graph

delaunay_graph(data: numpy.ndarray, edge_weight: typing.Callable[[numpy.ndarray, numpy.ndarray], float] = <function euclidean>)

The Delaunay triangulation of the data as networkx.Graph

Parameters

• data –

• edge_weight – function to compute weight given two coordinate points

Returns

instance of networx.Graph where the edges contain additional datapoints entries for “weight” and for constants.COORDINATE_PAIR_KEY

class SpanningTree(datapoints: numpy.ndarray, tree_finder: Callable[[networkx.Graph], networkx.Graph] = networkx.minimum_spanning_tree)

Bases: object

Wrapper around a tree-finding algorithm that will be applied on the Delaunay graph of the datapoints

__init__(datapoints: numpy.ndarray, tree_finder: Callable[[networkx.Graph], networkx.Graph] = networkx.minimum_spanning_tree)

Parameters

• datapoints –

• tree_finder – function mapping a graph to a subgraph. The default is minimum_spanning_tree

total_weight()

num_edges()

mean_edge_weight()

summary_dict() → Dict[str, float]

Dictionary containing coarse information about the tree

class CoordinateSpanningTree(datapoints: numpy.ndarray, tree_finder: Callable[[networkx.Graph], networkx.Graph] = networkx.minimum_spanning_tree)

Bases: sensai.geoanalytics.geopandas.graph.SpanningTree, sensai.geoanalytics.geopandas.coordinates.GeoDataFrameWrapper

Wrapper around a tree-finding algorithm that will be applied on the Delaunay graph of the coordinates. Enhances the SpanningTree class by adding methods and validation specific to geospatial coordinates.

__init__(datapoints: numpy.ndarray, tree_finder: Callable[[networkx.Graph], networkx.Graph] = networkx.minimum_spanning_tree)

Parameters

• datapoints –

• tree_finder – function mapping a graph to a subgraph. The default is minimum_spanning_tree

multi_line_string()

to_geodf(crs='epsg:3857')

Parameters

crs – projection. By default pseudo-mercator

Returns

GeoDataFrame of length 1 with the tree as MultiLineString instance