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 def create(shape, chunks=True, dtype=None, compressor='default', fill_value=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, **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 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 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 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 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 """ 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) 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) # 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 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) 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 >>> z[:2, :2] array([[0., 0.], [0., 0.]]) """ return create(shape=shape, fill_value=0, **kwargs) 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 >>> z[:2, :2] array([[1., 1.], [1., 1.]]) """ return create(shape=shape, fill_value=1, **kwargs) 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 >>> 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 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 """ # 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 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, **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. 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 >>> z2 = zarr.open_array('data/example.zarr', mode='r') >>> z2 >>> 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) 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') def empty_like(a, **kwargs): """Create an empty array like `a`.""" _like_args(a, kwargs) return empty(**kwargs) def zeros_like(a, **kwargs): """Create an array of zeros like `a`.""" _like_args(a, kwargs) return zeros(**kwargs) def ones_like(a, **kwargs): """Create an array of ones like `a`.""" _like_args(a, kwargs) return ones(**kwargs) 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) 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)