motile_plugin.backend.motile_run

Attributes

STAMP_FORMAT
PARAMS_FILENAME
IN_SEG_FILEANME
IN_POINTS_FILEANME
OUT_SEG_FILEANME
TRACKS_FILENAME
GAPS_FILENAME
SCALE_FILENAME

Classes

MotileRun

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

Module Contents

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

motile_plugin.backend.motile_run.PARAMS_FILENAME = 'solver_params.json'

motile_plugin.backend.motile_run.IN_SEG_FILEANME = 'input_segmentation.npy'

motile_plugin.backend.motile_run.IN_POINTS_FILEANME = 'input_points.npy'

motile_plugin.backend.motile_run.OUT_SEG_FILEANME = 'output_segmentation.npy'

motile_plugin.backend.motile_run.TRACKS_FILENAME = 'tracks.json'

motile_plugin.backend.motile_run.GAPS_FILENAME = 'gaps.txt'

motile_plugin.backend.motile_run.SCALE_FILENAME = 'scale.txt'

class motile_plugin.backend.motile_run.MotileRun(/, **data: Any)

Bases: pydantic.BaseModel

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: str

solver_params: motile_plugin.backend.solver_params.SolverParams | None = None

input_segmentation: numpy.ndarray | None = None

input_points: numpy.ndarray | None = None

tracks: motile_plugin.core.Tracks | None = None

time: datetime.datetime

gaps: list[float] | None = None

status: str = 'done'

scale: list[float] | None = None

model_config

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

_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) → 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.

Args:

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

static _load_params(run_dir: pathlib.Path) → motile_plugin.backend.solver_params.SolverParams

Load parameters from the parameters json file in the provided directory.

Args:

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

Raises:

FileNotFoundError: If the parameters file is not found in the

provided directory.

Returns:

SolverParams: The solver parameters loaded from disk.

_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_tracks_graph(run_dir: pathlib.Path, graph: networkx.DiGraph)

Save the tracks to file. Currently uses networkx node link data format (and saves it as json).

Args:

run_dir (Path): The directory in which to save the tracks file.

static _load_tracks_graph(run_dir: pathlib.Path, required: bool = True) → networkx.DiGraph | None

Load tracks from file. Currently uses networkx node link data format.

Args:

run_dir (Path): The directory in which to find the tracks file. required (bool, optional): Whether to fail if the tracks file is

not found. Defaults to True.

Raises:

FileNotFoundError: If the tracks file is not found in the run_dir

and it was required.

Returns:

nx.DiGraph | None: The tracks, or None if they were not present

and not required.

_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).