def price_scale_free(m, c=None, gamma=1, nodes=0, weighted=True, directed=True,
seed_graph=None, multigraph=False, name="PriceSF",
shape=None, positions=None, population=None,
from_graph=None, **kwargs):
"""
@todo: make the algorithm.
Generate a Price graph model (Barabasi-Albert if undirected).
Parameters
----------
m : int
The number of edges each new node will make.
c : double
Constant added to the probability of a vertex receiving an edge.
gamma : double
Preferential attachment power.
nodes : int, optional (default: None)
The number of nodes in the graph.
weighted : bool, optional (default: True)
@todo
Whether the graph edges have weights.
directed : bool, optional (default: True)
Whether the graph is directed or not.
multigraph : bool, optional (default: False)
Whether the graph can contain multiple edges between two
nodes.
name : string, optional (default: "ER")
Name of the created graph.
shape : :class:`~nngt.core.Shape`, optional (default: None)
Shape of the neurons' environment
positions : :class:`numpy.ndarray`, optional (default: None)
A 2D or 3D array containing the positions of the neurons in space.
population : :class:`~nngt.NeuralPop`, optional (default: None)
Population of neurons defining their biological properties (to create a
:class:`~nngt.Network`).
from_graph : :class:`~nngt.Graph` or subclass, optional (default: None)
Initial graph whose nodes are to be connected.
Returns
-------
graph_price : :class:`~nngt.Graph` or subclass.
Notes
-----
`nodes` is required unless `from_graph` or `population` is provided.
"""
nodes = ( ( population.size if population is not None else nodes )
if from_graph is None else from_graph.node_nb() )
#~ c = c if c is not None else 0 if directed else 1
graph_obj_price = GraphObject.to_graph_object(
price_network(nodes,m,c,gamma,directed,seed_graph))
if from_graph is not None:
from_graph.graph = graph_obj_price
graph_price = (Graph(name=name, libgraph=graph_obj_price, **kwargs)
if from_graph is None else from_graph)
if issubclass(graph_price.__class__, Network):
Connections.delays(graph_price, ia_edges)
elif population is not None:
Network.make_network(graph_price, population)
if shape is not None:
make_spatial(graph_price, shape, positions)
return graph_price
