subtr-actor is a versatile library designed to facilitate the process of
working with and extracting data from Rocket League replays. Utilizing the
powerful [boxcars] library for parsing, subtr-actor simplifies the
underlying complex actor-based structure of replay files, making them more
accessible and easier to manipulate.
[ReplayProcessor]: This struct is at the heart of subtr-actor's
replay processing capabilities. The [ReplayProcessor] traverses the actor
graph, pushing frames through an [ActorStateModeler] to capture the state of
all actors at any given moment. It provides a suite of helper methods to
assist in the navigation of the actor graph and the retrieval of
information about the game as it progresses.
[Collector]: This trait outlines the blueprint for data
collection from replays. The Collector interfaces with a ReplayProcessor,
handling frame data and guiding the pace of replay progression. It is
typically invoked repeatedly through the [ReplayProcessor::process] method
as the replay is processed frame by frame.
Notably, subtr-actor includes implementations of the [Collector] trait,
[NDArrayCollector]: This [Collector] implementations translates
frame-based replay data into a 2 dimensional array in the form of a
[::ndarray::Array2] instance. The exact data that is recorded in each frame
can be configured with the [FeatureAdder] and [PlayerFeatureAdder]
instances that are provided to its constructor ([NDArrayCollector::new]).
This representation is ideal for use with machine learning libraries like
pytorch and tensorflow.
[ReplayData]: This [Collector] implementation provides an easy way to
get a serializable to e.g. json (though [serde::Serialize]) representation
of the replay. The representation differs from what you might get from e.g.
raw boxcars in that it is not a complicated graph of actor objects and is
instead something more akin to the way a human might think of the data
contained in a replay.