NgPre

A (mostly pure) rust implementation of the Neuroglancer Precomputed n-dimensional tensor file system storage format created by the Jeremy Maitin-Shepard at Google. This library is based on the rust-n5 library and reused a lot of its infrastructure.

Differences from Java NgPre

Dataset paths may be relative. The root path in a dataset is addressable both by "/" and "".
Dataset paths are more strict. Calling methods with paths outside the dataset, e.g., "..", will return a Result::Err.

Minimum supported Rust version (MSRV)

Stable 1.39

Quick start

[dependencies]
ngpre = "0.1"

use ngpre::prelude::*;
use ngpre::smallvec::smallvec;

fn ngpre_roundtrip(root_path: &str) -> std::io::Result<()> {
    let n = NgPreFilesystem::open_or_create(root_path)?;

    let block_size = smallvec![44, 33, 22];
    let data_attrs = DatasetAttributes::new(
        smallvec![100, 200, 300],
        block_size.clone(),
        DataType::INT16,
        CompressionType::default(),
    );
    let block_data = vec![0i16; data_attrs.get_block_num_elements()];

    let block_in = SliceDataBlock::new(
        block_size,
        smallvec![0, 0, 0],
        &block_data);

    let path_name = "/test/dataset/group";

    n.create_dataset(path_name, &data_attrs)?;
    n.write_block(path_name, &data_attrs, &block_in)?;

    let block_out = n.read_block::<i16>(path_name, &data_attrs, smallvec![0, 0, 0])?
        .expect("Block is empty");
    assert_eq!(block_out.get_data(), &block_data[..]);

    Ok(())
}

fn main() {
    ngpre_roundtrip("tmp.ngpre").expect("NgPre roundtrip failed!");
}

Status

This library is compatible with all NgPre datasets the authors have encountered and is used in production services. However, some aspects of the library are still unergonomic and interfaces may still undergo rapid breaking changes.

License

Licensed under either of

Apache License, Version 2.0, (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.