spatial-graph 0.0.2

A spatial graph datastructure for python.

spatial-graph

spatial_graph provides a data structure for directed and undirected graphs, where each node has an nD position (in time or space).

Design Principles

Goals

• support for arbitrary number of dimensions
• typed node identifiers and attributes
  • any fixed-length type that is supported by numpy
• efficient node/edge queries by
  • ROI
  • kNN (by points / lines)
• numpy-like interface for efficient:
  • graph population and manipulation
  • query results
  • attribute access
• minimal memory footprint
• minimal dependencies
  • cython / witty / cheetah3 for runtime compilation
  • numpy for array interfaces
• PYX API for graph algorithms in C/C++

Non-Goals

• graph algorithms
• I/O
• non-typed arguments
• non-spatial graphs
• out-of-memory support
• networkx compatibility

Python API

Graph creation:

graph = sg.SpatialGraph(
    ndims=3,
    node_dtype="uint64",
    node_attr_dtypes={"position": "double[3]"},
    edge_attr_dtypes={"score": "float32"},
    position_attr="position",
    directed=False,
)

Adding nodes/edges:

graph.add_nodes(
    np.array([1, 2, 3, 4, 5], dtype="uint64"),
    position=np.array(
        [
            [0.1, 0.1, 0.1],
            [0.2, 0.2, 0.2],
            [0.3, 0.3, 0.3],
            [0.4, 0.4, 0.4],
            [0.5, 0.5, 0.5],
        ],
        dtype="double",
    ),
)

graph.add_edges(
    np.array([[1, 2], [3, 4], [5, 1]], dtype="uint64"),
    score=np.array([0.2, 0.3, 0.4], dtype="float32"),
)

Query nodes/edges in ROI:

# nodes/edges will be numpy arrays of dtype uint64 and shape (n,)/(n, 2)
nodes = graph.query_nodes_in_roi(np.array([[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]))
edges = graph.query_edges_in_roi(np.array([[0.0, 0.0, 0.0], [0.25, 0.25, 0.25]]))

Query nodes/edges by position:

nodes = graph.query_nearest_nodes(np.array([0.3, 0.3, 0.3]), k=3)
edges = graph.query_nearest_edges(np.array([0.3, 0.3, 0.3]), k=3)

Access node/edge attributes:

node_positions = graph.node_attrs[nodes].position
edge_scores = graph.edge_attrs[edges].score

Delete nodes/edges:

graph.remove_nodes(nodes[:1000])

Implementation Details

A SpatialGraph consists of three data structures:

• The Graph itself, holding nodes, edges, and their attributes (graphlite).
• Two R-trees for spatial node and edge queries (based on rtree.c).

For Developers

To create a new release, tag the current commit with a version number and push it to the upstream remote:

git tag -a "vX.Y.Z" -m "vX.Y.Z"
git push upstream --follow-tags

This will trigger the CI workflow, which will build the package and upload it to PyPI.