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tracksdata.functional

Functional utilities for graph operations.

Functions:

  • to_napari_format –

    Convert the subgraph of solution nodes to a napari-ready format.

to_napari_format 

to_napari_format(graph: BaseGraph, shape: tuple[int, ...], solution_key: str = DEFAULT_ATTR_KEYS.SOLUTION, output_track_id_key: str = DEFAULT_ATTR_KEYS.TRACK_ID) -> tuple[GraphArrayView, pl.DataFrame, dict[int, int]]

Convert the subgraph of solution nodes to a napari-ready format.

This includes: - a labels layer with the solution nodes - a tracks layer with the solution tracks - a graph with the parent-child relationships for the solution tracks

IMPORTANT: This function will reset the track ids if they already exist.

Parameters:

  • graph

    (BaseGraph) –

    The graph to convert.

  • shape

    (tuple[int, ...]) –

    The shape of the labels layer.

  • solution_key

    (str, default: SOLUTION ) –

    The key of the solution attribute.

  • output_track_id_key

    (str, default: TRACK_ID ) –

    The key of the output track id attribute.

Returns:

  • tuple[GraphArrayView, DataFrame, dict[int, int]] –
    • array_view: The array view of the solution graph.
    • tracks_data: The tracks data as a polars DataFrame.
    • dict_graph: A dictionary of parent -> child relationships.
Source code in src/tracksdata/functional/_napari.py 
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def to_napari_format(
    graph: BaseGraph,
    shape: tuple[int, ...],
    solution_key: str = DEFAULT_ATTR_KEYS.SOLUTION,
    output_track_id_key: str = DEFAULT_ATTR_KEYS.TRACK_ID,
) -> tuple[
    "GraphArrayView",
    pl.DataFrame,
    dict[int, int],
]:
    """
    Convert the subgraph of solution nodes to a napari-ready format.

    This includes:
    - a labels layer with the solution nodes
    - a tracks layer with the solution tracks
    - a graph with the parent-child relationships for the solution tracks

    IMPORTANT: This function will reset the track ids if they already exist.

    Parameters
    ----------
    graph : BaseGraph
        The graph to convert.
    shape : tuple[int, ...]
        The shape of the labels layer.
    solution_key : str, optional
        The key of the solution attribute.
    output_track_id_key : str, optional
        The key of the output track id attribute.

    Returns
    -------
    tuple[GraphArrayView, pl.DataFrame, dict[int, int]]
        - array_view: The array view of the solution graph.
        - tracks_data: The tracks data as a polars DataFrame.
        - dict_graph: A dictionary of parent -> child relationships.
    """
    solution_graph = graph.subgraph(NodeAttr(solution_key) == True, EdgeAttr(solution_key) == True)

    tracks_graph = solution_graph.assign_track_ids(output_track_id_key)
    dict_graph = {child: parent for parent, child in tracks_graph.edge_list()}

    spatial_cols = ["z", "y", "x"][-len(shape) + 1 :]

    tracks_data = solution_graph.node_attrs(
        attr_keys=[output_track_id_key, DEFAULT_ATTR_KEYS.T, *spatial_cols],
    )

    from tracksdata.array._graph_array import GraphArrayView

    array_view = GraphArrayView(
        solution_graph,
        shape,
        attr_key=output_track_id_key,
    )

    # sorting columns
    tracks_data = tracks_data.select([output_track_id_key, DEFAULT_ATTR_KEYS.T, *spatial_cols])

    return array_view, tracks_data, dict_graph