zarr.testing.stateful
=====================

.. py:module:: zarr.testing.stateful


Attributes
----------

.. autoapisummary::

   zarr.testing.stateful.F
   zarr.testing.stateful.MAX_BINARY_SIZE


Classes
-------

.. autoapisummary::

   zarr.testing.stateful.SyncStoreWrapper
   zarr.testing.stateful.ZarrHierarchyStateMachine
   zarr.testing.stateful.ZarrStoreStateMachine


Functions
---------

.. autoapisummary::

   zarr.testing.stateful.split_prefix_name
   zarr.testing.stateful.with_frequency


Module Contents
---------------

.. py:class:: SyncStoreWrapper(store: zarr.abc.store.Store)

   Bases: :py:obj:`zarr.core.sync.SyncMixin`


   .. py:method:: clear() -> None


   .. py:method:: delete(path: str) -> None


   .. py:method:: exists(key: str) -> bool


   .. py:method:: get(key: str, prototype: zarr.core.buffer.BufferPrototype) -> zarr.core.buffer.Buffer | None


   .. py:method:: get_partial_values(key_ranges: list[Any], prototype: zarr.core.buffer.BufferPrototype) -> list[zarr.core.buffer.Buffer | None]


   .. py:method:: is_empty(prefix: str) -> bool


   .. py:method:: list() -> list[str]


   .. py:method:: list_dir(prefix: str) -> None
      :abstractmethod:



   .. py:method:: list_prefix(prefix: str) -> None
      :abstractmethod:



   .. py:method:: set(key: str, data_buffer: zarr.core.buffer.Buffer) -> None


   .. py:method:: set_partial_values(key_start_values: Any) -> None
      :abstractmethod:



   .. py:property:: read_only
      :type: bool



   .. py:attribute:: store


   .. py:property:: supports_deletes
      :type: bool



   .. py:property:: supports_listing
      :type: bool



   .. py:property:: supports_partial_writes
      :type: bool



   .. py:property:: supports_writes
      :type: bool



.. py:class:: ZarrHierarchyStateMachine(store: zarr.abc.store.Store)

   Bases: :py:obj:`zarr.core.sync.SyncMixin`, :py:obj:`hypothesis.stateful.RuleBasedStateMachine`


   
   This state machine models operations that modify a zarr store's
   hierarchy. That is, user actions that modify arrays/groups as well
   as list operations. It is intended to be used by external stores, and
   compares their results to a MemoryStore that is assumed to be perfect.
















   ..
       !! processed by numpydoc !!

   .. py:method:: add_array(data: hypothesis.strategies.DataObject, name: str, array_and_chunks: tuple[numpy.ndarray[Any, Any], tuple[int, Ellipsis]]) -> None


   .. py:method:: add_group(name: str, data: hypothesis.strategies.DataObject) -> None


   .. py:method:: can_add(path: str) -> bool


   .. py:method:: check_array(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: check_list_dir(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: check_list_prefix_from_root() -> None


   .. py:method:: clear() -> None


   .. py:method:: delete_array_using_del(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: delete_chunk(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: delete_dir(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: delete_group_using_del(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: draw_directory(data: hypothesis.strategies.DataObject) -> str


   .. py:method:: init_store() -> None


   .. py:method:: overwrite_array_basic_indexing(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: overwrite_array_orthogonal_indexing(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: resize_array(data: hypothesis.strategies.DataObject) -> None


   .. py:attribute:: all_arrays
      :type:  set[str]


   .. py:attribute:: all_groups
      :type:  set[str]


   .. py:attribute:: model


   .. py:attribute:: store


.. py:class:: ZarrStoreStateMachine(store: zarr.abc.store.Store)

   Bases: :py:obj:`hypothesis.stateful.RuleBasedStateMachine`


   
   "
   Zarr store state machine

       This is a subclass of a Hypothesis RuleBasedStateMachine.
       It is testing a framework to ensure that the state of a Zarr store matches
       an expected state after a set of random operations. It contains a store
       (currently, a Zarr MemoryStore) and a model, a simplified version of a
       zarr store (in this case, a dict). It also contains rules which represent
       actions that can be applied to a zarr store. Rules apply an action to both
       the store and the model, and invariants assert that the state of the model
       is equal to the state of the store. Hypothesis then generates sequences of
       rules, running invariants after each rule. It raises an error if a sequence
       produces discontinuity between state of the model and state of the store
       (ie. an invariant is violated).
       https://hypothesis.readthedocs.io/en/latest/stateful.html















   ..
       !! processed by numpydoc !!

   .. py:method:: check_num_zarr_keys_equal() -> None


   .. py:method:: check_paths_equal() -> None


   .. py:method:: check_vals_equal() -> None


   .. py:method:: check_zarr_keys() -> None


   .. py:method:: clear() -> None


   .. py:method:: delete(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: exists(key: str) -> None


   .. py:method:: get(key: str, data: hypothesis.strategies.DataObject) -> None


   .. py:method:: get_invalid_zarr_keys(key: str, data: hypothesis.strategies.DataObject) -> None


   .. py:method:: get_partial_values(data: hypothesis.strategies.DataObject) -> None


   .. py:method:: init_store() -> None


   .. py:method:: is_empty() -> None


   .. py:method:: set(key: str, data: bytes) -> None


   .. py:attribute:: model
      :type:  dict[str, zarr.core.buffer.Buffer]


   .. py:attribute:: prototype


   .. py:attribute:: store


.. py:function:: split_prefix_name(path: str) -> tuple[str, str]

.. py:function:: with_frequency(frequency: float) -> collections.abc.Callable[[F], F]

   
   This needs to be deterministic for hypothesis replaying
















   ..
       !! processed by numpydoc !!

.. py:data:: F

.. py:data:: MAX_BINARY_SIZE
   :value: 100