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

"planemo upload commit 6c0a8142489327ece472c84e558c47da711a9142"

--- a/env/lib/python3.7/site-packages/networkx/linalg/spectrum.py	Thu May 14 16:47:39 2020 -0400
+++ /dev/null	Thu Jan 01 00:00:00 1970 +0000
@@ -1,172 +0,0 @@
-"""
-Eigenvalue spectrum 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
-__author__ = "\n".join(['Aric Hagberg <[e-mail]>',
-                        'Pieter Swart ([e-mail])',
-                        'Dan Schult([e-mail])',
-                        'Jean-Gabriel Young ([e-mail])'])
-
-__all__ = ['laplacian_spectrum', 'adjacency_spectrum', 'modularity_spectrum',
-           'normalized_laplacian_spectrum', 'bethe_hessian_spectrum']
-
-
-def laplacian_spectrum(G, weight='weight'):
-    """Returns eigenvalues of the Laplacian of G
-
-    Parameters
-    ----------
-    G : graph
-       A NetworkX graph
-
-    weight : string or None, optional (default='weight')
-       The edge data key used to compute each value in the matrix.
-       If None, then each edge has weight 1.
-
-    Returns
-    -------
-    evals : NumPy array
-      Eigenvalues
-
-    Notes
-    -----
-    For MultiGraph/MultiDiGraph, the edges weights are summed.
-    See to_numpy_matrix for other options.
-
-    See Also
-    --------
-    laplacian_matrix
-    """
-    from scipy.linalg import eigvalsh
-    return eigvalsh(nx.laplacian_matrix(G, weight=weight).todense())
-
-
-def normalized_laplacian_spectrum(G, weight='weight'):
-    """Return eigenvalues of the normalized Laplacian of G
-
-    Parameters
-    ----------
-    G : graph
-       A NetworkX graph
-
-    weight : string or None, optional (default='weight')
-       The edge data key used to compute each value in the matrix.
-       If None, then each edge has weight 1.
-
-    Returns
-    -------
-    evals : NumPy array
-      Eigenvalues
-
-    Notes
-    -----
-    For MultiGraph/MultiDiGraph, the edges weights are summed.
-    See to_numpy_matrix for other options.
-
-    See Also
-    --------
-    normalized_laplacian_matrix
-    """
-    from scipy.linalg import eigvalsh
-    return eigvalsh(nx.normalized_laplacian_matrix(G, weight=weight).todense())
-
-
-def adjacency_spectrum(G, weight='weight'):
-    """Returns eigenvalues of the adjacency matrix of G.
-
-    Parameters
-    ----------
-    G : graph
-       A NetworkX graph
-
-    weight : string or None, optional (default='weight')
-       The edge data key used to compute each value in the matrix.
-       If None, then each edge has weight 1.
-
-    Returns
-    -------
-    evals : NumPy array
-      Eigenvalues
-
-    Notes
-    -----
-    For MultiGraph/MultiDiGraph, the edges weights are summed.
-    See to_numpy_matrix for other options.
-
-    See Also
-    --------
-    adjacency_matrix
-    """
-    from scipy.linalg import eigvals
-    return eigvals(nx.adjacency_matrix(G, weight=weight).todense())
-
-
-def modularity_spectrum(G):
-    """Returns eigenvalues of the modularity matrix of G.
-
-    Parameters
-    ----------
-    G : Graph
-       A NetworkX Graph or DiGraph
-
-    Returns
-    -------
-    evals : NumPy array
-      Eigenvalues
-
-    See Also
-    --------
-    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.
-    """
-    from scipy.linalg import eigvals
-    if G.is_directed():
-        return eigvals(nx.directed_modularity_matrix(G))
-    else:
-        return eigvals(nx.modularity_matrix(G))
-
-
-def bethe_hessian_spectrum(G, r=None):
-    """Returns eigenvalues of the Bethe Hessian matrix of G.
-
-    Parameters
-    ----------
-    G : Graph
-       A NetworkX Graph or DiGraph
-
-    r : float
-       Regularizer parameter
-
-    Returns
-    -------
-    evals : NumPy array
-      Eigenvalues
-
-    See Also
-    --------
-    bethe_hessian_matrix
-
-    References
-    ----------
-    .. [1] A. Saade, F. Krzakala and L. Zdeborová
-       "Spectral clustering of graphs with the bethe hessian",
-       Advances in Neural Information Processing Systems. 2014.
-    """
-    from scipy.linalg import eigvalsh
-    return eigvalsh(nx.bethe_hessian_matrix(G, r).todense())
-
-
-# fixture for pytest
-def setup_module(module):
-    import pytest
-    scipy.linalg = pytest.importorskip('scipy.linalg')