diff env/lib/python3.7/site-packages/networkx/utils/rcm.py @ 5:9b1c78e6ba9c draft default tip

"planemo upload commit 6c0a8142489327ece472c84e558c47da711a9142"

--- a/env/lib/python3.7/site-packages/networkx/utils/rcm.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,165 +0,0 @@
-"""
-Cuthill-McKee ordering of graph nodes to produce sparse matrices
-"""
-#    Copyright (C) 2011-2014 by
-#    Aric Hagberg <[e-mail]>
-#    All rights reserved.
-#    BSD license.
-from collections import deque
-from operator import itemgetter
-
-import networkx as nx
-from ..utils import arbitrary_element
-
-__author__ = """\n""".join(['Aric Hagberg <[e-mail]>'])
-__all__ = ['cuthill_mckee_ordering',
-           'reverse_cuthill_mckee_ordering']
-
-
-def cuthill_mckee_ordering(G, heuristic=None):
-    """Generate an ordering (permutation) of the graph nodes to make
-    a sparse matrix.
-
-    Uses the Cuthill-McKee heuristic (based on breadth-first search) [1]_.
-
-    Parameters
-    ----------
-    G : graph
-      A NetworkX graph
-
-    heuristic : function, optional
-      Function to choose starting node for RCM algorithm.  If None
-      a node from a pseudo-peripheral pair is used.  A user-defined function
-      can be supplied that takes a graph object and returns a single node.
-
-    Returns
-    -------
-    nodes : generator
-       Generator of nodes in Cuthill-McKee ordering.
-
-    Examples
-    --------
-    >>> from networkx.utils import cuthill_mckee_ordering
-    >>> G = nx.path_graph(4)
-    >>> rcm = list(cuthill_mckee_ordering(G))
-    >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
-
-    Smallest degree node as heuristic function:
-
-    >>> def smallest_degree(G):
-    ...     return min(G, key=G.degree)
-    >>> rcm = list(cuthill_mckee_ordering(G, heuristic=smallest_degree))
-
-
-    See Also
-    --------
-    reverse_cuthill_mckee_ordering
-
-    Notes
-    -----
-    The optimal solution the the bandwidth reduction is NP-complete [2]_.
-
-
-    References
-    ----------
-    .. [1] E. Cuthill and J. McKee.
-       Reducing the bandwidth of sparse symmetric matrices,
-       In Proc. 24th Nat. Conf. ACM, pages 157-172, 1969.
-       http://doi.acm.org/10.1145/800195.805928
-    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
-       Springer-Verlag New York, Inc., New York, NY, USA.
-    """
-    for c in nx.connected_components(G):
-        for n in connected_cuthill_mckee_ordering(G.subgraph(c), heuristic):
-            yield n
-
-
-def reverse_cuthill_mckee_ordering(G, heuristic=None):
-    """Generate an ordering (permutation) of the graph nodes to make
-    a sparse matrix.
-
-    Uses the reverse Cuthill-McKee heuristic (based on breadth-first search)
-    [1]_.
-
-    Parameters
-    ----------
-    G : graph
-      A NetworkX graph
-
-    heuristic : function, optional
-      Function to choose starting node for RCM algorithm.  If None
-      a node from a pseudo-peripheral pair is used.  A user-defined function
-      can be supplied that takes a graph object and returns a single node.
-
-    Returns
-    -------
-    nodes : generator
-       Generator of nodes in reverse Cuthill-McKee ordering.
-
-    Examples
-    --------
-    >>> from networkx.utils import reverse_cuthill_mckee_ordering
-    >>> G = nx.path_graph(4)
-    >>> rcm = list(reverse_cuthill_mckee_ordering(G))
-    >>> A = nx.adjacency_matrix(G, nodelist=rcm) # doctest: +SKIP
-
-    Smallest degree node as heuristic function:
-
-    >>> def smallest_degree(G):
-    ...     return min(G, key=G.degree)
-    >>> rcm = list(reverse_cuthill_mckee_ordering(G, heuristic=smallest_degree))
-
-
-    See Also
-    --------
-    cuthill_mckee_ordering
-
-    Notes
-    -----
-    The optimal solution the the bandwidth reduction is NP-complete [2]_.
-
-    References
-    ----------
-    .. [1] E. Cuthill and J. McKee.
-       Reducing the bandwidth of sparse symmetric matrices,
-       In Proc. 24th Nat. Conf. ACM, pages 157-72, 1969.
-       http://doi.acm.org/10.1145/800195.805928
-    .. [2]  Steven S. Skiena. 1997. The Algorithm Design Manual.
-       Springer-Verlag New York, Inc., New York, NY, USA.
-    """
-    return reversed(list(cuthill_mckee_ordering(G, heuristic=heuristic)))
-
-
-def connected_cuthill_mckee_ordering(G, heuristic=None):
-    # the cuthill mckee algorithm for connected graphs
-    if heuristic is None:
-        start = pseudo_peripheral_node(G)
-    else:
-        start = heuristic(G)
-    visited = {start}
-    queue = deque([start])
-    while queue:
-        parent = queue.popleft()
-        yield parent
-        nd = sorted(list(G.degree(set(G[parent]) - visited)),
-                    key=itemgetter(1))
-        children = [n for n, d in nd]
-        visited.update(children)
-        queue.extend(children)
-
-
-def pseudo_peripheral_node(G):
-    # helper for cuthill-mckee to find a node in a "pseudo peripheral pair"
-    # to use as good starting node
-    u = arbitrary_element(G)
-    lp = 0
-    v = u
-    while True:
-        spl = dict(nx.shortest_path_length(G, v))
-        l = max(spl.values())
-        if l <= lp:
-            break
-        lp = l
-        farthest = (n for n, dist in spl.items() if dist == l)
-        v, deg = min(G.degree(farthest), key=itemgetter(1))
-    return v