Array creation (`zarr.creation`)#

zarr.creation.create(shape, chunks=True, dtype=None, compressor='default', fill_value: int | None = 0, order='C', store=None, synchronizer=None, overwrite=False, path=None, chunk_store=None, filters=None, cache_metadata=True, cache_attrs=True, read_only=False, object_codec=None, dimension_separator=None, write_empty_chunks=True, *, zarr_version=None, meta_array=None, storage_transformers=(), **kwargs)[source]#

Create an array.

Parameters:

shapeint or tuple of ints: Array shape.
chunksint or tuple of ints, optional: Chunk shape. If True, will be guessed from shape and dtype. If False, will be set to shape, i.e., single chunk for the whole array. If an int, the chunk size in each dimension will be given by the value of chunks. Default is True.
dtypestring or dtype, optional: NumPy dtype.
compressorCodec, optional: Primary compressor.
fill_valueobject: Default value to use for uninitialized portions of the array.
order{‘C’, ‘F’}, optional: Memory layout to be used within each chunk.
storeMutableMapping or string: Store or path to directory in file system or name of zip file.
synchronizerobject, optional: Array synchronizer.
overwritebool, optional: If True, delete all pre-existing data in store at path before creating the array.
pathstring, optional: Path under which array is stored.
chunk_storeMutableMapping, optional: Separate storage for chunks. If not provided, store will be used for storage of both chunks and metadata.
filterssequence of Codecs, optional: Sequence of filters to use to encode chunk data prior to compression.
cache_metadatabool, optional: If True, array configuration metadata will be cached for the lifetime of the object. If False, array metadata will be reloaded prior to all data access and modification operations (may incur overhead depending on storage and data access pattern).
cache_attrsbool, optional: If True (default), user attributes will be cached for attribute read operations. If False, user attributes are reloaded from the store prior to all attribute read operations.
read_onlybool, optional: True if array should be protected against modification.
object_codecCodec, optional: A codec to encode object arrays, only needed if dtype=object.
dimension_separator{‘.’, ‘/’}, optional: Separator placed between the dimensions of a chunk.

New in version 2.8.
write_empty_chunksbool, optional: If True (default), all chunks will be stored regardless of their contents. If False, each chunk is compared to the array’s fill value prior to storing. If a chunk is uniformly equal to the fill value, then that chunk is not be stored, and the store entry for that chunk’s key is deleted. This setting enables sparser storage, as only chunks with non-fill-value data are stored, at the expense of overhead associated with checking the data of each chunk.

New in version 2.11.
storage_transformerssequence of StorageTransformers, optional: Setting storage transformers, changes the storage structure and behaviour of data coming from the underlying store. The transformers are applied in the order of the given sequence. Supplying an empty sequence is the same as omitting the argument or setting it to None. May only be set when using zarr_version 3.

New in version 2.13.
zarr_version{None, 2, 3}, optional: The zarr protocol version of the created array. If None, it will be inferred from store or chunk_store if they are provided, otherwise defaulting to 2.

New in version 2.12.
meta_arrayarray-like, optional: An array instance to use for determining arrays to create and return to users. Use numpy.empty(()) by default.

New in version 2.13.

Returns:

zzarr.core.Array

Examples

Create an array with default settings:

>>> import zarr
>>> z = zarr.create((10000, 10000), chunks=(1000, 1000))
>>> z
<zarr.core.Array (10000, 10000) float64>

Create an array with different some different configuration options:

>>> from numcodecs import Blosc
>>> compressor = Blosc(cname='zstd', clevel=1, shuffle=Blosc.BITSHUFFLE)
>>> z = zarr.create((10000, 10000), chunks=(1000, 1000), dtype='i1', order='F',
...                 compressor=compressor)
>>> z
<zarr.core.Array (10000, 10000) int8>

To create an array with object dtype requires a filter that can handle Python object encoding, e.g., MsgPack or Pickle from numcodecs:

>>> from numcodecs import MsgPack
>>> z = zarr.create((10000, 10000), chunks=(1000, 1000), dtype=object,
...                 object_codec=MsgPack())
>>> z
<zarr.core.Array (10000, 10000) object>

Example with some filters, and also storing chunks separately from metadata:

>>> from numcodecs import Quantize, Adler32
>>> store, chunk_store = dict(), dict()
>>> z = zarr.create((10000, 10000), chunks=(1000, 1000), dtype='f8',
...                 filters=[Quantize(digits=2, dtype='f8'), Adler32()],
...                 store=store, chunk_store=chunk_store)
>>> z
<zarr.core.Array (10000, 10000) float64>

zarr.creation.empty(shape, **kwargs)[source]#

Create an empty array.

For parameter definitions see zarr.creation.create().

Notes

The contents of an empty Zarr array are not defined. On attempting to retrieve data from an empty Zarr array, any values may be returned, and these are not guaranteed to be stable from one access to the next.

zarr.creation.zeros(shape, **kwargs)[source]#

Create an array, with zero being used as the default value for uninitialized portions of the array.

For parameter definitions see zarr.creation.create().

Examples

>>> import zarr
>>> z = zarr.zeros((10000, 10000), chunks=(1000, 1000))
>>> z
<zarr.core.Array (10000, 10000) float64>
>>> z[:2, :2]
array([[0., 0.],
       [0., 0.]])

zarr.creation.ones(shape, **kwargs)[source]#

Create an array, with one being used as the default value for uninitialized portions of the array.

For parameter definitions see zarr.creation.create().

Examples

>>> import zarr
>>> z = zarr.ones((10000, 10000), chunks=(1000, 1000))
>>> z
<zarr.core.Array (10000, 10000) float64>
>>> z[:2, :2]
array([[1., 1.],
       [1., 1.]])

zarr.creation.full(shape, fill_value, **kwargs)[source]#

Create an array, with fill_value being used as the default value for uninitialized portions of the array.

For parameter definitions see zarr.creation.create().

Examples

>>> import zarr
>>> z = zarr.full((10000, 10000), chunks=(1000, 1000), fill_value=42)
>>> z
<zarr.core.Array (10000, 10000) float64>
>>> z[:2, :2]
array([[42., 42.],
       [42., 42.]])

zarr.creation.array(data, **kwargs)[source]#

Create an array filled with data.

The data argument should be a NumPy array or array-like object. For other parameter definitions see zarr.creation.create().

Examples

>>> import numpy as np
>>> import zarr
>>> a = np.arange(100000000).reshape(10000, 10000)
>>> z = zarr.array(a, chunks=(1000, 1000))
>>> z
<zarr.core.Array (10000, 10000) int64>

zarr.creation.open_array(store=None, mode='a', shape=None, chunks=True, dtype=None, compressor='default', fill_value=0, order='C', synchronizer=None, filters=None, cache_metadata=True, cache_attrs=True, path=None, object_codec=None, chunk_store=None, storage_options=None, partial_decompress=False, write_empty_chunks=True, *, zarr_version=None, dimension_separator=None, meta_array=None, **kwargs)[source]#

Open an array using file-mode-like semantics.

Parameters:

storeMutableMapping or string, optional: Store or path to directory in file system or name of zip file.
mode{‘r’, ‘r+’, ‘a’, ‘w’, ‘w-‘}, optional: Persistence mode: ‘r’ means read only (must exist); ‘r+’ means read/write (must exist); ‘a’ means read/write (create if doesn’t exist); ‘w’ means create (overwrite if exists); ‘w-’ means create (fail if exists).
shapeint or tuple of ints, optional: Array shape.
chunksint or tuple of ints, optional: Chunk shape. If True, will be guessed from shape and dtype. If False, will be set to shape, i.e., single chunk for the whole array. If an int, the chunk size in each dimension will be given by the value of chunks. Default is True.
dtypestring or dtype, optional: NumPy dtype.
compressorCodec, optional: Primary compressor.
fill_valueobject, optional: Default value to use for uninitialized portions of the array.
order{‘C’, ‘F’}, optional: Memory layout to be used within each chunk.
synchronizerobject, optional: Array synchronizer.
filterssequence, optional: Sequence of filters to use to encode chunk data prior to compression.
cache_metadatabool, optional: If True, array configuration metadata will be cached for the lifetime of the object. If False, array metadata will be reloaded prior to all data access and modification operations (may incur overhead depending on storage and data access pattern).
cache_attrsbool, optional: If True (default), user attributes will be cached for attribute read operations. If False, user attributes are reloaded from the store prior to all attribute read operations.
pathstring, optional: Array path within store.
object_codecCodec, optional: A codec to encode object arrays, only needed if dtype=object.
chunk_storeMutableMapping or string, optional: Store or path to directory in file system or name of zip file.
storage_optionsdict: If using an fsspec URL to create the store, these will be passed to the backend implementation. Ignored otherwise.
partial_decompressbool, optional: If True and while the chunk_store is a FSStore and the compression used is Blosc, when getting data from the array chunks will be partially read and decompressed when possible.
write_empty_chunksbool, optional: If True (default), all chunks will be stored regardless of their contents. If False, each chunk is compared to the array’s fill value prior to storing. If a chunk is uniformly equal to the fill value, then that chunk is not be stored, and the store entry for that chunk’s key is deleted. This setting enables sparser storage, as only chunks with non-fill-value data are stored, at the expense of overhead associated with checking the data of each chunk.

New in version 2.11.
zarr_version{None, 2, 3}, optional: The zarr protocol version of the array to be opened. If None, it will be inferred from store or chunk_store if they are provided, otherwise defaulting to 2.
dimension_separator{None, ‘.’, ‘/’}, optional: Can be used to specify whether the array is in a flat (‘.’) or nested (‘/’) format. If None, the appropriate value will be read from store when present. Otherwise, defaults to ‘.’ when zarr_version == 2 and / otherwise.
meta_arrayarray-like, optional: An array instance to use for determining arrays to create and return to users. Use numpy.empty(()) by default.

New in version 2.15.

Returns:

zzarr.core.Array

Notes

There is no need to close an array. Data are automatically flushed to the file system.

Examples

>>> import numpy as np
>>> import zarr
>>> z1 = zarr.open_array('data/example.zarr', mode='w', shape=(10000, 10000),
...                      chunks=(1000, 1000), fill_value=0)
>>> z1[:] = np.arange(100000000).reshape(10000, 10000)
>>> z1
<zarr.core.Array (10000, 10000) float64>
>>> z2 = zarr.open_array('data/example.zarr', mode='r')
>>> z2
<zarr.core.Array (10000, 10000) float64 read-only>
>>> np.all(z1[:] == z2[:])
True

zarr.creation.empty_like(a, **kwargs)[source]#: Create an empty array like a.

zarr.creation.zeros_like(a, **kwargs)[source]#: Create an array of zeros like a.

zarr.creation.ones_like(a, **kwargs)[source]#: Create an array of ones like a.

zarr.creation.full_like(a, **kwargs)[source]#: Create a filled array like a.

zarr.creation.open_like(a, path, **kwargs)[source]#: Open a persistent array like a.

Array creation (zarr.creation)#

Array creation (`zarr.creation`)#