Added Google style docstrings

onair/src/ai_components/ai_plugin_abstract/ai_plugin.py

@@ -1,40 +1,63 @@
 # GSC-19165-1, "The On-Board Artificial Intelligence Research (OnAIR) Platform"
 #
-# Copyright © 2023 United States Government as represented by the Administrator of
-# the National Aeronautics and Space Administration. No copyright is claimed in the
-# United States under Title 17, U.S. Code. All Other Rights Reserved.
+# Copyright © 2023 United States Government as represented by the Administrator
+# of the National Aeronautics and Space Administration. No copyright is claimed
+# in the United States under Title 17, U.S. Code. All Other Rights Reserved.
 #
 # Licensed under the NASA Open Source Agreement version 1.3
 # See "NOSA GSC-19165-1 OnAIR.pdf"
 
+
 from abc import ABC, abstractmethod
 
-"""This object serves as a proxy for all plugins.
-   Therefore, the AIPlugin object is meant to induce
-   standards and structures of compliance for user-created
-   and/or imported plugins/libraries
-"""
 
+class AIPlugIn(ABC):
+    """This serves as a base for all plugins.
+
+    This is the superclass for data driven components: VAE, PPO, etc. It is
+    meant to induce standards and structures of compliance for imported
+    plugins: KnowledgeRep, Learner, Planner or Complex. Those plugins must
+    inherit this class.
+
+    Attributes:
+        component_name (str): Name given to instance of plugin.
+        headers (list[str]): Names for each data point in a data frame.
+
+    """
+    def __init__(self, _name: str, _headers: list[str]):
+        """Initializes a new AIPlugIn object.
+
+        Args:
+            _name: The name of this plugin instance.
+            _headers: Sequenced names of each item in OnAIR data frame.
 
-class AIPlugin(ABC):
-    def __init__(self, _name, _headers):
-        """
-        Superclass for data driven components: VAE, PPO, etc. Allows for easier modularity.
         """
         assert len(_headers) > 0
         self.component_name = _name
         self.headers = _headers
 
     @abstractmethod
-    def update(self, low_level_data=[], high_level_data={}):
-        """
-        Given streamed data point, system should update internally
+    def update(
+        self, low_level_data: list[float] = [],
+        high_level_data: dict[str, dict[str, list[object]]] = {}
+    ) -> None:
+        """Updates plugin's data using provided data.
+
+        Args:
+            low_level_data: Frame of floats, one for each header.
+            high_level_data: Reasoned data results from previous plugins.
+
+        Returns:
+        None: This function only updates the data for this plugin
         """
         raise NotImplementedError
 
     @abstractmethod
-    def render_reasoning(self):
-        """
-        System should return its diagnosis
+    def render_reasoning(self) -> list[object]:
+        """Plugin reasons with current data and provides analysis.
+
+        Returns:
+            list[object]: The list of reasoned outcomes, where contents are
+            relevant to this plugin. May be an empty list.
         """
         raise NotImplementedError