created_at2017-05-06 13:28:08.404512
updated_at2017-05-06 13:28:08.404512
descriptionAn experimental uniform binary format for particle physics
publish (github:pistondevelopers:publish)




An experimental uniform binary format for particle physics analysis

When running particle physics simulations, it is sometimes handy to record the data and perform analysis separately from the simulation process.

This binary format is designed to read and write particle physics data to files or streams, such that the tools for analysis can be reused with minimum setup.

Particle physics data is similar to video or animation streams with the difference that time is arbitrarily defined. This format allows precise control over data changes, by changing the offset instance id and range.

10 built-in Rust number types are supported, using array notation, with vector and matrix dimensions up to 80x80. You can also define custom binary formats.

You can repeat the same data multiple times, or write only ranges that changes. Semantics is controlled by the application, but the format is generic enough to reuse algorithms across projects.


type format == 0 => end of stream
type format: u16, property id: u16
|- bytes == 0 => no more data, next property
|- bytes: u64, offset instance id: u64, data: [u8; bytes]

Integers are stored in little-endian format.

The number of items in the data are inferred from the number of bytes and knowledge about the type format.

Motivation for the design

A file format is uniform when it organized the same way everywhere.

One benefit with a uniform format is that you can easily split data up into multiple files, or stream it across a network. It is also easy to generate while running a physics simulation.

This file format assumes that the application has some form of data structure, where each particle instance is assigned a unique id, and their object relations can be derived from a property id. Since the property ids are known, one can use external tools to analyze the physical properties relative to each other, without any knowledge about the data structure.

To describe time, just create a new property id, e.g. f32 scalar, and write out this value before other data in the same time step.


The traits Scalar, Vector and Matrix are implemented for array types of primitive integer and float formats.

When you write data to a file the order is preserved.

use binpool::Scalar;

let prop_id = 0; // A unique property id.
let data: Vec<f32> = vec![1.0, 2.0, 3.0];
Scalar::write_array(prop_id, &data, &mut file).unwrap();

When you read from a file, e.g. to replay a recorded simulation, you often read a single frame at a time then wait for the time to read next frame. To do this, use a loop with flags for each property and break when all read flags are set.

use binpool::State;

let prop_id = 0; // A unique property id.
let mut read_prop_id = false;
let mut data: Vec<[f32; 2]> = vec![];
while let Ok((Some(state), ty, prop)) = State::read(&mut file) {
    match prop {
        prop_id if !read_prop_id => {
            Vector::read_array(state, ty, &mut data, &mut file).unwrap();
            read_prop_id = true;
        _ => break,

Data is often stored in a struct and overwritten for each frame. The example above uses a local variable just for showing how to read data.


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Commit count: 4

cargo fmt