#! /usr/bin/python

import networkx as nx
from scipy.sparse import spdiags, coo_matrix
import scipy as sp
import numpy as np

## Now the layout function
def forceatlas2_layout(G, iterations=10, linlog=False, pos=None, nohubs=False,
                       kr=0.001, k=None, dim=2):
    """
    Options values are

    g                The graph to layout
    iterations       Number of iterations to do
    linlog           Whether to use linear or log repulsion
    random_init      Start with a random position
                     If false, start with FR
    avoidoverlap     Whether to avoid overlap of points
    degreebased      Degree based repulsion
    """
    # We add attributes to store the current and previous convergence speed
    for n in G:
        G.node[n]['prevcs'] = 0
        G.node[n]['currcs'] = 0
        # To numpy matrix
    # This comes from the spares FR layout in nx
    A = nx.to_scipy_sparse_matrix(G, dtype='f')
    nnodes, _ = A.shape

    try:
        A = A.tolil()
    except Exception as e:
        A = (coo_matrix(A)).tolil()
    if pos is None:
        pos = np.asarray(np.random.random((nnodes, dim)), dtype=A.dtype)
    else:
        pos = pos.astype(A.dtype)
    if k is None:
        k = np.sqrt(1.0 / nnodes)
        # Iterations
    # the initial "temperature" is about .1 of domain area (=1x1)
    # this is the largest step allowed in the dynamics.
    t = 0.1
    # simple cooling scheme.
    # linearly step down by dt on each iteration so last iteration is size dt.
    dt = t / float(iterations + 1)
    displacement = np.zeros((dim, nnodes))
    for iteration in range(iterations):
        displacement *= 0
        # loop over rows
        for i in range(A.shape[0]):
            # difference between this row's node position and all others
            delta = (pos[i] - pos).T
            # distance between points
            distance = np.sqrt((delta ** 2).sum(axis=0))
            # enforce minimum distance of 0.01
            distance = np.where(distance < 0.01, 0.01, distance)
            # the adjacency matrix row
            Ai = np.asarray(A.getrowview(i).toarray())
            # displacement "force"
            Dist = k * k / distance ** 2
            if nohubs:
                Dist = Dist / float(Ai.sum(axis=1) + 1)
            if linlog:
                Dist = np.log(Dist + 1)
            displacement[:, i] += \
                (delta * (Dist - Ai * distance / k)).sum(axis=1)
            # update positions
        length = np.sqrt((displacement ** 2).sum(axis=0))
        length = np.where(length < 0.01, 0.01, length)
        pos += (displacement * t / length).T
        # cool temperature
        t -= dt
        # Return the layout
    return dict(zip(G, pos))
