updated docstring for holography (#316)

* u

* u

* u

Author: cmanzo

deeptrack/features.py

@@ -4,7 +4,7 @@
 manipulate features in DeepTrack2, enabling users to build sophisticated data 
 processing pipelines with modular, reusable, and composable components.
 
-Main Concepts
+Key Features
 -------------
 - **Features**
 
@@ -20,51 +20,79 @@
     They enable the construction of pipelines with advanced data flow 
     requirements.
 
-Key Classes
------------
-- `Feature`: 
+- **Feature Properties**
+
+    Features in DeepTrack2 can have dynamically sampled properties, enabling 
+    parameterization of transformations. These properties are defined at 
+    initialization and can be updated during pipeline execution.
+
+- **Pipeline Composition**
+
+    Features can be composed into flexible pipelines using intuitive operators 
+    (`>>`, `&`, etc.), making it easy to define complex data processing 
+    workflows.
+
+- **Lazy Evaluation**
+
+    DeepTrack2 supports lazy evaluation of features, ensuring that data is 
+    processed only when needed, which improves performance and scalability.
+
+Module Structure
+----------------
+Key Classes: 
+
+- `Feature`:
+
     Base class for all features in DeepTrack2. Represents a modular data 
     transformation with properties and methods for customization.
 
-- `StructuralFeature`: 
+- `StructuralFeature`:
+
     A specialized feature for organizing and managing hierarchical or logical 
     structures in the pipeline.
 
-- `Value`: 
+- `Value`:
+
     Stores a constant value as a feature. Useful for passing parameters through
     the pipeline.
 
-- `Chain`: 
+- `Chain`:
+
     Sequentially applies multiple features to the input data (>>).
 
-- `DummyFeature`: 
+- `DummyFeature`:
+
     A no-op feature that passes the input data unchanged.
 
 - `ArithmeticOperationFeature`:
+
     A parent class for features performing arithmetic operations like addition,
     subtraction, multiplication, and division.
 
-Module Highlights
------------------
-- **Feature Properties**
+Functions:
 
-    Features in DeepTrack2 can have dynamically sampled properties, enabling 
-    parameterization of transformations. These properties are defined at 
-    initialization and can be updated during pipeline execution.
+- `propagate_data_to_dependencies`:
 
-- **Pipeline Composition**
+    def propagate_data_to_dependencies(
+        feature: Feature,
+        **kwargs: Any
+    ) -> None
 
-    Features can be composed into flexible pipelines using intuitive operators 
-    (`>>`, `&`, etc.), making it easy to define complex data processing 
-    workflows.
+    Propagates data to all dependencies of a feature, updating their properties
+    with the provided values.
 
-- **Lazy Evaluation**
+- `merge_features`:
 
-    DeepTrack2 supports lazy evaluation of features, ensuring that data is 
-    processed only when needed, which improves performance and scalability.
+    def merge_features(
+        features: list[Feature],
+        merge_strategy: int = MERGE_STRATEGY_OVERRIDE,
+    ) -> Feature
+
+    Merges multiple features into a single feature using the specified merge
+    strategy.
 
-Example
--------
+Examples
+--------
 Define a simple pipeline with features:
 >>> import deeptrack as dt
 >>> import numpy as np
@@ -115,9 +143,6 @@
 from deeptrack.sources import SourceItem
 from deeptrack.types import ArrayLike, PropertyLike
 
-
-#TODO: for all features check whether image should be Image, np.ndarray, or both.
-
 MERGE_STRATEGY_OVERRIDE: int = 0
 MERGE_STRATEGY_APPEND: int = 1
 
@@ -4630,7 +4655,7 @@ class SampleToMasks(Feature):
     ValueError
         If `merge_method` is invalid.
 
-    Example
+    Examples
     -------
     >>> import deeptrack as dt
     >>> import matplotlib.pyplot as plt