def connect_neural_types(network, source_type, target_type, graph_model,
model_param):
'''
Function to connect excitatory and inhibitory population with a given graph
model.
@todo: make the modifications for only a set of edges
Parameters
----------
network : :class:`Network` or :class:`SpatialNetwork`
The network to connect.
source_type : int
The type of source neurons (``1`` for excitatory, ``-1 for
inhibitory neurons).
source_type : int
The type of target neurons.
graph_model : string
The name of the connectivity model (among "erdos_renyi",
"random_scale_free", "price_scale_free", and "newman_watts").
model_param : dict
Dictionary containing the model parameters (the keys are the keywords
of the associated generation function --- see above).
'''
edges, source_ids, target_ids = None, [], []
di_param = di_default.copy()
di_param.update(model_param)
for group in network._population.itervalues():
if group.neuron_type == source_type:
source_ids.extend(group._id_list)
elif group.neuron_type == target_type:
target_ids.extend(group._id_list)
if source_type == target_type:
edges = di_gen_func[graph_model](source_ids,source_ids,**di_param)
network.add_edges(edges)
else:
edges = di_gen_func[graph_model](source_ids,target_ids,**di_param)
network.add_edges(edges)
#~ network.set_weights(edges)
network.set_weights()
#~ Connections.delays(network, elist=edges)
Connections.delays(network)
if issubclass(network.__class__, SpatialGraph):
Connections.distances(network)
def distance_rule(scale, rule="exp", shape=None, neuron_density=1000., nodes=0,
density=0.1, edges=-1, avg_deg=-1., weighted=True,
directed=True, multigraph=False, name="DR", positions=None,
population=None, from_graph=None, **kwargs):
"""
Create a graph using a 2D distance rule to create the connection between
neurons. Available rules are linear (@todo) and exponential.
Parameters
----------
scale : float
Characteristic scale for the distance rule. E.g for linear distance-
rule, :math:`P(i,j) \propto (1-d_{ij}/scale))`, whereas for the
exponential distance-rule, :math:`P(i,j) \propto e^{-d_{ij}/scale}`.
rule : string, optional (default: 'exp')
Rule that will be apply to draw the connections between neurons.
Choose among "exp" (exponential), "lin" (linear, not implemented yet),
"power" (power-law, not implemented yet).
shape : :class:`~nngt.core.Shape`, optional (default: None)
Shape of the neurons' environment. If not specified, a square will be
created with the appropriate dimensions for the number of neurons and
the neuron spatial density.
neuron_density : float, optional (default: 1000.)
Density of neurons in space (:math:`neurons \cdot mm^{-2}).
nodes : int, optional (default: None)
The number of nodes in the graph.
density: double, optional (default: 0.1)
Structural density given by `edges` / (`nodes`*`nodes`).
edges : int (optional)
The number of edges between the nodes
avg_deg : double, optional
Average degree of the neurons given by `edges` / `nodes`.
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.
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:`Graph` or subclass, optional (default: None)
Initial graph whose nodes are to be connected.
"""
# set node number and library graph
graph_obj_dr, graph_dr = None, from_graph
if graph_dr is not None:
nodes = graph_dr.node_nb()
graph_dr.clear_edges()
graph_obj_dr = graph_dr.graph
else:
nodes = population.size if population is not None else nodes
graph_obj_dr = GraphObject(nodes, directed=directed)
# generate container
h = w = np.sqrt(float(nodes)/neuron_density)
if graph_dr is None:
shape = shape if shape is not None else Shape.rectangle(None,h,w)
graph_dr = SpatialGraph(name=name, libgraph=graph_obj_dr, shape=shape,
**kwargs)
else:
if not issubclass(graph_dr.__class__, SpatialGraph):
shape = shape if shape is not None else Shape.rectangle(self,h,w)
SpatialGraph.make_spatial(graph_dr, shape, positions)
# add edges
positions = graph_dr.position
ia_edges = None
if nodes > 1:
ids = range(nodes)
ia_edges = _distance_rule(ids, ids, density, edges, avg_deg, scale,
rule, shape, positions, directed, multigraph)
graph_obj_dr.new_edges(ia_edges)
# set options
if weighted:
graph_dr.set_weights()
if issubclass(graph_dr.__class__, Network):
Connections.delays(graph_dr)
elif population is not None:
Network.make_network(graph_dr, population)
return graph_dr
def newman_watts(coord_nb, proba_shortcut, nodes=0, directed=True,
multigraph=False, name="ER", shape=None, positions=None,
population=None, from_graph=None, **kwargs):
"""
Generate a small-world graph using the Newman-Watts algorithm.
@todo: generate the edges of a circular graph to not replace the graph
of the `from_graph` and implement chosen reciprocity.
Parameters
----------
coord_nb : int
The number of neighbours for each node on the initial topological
lattice.
proba_shortcut : double
Probability of adding a new random (shortcut) edge for each existing
edge on the initial lattice.
nodes : int, optional (default: None)
The number of nodes in the graph.
density: double, optional (default: 0.1)
Structural density given by `edges` / (`nodes`*`nodes`).
edges : int (optional)
The number of edges between the nodes
avg_deg : double, optional
Average degree of the neurons given by `edges` / `nodes`.
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:`Graph` or subclass, optional (default: None)
Initial graph whose nodes are to be connected.
Returns
-------
graph_nw : :class:`~nngt.Graph` or subclass
Notes
-----
`nodes` is required unless `from_graph` or `population` is provided.
"""
# set node number and library graph
graph_obj_nw, graph_nw = None, from_graph
if graph_nw is not None:
nodes = graph_nw.node_nb()
graph_nw.clear_edges()
graph_obj_nw = graph_nw.graph
else:
nodes = population.size if population is not None else nodes
graph_obj_nw = GraphObject(nodes, directed=directed)
# add edges
ia_edges = None
if nodes > 1:
ids = range(nodes)
ia_edges = _newman_watts(ids, ids, coord_nb, proba_shortcut, directed,
multigraph)
graph_obj_nw.new_edges(ia_edges)
# generate container
if graph_nw is None:
graph_nw = Graph(name=name, libgraph=graph_obj_nw, **kwargs)
else:
graph_nw.set_weights()
# set options
if issubclass(graph_nw.__class__, Network):
Connections.delays(graph_nw)
elif population is not None:
Network.make_network(graph_nw, population)
if shape is not None:
SpatialGraph.make_spatial(graph_nw, shape, positions)
return graph_nw
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
def erdos_renyi(nodes=0, density=0.1, edges=-1, avg_deg=-1., reciprocity=-1.,
weighted=True, directed=True, multigraph=False, name="ER",
shape=None, positions=None, population=None, from_graph=None,
**kwargs):
"""
Generate a random graph as defined by Erdos and Renyi but with a
reciprocity that can be chosen.
Parameters
----------
nodes : int, optional (default: None)
The number of nodes in the graph.
density : double, optional (default: 0.1)
Structural density given by `edges` / `nodes`:math:`^2`.
edges : int (optional)
The number of edges between the nodes
avg_deg : double, optional
Average degree of the neurons given by `edges` / `nodes`.
reciprocity : double, optional (default: -1 to let it free)
Fraction of edges that are bidirectional (only for
directed graphs -- undirected graphs have a reciprocity of 1 by
definition)
weighted : bool, optional (default: True)
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:`Graph` or subclass, optional (default: None)
Initial graph whose nodes are to be connected.
Returns
-------
graph_er : :class:`~nngt.Graph`, or subclass
A new generated graph or the modified `from_graph`.
Notes
-----
`nodes` is required unless `from_graph` or `population` is provided.
If an `from_graph` is provided, all preexistant edges in the
object will be deleted before the new connectivity is implemented.
"""
# set node number and library graph
graph_obj_er, graph_er = None, from_graph
if graph_er is not None:
nodes = graph_er.node_nb()
graph_er.clear_edges()
graph_obj_er = graph_er.graph
else:
nodes = population.size if population is not None else nodes
graph_obj_er = GraphObject(nodes, directed=directed)
# add edges
ia_edges = None
if nodes > 1:
ids = range(nodes)
ia_edges = _erdos_renyi(ids, ids, density, edges, avg_deg, reciprocity,
directed, multigraph)
graph_obj_er.new_edges(ia_edges)
# generate container
if graph_er is None:
graph_er = Graph(name=name, libgraph=graph_obj_er, **kwargs)
else:
graph_er.set_weights()
# set options
if issubclass(graph_er.__class__, Network):
Connections.delays(graph_er)
elif population is not None:
Network.make_network(graph_er, population)
if shape is not None:
SpatialGraph.make_spatial(graph_er, shape, positions)
return graph_er
def random_scale_free(in_exp, out_exp, nodes=0, density=0.1, edges=-1,
avg_deg=-1, reciprocity=0., weighted=True, directed=True,
multigraph=False, name="RandomSF", shape=None,
positions=None, population=None, from_graph=None,
**kwargs):
"""
Generate a free-scale graph of given reciprocity and otherwise
devoid of correlations.
Parameters
----------
in_exp : float
Absolute value of the in-degree exponent :math:`\gamma_i`, such that
:math:`p(k_i) \propto k_i^{-\gamma_i}
out_exp : float
Absolute value of the out-degree exponent :math:`\gamma_o`, such that
:math:`p(k_o) \propto k_o^{-\gamma_o}
nodes : int, optional (default: None)
The number of nodes in the graph.
density: double, optional (default: 0.1)
Structural density given by `edges` / (`nodes`*`nodes`).
edges : int (optional)
The number of edges between the nodes
avg_deg : double, optional
Average degree of the neurons given by `edges` / `nodes`.
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. can contain multiple edges between two
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:`Graph` or subclass, optional (default: None)
Initial graph whose nodes are to be connected.
Returns
-------
graph_fs : :class:`~nngt.Graph`
Notes
-----
As reciprocity increases, requested values of `in_exp` and `out_exp` will
be less and less respected as the distribution will converge to a common
exponent :math:`\gamma = \frac{\gamma_i + \gamma_o}{2}`.
Parameter `nodes` is required unless `from_graph` or `population` is
provided.
"""
# set node number and library graph
graph_obj_rsf, graph_rsf = None, from_graph
if graph_rsf is not None:
nodes = graph_rsf.node_nb()
graph_rsf.clear_edges()
graph_obj_rsf = graph_rsf.graph
else:
nodes = population.size if population is not None else nodes
graph_obj_rsf = GraphObject(nodes, directed=directed)
# add edges
ia_edges = None
if nodes > 1:
ids = range(nodes)
ia_edges = _random_scale_free(ids, ids, in_exp, out_exp, density,
edges, avg_deg, reciprocity, directed, multigraph)
graph_obj_rsf.new_edges(ia_edges)
# generate container
if graph_rsf is None:
graph_rsf = Graph(name=name, libgraph=graph_obj_rsf, **kwargs)
else:
graph_rsf.set_weights()
# set options
if issubclass(graph_rsf.__class__, Network):
Connections.delays(graph_rsf)
elif population is not None:
Network.make_network(graph_rsf, population)
if shape is not None:
SpatialGraph.make_spatial(graph_rsf, shape, positions)
return graph_rsf