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(typed=True, weighted=True)

Returns the adjacency matrix of the graph as a scipy.sparse.csr_matrix.

Parameters:weighted (bool or string, optional (default: True)) – If True, each entry adj[i,j] = w_ij where w_ij is the strength of the connection from i to j, if False, adj[i,j] = 0. or 1.. Weighted can also be a string describing an edge attribute (e.g. if “distance” refers to an edge attribute dist, then ajacency_matrix("distance") will return adj[i,i] = dist_ij). Returns:adj (scipy.sparse.csr_matrix) – The adjacency matrix of the graph.

attributes()

List of the graph’s attributes (synaptic weights, delays...)

clear_edges()

Remove all the edges in the graph.

copy()

Returns a deepcopy of the current Graph instance

edge_nb()

Number of edges in the graph

edges

excitatory_subgraph()

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

get_betweenness(use_weights=True)

Betweenness centrality sequence of all nodes and edges.

Parameters:use_weights (bool, optional (default: True)) – Whether to use weighted (True) or simple degrees (False). Returns:

• node_betweenness (numpy.array) – Betweenness of the nodes.
• edge_betweenness (numpy.array) – Betweenness of the edges.

get_degrees(node_list=None, deg_type='total', use_weights=True)

Degree sequence of all the nodes.

Parameters:

• node_list (list, optional (default: None)) – List of the nodes which degree should be returned
• deg_type (string, optional (default: “total”)) – Degree type (among ‘in’, ‘out’ or ‘total’).
• use_weights (bool, optional (default: True)) – Whether to use weighted (True) or simple degrees (False).

Returns:

numpy.array or None (if an invalid type is asked).

get_density()

Density of the graph: \(\frac{E}{N^2}\), where E is the number of edges and N the number of nodes.

get_edge_types()

get_name()

Get the name of the graph

get_weights()

Returns the weighted adjacency matrix as a scipy.sparse.lil_matrix.

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()

Whether the graph is directed or not

is_spatial()

Whether the graph is embedded in space (has a Shape attribute).

is_weighted()

Whether the edges have weights

name

name of the graph

node_nb()

Number of nodes in the graph

classmethod num_graphs()

Returns the number of alive instances.

set_edge_attribute(attribute, values=None, val=None, value_type=None)

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)

position

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:

• population – NeuralPop Object reparting the neurons into groups with specific properties.
• graph – GraphObject Main attribute of the class instance
• nest_gid – numpy.array Array containing the NEST gid associated to each neuron; it is None until a NEST network has been created.
• id_from_nest_gid – dict Dictionary mapping each NEST gid to the corresponding neuron index in the nngt.~Network

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

Generate a network containing a population of two neural groups: inhibitory and excitatory neurons.

Parameters:

• size (int) – Number of neurons in the network.
• ei_ratio (double, optional (default: 0.2)) – Ratio of inhibitory neurons: \(\frac{N_i}{N_e+N_i}\).
• en_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the excitatory neuron.
• en_param (dict, optional (default: {})) – Dictionary of parameters for the the excitatory neuron.
• es_model (string, optional (default: ‘static_synapse’)) – NEST model for the excitatory synapse.
• es_param (dict, optional (default: {})) – Dictionary containing the excitatory synaptic parameters.
• in_model (string, optional (default: ‘aeif_cond_alpha’)) – Nest model for the inhibitory neuron.
• in_param (dict, optional (default: {})) – Dictionary of parameters for the the inhibitory neuron.
• is_model (string, optional (default: ‘static_synapse’)) – NEST model for the inhibitory synapse.
• is_param (dict, optional (default: {})) – Dictionary containing the inhibitory synaptic parameters.

Returns:

net (Network or subclass) – Network of disconnected excitatory and inhibitory neurons.

static make_network(graph, neural_pop)

Turn a Graph object into a Network, or a SpatialGraph into a SpatialNetwork.

Parameters:

• graph (Graph or SpatialGraph) – Graph to convert
• neural_pop (NeuralPop) – Population to associate to the new Network

Notes

In-place operation that directly converts the original graph.

neuron_properties(idx_neuron)

Properties of a neuron in the graph.

Parameters:idx_neuron (int) – Index of a neuron in the graph. Returns:dict of the neuron properties.

classmethod num_networks()

Returns the number of alive instances.

population

NeuralPop that divides the neurons into groups with specific properties.

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

Generate a network containing only one type of neurons.

Parameters:

• size (int) – Number of neurons in the network.
• neuron_model (string, optional (default: ‘aief_cond_alpha’)) – Name of the NEST neural model to use when simulating the activity.
• neuron_param (dict, optional (default: {})) – Dictionary containing the neural parameters; the default value will make NEST use the default parameters of the model.
• syn_model (string, optional (default: ‘static_synapse’)) – NEST synaptic model to use when simulating the activity.
• syn_param (dict, optional (default: {})) – Dictionary containing the synaptic parameters; the default value will make NEST use the default parameters of the model.

Returns:

net (Network or subclass) – Uniform network of disconnected neurons.

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.
• population – NeuralPop Object reparting the neurons into groups with specific properties.
• graph – GraphObject Main attribute of the class instance.
• nest_gid – numpy.array Array containing the NEST gid associated to each neuron; it is None until a NEST network has been created.
• id_from_nest_gid – dict Dictionary mapping each NEST gid to the corresponding neuron index in the nngt.~SpatialNetwork