1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209
|
.. only:: doctest
>>> import shutil
>>> shutil.rmtree('data', ignore_errors=True)
>>>
>>> import numpy as np
>>> np.random.seed(0)
Quickstart
==========
Welcome to the Zarr-Python Quickstart guide! This page will help you get up and running with
the Zarr library in Python to efficiently manage and analyze multi-dimensional arrays.
Zarr is a powerful library for storage of n-dimensional arrays, supporting chunking,
compression, and various backends, making it a versatile choice for scientific and
large-scale data.
Installation
------------
Zarr requires Python 3.11 or higher. You can install it via `pip`:
.. code-block:: bash
pip install zarr
or `conda`:
.. code-block:: bash
conda install --channel conda-forge zarr
Creating an Array
-----------------
To get started, you can create a simple Zarr array::
>>> import zarr
>>> import numpy as np
>>>
>>> # Create a 2D Zarr array
>>> z = zarr.create_array(
... store="data/example-1.zarr",
... shape=(100, 100),
... chunks=(10, 10),
... dtype="f4"
... )
>>>
>>> # Assign data to the array
>>> z[:, :] = np.random.random((100, 100))
>>> z.info
Type : Array
Zarr format : 3
Data type : DataType.float32
Shape : (100, 100)
Chunk shape : (10, 10)
Order : C
Read-only : False
Store type : LocalStore
Codecs : [{'endian': <Endian.little: 'little'>}, {'level': 0, 'checksum': False}]
No. bytes : 40000 (39.1K)
Here, we created a 2D array of shape ``(100, 100)``, chunked into blocks of
``(10, 10)``, and filled it with random floating-point data. This array was
written to a ``LocalStore`` in the ``data/example-1.zarr`` directory.
Compression and Filters
~~~~~~~~~~~~~~~~~~~~~~~
Zarr supports data compression and filters. For example, to use Blosc compression::
>>> z = zarr.create_array(
... "data/example-3.zarr",
... mode="w", shape=(100, 100),
... chunks=(10, 10), dtype="f4",
... compressors=zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.shuffle)
... )
>>> z[:, :] = np.random.random((100, 100))
>>>
>>> z.info
Type : Array
Zarr format : 3
Data type : DataType.float32
Shape : (100, 100)
Chunk shape : (10, 10)
Order : C
Read-only : False
Store type : LocalStore
Codecs : [{'endian': <Endian.little: 'little'>}, {'level': 0, 'checksum': False}]
No. bytes : 40000 (39.1K)
This compresses the data using the Zstandard codec with shuffle enabled for better compression.
Hierarchical Groups
-------------------
Zarr allows you to create hierarchical groups, similar to directories::
>>> # Create nested groups and add arrays
>>> root = zarr.group("data/example-2.zarr")
>>> foo = root.create_group(name="foo")
>>> bar = root.create_array(
... name="bar", shape=(100, 10), chunks=(10, 10), dtype="f4"
... )
>>> spam = foo.create_array(name="spam", shape=(10,), dtype="i4")
>>>
>>> # Assign values
>>> bar[:, :] = np.random.random((100, 10))
>>> spam[:] = np.arange(10)
>>>
>>> # print the hierarchy
>>> root.tree()
/
├── bar (100, 10) float32
└── foo
└── spam (10,) int32
<BLANKLINE>
This creates a group with two datasets: ``foo`` and ``bar``.
Batch Hierarchy Creation
~~~~~~~~~~~~~~~~~~~~~~~~
Zarr provides tools for creating a collection of arrays and groups with a single function call.
Suppose we want to copy existing groups and arrays into a new storage backend:
>>> # Create nested groups and add arrays
>>> root = zarr.group("data/example-3.zarr", attributes={'name': 'root'})
>>> foo = root.create_group(name="foo")
>>> bar = root.create_array(
... name="bar", shape=(100, 10), chunks=(10, 10), dtype="f4"
... )
>>> nodes = {'': root.metadata} | {k: v.metadata for k,v in root.members()}
>>> print(nodes)
>>> from zarr.storage import MemoryStore
>>> new_nodes = dict(zarr.create_hierarchy(store=MemoryStore(), nodes=nodes))
>>> new_root = new_nodes['']
>>> assert new_root.attrs == root.attrs
Note that :func:`zarr.create_hierarchy` will only initialize arrays and groups -- copying array data must
be done in a separate step.
Persistent Storage
------------------
Zarr supports persistent storage to disk or cloud-compatible backends. While examples above
utilized a :class:`zarr.storage.LocalStore`, a number of other storage options are available.
Zarr integrates seamlessly with cloud object storage such as Amazon S3 and Google Cloud Storage
using external libraries like `s3fs <https://s3fs.readthedocs.io>`_ or
`gcsfs <https://gcsfs.readthedocs.io>`_::
>>> import s3fs # doctest: +SKIP
>>>
>>> z = zarr.create_array("s3://example-bucket/foo", mode="w", shape=(100, 100), chunks=(10, 10), dtype="f4") # doctest: +SKIP
>>> z[:, :] = np.random.random((100, 100)) # doctest: +SKIP
A single-file store can also be created using the the :class:`zarr.storage.ZipStore`::
>>> # Store the array in a ZIP file
>>> store = zarr.storage.ZipStore("data/example-3.zip", mode='w')
>>>
>>> z = zarr.create_array(
... store=store,
... mode="w",
... shape=(100, 100),
... chunks=(10, 10),
... dtype="f4"
... )
>>>
>>> # write to the array
>>> z[:, :] = np.random.random((100, 100))
>>>
>>> # the ZipStore must be explicitly closed
>>> store.close()
To open an existing array from a ZIP file::
>>> # Open the ZipStore in read-only mode
>>> store = zarr.storage.ZipStore("data/example-3.zip", read_only=True)
>>>
>>> z = zarr.open_array(store, mode='r')
>>>
>>> # read the data as a NumPy Array
>>> z[:]
array([[0.66734236, 0.15667458, 0.98720884, ..., 0.36229587, 0.67443246,
0.34315267],
[0.65787303, 0.9544212 , 0.4830079 , ..., 0.33097172, 0.60423803,
0.45621237],
[0.27632037, 0.9947008 , 0.42434934, ..., 0.94860053, 0.6226942 ,
0.6386924 ],
...,
[0.12854576, 0.934397 , 0.19524333, ..., 0.11838563, 0.4967675 ,
0.43074256],
[0.82029045, 0.4671437 , 0.8090906 , ..., 0.7814118 , 0.42650765,
0.95929915],
[0.4335856 , 0.7565437 , 0.7828931 , ..., 0.48119593, 0.66220033,
0.6652362 ]], shape=(100, 100), dtype=float32)
Read more about Zarr's storage options in the :ref:`User Guide <user-guide-storage>`.
Next Steps
----------
Now that you're familiar with the basics, explore the following resources:
- `User Guide <user-guide>`_
- `API Reference <api>`_
|