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# spring_layout

spring_layout(G, k=None, pos=None, fixed=None, iterations=50, threshold=0.0001, weight='weight', scale=1, center=None, dim=2, seed=None)

  Position nodes using Fruchterman-Reingold force-directed algorithm.

  The algorithm simulates a force-directed representation of the network treating edges as springs holding nodes close, while treating nodes as repelling objects, sometimes called an anti-gravity force. Simulation continues until the positions are close to an equilibrium.

  There are some hard-coded values: minimal distance between nodes (0.01) and “temperature” of 0.1 to ensure nodes don’t fly away. During the simulation, `k` helps determine the distance between nodes, though `scale` and `center` determine the size and place after rescaling occurs at the end of the simulation.

  Fixing some nodes doesn’t allow them to move in the simulation. It also turns off the rescaling feature at the simulation’s end. In addition, setting `scale` to `None` turns off rescaling.

  Parameters:
    G NetworkX graph or list of nodes

      A position will be assigned to every node in G.

    k float (default=None)

      Optimal distance between nodes. If None the distance is set to 1/sqrt(n) where n is the number of nodes. Increase this value to move nodes farther apart.

    pos dict or None optional (default=None)

      Initial positions for nodes as a dictionary with node as keys and values as a coordinate list or tuple. If None, then use random initial positions.

    fixed list or None optional (default=None)

      Nodes to keep fixed at initial position. Nodes not in `G.nodes` are ignored. ValueError raised if `fixed` specified and `pos` not.

    iterations int optional (default=50)

      Maximum number of iterations taken

    threshold: float optional (default = 1e-4)

      Threshold for relative error in node position changes. The iteration stops if the error is below this threshold.

    weight string or None optional (default=’weight’)

      The edge attribute that holds the numerical value used for the edge weight. Larger means a stronger attractive force. If None, then all edge weights are 1.

    scale number or None (default: 1)

      Scale factor for positions. Not used unless `fixed is None`. If scale is None, no rescaling is performed.

    center array-like or None

      Coordinate pair around which to center the layout. Not used unless `fixed is None`.

    dim int

      Dimension of layout.

    seed int, RandomState instance or None optional (default=None)

      Used only for the initial positions in the algorithm. Set the random state for deterministic node layouts. If int, `seed` is the seed used by the random number generator, if numpy.random.RandomState instance, `seed` is the random number generator, if None, the random number generator is the RandomState instance used by numpy.random.

  Returns:
    pos dict

      A dictionary of positions keyed by node

  Examples

      >>> G = nx.path_graph(4)
      >>> pos = nx.spring_layout(G)

  # The same using longer but equivalent function name >>> pos = nx.fruchterman_reingold_layout(G)