortest_path_length",
+    "predecessor",
+]
+
+
+@nx._dispatchable
+def single_source_shortest_path_length(G, source, cutoff=None):
+    """Compute the shortest path lengths from source to all reachable nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node
+       Starting node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    lengths : dict
+        Dict keyed by node to shortest path length to source.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = nx.single_source_shortest_path_length(G, 0)
+    >>> length[4]
+    4
+    >>> for node in length:
+    ...     print(f"{node}: {length[node]}")
+    0: 0
+    1: 1
+    2: 2
+    3: 3
+    4: 4
+
+    See Also
+    --------
+    shortest_path_length
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = [source]
+    return dict(_single_shortest_path_length(G._adj, nextlevel, cutoff))
+
+
+def _single_shortest_path_length(adj, firstlevel, cutoff):
+    """Yields (node, level) in a breadth first search
+
+    Shortest Path Length helper function
+    Parameters
+    ----------
+        adj : dict
+            Adjacency dict or view
+        firstlevel : list
+            starting nodes, e.g. [source] or [target]
+        cutoff : int or float
+            level at which we stop the process
+    """
+    seen = set(firstlevel)
+    nextlevel = firstlevel
+    level = 0
+    n = len(adj)
+    for v in nextlevel:
+        yield (v, level)
+    while nextlevel and cutoff > level:
+        level += 1
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in adj[v]:
+                if w not in seen:
+                    seen.add(w)
+                    nextlevel.append(w)
+                    yield (w, level)
+            if len(seen) == n:
+                return
+
+
+@nx._dispatchable
+def single_target_shortest_path_length(G, target, cutoff=None):
+    """Compute the shortest path lengths to target from all reachable nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    target : node
+       Target node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    lengths : iterator
+        (source, shortest path length) iterator
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> length = dict(nx.single_target_shortest_path_length(G, 4))
+    >>> length[0]
+    4
+    >>> for node in range(5):
+    ...     print(f"{node}: {length[node]}")
+    0: 4
+    1: 3
+    2: 2
+    3: 1
+    4: 0
+
+    See Also
+    --------
+    single_source_shortest_path_length, shortest_path_length
+    """
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+
+    warnings.warn(
+        (
+            "\n\nsingle_target_shortest_path_length will return a dict instead of"
+            "\nan iterator in version 3.5"
+        ),
+        FutureWarning,
+        stacklevel=3,
+    )
+
+    if cutoff is None:
+        cutoff = float("inf")
+    # handle either directed or undirected
+    adj = G._pred if G.is_directed() else G._adj
+    nextlevel = [target]
+    # for version 3.3 we will return a dict like this:
+    # return dict(_single_shortest_path_length(adj, nextlevel, cutoff))
+    return _single_shortest_path_length(adj, nextlevel, cutoff)
+
+
+@nx._dispatchable
+def all_pairs_shortest_path_length(G, cutoff=None):
+    """Computes the shortest path lengths between all nodes in `G`.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer, optional
+        Depth at which to stop the search. Only paths of length at most
+        `cutoff` are returned.
+
+    Returns
+    -------
+    lengths : iterator
+        (source, dictionary) iterator with dictionary keyed by target and
+        shortest path length as the key value.
+
+    Notes
+    -----
+    The iterator returned only has reachable node pairs.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> length = dict(nx.all_pairs_shortest_path_length(G))
+    >>> for node in [0, 1, 2, 3, 4]:
+    ...     print(f"1 - {node}: {length[1][node]}")
+    1 - 0: 1
+    1 - 1: 0
+    1 - 2: 1
+    1 - 3: 2
+    1 - 4: 3
+    >>> length[3][2]
+    1
+    >>> length[2][2]
+    0
+
+    """
+    length = single_source_shortest_path_length
+    # TODO This can be trivially parallelized.
+    for n in G:
+        yield (n, length(G, n, cutoff=cutoff))
+
+
+@nx._dispatchable
+def bidirectional_shortest_path(G, source, target):
+    """Returns a list of nodes in a shortest path between source and target.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       starting node for path
+
+    target : node label
+       ending node for path
+
+    Returns
+    -------
+    path: list
+       List of nodes in a path from source to target.
+
+    Raises
+    ------
+    NetworkXNoPath
+       If no path exists between source and target.
+
+    Examples
+    --------
+    >>> G = nx.Graph()
+    >>> nx.add_path(G, [0, 1, 2, 3, 0, 4, 5, 6, 7, 4])
+    >>> nx.bidirectional_shortest_path(G, 2, 6)
+    [2, 1, 0, 4, 5, 6]
+
+    See Also
+    --------
+    shortest_path
+
+    Notes
+    -----
+    This algorithm is used by shortest_path(G, source, target).
+    """
+
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} is not in G")
+
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} is not in G")
+
+    # call helper to do the real work
+    results = _bidirectional_pred_succ(G, source, target)
+    pred, succ, w = results
+
+    # build path from pred+w+succ
+    path = []
+    # from source to w
+    while w is not None:
+        path.append(w)
+        w = pred[w]
+    path.reverse()
+    # from w to target
+    w = succ[path[-1]]
+    while w is not None:
+        path.append(w)
+        w = succ[w]
+
+    return path
+
+
+def _bidirectional_pred_succ(G, source, target):
+    """Bidirectional shortest path helper.
+
+    Returns (pred, succ, w) where
+    pred is a dictionary of predecessors from w to the source, and
+    succ is a dictionary of successors from w to the target.
+    """
+    # does BFS from both source and target and meets in the middle
+    if target == source:
+        return ({target: None}, {source: None}, source)
+
+    # handle either directed or undirected
+    if G.is_directed():
+        Gpred = G.pred
+        Gsucc = G.succ
+    else:
+        Gpred = G.adj
+        Gsucc = G.adj
+
+    # predecessor and successors in search
+    pred = {source: None}
+    succ = {target: None}
+
+    # initialize fringes, start with forward
+    forward_fringe = [source]
+    reverse_fringe = [target]
+
+    while forward_fringe and reverse_fringe:
+        if len(forward_fringe) <= len(reverse_fringe):
+            this_level = forward_fringe
+            forward_fringe = []
+            for v in this_level:
+                for w in Gsucc[v]:
+                    if w not in pred:
+                        forward_fringe.append(w)
+                        pred[w] = v
+                    if w in succ:  # path found
+                        return pred, succ, w
+        else:
+            this_level = reverse_fringe
+            reverse_fringe = []
+            for v in this_level:
+                for w in Gpred[v]:
+                    if w not in succ:
+                        succ[w] = v
+                        reverse_fringe.append(w)
+                    if w in pred:  # found path
+                        return pred, succ, w
+
+    raise nx.NetworkXNoPath(f"No path between {source} and {target}.")
+
+
+@nx._dispatchable
+def single_source_shortest_path(G, source, cutoff=None):
+    """Compute shortest path between source
+    and all other nodes reachable from source.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       Starting node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary, keyed by target, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = nx.single_source_shortest_path(G, 0)
+    >>> path[4]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    The shortest path is not necessarily unique. So there can be multiple
+    paths between the source and each target node, all of which have the
+    same 'shortest' length. For each target node, this function returns
+    only one of those paths.
+
+    See Also
+    --------
+    shortest_path
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} not in G")
+
+    def join(p1, p2):
+        return p1 + p2
+
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = {source: 1}  # list of nodes to check at next level
+    paths = {source: [source]}  # paths dictionary  (paths to key from source)
+    return dict(_single_shortest_path(G.adj, nextlevel, paths, cutoff, join))
+
+
+def _single_shortest_path(adj, firstlevel, paths, cutoff, join):
+    """Returns shortest paths
+
+    Shortest Path helper function
+    Parameters
+    ----------
+        adj : dict
+            Adjacency dict or view
+        firstlevel : dict
+            starting nodes, e.g. {source: 1} or {target: 1}
+        paths : dict
+            paths for starting nodes, e.g. {source: [source]}
+        cutoff : int or float
+            level at which we stop the process
+        join : function
+            function to construct a path from two partial paths. Requires two
+            list inputs `p1` and `p2`, and returns a list. Usually returns
+            `p1 + p2` (forward from source) or `p2 + p1` (backward from target)
+    """
+    level = 0  # the current level
+    nextlevel = firstlevel
+    while nextlevel and cutoff > level:
+        thislevel = nextlevel
+        nextlevel = {}
+        for v in thislevel:
+            for w in adj[v]:
+                if w not in paths:
+                    paths[w] = join(paths[v], [w])
+                    nextlevel[w] = 1
+        level += 1
+    return paths
+
+
+@nx._dispatchable
+def single_target_shortest_path(G, target, cutoff=None):
+    """Compute shortest path to target from all nodes that reach target.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    target : node label
+       Target node for path
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    Returns
+    -------
+    paths : dictionary
+        Dictionary, keyed by target, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5, create_using=nx.DiGraph())
+    >>> path = nx.single_target_shortest_path(G, 4)
+    >>> path[0]
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    The shortest path is not necessarily unique. So there can be multiple
+    paths between the source and each target node, all of which have the
+    same 'shortest' length. For each target node, this function returns
+    only one of those paths.
+
+    See Also
+    --------
+    shortest_path, single_source_shortest_path
+    """
+    if target not in G:
+        raise nx.NodeNotFound(f"Target {target} not in G")
+
+    def join(p1, p2):
+        return p2 + p1
+
+    # handle undirected graphs
+    adj = G.pred if G.is_directed() else G.adj
+    if cutoff is None:
+        cutoff = float("inf")
+    nextlevel = {target: 1}  # list of nodes to check at next level
+    paths = {target: [target]}  # paths dictionary  (paths to key from source)
+    return dict(_single_shortest_path(adj, nextlevel, paths, cutoff, join))
+
+
+@nx._dispatchable
+def all_pairs_shortest_path(G, cutoff=None):
+    """Compute shortest paths between all nodes.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    cutoff : integer, optional
+        Depth at which to stop the search. Only paths of length at most
+        `cutoff` are returned.
+
+    Returns
+    -------
+    paths : iterator
+        Dictionary, keyed by source and target, of shortest paths.
+
+    Examples
+    --------
+    >>> G = nx.path_graph(5)
+    >>> path = dict(nx.all_pairs_shortest_path(G))
+    >>> print(path[0][4])
+    [0, 1, 2, 3, 4]
+
+    Notes
+    -----
+    There may be multiple shortest paths with the same length between
+    two nodes. For each pair, this function returns only one of those paths.
+
+    See Also
+    --------
+    floyd_warshall
+    all_pairs_all_shortest_paths
+
+    """
+    # TODO This can be trivially parallelized.
+    for n in G:
+        yield (n, single_source_shortest_path(G, n, cutoff=cutoff))
+
+
+@nx._dispatchable
+def predecessor(G, source, target=None, cutoff=None, return_seen=None):
+    """Returns dict of predecessors for the path from source to all nodes in G.
+
+    Parameters
+    ----------
+    G : NetworkX graph
+
+    source : node label
+       Starting node for path
+
+    target : node label, optional
+       Ending node for path. If provided only predecessors between
+       source and target are returned
+
+    cutoff : integer, optional
+        Depth to stop the search. Only paths of length <= cutoff are returned.
+
+    return_seen : bool, optional (default=None)
+        Whether to return a dictionary, keyed by node, of the level (number of
+        hops) to reach the node (as seen during breadth-first-search).
+
+    Returns
+    -------
+    pred : dictionary
+        Dictionary, keyed by node, of predecessors in the shortest path.
+
+
+    (pred, seen): tuple of dictionaries
+        If `return_seen` argument is set to `True`, then a tuple of dictionaries
+        is returned. The first element is the dictionary, keyed by node, of
+        predecessors in the shortest path. The second element is the dictionary,
+        keyed by node, of the level (number of hops) to reach the node (as seen
+        during breadth-first-search).
+
+    Examples
+    --------
+    >>> G = nx.path_graph(4)
+    >>> list(G)
+    [0, 1, 2, 3]
+    >>> nx.predecessor(G, 0)
+    {0: [], 1: [0], 2: [1], 3: [2]}
+    >>> nx.predecessor(G, 0, return_seen=True)
+    ({0: [], 1: [0], 2: [1], 3: [2]}, {0: 0, 1: 1, 2: 2, 3: 3})
+
+
+    """
+    if source not in G:
+        raise nx.NodeNotFound(f"Source {source} not in G")
+
+    level = 0  # the current level
+    nextlevel = [source]  # list of nodes to check at next level
+    seen = {source: level}  # level (number of hops) when seen in BFS
+    pred = {source: []}  # predecessor dictionary
+    while nextlevel:
+        level = level + 1
+        thislevel = nextlevel
+        nextlevel = []
+        for v in thislevel:
+            for w in G[v]:
+                if w not in seen:
+                    pred[w] = [v]
+                    seen[w] = level
+                    nextlevel.append(w)
+                elif seen[w] == level:  # add v to predecessor list if it
+                    pred[w].append(v)  # is at the correct level
+        if cutoff and cutoff <= level:
+            break
+
+    if target is not None:
+        if return_seen:
+            if target not in pred:
+                return ([], -1)  # No predecessor
+            return (pred[target], seen[target])
+        else:
+            if target not in pred:
+                return []  # No predecessor
+            return pred[target]
+    else:
+        if return_seen:
+            return (pred, seen)
+        else:
+            return pred