ConditionalSetFeature#

Bases: StructuralFeature

Conditionally resolve one of two features.

Deprecated since version 2.0: This feature is deprecated and may be removed in a future release. It is recommended to use Arguments instead.

This feature allows dynamically selecting and resolving one of two child features depending on whether a specified condition evaluates to True or False.

The condition parameter specifies either: - A boolean value (default is True). - The name of a property to listen to. For example, if condition=”is_label”, the selected feature can be toggled as follows:

>>> feature.resolve(is_label=True)   # Resolves `on_true`
>>> feature.resolve(is_label=False)  # Resolves `on_false`
>>> feature.update(is_label=True)    # Updates both features

Both on_true and on_false are updated during each call, even if only one is resolved.

It is advisable to use Arguments instead when possible.

Parameters#

on_false: Feature, optional: The feature to resolve if the condition is False. If not provided, the input image remains unchanged.
on_true: Feature, optional: The feature to resolve if the condition is True. If not provided, the input image remains unchanged.
condition: str | bool, optional: The name of the conditional property or a boolean value. If a string is provided, its value is retrieved from kwargs or self.properties. If not found, the default value is True.
**kwargs: Any: Additional keyword arguments passed to the parent StructuralFeature.

Methods#

get(image: Any, condition: str or bool, **kwargs: Any) -> Any: Resolves the appropriate feature based on the condition.

Examples#

>>> import deeptrack as dt

Define an image:

>>> import numpy as np
>>>
>>> image = np.ones((512, 512))

Define two Gaussian noise features:

>>> true_feature = dt.Gaussian(sigma=0)
>>> false_feature = dt.Gaussian(sigma=5)

— Using a boolean condition — Combine the features into a conditional set feature. If not provided explicitely, the condition is assumed to be True:

>>> conditional_feature = dt.ConditionalSetFeature(
...     on_true=true_feature,
...     on_false=false_feature,
... )

Resolve based on the condition. If not specified, default is True:

>>> clean_image = conditional_feature(image)
>>> round(clean_image.std(), 1)
0.0

>>> noisy_image = conditional_feature(image, condition=False)
>>> round(noisy_image.std(), 1)
5.0

>>> clean_image = conditional_feature(image, condition=True)
>>> round(clean_image.std(), 1)
0.0

— Using a string-based condition — Define condition as a string:

>>> conditional_feature = dt.ConditionalSetFeature(
...     on_true=true_feature,
...     on_false=false_feature,
...     condition = "is_noisy",
... )

Resolve based on the conditions:

>>> noisy_image = conditional_feature(image, is_noisy=False)
>>> round(noisy_image.std(), 1)
5.0

>>> clean_image = conditional_feature(image, is_noisy=True)
>>> round(clean_image.std(), 1)
0.0

Methods Summary

get(inputs, *, condition, **kwargs)

Resolve the appropriate feature based on the condition.

Methods Documentation

get(inputs: Any, *, condition: str | bool, **kwargs: Any)#

Resolve the appropriate feature based on the condition.

Parameters#

inputs: Any: The inputs to process.
condition: str or bool: The name of the conditional property or a boolean value. If a string is provided, it is looked up in kwargs to get the actual boolean value.
**kwargs:: Any: Additional keyword arguments to pass to the resolved feature.

Returns#

Any: The processed data after resolving the appropriate feature. If neither on_true nor on_false is provided for the corresponding condition, the input is returned unchanged.