from typing import Optional
from warnings import warn
import numpy as np
from numcodecs.registry import codec_registry
from zarr._storage.store import DEFAULT_ZARR_VERSION
from zarr.core import Array
from zarr.errors import (
ArrayNotFoundError,
ContainsArrayError,
ContainsGroupError,
)
from zarr.storage import (
contains_array,
contains_group,
default_compressor,
init_array,
normalize_storage_path,
normalize_store_arg,
)
from zarr.util import normalize_dimension_separator
[docs]
def create(
shape,
chunks=True,
dtype=None,
compressor="default",
fill_value: Optional[int] = 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,
):
"""Create an array.
Parameters
----------
shape : int or tuple of ints
Array shape.
chunks : int 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.
dtype : string or dtype, optional
NumPy dtype.
compressor : Codec, optional
Primary compressor.
fill_value : object
Default value to use for uninitialized portions of the array.
order : {'C', 'F'}, optional
Memory layout to be used within each chunk.
store : MutableMapping or string
Store or path to directory in file system or name of zip file.
synchronizer : object, optional
Array synchronizer.
overwrite : bool, optional
If True, delete all pre-existing data in `store` at `path` before
creating the array.
path : string, optional
Path under which array is stored.
chunk_store : MutableMapping, optional
Separate storage for chunks. If not provided, `store` will be used
for storage of both chunks and metadata.
filters : sequence of Codecs, optional
Sequence of filters to use to encode chunk data prior to compression.
cache_metadata : bool, 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_attrs : bool, 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_only : bool, optional
True if array should be protected against modification.
object_codec : Codec, optional
A codec to encode object arrays, only needed if dtype=object.
dimension_separator : {'.', '/'}, optional
Separator placed between the dimensions of a chunk.
.. versionadded:: 2.8
write_empty_chunks : bool, 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.
.. versionadded:: 2.11
storage_transformers : sequence 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.
.. versionadded:: 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.
.. versionadded:: 2.12
meta_array : array-like, optional
An array instance to use for determining arrays to create and return
to users. Use `numpy.empty(())` by default.
.. versionadded:: 2.13
Returns
-------
z : zarr.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>
"""
if zarr_version is None and store is None:
zarr_version = getattr(chunk_store, "_store_version", DEFAULT_ZARR_VERSION)
# handle polymorphic store arg
store = normalize_store_arg(store, zarr_version=zarr_version, mode="w")
zarr_version = getattr(store, "_store_version", DEFAULT_ZARR_VERSION)
# API compatibility with h5py
compressor, fill_value = _kwargs_compat(compressor, fill_value, kwargs)
# optional array metadata
if dimension_separator is None:
dimension_separator = getattr(store, "_dimension_separator", None)
else:
store_separator = getattr(store, "_dimension_separator", None)
if store_separator not in (None, dimension_separator):
raise ValueError(
f"Specified dimension_separator: {dimension_separator}"
f"conflicts with store's separator: "
f"{store_separator}"
)
dimension_separator = normalize_dimension_separator(dimension_separator)
if zarr_version > 2 and path is None:
path = "/"
# initialize array metadata
init_array(
store,
shape=shape,
chunks=chunks,
dtype=dtype,
compressor=compressor,
fill_value=fill_value,
order=order,
overwrite=overwrite,
path=path,
chunk_store=chunk_store,
filters=filters,
object_codec=object_codec,
dimension_separator=dimension_separator,
storage_transformers=storage_transformers,
)
# instantiate array
z = Array(
store,
path=path,
chunk_store=chunk_store,
synchronizer=synchronizer,
cache_metadata=cache_metadata,
cache_attrs=cache_attrs,
read_only=read_only,
write_empty_chunks=write_empty_chunks,
meta_array=meta_array,
)
return z
def _kwargs_compat(compressor, fill_value, kwargs):
# to be compatible with h5py, as well as backwards-compatible with Zarr
# 1.x, accept 'compression' and 'compression_opts' keyword arguments
if compressor != "default":
# 'compressor' overrides 'compression'
if "compression" in kwargs:
warn(
"'compression' keyword argument overridden by 'compressor'",
stacklevel=3,
)
del kwargs["compression"]
if "compression_opts" in kwargs:
warn(
"'compression_opts' keyword argument overridden by 'compressor'",
stacklevel=3,
)
del kwargs["compression_opts"]
elif "compression" in kwargs:
compression = kwargs.pop("compression")
compression_opts = kwargs.pop("compression_opts", None)
if compression is None or compression == "none":
compressor = None
elif compression == "default":
compressor = default_compressor
elif isinstance(compression, str):
codec_cls = codec_registry[compression]
# handle compression_opts
if isinstance(compression_opts, dict):
compressor = codec_cls(**compression_opts)
elif isinstance(compression_opts, (list, tuple)):
compressor = codec_cls(*compression_opts)
elif compression_opts is None:
compressor = codec_cls()
else:
# assume single argument, e.g., int
compressor = codec_cls(compression_opts)
# be lenient here if user gives compressor as 'compression'
elif hasattr(compression, "get_config"):
compressor = compression
else:
raise ValueError("bad value for compression: %r" % compression)
# handle 'fillvalue'
if "fillvalue" in kwargs:
# to be compatible with h5py, accept 'fillvalue' instead of
# 'fill_value'
fill_value = kwargs.pop("fillvalue")
# ignore other keyword arguments
for k in kwargs:
warn("ignoring keyword argument %r" % k)
return compressor, fill_value
[docs]
def empty(shape, **kwargs):
"""Create an empty array.
For parameter definitions see :func:`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.
"""
return create(shape=shape, fill_value=None, **kwargs)
[docs]
def zeros(shape, **kwargs):
"""Create an array, with zero being used as the default value for
uninitialized portions of the array.
For parameter definitions see :func:`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.]])
"""
return create(shape=shape, fill_value=0, **kwargs)
[docs]
def ones(shape, **kwargs):
"""Create an array, with one being used as the default value for
uninitialized portions of the array.
For parameter definitions see :func:`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.]])
"""
return create(shape=shape, fill_value=1, **kwargs)
[docs]
def full(shape, fill_value, **kwargs):
"""Create an array, with `fill_value` being used as the default value for
uninitialized portions of the array.
For parameter definitions see :func:`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.]])
"""
return create(shape=shape, fill_value=fill_value, **kwargs)
def _get_shape_chunks(a):
shape = None
chunks = None
if hasattr(a, "shape") and isinstance(a.shape, tuple):
shape = a.shape
if hasattr(a, "chunks") and isinstance(a.chunks, tuple) and (len(a.chunks) == len(a.shape)):
chunks = a.chunks
elif hasattr(a, "chunklen"):
# bcolz carray
chunks = (a.chunklen,) + a.shape[1:]
return shape, chunks
[docs]
def array(data, **kwargs):
"""Create an array filled with `data`.
The `data` argument should be a NumPy array or array-like object. For
other parameter definitions see :func:`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>
"""
# ensure data is array-like
if not hasattr(data, "shape") or not hasattr(data, "dtype"):
data = np.asanyarray(data)
# setup dtype
kw_dtype = kwargs.get("dtype")
if kw_dtype is None:
kwargs["dtype"] = data.dtype
else:
kwargs["dtype"] = kw_dtype
# setup shape and chunks
data_shape, data_chunks = _get_shape_chunks(data)
kwargs["shape"] = data_shape
kw_chunks = kwargs.get("chunks")
if kw_chunks is None:
kwargs["chunks"] = data_chunks
else:
kwargs["chunks"] = kw_chunks
# pop read-only to apply after storing the data
read_only = kwargs.pop("read_only", False)
# instantiate array
z = create(**kwargs)
# fill with data
z[...] = data
# set read_only property afterwards
z.read_only = read_only
return z
[docs]
def 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,
):
"""Open an array using file-mode-like semantics.
Parameters
----------
store : MutableMapping 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).
shape : int or tuple of ints, optional
Array shape.
chunks : int 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.
dtype : string or dtype, optional
NumPy dtype.
compressor : Codec, optional
Primary compressor.
fill_value : object, optional
Default value to use for uninitialized portions of the array.
order : {'C', 'F'}, optional
Memory layout to be used within each chunk.
synchronizer : object, optional
Array synchronizer.
filters : sequence, optional
Sequence of filters to use to encode chunk data prior to compression.
cache_metadata : bool, 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_attrs : bool, 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.
path : string, optional
Array path within store.
object_codec : Codec, optional
A codec to encode object arrays, only needed if dtype=object.
chunk_store : MutableMapping or string, optional
Store or path to directory in file system or name of zip file.
storage_options : dict
If using an fsspec URL to create the store, these will be passed to
the backend implementation. Ignored otherwise.
partial_decompress : bool, 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_chunks : bool, 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.
.. versionadded:: 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_array : array-like, optional
An array instance to use for determining arrays to create and return
to users. Use `numpy.empty(())` by default.
.. versionadded:: 2.15
Returns
-------
z : zarr.core.Array
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
Notes
-----
There is no need to close an array. Data are automatically flushed to the
file system.
"""
# use same mode semantics as h5py
# r : read only, must exist
# r+ : read/write, must exist
# w : create, delete if exists
# w- or x : create, fail if exists
# a : read/write if exists, create otherwise (default)
if zarr_version is None and store is None:
zarr_version = getattr(chunk_store, "_store_version", DEFAULT_ZARR_VERSION)
# handle polymorphic store arg
store = normalize_store_arg(
store, storage_options=storage_options, mode=mode, zarr_version=zarr_version
)
zarr_version = getattr(store, "_store_version", DEFAULT_ZARR_VERSION)
if chunk_store is not None:
chunk_store = normalize_store_arg(
chunk_store, storage_options=storage_options, mode=mode, zarr_version=zarr_version
)
# respect the dimension separator specified in a store, if present
if dimension_separator is None:
if hasattr(store, "_dimension_separator"):
dimension_separator = store._dimension_separator
else:
dimension_separator = "." if zarr_version == 2 else "/"
if zarr_version == 3 and path is None:
path = "array" # TODO: raise ValueError instead?
path = normalize_storage_path(path)
# API compatibility with h5py
compressor, fill_value = _kwargs_compat(compressor, fill_value, kwargs)
# ensure fill_value of correct type
if fill_value is not None:
fill_value = np.array(fill_value, dtype=dtype)[()]
# ensure store is initialized
if mode in ["r", "r+"]:
if not contains_array(store, path=path):
if contains_group(store, path=path):
raise ContainsGroupError(path)
raise ArrayNotFoundError(path)
elif mode == "w":
init_array(
store,
shape=shape,
chunks=chunks,
dtype=dtype,
compressor=compressor,
fill_value=fill_value,
order=order,
filters=filters,
overwrite=True,
path=path,
object_codec=object_codec,
chunk_store=chunk_store,
dimension_separator=dimension_separator,
)
elif mode == "a":
if not contains_array(store, path=path):
if contains_group(store, path=path):
raise ContainsGroupError(path)
init_array(
store,
shape=shape,
chunks=chunks,
dtype=dtype,
compressor=compressor,
fill_value=fill_value,
order=order,
filters=filters,
path=path,
object_codec=object_codec,
chunk_store=chunk_store,
dimension_separator=dimension_separator,
)
elif mode in ["w-", "x"]:
if contains_group(store, path=path):
raise ContainsGroupError(path)
elif contains_array(store, path=path):
raise ContainsArrayError(path)
else:
init_array(
store,
shape=shape,
chunks=chunks,
dtype=dtype,
compressor=compressor,
fill_value=fill_value,
order=order,
filters=filters,
path=path,
object_codec=object_codec,
chunk_store=chunk_store,
dimension_separator=dimension_separator,
)
# determine read only status
read_only = mode == "r"
# instantiate array
z = Array(
store,
read_only=read_only,
synchronizer=synchronizer,
cache_metadata=cache_metadata,
cache_attrs=cache_attrs,
path=path,
chunk_store=chunk_store,
write_empty_chunks=write_empty_chunks,
meta_array=meta_array,
)
return z
def _like_args(a, kwargs):
shape, chunks = _get_shape_chunks(a)
if shape is not None:
kwargs.setdefault("shape", shape)
if chunks is not None:
kwargs.setdefault("chunks", chunks)
if hasattr(a, "dtype"):
kwargs.setdefault("dtype", a.dtype)
if isinstance(a, Array):
kwargs.setdefault("compressor", a.compressor)
kwargs.setdefault("order", a.order)
kwargs.setdefault("filters", a.filters)
kwargs.setdefault("zarr_version", a._version)
else:
kwargs.setdefault("compressor", "default")
kwargs.setdefault("order", "C")
[docs]
def empty_like(a, **kwargs):
"""Create an empty array like `a`."""
_like_args(a, kwargs)
return empty(**kwargs)
[docs]
def zeros_like(a, **kwargs):
"""Create an array of zeros like `a`."""
_like_args(a, kwargs)
return zeros(**kwargs)
[docs]
def ones_like(a, **kwargs):
"""Create an array of ones like `a`."""
_like_args(a, kwargs)
return ones(**kwargs)
[docs]
def full_like(a, **kwargs):
"""Create a filled array like `a`."""
_like_args(a, kwargs)
if isinstance(a, Array):
kwargs.setdefault("fill_value", a.fill_value)
return full(**kwargs)
[docs]
def open_like(a, path, **kwargs):
"""Open a persistent array like `a`."""
_like_args(a, kwargs)
if isinstance(a, Array):
kwargs.setdefault("fill_value", a.fill_value)
return open_array(path, **kwargs)
