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)
