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nd-image-codecs

Composable Zarr v3 codecs for ND scientific images — Python binding.

A family of Zarr v3 codecs that capture correlation along z, time, and channel axes explicitly — as ordinary, independently specified array-to-array and array-to-bytes codecs — then store the result with a fast entropy backend, High-Throughput JPEG 2000 (ISO/IEC 15444-15) coefficient planes, or ZFP blocks. Built for OME-Zarr / OME-NGFF.

No JPEG 2000 Part 2 (MCT) syntax anywhere — cross-axis decorrelation is an explicit Zarr codec, sidestepping Part 2 IP entirely.

The three codec families

codec_series assembles a series (pipeline) of Zarr v3 codecs from an array's axis metadata:

Family Series (pipeline) Built for
nd-delta transpose → numcodecs.delta → bitshuffle → zstd/lz4 Fast lossless storage from existing Zarr codecs only
nd-lift-ht transpose → nd_lift → htj2k Scalable microscopy & volume visualization
nd-zfp transpose → reshape → zfp GPU volume rendering, random access, fixed-rate memory

Install

pip install nd-image-codecs           # NumPy only
pip install "nd-image-codecs[zarr]"   # + zarr-python v3, for the codec pipeline

Wheels cover manylinux, musllinux, macOS, and Windows on x86-64 and arm64. The base install requires only NumPy, so codec_series and the htj2k, zfp, and nd_lift codecs are usable anywhere that wheel lands; the zarr extra adds zarr-python (and, through it, numcodecs) to run pipelines.

Usage

from nd_image_codecs import codec_series

codecs = codec_series(["t", "c", "z", "y", "x"], [8, 1, 32, 256, 256],
                      "uint16", "nd-lift-ht")

The three codec classes (NdLift, Htj2k, NdZfp) register with zarr-python v3 through the zarr.codecs entry-point group, so pipelines produced by codec_series resolve by name.

Status

Alpha. nd_lift, htj2k, and zfp encode and decode for real, and the codec_series builder is cross-checked byte-identical against the Rust and TypeScript implementations in CI. The API is not stable before 1.0.

Links

License

MIT — Copyright (c) Fideus Labs LLC.

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