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# Source code for fnss.topologies.randmodels

    """
    Provides functions to generate random topologies according to a number of
    models.

    The generated topologies are either Topology or DIrectedTopology objects.
    """
    import math
    import random

    import networkx as nx

    from fnss.util import random_from_pdf
    from fnss.topologies.topology import Topology

    __all__ = [
        'erdos_renyi_topology',
        'waxman_1_topology',
        'waxman_2_topology',
        'barabasi_albert_topology',
        'extended_barabasi_albert_topology',
        'glp_topology'
              ]

    def erdos_renyi_topology(n, p, seed=None, fast=False):
        r"""Return a random graph :math:`G_{n,p}` (Erdos-Renyi graph, binomial
        graph).

        Chooses each of the possible edges with probability p.

        Parameters
        ----------
        n : int
            The number of nodes.
        p : float
            Probability for edge creation.
        seed : int, optional
            Seed for random number generator (default=None).
        fast : boolean, optional
            Uses the algorithm proposed by [3]_, which is faster for small p

        References
        ----------
        .. [1] P. Erdos and A. Renyi, On Random Graphs, Publ. Math. 6, 290 (1959).
        .. [2] E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).
        .. [3] Vladimir Batagelj and Ulrik Brandes,
           "Efficient generation of large random networks",
           Phys. Rev. E, 71, 036113, 2005.
        """
        # validate input parameters
        if not isinstance(n, int) or n < 0:
            raise ValueError('n must be a positive integer')
        if p > 1 or p < 0:
            raise ValueError('p must be a value in (0,1)')
        if fast:
            G = Topology(nx.fast_gnp_random_graph(n, p, seed=seed))
        else:
            G = Topology(nx.gnp_random_graph(n, p, seed=seed))
        G.name = "erdos_renyi_topology(%s, %s)" % (n, p)
        G.graph['type'] = 'er'
        return G

    def waxman_1_topology(n, alpha=0.4, beta=0.1, L=1.0,
                          distance_unit='Km', seed=None):
        r"""
        Return a Waxman-1 random topology.

        The Waxman-1 random topology models assigns link between nodes with
        probability

        .. math::
                p = \alpha*exp(-d/(\beta*L)).

        where the distance *d* is chosen randomly in *[0,L]*.

        Parameters
        ----------
        n : int
            Number of nodes
        alpha : float
            Model parameter chosen in *(0,1]* (higher alpha increases link density)
        beta : float
            Model parameter chosen in *(0,1]* (higher beta increases difference
            between density of short and long links)
        L : float
            Maximum distance between nodes.
        seed : int, optional
            Seed for random number generator (default=None).

        Returns
        -------
        G : Topology

        Notes
        -----
        Each node of G has the attributes *latitude* and *longitude*. These
        attributes are not expressed in degrees but in *distance_unit*.

        Each edge of G has the attribute *length*, which is also expressed in
        *distance_unit*.

        References
        ----------
        .. [1]  B. M. Waxman, Routing of multipoint connections.
           IEEE J. Select. Areas Commun. 6(9),(1988) 1617-1622.
        """
        # validate input parameters
        if not isinstance(n, int) or n <= 0:
            raise ValueError('n must be a positive integer')
        if alpha > 1 or alpha <= 0 or beta > 1 or beta <= 0:
            raise ValueError('alpha and beta must be float values in (0,1]')
        if L <= 0:
            raise ValueError('L must be a positive number')
        if seed is not None:
            random.seed(seed)

        G = Topology(type='waxman_1', distance_unit=distance_unit)

        G.name = "waxman_1_topology(%s, %s, %s, %s)" % (n, alpha, beta, L)
        G.add_nodes_from(range(n))
        nodes = G.nodes()
        while nodes:
            u = nodes.pop()
            for v in nodes:
                d = L * random.random()
                if random.random() < alpha * math.exp(-d/(beta * L)):
                    G.add_edge(u, v, length=d)
        return G

    def waxman_2_topology(n, alpha=0.4, beta=0.1, domain=(0, 0, 1, 1),
                          distance_unit='Km', seed=None):
        r"""Return a Waxman-2 random topology.

        The Waxman-2 random topology models place n nodes uniformly at random
        in a rectangular domain. Two nodes u, v are connected with a link
        with probability

        .. math::
                p = \alpha*exp(-d/(\beta*L)).

        where the distance *d* is the Euclidean distance between the nodes u and v.
        and *L* is the maximum distance between all nodes in the graph.

        Parameters
        ----------
        n : int
            Number of nodes
        alpha : float
            Model parameter chosen in *(0,1]* (higher alpha increases link density)
        beta : float
            Model parameter chosen in *(0,1]* (higher beta increases difference
            between density of short and long links)
        domain : tuple of numbers, optional
             Domain size (xmin, ymin, xmax, ymax)
        seed : int, optional
            Seed for random number generator (default=None).

        Returns
        -------
        G : Topology

        Notes
        -----
        Each edge of G has the attribute *length*

        References
        ----------
        .. [1]  B. M. Waxman, Routing of multipoint connections.
           IEEE J. Select. Areas Commun. 6(9),(1988) 1617-1622.
        """
        # validate input parameters
        if not isinstance(n, int) or n <= 0:
            raise ValueError('n must be a positive integer')
        if alpha > 1 or alpha <= 0 or beta > 1 or beta <= 0:
            raise ValueError('alpha and beta must be float values in (0,1]')
        if not isinstance(domain, tuple) or len(domain) != 4:
            raise ValueError('domain must be a tuple of 4 number')
        (xmin, ymin, xmax, ymax) = domain
        if xmin > xmax:
            raise ValueError('In domain, xmin cannot be greater than xmax')
        if  ymin > ymax:
            raise ValueError('In domain, ymin cannot be greater than ymax')
        if seed is not None:
            random.seed(seed)

        G = Topology(type='waxman_2', distance_unit=distance_unit)
        G.name = "waxman_2_topology(%s, %s, %s)" % (n, alpha, beta)
        G.add_nodes_from(range(n))

        for v in G.nodes_iter():
            G.node[v]['latitude'] = (ymin + (ymax - ymin)) * random.random()
            G.node[v]['longitude'] = (xmin + (xmax - xmin)) * random.random()

        l = {}
        nodes = G.nodes()
        while nodes:
            u = nodes.pop()
            for v in nodes:
                x_u = G.node[u]['longitude']
                x_v = G.node[v]['longitude']
                y_u = G.node[u]['latitude']
                y_v = G.node[v]['latitude']
                l[(u, v)] = math.sqrt((x_u - x_v)**2 + (y_u - y_v)**2)
        L = max(l.values())
        for (u, v), d in l.items():
            if random.random() < alpha * math.exp(-d/(beta*L)):
                G.add_edge(u, v, length=d)

        return G

    # This is the classical BA model, without rewiring and add
    def barabasi_albert_topology(n, m, m0, seed=None):
        r"""
        Return a random topology using Barabasi-Albert preferential attachment
        model.

        A topology of n nodes is grown by attaching new nodes each with m links
        that are preferentially attached to existing nodes with high degree.

        More precisely, the Barabasi-Albert topology is built as follows. First, a
        line topology with m0 nodes is created. Then at each step, one node is
        added and connected to m existing nodes. These nodes are selected randomly
        with probability

        .. math::
                \Pi(i) = \frac{deg(i)}{sum_{v \in V} deg V}.

        Where i is the selected node and V is the set of nodes of the graph.

        Parameters
        ----------
        n : int
            Number of nodes
        m : int
            Number of edges to attach from a new node to existing nodes
        m0 : int
            Number of edges initially attached to the network
        seed : int, optional
            Seed for random number generator (default=None).

        Returns
        -------
        G : Topology

        Notes
        -----
        The initialization is a graph with with m nodes connected by :math:`m -1`
        edges.
        It does not use the Barabasi-Albert method provided by NetworkX because it
        does not allow to specify *m0* parameter.
        There are no disconnected subgraphs in the topology.

        References
        ----------
        .. [1] A. L. Barabasi and R. Albert "Emergence of scaling in
           random networks", Science 286, pp 509-512, 1999.
        """
        def calc_pi(G):
            """Calculate BA Pi function for all nodes of the graph"""
            degree = G.degree()
            den = float(sum(degree.values()))
            return dict((node, degree[node]/den) for node in G.nodes_iter())

        # input parameters
        if n < 1 or m < 1 or m0 < 1:
            raise ValueError('n, m and m0 must be positive integers')
        if m >= m0:
            raise ValueError('m must be <= m0')
        if n < m0:
            raise ValueError('n must be > m0')
        if seed is not None:
            random.seed(seed)
        # Step 1: Add m0 nodes. These nodes are interconnected together
        # because otherwise they will end up isolated at the end
        G = Topology(nx.path_graph(m0))
        G.name = "ba_topology(%d,%d,%d)" % (n, m, m0)
        G.graph['type'] = 'ba'

        # Step 2: Add one node and connect it with m links
        while G.number_of_nodes() < n:
            pi = calc_pi(G)
            u = G.number_of_nodes()
            G.add_node(u)
            new_links = 0
            while new_links < m:
                v = random_from_pdf(pi)
                if not G.has_edge(u, v):
                    G.add_edge(u, v)
                    new_links += 1
        return G

    # This is the extended BA model, with rewiring and add
    def extended_barabasi_albert_topology(n, m, m0, p, q, seed=None):
        r"""
        Return a random topology using the extended Barabasi-Albert preferential
        attachment model.

        Differently from the original Barabasi-Albert model, this model takes into
        account the presence of local events, such as the addition of new links or
        the rewiring of existing links.

        More precisely, the Barabasi-Albert topology is built as follows. First, a
        topology with *m0* isolated nodes is created. Then, at each step:
        with probability *p* add *m* new links between existing nodes, selected
        with probability:

        .. math::
            \Pi(i) = \frac{deg(i) + 1}{\sum_{v \in V} (deg(v) + 1)}

        with probability *q* rewire *m* links. Each link to be rewired is selected as
        follows: a node i is randomly selected and a link is randomly removed from
        it. The node i is then connected to a new node randomly selected with
        probability :math:`\Pi(i)`,
        with probability :math:`1-p-q` add a new node and attach it to m nodes of
        the existing topology selected with probability :math:`\Pi(i)`

        Repeat the previous step until the topology comprises n nodes in total.

        Parameters
        ----------
        n : int
            Number of nodes
        m : int
            Number of edges to attach from a new node to existing nodes
        m0 : int
            Number of edges initially attached to the network
        p : float
            The probability that new links are added
        q : float
            The probability that existing links are rewired
        seed : int, optional
            Seed for random number generator (default=None).

        Returns
        -------
        G : Topology

        References
        ----------
        .. [1] A. L. Barabasi and R. Albert "Topology of evolving networks: local
           events and universality", Physical Review Letters 85(24), 2000.
        """
        def calc_pi(G):
            """Calculate extended-BA Pi function for all nodes of the graph"""
            degree = G.degree()
            den = float(sum(degree.values()) + G.number_of_nodes())
            return dict((node, (degree[node] + 1)/den) for node in G.nodes_iter())

        # input parameters
        if n < 1 or m < 1 or m0 < 1:
            raise ValueError('n, m and m0 must be a positive integer')
        if m >= m0:
            raise ValueError('m must be <= m0')
        if n < m0:
            raise ValueError('n must be > m0')
        if p > 1 or p < 0:
            raise ValueError('p must be included between 0 and 1')
        if q > 1 or q < 0:
            raise ValueError('q must be included between 0 and 1')
        if p + q > 1:
            raise ValueError('p + q must be <= 1')
        if seed is not None:
            random.seed(seed)
        G = Topology(type='extended_ba')
        G.name = "ext_ba_topology(%d, %d, %d, %f, %f)" % (n, m, m0, p, q)
        # Step 1: Add m0 isolated nodes
        G.add_nodes_from(range(m0))

        while G.number_of_nodes() < n:
            pi = calc_pi(G)
            r = random.random()

            if r <= p:
                # add m new links with probability p
                n_nodes = G.number_of_nodes()
                n_edges = G.number_of_edges()
                max_n_edges = (n_nodes * (n_nodes - 1)) / 2
                if n_edges + m > max_n_edges: # cannot add m links
                    continue # rewire or add nodes
                new_links = 0
                while new_links < m:
                    u = random_from_pdf(pi)
                    v = random_from_pdf(pi)
                    if u is not v and not G.has_edge(u, v):
                        G.add_edge(u, v)
                        new_links += 1

            elif r > p and r <= p + q:
                # rewire m links with probability q
                rewired_links = 0
                while rewired_links < m:
                    i = random.choice(G.nodes()) # pick up node randomly (uniform)
                    if len(G.edge[i]) is 0: # if i has no edges, I cannot rewire
                        break
                    j = random.choice(list(G.edge[i].keys())) # node to be disconnected
                    k = random_from_pdf(pi) # new node to be connected
                    if i is not k and j is not k and not G.has_edge(i, k):
                        G.remove_edge(i, j)
                        G.add_edge(i, k)
                        rewired_links += 1
            else:
                # add a new node with probability 1 - p - q
                new_node = G.number_of_nodes()
                G.add_node(new_node)
                new_links = 0
                while new_links < m:
                    existing_node = random_from_pdf(pi)
                    if not G.has_edge(new_node, existing_node):
                        G.add_edge(new_node, existing_node)
                        new_links += 1
        return G

    def glp_topology(n, m, m0, p, beta, seed=None):
        r"""
        Return a random topology using the Generalized Linear Preference (GLP)
        preferential attachment model.

        It differs from the extended Barabasi-Albert model in that there is link
        rewiring and a beta parameter is introduced to fine-tune preferential
        attachment.

        More precisely, the GLP topology is built as follows. First, a
        line topology with *m0* nodes is created. Then, at each step:
        with probability *p*, add *m* new links between existing nodes, selected
        with probability:

        .. math::
            \Pi(i) = \frac{deg(i) - \beta 1}{\sum_{v \in V} (deg(v) - \beta)}

        with probability :math:`1-p`, add a new node and attach it to m nodes of
        the existing topology selected with probability :math:`\Pi(i)`

        Repeat the previous step until the topology comprises n nodes in total.

        Parameters
        ----------
        n : int
            Number of nodes
        m : int
            Number of edges to attach from a new node to existing nodes
        m0 : int
            Number of edges initially attached to the network
        p : float
            The probability that new links are added
        beta : float
            Parameter to fine-tune preferntial attachment: beta < 1
        seed : int, optional
            Seed for random number generator (default=None).

        Returns
        -------
        G : Topology

        References
        ----------
        .. [1] T. Bu and D. Towsey "On distinguishing between Internet power law
           topology generators", Proceeding od the 21st IEEE INFOCOM conference.
           IEEE, volume 2, pages 638-647, 2002.
        """
        def calc_pi(G, beta):
            """Calculate GLP Pi function for all nodes of the graph"""
            # validate input parameter
            if beta >= 1:
                raise ValueError('beta must be < 1')
            degree = G.degree()
            den = float(sum(degree.values()) - (G.number_of_nodes() * beta))
            return dict((node, (degree[node]-beta)/den) for node in G.nodes_iter())

        def add_m_links(G, pi):
            """Add m links between existing nodes to the graph"""
            n_nodes = G.number_of_nodes()
            n_edges = G.number_of_edges()
            max_n_edges = (n_nodes * (n_nodes - 1)) / 2
            if n_edges + m > max_n_edges: # cannot add m links
                add_node(G, pi) # add a new node instead
                # return in any case because before doing another operation
                # (add node or links) we need to recalculate pi
                return
            new_links = 0
            while new_links < m:
                u = random_from_pdf(pi)
                v = random_from_pdf(pi)
                if u != v and not G.has_edge(u, v):
                    G.add_edge(u, v)
                    new_links += 1

        def add_node(G, pi):
            """Add one node to the graph and connect it to m existing nodes"""
            new_node = G.number_of_nodes()
            G.add_node(new_node)
            new_links = 0
            while new_links < m:
                existing_node = random_from_pdf(pi)
                if not G.has_edge(new_node, existing_node):
                    G.add_edge(new_node, existing_node)
                    new_links += 1

        # validate input parameters
        if n < 1 or m < 1 or m0 < 1:
            raise ValueError('n, m and m0 must be a positive integers')
        if beta >= 1:
            raise ValueError('beta must be < 1')
        if m >= m0:
            raise ValueError('m must be <= m0')
        if p > 1 or p < 0:
            raise ValueError('p must be included between 0 and 1')
        if seed is not None:
            random.seed(seed)
        # step 1: create a graph of m0 nodes connected by n-1 edges
        G = Topology(nx.path_graph(m0))
        G.graph['type'] = 'glp'
        G.name = "glp_topology(%d, %d, %d, %f, %f)" % (n, m, m0, p, beta)
        # Add nodes and links now
        while G.number_of_nodes() < n:
            pi = calc_pi(G, beta)
            if random.random() < p:
                # add m new links with probability p
                add_m_links(G, pi)
            else:
                # add a new node with m new links with probability 1 - p
                add_node(G, pi)
        return G

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