spacenet.utils.query_nodes#

spacenet.utils.query_nodes(spatial_network, node_label_name=None, relation='is', node_label_value=None)#

Query nodes in a spatial network based on a specified node attribute, relation, and value. This function allows you to filter nodes in the spatial network according to various criteria, such as equality, containment, and comparison operations.

Parameters:
spatial_networknetworkx graph

A spatial network represented as a NetworkX graph. Nodes should have attributes that can be queried.

node_label_namestr

The name of the node attribute to query. This should be a valid node attribute in the spatial network.

relationstr

The relation to use for querying. Supported relations include: ‘is’ (or ‘==’), ‘contains’ (or ‘in’), ‘is not’ (or ‘!=’), ‘does not contain’ (or ‘not in’), ‘greater than’ (or ‘>’), ‘less than’ (or ‘<’), ‘greater than or equal to’ (or ‘>=’), and ‘less than or equal to’ (or ‘<=’).

node_label_valueint, float, str, or list

The value to compare the node attribute against. The type of this value should be compatible with the type of the node attribute being queried.

Returns:
np.ndarray

An array of node indices that meet the specified relation and value criteria.

Examples

You can use the query_nodes function to filter nodes in a spatial network based on their attributes. Below are examples of how to use this function to query nodes with specific categorical and continuous labels. The first example demonstrates how to query nodes with a categorical label.

import spacenet as sn

# get data from the Spiral dataset
sprial_df = sn.datasets.load_dataset('spiral')
points = sprial_df[['x','y']].values
categorical_labels = sprial_df['Marker (categorical)'].values

# generate a spatial network using the delaunay method and add labels
G = sn.utils.spatial_network_from_points(points,network_type='delaunay',max_edge_distance=75)
sn.utils.add_node_labels(G,categorical_labels,node_label_name='Marker (categorical)')

# use query to get node ids for nodes with categorical label A
nodes_a = sn.utils.query_nodes(G,node_label_name='Marker (categorical)',relation='is',node_label_value='A')

print(f'First 10 Nodes with categorical label A: {nodes_a[0:10]}')

This example shows how to query nodes with a continuous labels using relational comparisons.

import spacenet as sn

# get data from the Spiral dataset
sprial_df = sn.datasets.load_dataset('spiral')
points = sprial_df[['x','y']].values
categorical_labels = sprial_df['Marker (continuous)'].values

# generate a spatial network using the delaunay method and add labels
G = sn.utils.spatial_network_from_points(points,network_type='delaunay',max_edge_distance=75)
sn.utils.add_node_labels(G,categorical_labels,node_label_name='Marker (continuous)')

# use query to get node ids for nodes with continuous label > 0.5
nodes_of_interest = sn.utils.query_nodes(G,node_label_name='Marker (continuous)',relation='>',node_label_value=0.5)

print(f'First 10 Nodes with continuous label > 0.5: {nodes_a[0:10]}')
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