motile_plugin.data_model.tracks_controller

Classes

TracksController

A set of high level functions to change the data model.

Module Contents

class motile_plugin.data_model.tracks_controller.TracksController(tracks: motile_plugin.data_model.solution_tracks.SolutionTracks)

A set of high level functions to change the data model. All changes to the data should go through this API.

tracks

action_history

node_id_counter = 1

add_nodes(attributes: motile_plugin.data_model.tracks.Attrs, pixels: list[motile_plugin.data_model.tracks.SegMask] | None = None) → None

Calls the _add_nodes function to add nodes. Calls the refresh signal when finished.

Args:

nodes (np.ndarray[int]):an array of node ids attributes (dict[str, np.ndarray]): dictionary containing at least time and position attributes

_get_pred_and_succ(track_id: int, time: int) → tuple[motile_plugin.data_model.tracks.Node | None, motile_plugin.data_model.tracks.Node | None]

Get the last node with the given track id before time, and the first node with the track id after time, if any. Does not assume that a node with the given track_id and time is already in tracks, but it can be.

Args:

track_id (int): The track id to search for time (int): The time point to find the immediate predecessor and successor

for

Returns:

tuple[Node | None, Node | None]: The last node before time with the given track id, and the first node after time with the given track id, or Nones if there are no such nodes.

_add_nodes(attributes: motile_plugin.data_model.tracks.Attrs, pixels: list[motile_plugin.data_model.tracks.SegMask] | None = None) → tuple[motile_plugin.data_model.actions.TracksAction, list[motile_plugin.data_model.tracks.Node]]

Add nodes to the graph. Includes all attributes and the segmentation. Will return the actions needed to add the nodes, and the node ids generated for the new nodes. If there is a segmentation, the attributes must include: - time - seg_id - track_id If there is not a segmentation, the attributes must include: - time - pos - track_id

Logic of the function: - remove edges (when we add a node in a track between two nodes

connected by a skip edge)

• add the nodes

• add edges (to connect each node to its immediate

  predecessor and successor with the same track_id, if any)

Args:

nodes (list[Node]): a list of node ids attributes (Attributes): dictionary containing at least time and track id,

and either seg_id (if pixels are provided) or position (if not)

pixels (list[SegMask] | None): A list of pixels associated with the node,

or None if there is no segmentation. These pixels will be updated in the tracks.segmentation, set to the provided seg_id

delete_nodes(nodes: collections.abc.Iterable[Any]) → None

Calls the _delete_nodes function and then emits the refresh signal

Args:

nodes (np.ndarray): array of node_ids to be deleted

_delete_nodes(nodes: numpy.ndarray[Any], pixels: list[motile_plugin.data_model.tracks.SegMask] | None = None) → motile_plugin.data_model.actions.TracksAction

Delete the nodes provided by the array from the graph but maintain successor track_ids. Reconnect to the nearest predecessor and/or nearest successor on the same track, if any.

Function logic: - delete all edges incident to the nodes - delete the nodes - add edges to preds and succs of nodes if they have the same track id - update track ids if we removed a division by deleting the dge

Args:

nodes (np.ndarray): array of node_ids to be deleted

update_node_segs(nodes: collections.abc.Iterable[motile_plugin.data_model.tracks.Node], attributes: dict[str, numpy.ndarray]) → None

Calls the _update_node_segs function to update the node attributtes in given array. Then calls the refresh signal.

Args: nodes (np.ndarray[int]):an array of node ids attributes (dict[str, np.ndarray]): dictionary containing the attributes to be updated

update_node_attrs(nodes: collections.abc.Iterable[motile_plugin.data_model.tracks.Node], attributes: motile_plugin.data_model.tracks.Attrs)

_update_node_attrs(nodes: collections.abc.Iterable[motile_plugin.data_model.tracks.Node], attributes: motile_plugin.data_model.tracks.Attrs)

_update_node_segs(nodes: numpy.ndarray[Any], pixels: list[motile_plugin.data_model.tracks.SegMask], added=False) → motile_plugin.data_model.actions.TracksAction

Update the segmentation and segmentation-managed attributes for a set of nodes.

Args: nodes (np.ndarray[int]):an array of node ids attributes (dict[str, np.ndarray]): dictionary containing the attributes to be updated

add_edges(edges: numpy.ndarray[int]) → None

Add edges and attributes to the graph. Also update the track ids and corresponding segmentations if applicable

Args:

edges (np.array[int]): An Nx2 array of N edges, each with source and target

node ids

_add_edges(edges: numpy.ndarray[int]) → motile_plugin.data_model.actions.TracksAction

Add edges and attributes to the graph. Also update the track ids and corresponding segmentations of the target node tracks and potentially sibling tracks.

Args:

edges (np.array[int]): An Nx2 array of N edges, each with source and target

node ids

attributes (dict[str, np.ndarray]): dictionary mapping attribute names to

an array of values, where the index in the array matches the edge index

Returns:

True if the edges were successfully added, False if any edge was invalid.

is_valid(edge) → tuple[bool, motile_plugin.data_model.actions.TracksAction | None]

Check if this edge is valid. Criteria: - not horizontal - not existing yet - no merges - no triple divisions - new edge should be the shortest possible connection between two nodes, given their track_ids. (no skipping/bypassing any nodes of the same track_id). Check if there are any nodes of the same source or target track_id between source and target

Args:

edge (np.ndarray[(int, int)]: edge to be validated

Returns:

True if the edge is valid, false if invalid

delete_edges(edges: numpy.ndarray)

Delete edges from the graph.

Args:

edges (np.ndarray): _description_

_delete_edges(edges: numpy.ndarray) → motile_plugin.data_model.actions.ActionGroup

update_segmentations(to_remove: list[motile_plugin.data_model.tracks.Node], to_update_smaller: list[tuple], to_update_bigger: list[tuple], to_add: list[tuple], current_timepoint: int) → None

Handle a change in the segmentation mask, checking for node addition, deletion, and attribute updates. Args:

updated_pixels (list[(tuple(np.ndarray, np.ndarray, np.ndarray), np.ndarray, int)]):

list holding the operations that updated the segmentation (directly from the napari labels paint event). Each element in the list consists of a tuple of np.ndarrays representing indices for each dimension, an array of the previous values, and an array or integer representing the new value(s)

current_timepoint (int): the current time point in the viewer, used to set the selected node.

undo() → None

Obtain the action to undo from the history, and invert

redo() → None

Obtain the action to redo from the history

_get_new_node_ids(n: int) → list[motile_plugin.data_model.tracks.Node]

Get a list of new node ids for creating new nodes. They will be unique from all existing nodes, but have no other guarantees.

Args:

n (int): The number of new node ids to return

Returns:

list[Node]: A list of new node ids.