--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/env/lib/python3.7/site-packages/networkx/linalg/spectrum.py	Thu May 14 14:56:58 2020 -0400
@@ -0,0 +1,172 @@
+"""
+Eigenvalue spectrum of graphs.
+"""
+#    Copyright (C) 2004-2019 by
+#    Aric Hagberg <hagberg@lanl.gov>
+#    Dan Schult <dschult@colgate.edu>
+#    Pieter Swart <swart@lanl.gov>
+#    All rights reserved.
+#    BSD license.
+import networkx as nx
+__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
+                        'Pieter Swart (swart@lanl.gov)',
+                        'Dan Schult(dschult@colgate.edu)',
+                        'Jean-Gabriel Young (jean.gabriel.young@gmail.com)'])
+
+__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')