Module Contents

class noc.core.topology.layout.spring.SpringLayout

Bases: noc.core.topology.layout.base.LayoutBase

classmethod get_weight(cls, l1, l2)
noc.core.topology.layout.spring.fruchterman_reingold_layout(G, k=None, pos=None, fixed=None, iterations=50, threshold=0.0001, weight='weight', scale=1, center=None, dim=2, seed=None, min_dist=0.01)
Position nodes using Fruchterman-Reingold force-directed algorithm.
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.
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. If None, then all edge weights are 1.
scale : number (default: 1)
Scale factor for positions. Not used unless `fixed is None`.
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)
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.
pos : dict
A dictionary of positions keyed by node
>>> G = nx.path_graph(4)
>>> pos = nx.spring_layout(G)
# The same using longer but equivalent function name
>>> pos = nx.fruchterman_reingold_layout(G)
noc.core.topology.layout.spring._fruchterman_reingold(A, k=None, pos=None, fixed=None, iterations=50, threshold=0.0001, dim=2, seed=None, min_dist=0.01, cycles=None)
Position nodes in adjacency matrix A using Fruchterman-Reingold