backend.motile_run

Attributes

STAMP_FORMAT
PARAMS_FILENAME
IN_POINTS_FILENAME
GAPS_FILENAME
ATTRS_FILENAME
_TRACKSDATA_INTERNAL_EDGE_KEYS

Classes

MotileRun

An object representing a motile tracking run. Contains a name,

Module Contents

backend.motile_run.STAMP_FORMAT = '%m%d%Y_%H%M%S'

backend.motile_run.PARAMS_FILENAME = 'solver_params.json'

backend.motile_run.IN_POINTS_FILENAME = 'input_points.npy'

backend.motile_run.GAPS_FILENAME = 'gaps.txt'

backend.motile_run.ATTRS_FILENAME = 'attrs.json'

backend.motile_run._TRACKSDATA_INTERNAL_EDGE_KEYS

class backend.motile_run.MotileRun(graph: tracksdata.graph.GraphView, run_name: str, time_attr: str = 't', pos_attr: str | tuple[str] | list[str] = 'pos', scale: list[float] | None = None, ndim: int | None = None, solver_params: backend.solver_params.SolverParams | None = None, input_segmentation: numpy.ndarray | None = None, input_points: numpy.ndarray | None = None, time: datetime.datetime | None = None, gaps: list[float] | None = None, status: str = 'done', _features=None, _segmentation=None)

Bases: funtracks.data_model.SolutionTracks

An object representing a motile tracking run. Contains a name, parameters, time of creation, information about the solving process (status and list of solver gaps), and optionally the input and output segmentations and tracks. Mostly used for passing around the set of attributes needed to specify a run, as well as saving and loading.

run_name

solver_params = None

input_segmentation = None

input_points = None

gaps = None

status = 'done'

time

_make_id() → str

Combine the time and run name into a unique id for the run

Returns:

str: A unique id combining the timestamp and run name

static _unpack_id(_id: str) → tuple[datetime.datetime, str]

Unpack a string id created with _make_id into the time and run name

Args:

_id (str): The id to unpack into time and run name

Raises:

ValueError: If the provided id is not in the expected format

Returns:

tuple[datetime, str]: A tuple of time and run name

save(base_path: str | pathlib.Path, save_segmentation: bool = False) → pathlib.Path

Save the run in the provided directory. Creates a subdirectory from the timestamp and run name and stores one file for each element of the run in that subdirectory.

Args:

base_path (str | Path): The directory to save the run in.

Returns:

(Path): The Path that the run was saved in. The last part of the path is the directory that was created to store the run.

classmethod load(run_dir: pathlib.Path | str, output_required: bool = True)

Load a run from disk into memory.

Args:

run_dir (Path | str): A directory containing the saved run.

Should be the subdirectory created by MotileRun.save that includes the timestamp and run name.

output_required (bool): If the model outputs are required.

If true, will raise an error if the output files are not found. Defualts to True.

Returns:

MotileRun: The run saved in the provided directory.

_save_params(run_dir: pathlib.Path)

Save the run parameters in the provided run directory. Currently dumps the parameters dict into a json file. Skips writing if there are no params (e.g. tracks imported from CSV/geff that never went through the solver).

Args:

run_dir (Path): A directory in which to save the parameters file.

static _load_params(run_dir: pathlib.Path) → backend.solver_params.SolverParams | None

Load parameters from the parameters json file in the provided directory. Returns None if the file is absent — runs imported from CSV/geff are saved without solver params.

Args:

run_dir (Path): The directory in which to find the parameters file.

Returns:

SolverParams | None: The solver parameters, or None if no params

file exists in the run directory.

_save_array(run_dir: pathlib.Path, filename: str, array: numpy.ndarray)

Save a segmentation as a numpy array using np.save. In the future, could be changed to use zarr or other file types.

Args:

run_dir (Path): The directory in which to save the segmentation filename (str): The filename to use array (np.array): The array to save

static _load_array(run_dir: pathlib.Path, filename: str, required: bool = True) → numpy.ndarray | None

Load an array from file using np.load. In the future, could be lazy loading from a zarr.

Args:

run_dir (Path): The base run directory containing the array filename (str): The name of the file to load required (bool, optional): If true, will fail if the array

file is not present. If false, will return None if the file is not present. Defaults to True.

Raises:

FileNotFoundError: If the array file is not found, and

it was required.

Returns:

np.ndarray | None: The array, or None if the file was

not found and not required.

_save_attrs(directory: pathlib.Path)

Save the time_attr, pos_attr, scale, and segmentation_shape in a json file.

Args:

directory (Path): The directory in which to save the attributes

static _load_attrs(run_dir: pathlib.Path) → dict | None

Load attrs from the attrs json file in the provided directory, if present.

Args:

run_dir (Path): The directory in which to find the attrs file.

Returns:

dict | None: The attrs dict, or None if the file was not found.

_save_list(list_to_save: list | None, run_dir: pathlib.Path, filename: str)

static _load_list(run_dir: pathlib.Path, filename: str, required: bool = True) → list[float]

delete(base_path: str | pathlib.Path)

Delete this run from the file system. Will look inside base_path for the directory corresponding to this run and delete it.

Args:

base_path (str | Path): The parent directory where the run is saved

(not the one created by self.save).