zarr.testing.strategies#

Attributes#

array_names

array_shapes

attrs

compressors

node_names

short_node_names

stores

zarr_formats

zarr_key_chars

Functions#

array_metadata(...)

arrays(, array_names, arrays, attrs, zarr_formats)

basic_indices(→ Any)

Basic indices without unsupported negative slices.

chunk_shapes(→ tuple[int, Ellipsis])

clear_store(→ zarr.abc.store.Store)

dimension_names(→ list[None | str] | None)

end_slices(→ Any)

A strategy that slices ranges that include the last chunk.

is_negative_slice(→ bool)

key_ranges(...)

Function to generate key_ranges strategy for get_partial_values()

keys(→ str)

np_array_and_chunks() → tuple[numpy.ndarray, ...)

A hypothesis strategy to generate small sized random arrays.

numpy_arrays(→ numpy.typing.NDArray[Any])

Generate numpy arrays that can be saved in the provided Zarr format.

orthogonal_indices(→ tuple[tuple[numpy.ndarray[Any, ...)

Strategy that returns

paths(→ str)

safe_unicode_for_dtype(...)

Generate UTF-8-safe text constrained to max_len of dtype.

shard_shapes(→ tuple[int, Ellipsis])

simple_arrays(→ Any)

v2_dtypes(...)

v3_dtypes(...)

Module Contents#

zarr.testing.strategies.array_metadata(
*,
array_shapes: collections.abc.Callable[Ellipsis, hypothesis.strategies.SearchStrategy[tuple[int, Ellipsis]]] = npst.array_shapes,
zarr_formats: hypothesis.strategies.SearchStrategy[Literal[2, 3]] = zarr_formats,
attributes: hypothesis.strategies.SearchStrategy[collections.abc.Mapping[str, zarr.core.common.JSON] | None] = attrs,
) → zarr.core.metadata.ArrayV2Metadata | zarr.core.metadata.ArrayV3Metadata[source]#
zarr.testing.strategies.arrays(
*,
shapes: hypothesis.strategies.SearchStrategy[tuple[int, Ellipsis]] = array_shapes,
compressors: hypothesis.strategies.SearchStrategy = compressors,
stores: hypothesis.strategies.SearchStrategy[zarr.storage.StoreLike] = stores,
paths: hypothesis.strategies.SearchStrategy[str] = paths(),
array_names: hypothesis.strategies.SearchStrategy = array_names,
arrays: hypothesis.strategies.SearchStrategy | None = None,
attrs: hypothesis.strategies.SearchStrategy = attrs,
zarr_formats: hypothesis.strategies.SearchStrategy = zarr_formats,
) → zarr.core.array.Array[source]#
zarr.testing.strategies.basic_indices(
*,
shape: tuple[int],
min_dims: int = 0,
max_dims: int | None = None,
allow_newaxis: bool = False,
allow_ellipsis: bool = True,
) → Any[source]#

Basic indices without unsupported negative slices.

zarr.testing.strategies.chunk_shapes(*, shape: tuple[int, Ellipsis]) → tuple[int, Ellipsis][source]#
zarr.testing.strategies.clear_store(x: zarr.abc.store.Store) → zarr.abc.store.Store[source]#
zarr.testing.strategies.dimension_names(*, ndim: int | None = None) → list[None | str] | None[source]#
zarr.testing.strategies.end_slices(*, shape: tuple[int]) → Any[source]#

A strategy that slices ranges that include the last chunk. This is intended to stress-test handling of a possibly smaller last chunk.

zarr.testing.strategies.is_negative_slice(idx: Any) → bool[source]#
zarr.testing.strategies.key_ranges(
keys: hypothesis.strategies.SearchStrategy[str] = node_names,
max_size: int = sys.maxsize,
) → hypothesis.strategies.SearchStrategy[list[tuple[str, zarr.abc.store.RangeByteRequest]]][source]#

Function to generate key_ranges strategy for get_partial_values() returns list strategy w/ form:

[(key, (range_start, range_end)),
 (key, (range_start, range_end)),...]
zarr.testing.strategies.keys(*, max_num_nodes: int | None = None) → str[source]#
zarr.testing.strategies.np_array_and_chunks(
*,
arrays: hypothesis.strategies.SearchStrategy[numpy.typing.NDArray[Any]] = numpy_arrays(),
) → tuple[numpy.ndarray, tuple[int, Ellipsis]][source]#

A hypothesis strategy to generate small sized random arrays.

Returns: a tuple of the array and a suitable random chunking for it.

zarr.testing.strategies.numpy_arrays(
*,
shapes: hypothesis.strategies.SearchStrategy[tuple[int, Ellipsis]] = array_shapes,
dtype: numpy.dtype[Any] | None = None,
zarr_formats: hypothesis.strategies.SearchStrategy[zarr.core.common.ZarrFormat] = zarr_formats,
) → numpy.typing.NDArray[Any][source]#

Generate numpy arrays that can be saved in the provided Zarr format.

zarr.testing.strategies.orthogonal_indices(
*,
shape: tuple[int],
) → tuple[tuple[numpy.ndarray[Any, Any], Ellipsis], tuple[numpy.ndarray[Any, Any], Ellipsis]][source]#

Strategy that returns (1) a tuple of integer arrays used for orthogonal indexing of Zarr arrays. (2) an tuple of integer arrays that can be used for equivalent indexing of numpy arrays

zarr.testing.strategies.paths(*, max_num_nodes: int | None = None) → str[source]#
zarr.testing.strategies.safe_unicode_for_dtype(
dtype: numpy.dtype[numpy.str_],
) → hypothesis.strategies.SearchStrategy[str][source]#

Generate UTF-8-safe text constrained to max_len of dtype.

zarr.testing.strategies.shard_shapes(
*,
shape: tuple[int, Ellipsis],
chunk_shape: tuple[int, Ellipsis],
) → tuple[int, Ellipsis][source]#
zarr.testing.strategies.simple_arrays(
*,
shapes: hypothesis.strategies.SearchStrategy[tuple[int, Ellipsis]] = array_shapes,
) → Any[source]#
zarr.testing.strategies.v2_dtypes() → hypothesis.strategies.SearchStrategy[numpy.dtype[Any]][source]#
zarr.testing.strategies.v3_dtypes() → hypothesis.strategies.SearchStrategy[numpy.dtype[Any]][source]#
zarr.testing.strategies.array_names[source]#
zarr.testing.strategies.array_shapes[source]#
zarr.testing.strategies.attrs: hypothesis.strategies.SearchStrategy[collections.abc.Mapping[str, zarr.core.common.JSON] | None][source]#
zarr.testing.strategies.compressors[source]#
zarr.testing.strategies.node_names[source]#
zarr.testing.strategies.short_node_names[source]#
zarr.testing.strategies.stores[source]#
zarr.testing.strategies.zarr_formats: hypothesis.strategies.SearchStrategy[zarr.core.common.ZarrFormat][source]#
zarr.testing.strategies.zarr_key_chars[source]#