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

"planemo upload commit 6c0a8142489327ece472c84e558c47da711a9142"

--- a/env/lib/python3.7/site-packages/networkx/linalg/modularitymatrix.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,166 +0,0 @@
-"""Modularity matrix of graphs.
-"""
-#    Copyright (C) 2004-2019 by
-#    Aric Hagberg <[e-mail]>
-#    Dan Schult <[e-mail]>
-#    Pieter Swart <[e-mail]>
-#    All rights reserved.
-#    BSD license.
-import networkx as nx
-from networkx.utils import not_implemented_for
-__author__ = "\n".join(['Aric Hagberg <[e-mail]>',
-                        'Pieter Swart ([e-mail])',
-                        'Dan Schult ([e-mail])',
-                        'Jean-Gabriel Young ([e-mail])'])
-__all__ = ['modularity_matrix', 'directed_modularity_matrix']
-
-
-@not_implemented_for('directed')
-@not_implemented_for('multigraph')
-def modularity_matrix(G, nodelist=None, weight=None):
-    r"""Returns the modularity matrix of G.
-
-    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
-    matrix and <A> is the average adjacency matrix, assuming that the graph
-    is described by the configuration model.
-
-    More specifically, the element B_ij of B is defined as
-
-    .. math::
-        A_{ij} - {k_i k_j \over 2 m}
-
-    where k_i is the degree of node i, and where m is the number of edges
-    in the graph. When weight is set to a name of an attribute edge, Aij, k_i,
-    k_j and m are computed using its value.
-
-    Parameters
-    ----------
-    G : Graph
-       A NetworkX graph
-
-    nodelist : list, optional
-       The rows and columns are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
-
-    weight : string or None, optional (default=None)
-       The edge attribute that holds the numerical value used for
-       the edge weight.  If None then all edge weights are 1.
-
-    Returns
-    -------
-    B : Numpy matrix
-      The modularity matrix of G.
-
-    Examples
-    --------
-    >>> import networkx as nx
-    >>> k =[3, 2, 2, 1, 0]
-    >>> G = nx.havel_hakimi_graph(k)
-    >>> B = nx.modularity_matrix(G)
-
-
-    See Also
-    --------
-    to_numpy_matrix
-    modularity_spectrum
-    adjacency_matrix
-    directed_modularity_matrix
-
-    References
-    ----------
-    .. [1] M. E. J. Newman, "Modularity and community structure in networks",
-           Proc. Natl. Acad. Sci. USA, vol. 103, pp. 8577-8582, 2006.
-    """
-    if nodelist is None:
-        nodelist = list(G)
-    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
-                                  format='csr')
-    k = A.sum(axis=1)
-    m = k.sum() * 0.5
-    # Expected adjacency matrix
-    X = k * k.transpose() / (2 * m)
-    return A - X
-
-
-@not_implemented_for('undirected')
-@not_implemented_for('multigraph')
-def directed_modularity_matrix(G, nodelist=None, weight=None):
-    """Returns the directed modularity matrix of G.
-
-    The modularity matrix is the matrix B = A - <A>, where A is the adjacency
-    matrix and <A> is the expected adjacency matrix, assuming that the graph
-    is described by the configuration model.
-
-    More specifically, the element B_ij of B is defined as
-
-    .. math::
-        B_{ij} = A_{ij} - k_i^{out} k_j^{in} / m
-
-    where :math:`k_i^{in}` is the in degree of node i, and :math:`k_j^{out}` is the out degree
-    of node j, with m the number of edges in the graph. When weight is set
-    to a name of an attribute edge, Aij, k_i, k_j and m are computed using
-    its value.
-
-    Parameters
-    ----------
-    G : DiGraph
-       A NetworkX DiGraph
-
-    nodelist : list, optional
-       The rows and columns are ordered according to the nodes in nodelist.
-       If nodelist is None, then the ordering is produced by G.nodes().
-
-    weight : string or None, optional (default=None)
-       The edge attribute that holds the numerical value used for
-       the edge weight.  If None then all edge weights are 1.
-
-    Returns
-    -------
-    B : Numpy matrix
-      The modularity matrix of G.
-
-    Examples
-    --------
-    >>> import networkx as nx
-    >>> G = nx.DiGraph()
-    >>> G.add_edges_from(((1,2), (1,3), (3,1), (3,2), (3,5), (4,5), (4,6),
-    ...                   (5,4), (5,6), (6,4)))
-    >>> B = nx.directed_modularity_matrix(G)
-
-
-    Notes
-    -----
-    NetworkX defines the element A_ij of the adjacency matrix as 1 if there
-    is a link going from node i to node j. Leicht and Newman use the opposite
-    definition. This explains the different expression for B_ij.
-
-    See Also
-    --------
-    to_numpy_matrix
-    modularity_spectrum
-    adjacency_matrix
-    modularity_matrix
-
-    References
-    ----------
-    .. [1] E. A. Leicht, M. E. J. Newman,
-        "Community structure in directed networks",
-        Phys. Rev Lett., vol. 100, no. 11, p. 118703, 2008.
-    """
-    if nodelist is None:
-        nodelist = list(G)
-    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
-                                  format='csr')
-    k_in = A.sum(axis=0)
-    k_out = A.sum(axis=1)
-    m = k_in.sum()
-    # Expected adjacency matrix
-    X = k_out * k_in / m
-    return A - X
-
-
-# fixture for pytest
-def setup_module(module):
-    import pytest
-    numpy = pytest.importorskip('numpy')
-    scipy = pytest.importorskip('scipy')