subtr-actor

subtr-actor

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 actor-based structure of replay files, making them more accessible and easier to manipulate.

Overview of Key Components

• ReplayProcessor: This struct is at the heart of subtr-actor's replay processing capabilities. In its main entry point, ReplayProcessor::process, it pushes network frames from the boxcars::Replay that it is initialized with though an ActorStateModeler instance, calling the Collector instance that is provided as an argument as it does so. The Collector is provided with a reference to the ReplayProcessor each time the it is invoked, which allows it to use the suite of helper methods which greatly assist in the navigation of the actor graph and the retrieval of information about the current game state.

• 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 with TimeAdvance. It is typically invoked repeatedly through the ReplayProcessor::process method as the replay is processed frame by frame.

• FrameRateDecorator: This struct decorates a Collector implementation with a target frame duration, controlling the frame rate of the replay processing.

Collector implementations

subtr-actor also 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). Extending the exact behavior of NDArrayCollector is thus possible with user defined FeatureAdder and PlayerFeatureAdder, which is made easy with the build_global_feature_adder! and build_player_feature_adder! macros. The ::ndarray::Array2 produced by NDArrayCollector 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, but instead something more natural where the data associated with

each entity in the game is grouped together.

Example

In the following example, we demonstrate how to use boxcars, NDArrayCollector and FrameRateDecorator to write a function that takes a replay filepath and collections of features adders and returns a ReplayMetaWithHeaders along with a ::ndarray::Array2. The resulting ::ndarray::Array2 would be appropriate for use in a machine learning context. Note that ReplayProcessor is also used implicitly here in the Collector::process_replay

```rust use subtr_actor::*;

fn getndarraywithinfofromreplayfilepath( filepath: std::path::PathBuf, featureadders: FeatureAdders, playerfeatureadders: PlayerFeatureAdders, fps: Option, ) -> anyhow::Result<(ReplayMetaWithHeaders, ::ndarray::Array2)> { let data = std::fs::read(filepath.aspath())?; let replay = boxcars::ParserBuilder::new(&data) .mustparsenetworkdata() .onerrorcheckcrc() .parse()?;

let mut collector = NDArrayCollector::new(feature_adders, player_feature_adders);

FrameRateDecorator::new_from_fps(fps.unwrap_or(10.0), &mut collector)
    .process_replay(&replay)
    .map_err(|e| e.variant)?;

Ok(collector.get_meta_and_ndarray().map_err(|e| e.variant)?)

} ```