"""Laplacian matrix of graphs.
"""
#    Copyright (C) 2004-2015 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
from networkx.utils import not_implemented_for
__author__ = "\n".join(['Aric Hagberg <aric.hagberg@gmail.com>',
                        'Pieter Swart (swart@lanl.gov)',
                        'Dan Schult (dschult@colgate.edu)',
                        'Alejandro Weinstein <alejandro.weinstein@gmail.com>'])
__all__ = ['laplacian_matrix',
           'normalized_laplacian_matrix',
           'directed_laplacian_matrix']

@not_implemented_for('directed')
def laplacian_matrix(G, nodelist=None, weight='weight'):
    """Return the Laplacian matrix of G.

    The graph Laplacian is the matrix L = D - A, where
    A is the adjacency matrix and D is the diagonal matrix of node degrees.

    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='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    L : SciPy sparse matrix
      The Laplacian matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges weights are summed.

    See Also
    --------
    to_numpy_matrix
    normalized_laplacian_matrix
    """
    import scipy.sparse
    if nodelist is None:
        nodelist = G.nodes()
    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  format='csr')
    n,m = A.shape
    diags = A.sum(axis=1)
    D = scipy.sparse.spdiags(diags.flatten(), [0], m, n, format='csr')
    return  D - A

@not_implemented_for('directed')
def normalized_laplacian_matrix(G, nodelist=None, weight='weight'):
    r"""Return the normalized Laplacian matrix of G.

    The normalized graph Laplacian is the matrix

    .. math::

        N = D^{-1/2} L D^{-1/2}

    where `L` is the graph Laplacian and `D` is the diagonal matrix of
    node degrees.

    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='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    Returns
    -------
    N : NumPy matrix
      The normalized Laplacian matrix of G.

    Notes
    -----
    For MultiGraph/MultiDiGraph, the edges weights are summed.
    See to_numpy_matrix for other options.

    If the Graph contains selfloops, D is defined as diag(sum(A,1)), where A is
    the adjacency matrix [2]_.

    See Also
    --------
    laplacian_matrix

    References
    ----------
    .. [1] Fan Chung-Graham, Spectral Graph Theory,
       CBMS Regional Conference Series in Mathematics, Number 92, 1997.
    .. [2] Steve Butler, Interlacing For Weighted Graphs Using The Normalized
       Laplacian, Electronic Journal of Linear Algebra, Volume 16, pp. 90-98,
       March 2007.
    """
    import scipy
    import scipy.sparse
    if nodelist is None:
        nodelist = G.nodes()
    A = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  format='csr')
    n,m = A.shape
    diags = A.sum(axis=1).flatten()
    D = scipy.sparse.spdiags(diags, [0], m, n, format='csr')
    L = D - A
    with scipy.errstate(divide='ignore'):
       diags_sqrt = 1.0/scipy.sqrt(diags)
    diags_sqrt[scipy.isinf(diags_sqrt)] = 0
    DH = scipy.sparse.spdiags(diags_sqrt, [0], m, n, format='csr')
    return DH.dot(L.dot(DH))

###############################################################################
# Code based on
# https://bitbucket.org/bedwards/networkx-community/src/370bd69fc02f/networkx/algorithms/community/

@not_implemented_for('undirected')
@not_implemented_for('multigraph')
def directed_laplacian_matrix(G, nodelist=None, weight='weight',
                              walk_type=None, alpha=0.95):
    r"""Return the directed Laplacian matrix of G.

    The graph directed Laplacian is the matrix

    .. math::

        L = I - (\Phi^{1/2} P \Phi^{-1/2} + \Phi^{-1/2} P^T \Phi^{1/2} ) / 2

    where `I` is the identity matrix, `P` is the transition matrix of the
    graph, and `\Phi` a matrix with the Perron vector of `P` in the diagonal and
    zeros elsewhere.

    Depending on the value of walk_type, `P` can be the transition matrix
    induced by a random walk, a lazy random walk, or a random walk with
    teleportation (PageRank).

    Parameters
    ----------
    G : DiGraph
       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='weight')
       The edge data key used to compute each value in the matrix.
       If None, then each edge has weight 1.

    walk_type : string or None, optional (default=None)
       If None, `P` is selected depending on the properties of the
       graph. Otherwise is one of 'random', 'lazy', or 'pagerank'

    alpha : real
       (1 - alpha) is the teleportation probability used with pagerank

    Returns
    -------
    L : NumPy array
      Normalized Laplacian of G.

    Raises
    ------
    NetworkXError
        If NumPy cannot be imported

    NetworkXNotImplemnted
        If G is not a DiGraph

    Notes
    -----
    Only implemented for DiGraphs

    See Also
    --------
    laplacian_matrix

    References
    ----------
    .. [1] Fan Chung (2005).
       Laplacians and the Cheeger inequality for directed graphs.
       Annals of Combinatorics, 9(1), 2005
    """
    import scipy as sp
    from scipy.sparse import identity, spdiags, linalg
    if walk_type is None:
        if nx.is_strongly_connected(G):
            if nx.is_aperiodic(G):
                walk_type = "random"
            else:
                walk_type = "lazy"
        else:
            walk_type = "pagerank"

    M = nx.to_scipy_sparse_matrix(G, nodelist=nodelist, weight=weight,
                                  dtype=float)
    n, m = M.shape
    if walk_type in ["random", "lazy"]:
        DI = spdiags(1.0/sp.array(M.sum(axis=1).flat), [0], n, n)
        if walk_type == "random":
            P =  DI * M
        else:
            I = identity(n)
            P = (I + DI * M) / 2.0

    elif walk_type == "pagerank":
        if not (0 < alpha < 1):
            raise nx.NetworkXError('alpha must be between 0 and 1')
        # this is using a dense representation
        M = M.todense()
        # add constant to dangling nodes' row
        dangling = sp.where(M.sum(axis=1) == 0)
        for d in dangling[0]:
            M[d] = 1.0 / n
        # normalize
        M = M / M.sum(axis=1)
        P = alpha * M + (1 - alpha) / n
    else:
        raise nx.NetworkXError("walk_type must be random, lazy, or pagerank")

    evals, evecs = linalg.eigs(P.T, k=1)
    v = evecs.flatten().real
    p =  v / v.sum()
    sqrtp = sp.sqrt(p)
    Q = spdiags(sqrtp, [0], n, n) * P * spdiags(1.0/sqrtp, [0], n, n)
    I = sp.identity(len(G))

    return I  - (Q + Q.T) /2.0

# fixture for nose tests
def setup_module(module):
    from nose import SkipTest
    try:
        import numpy
    except:
        raise SkipTest("NumPy not available")
