Graph container classes

class nngt.Graph(nodes=0, name='Graph', weighted=True, directed=True, libgraph=None, **kwargs)

The basic class that contains a graph_tool.Graph and some of is properties or methods to easily access them.

Variables:

• id – int unique id that identifies the instance.
• graph – GraphObject main attribute of the class instance.

add_edges(lst_edges)

Add a list of edges to the graph.

Parameters:

• lst_edges (list of 2-tuples or np.array of shape (edge_nb, 2)) – List of the edges that should be added as tuples (source, target)
• @todo (add example, check the edges for self-loops and multiple edges)

adjacency_matrix(weighted=True)

attributes()

copy()

Returns a deepcopy of the current Graph instance

edge_nb()

excitatory_subgraph()

create a Graph instance which graph contains only the excitatory edges of the current instance’s GraphObject

get_betweenness(use_weights=True)

get_degrees(strType='total', use_weights=True)

get_density()

get_edge_types()

get_name()

get_properties(a_properties)

Return a dictionary containing the desired properties

Parameters:a_properties (sequence) – List or tuple of strings of the property names. Returns:di_result (dict) – A dictionary of values with the property names as keys.

get_property(s_property)

Return the desired property or None for an incorrect one.

get_weights()

graph

graph_tool.Graph attribute of the instance

id

unique int identifying the instance

inhibitory_subgraph()

Create a Graph instance which graph contains only the inhibitory edges of the current instance’s graph_tool.Graph

is_directed()

is_spatial()

is_weighted()

name

node_nb()

classmethod num_graphs()

Returns the number of alive instances.

set_name(name='')

set graph name

set_weights(elist=None, wlist=None, distrib=None, distrib_prop=None, correl=None, noise_scale=None)

Set the synaptic weights.

Parameters:

• elist (class:numpy.array, optional (default: None)) – List of the edges (for user defined weights).
• wlist (class:numpy.array, optional (default: None)) – List of the weights (for user defined weights).
• distrib (class:string, optional (default: None)) – Type of distribution (choose among “constant”, “uniform”, “gaussian”, “lognormal”, “lin_corr”, “log_corr”).
• distrib_prop (dict, optional (default: {})) – Dictoinary containing the properties of the weight distribution.
• correl (class:string, optional (default: None)) – Property to which the weights should be correlated.
• noise_scale (class:int, optional (default: None)) – Scale of the multiplicative Gaussian noise that should be applied on the weights.

class nngt.SpatialGraph(nodes=0, name='Graph', weighted=True, directed=True, libgraph=None, shape=None, positions=None, **kwargs)

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Variables:

• shape – Shape Shape of the neurons environment.
• positions – numpy.array Positions of the neurons.
• graph – GraphObject Main attribute of the class instance.

classmethod make_spatial(graph, shape=<nngt.core.graph_datastruct.Shape instance>, positions=None)

shape

class nngt.Network(name='Graph', weighted=True, directed=True, libgraph=None, population=None, **kwargs)

The detailed class that inherits from Graph and implements additional properties to describe various biological functions and interact with the NEST simulator.

Variables:

• neural_model – list List of the NEST neural models for each neuron.
• syn_model – list List of the NEST synaptic models for each edge.
• graph – GraphObject Main attribute of the class instance

classmethod ei_network(size, ei_ratio=0.2, en_model='aeif_neuron', en_param={}, es_model='static_synapse', es_param={}, in_model='aeif_neuron', in_param={}, is_model='static_synapse', is_param={})

classmethod make_network(graph, neural_pop)

neuron_properties(idx_neuron)

classmethod num_networks()

Returns the number of alive instances.

population

classmethod uniform_network(size, neuron_model='iaf_neuron', neuron_param={}, syn_model='static_synapse', syn_param={})

class nngt.SpatialNetwork(population, name='Graph', weighted=True, directed=True, shape=None, graph=None, positions=None, **kwargs)

Class that inherits from Network and SpatialGraph to provide a detailed description of a real neural network in space, i.e. with positions and biological properties to interact with NEST.

Variables:

• shape – nngt.core.Shape Shape of the neurons environment.
• positions – numpy.array Positions of the neurons.
• neural_model – list List of the NEST neural models for each neuron.
• syn_model – list List of the NEST synaptic models for each edge.
• graph – GraphObject Main attribute of the class instance.