bluepysnap.edges.edges

Edges access.

Classes

Edges(circuit)

The top level Edges accessor.

class bluepysnap.edges.edges.Edges(circuit)

The top level Edges accessor.

Initialize the top level Edges accessor.

afferent_edges(node_id, properties=None)

Get afferent edges for given node_id.

Parameters:

• node_id (int) – Target node ID.

• properties – An edge property name, a list of edge property names, or None.

Returns:

• A pandas Series indexed by edge ID if properties is a string.

• A pandas DataFrame indexed by edge ID if properties is a list.

• A list of edge IDs, if properties is None.

Return type:

pandas.Series/pandas.DataFrame/list

afferent_nodes(target, unique=True)

Get afferent CircuitNodeIDs for given target node_id.

Notes

Afferent nodes are nodes projecting an outgoing edge to one of the target node.

Parameters:

• target (CircuitNodeIds/int/sequence/str/mapping/None) – the target you want to resolve

• nodes. (and use as target) –

• unique (bool) – If True, return only unique afferent node IDs.

Returns:

Afferent CircuitNodeIDs for all the targets from all edge population.

Return type:

CircuitNodeIDs

efferent_edges(node_id, properties=None)

Get efferent edges for given node_id.

Parameters:

• node_id – source node ID

• properties – None / edge property name / list of edge property names

Returns:

• List of edge IDs, if properties is None;

• Pandas Series indexed by edge IDs if properties is string;

• Pandas DataFrame indexed by edge IDs if properties is list.

efferent_nodes(source, unique=True)

Get efferent node IDs for given source node_id.

Notes

Efferent nodes are nodes receiving an incoming edge from one of the source node.

Parameters:

• source (CircuitNodeIds/int/sequence/str/mapping/None) – the source you want to resolve and use as source nodes.

• unique (bool) – If True, return only unique afferent node IDs.

Returns:

Efferent node IDs for all the sources.

Return type:

numpy.ndarray

get(edge_ids=None, properties=None)

Edge properties as pandas DataFrame.

Parameters:

• edge_ids (int/CircuitEdgeId/CircuitEdgeIds/sequence) – same as Edges.ids().

• properties (None/str/list) – an edge property name or a list of edge property names. If set to None ids are returned.

Returns:

• A pandas Series indexed by edge IDs if properties is scalar.

• A pandas DataFrame indexed by edge IDs if properties is list.

Return type:

pandas.Series/pandas.DataFrame

Notes

The Edges.property_names function will give you all the usable properties for the properties argument.

ids(group=None, sample=None, limit=None)

Edge CircuitEdgeIds corresponding to edges edge_ids.

Parameters:

• group (None/int/CircuitEdgeId/CircuitEdgeIds/sequence) –

  Which IDs will be returned depends on the type of the group argument:

  • None: return all CircuitEdgeIds.

  • CircuitEdgeId: return the ID in a CircuitEdgeIds object.

  • CircuitEdgeIds: return the IDs in a CircuitNodeIds object.

  • int: returns a CircuitEdgeIds object containing the corresponding edge ID for all populations.

  • sequence: returns a CircuitEdgeIds object containing the corresponding edge IDs for all populations.

• sample (int) – If specified, randomly choose sample number of IDs from the match result. If the size of the sample is greater than the size of all the EdgePopulations then all ids are taken and shuffled.

• limit (int) – If specified, return the first limit number of IDs from the match result. If limit is greater than the size of all the populations all node IDs are returned.

Returns:

returns a CircuitEdgeIds containing all the edge IDs and the corresponding populations. For performance reasons we do not test if the edge ids are present or not in the circuit.

Return type:

CircuitEdgeIds

Notes

This envision also the maybe future selection of edges on queries.

items()

Returns iterator on the tuples (population name, EdgePopulations).

Made to simulate the behavior of a dict.items().

iter_connections(source=None, target=None, return_edge_ids=False, return_edge_count=False)

Iterate through source -> target connections.

Parameters:

• source (CircuitNodeIds/int/sequence/str/mapping/None) – source node group

• target (CircuitNodeIds/int/sequence/str/mapping/None) – target node group

• return_edge_count – if True, edge count is added to yield result

• return_edge_ids – if True, edge ID list is added to yield result

return_edge_count and return_edge_ids are mutually exclusive.

Yields:

• (source_node_id, target_node_id, edge_ids) if return_edge_ids is True;

• (source_node_id, target_node_id, edge_count) if return_edge_count is True;

• (source_node_id, target_node_id) otherwise.

keys()

Returns iterator on the EdgePopulation names.

Made to simulate the behavior of a dict.keys().

pair_edges(source_node_id, target_node_id, properties=None)

Get edges corresponding to source_node_id -> target_node_id connection.

Parameters:

• source_node_id – source node ID

• target_node_id – target node ID

• properties – None / edge property name / list of edge property names

Returns:

• List of edge IDs, if properties is None;

• Pandas Series indexed by edge IDs if properties is string;

• Pandas DataFrame indexed by edge IDs if properties is list.

pathway_edges(source=None, target=None, properties=None)

Get edges corresponding to source -> target connections.

Parameters:

• source – source node group

• target – target node group

• properties – None / edge property name / list of edge property names

Returns:

• CircuitEdgeIDs, if properties is None;

• Pandas Series indexed by CircuitEdgeIDs if properties is string;

• Pandas DataFrame indexed by CircuitEdgeIDs if properties is list.

property population_names

Defines all sorted edge population names from the Circuit.

values()

Returns iterator on the EdgePopulations.

Made to simulate the behavior of a dict.values().