Base Classes

class pyclm.core.patterns.pattern.PatternMethod(experiment_name=None, camera_properties: CameraProperties = None, **kwargs)

Bases: object

add_requirement(channel_name: str, raw: bool = False, seg: bool = False)

Declarative way to add requirements.

center_um() → tuple[float, float]

configure_system(experiment_name: str, camera_properties: CameraProperties, experiment: Experiment)

Called by the system to inject dependencies.

generate(data_dock: DataDock | PatternContext) → ndarray

get_um_meshgrid() → tuple[ndarray, ndarray]

initialize(experiment: Experiment) → list[AcquiredImageRequest]

name = 'base'

request_stim(raw: bool = False, seg: bool = False)

Request the imaged stimulation

update_binning(binning: int)

class pyclm.core.patterns.pattern.PatternContext(data_dock: DataDock, experiment: Experiment)

Bases: object

raw(channel_name: str) → ndarray

Get raw image for a channel.

segmentation(channel_name: str) → ndarray

Get segmentation mask for a channel.

class pyclm.core.patterns.zoo.ZooMeta(source: str, kwargs: dict = <factory>, time_seconds: float = 0.0, title: str = '', description: str = '')

Bases: object

Declares how a PatternMethod should appear in the documentation zoo.

Parameters:

• source (str) – Name of the sample image set, e.g. "mdck", "fly", or "mcf10a". A file documentation/zoo_sources/<source>.tif must exist; <source>_seg.tif is optional (synthetic blobs are used when absent).

• kwargs (dict) – Keyword arguments forwarded to the pattern-method constructor.

• time_seconds (float) – Experiment time (seconds) injected into the context.

• title (str) – Gallery-card heading. Defaults to the class name attribute.

• description (str) – One-sentence description shown beneath the image.