zarrita 0.1.0a16

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Zarrita

Zarrita is an experimental implementation of Zarr v3 including sharding. This is only a technical proof of concept meant for generating sample datasets. Not recommended for production use.

Setup

import zarrita
import numpy as np

store = zarrita.LocalStore('testoutput') # or zarrita.RemoteStore('s3://bucket/test')

Create an array

a = zarrita.Array.create(
    store / 'array',
    shape=(6, 10),
    dtype='int32',
    chunk_shape=(2, 5),
    codecs=[
        zarrita.codecs.endian_codec(),
        zarrita.codecs.blosc_codec(typesize=4),
    ],
    attributes={'question': 'life', 'answer': 42}
)
a[:, :] = np.ones((6, 10), dtype='int32')

Open an array

a = zarrita.Array.open(store / 'array')
assert np.array_equal(a[:, :], np.ones((6, 10), dtype='int32'))

Create an array with sharding

a = zarrita.Array.create(
    store / 'sharding',
    shape=(16, 16),
    dtype='int32',
    chunk_shape=(16, 16),
    chunk_key_encoding=('v2', '.'),
    codecs=[
        zarrita.codecs.sharding_codec(
            chunk_shape=(8, 8),
            codecs=[
                zarrita.codecs.endian_codec(),
                zarrita.codecs.blosc_codec(typesize=4),
            ]
        ),
    ],
)
data = np.arange(0, 16 * 16, dtype='int32').reshape((16, 16))
a[:, :] = data
assert np.array_equal(a[:, :], data)

Create a group

g = zarrita.Group.create(store / 'group')
g2 = g.create_group('group2')
a = g2.create_array(
    'array',
    shape=(16, 16),
    dtype='int32',
    chunk_shape=(16, 16),
)
a[:, :] = np.arange(0, 16 * 16, dtype='int32').reshape((16, 16))

Open a group

g = zarrita.Group.open(store / 'group')
g2 = g['group2']
a = g['group2']['array']
assert np.array_equal(a[:, :], np.arange(0, 16 * 16, dtype='int32').reshape((16, 16)))

Resize array

a.resize((10, 10))

Update attributes

a.update_attributes({'question': 'life', 'answer': 0})

Zarr v2

a = zarrita.ArrayV2.create(
    store / 'array',
    shape=(6, 10),
    dtype='int32',
    chunks=(2, 5),
)
a[:, :] = np.ones((6, 10), dtype='int32')

a3 = a.convert_to_v3()
assert a3.metadata.shape == a.shape

Async methods

a = await zarrita.Array.create_async(
    store / 'array_async',
    shape=(6, 10),
    dtype='int32',
    chunk_shape=(2, 5),
)
await a.async_[:, :].set(np.ones((6, 10), dtype='int32'))
await a.async_[:, :].get()

Credits

This is a largely-rewritten fork of zarrita by @alimanfoo. It implements the Zarr v3 draft specification created by @alimanfoo, @jstriebel, @jbms et al.

Licensed under MIT