zarr-indexing

This library is for modelling and transforming NumPy-style array indexing expressions. It separates the declaration of an array indexing expression from the result of that expression.

Developed for use in zarr.

Inspired by TensorStore's index-transform model.

Install

zarr-indexing is developed in the zarr-python repository and released independently of zarr itself:

pip install zarr-indexing

Quickstart

Wrap an array, compose a lazy view through .lazy, and call result() when you want its values:

import numpy as np

from zarr_indexing import LazyArray

source = np.array([10, 11, 12, 13, 14, 15])
view = LazyArray.from_numpy(source).lazy[2:5]

view.result()
# array([12, 13, 14])

Composing these selections does not read source values; the example reads them at result(). Construction inspects source metadata, and Dask tokenization can inspect source values. Lazy views compose shows how the chain stays one description, and where the materialization boundary is.

Learn more

• Visual guide — one selection followed from coordinates to chunk plan. Using lazy indexing, start at An index selects coordinates; integrating a chunked backend, start at A request becomes a chunk plan and finish with the per-axis tables a plan is built from.
• Indexing pattern reference — every selection form with its NumPy-verified result.
• Integration boundaries — what a reader, writer, or scheduler owns, and what the plan owns.
• Lazy indexing a NumPy array and with Dask — runnable examples.
• The ndsel wire format — the JSON form of a selection.
• Design notes — TensorStore lineage, box vs query, and deliberate limits.
• API reference
• Release notes · License (MIT)