Merge branch 'main' into dependabot/github_actions/actions-4d91c028e1

Author: d-v-b

changes/3899.bugfix.md

@@ -0,0 +1,7 @@
+Make chunk normalization properly handle `-1` as a compact representation of the
+length of an entire axis. Reject several previously-accepted but ill-defined
+chunk specifications: `chunks=True` (previously silently produced size-1 chunks),
+chunk tuples shorter than the array's number of dimensions (previously padded to
+the array's shape), and `None` as a per-dimension chunk size. These all now
+raise informative errors. Also fix chunk handling for 0-length array dimensions,
+and add explicit rejection of 0-length chunks.

docs/contributing.md

@@ -255,7 +255,7 @@ print("Hello world")
 
 #### Building documentation without executing code blocks
 
-Sometimes, you may want the documentation to build quicker. You can disable code block execution by commenting out the [markdown-exec](https://github.com/zarr-developers/zarr-python/blob/884a8c91afcc3efe28b3da952be3b85125c453cb/mkdocs.yml#L132 plugin in the mkdocs configuration file). This will make code blocks and cross references render incorrectly (i.e., expect build warnings), but also reduces build time by ~3x. Be sure to undo the commenting out before opening your pull request.
+Sometimes, you may want the documentation to build quicker. You can disable code block execution by commenting out the [markdown-exec plugin](https://github.com/zarr-developers/zarr-python/blob/884a8c91afcc3efe28b3da952be3b85125c453cb/mkdocs.yml#L132) in the mkdocs configuration file. This will make code blocks and cross references render incorrectly (i.e., expect build warnings), but also reduces build time by ~3x. Be sure to undo the commenting out before opening your pull request.
 
 ### Changelog
 

docs/user-guide/arrays.md

@@ -512,7 +512,7 @@ print(np.all(z.oindex[[0, 2], :] == z[[0, 2], :]))
 
 ### Block Indexing
 
-Zarr also support block indexing, which allows selections of whole chunks based on their
+Zarr also supports block indexing, which allows selections of whole chunks based on their
 logical indices along each dimension of an array. For example, this allows selecting
 a subset of chunk aligned rows and/or columns from a 2-dimensional array. E.g.:
 

docs/user-guide/consolidated_metadata.md

@@ -17,7 +17,7 @@ If consolidated metadata is present in a Zarr Group's metadata then it is used
 by default.  The initial read to open the group will need to communicate with
 the store (reading from a file for a [`zarr.storage.LocalStore`][], making a
 network request for a [`zarr.storage.FsspecStore`][]). After that, any subsequent
-metadata reads get child Group or Array nodes will *not* require reads from the store.
+metadata reads to get child Group or Array nodes will *not* require reads from the store.
 
 In Python, the consolidated metadata is available on the `.consolidated_metadata`
 attribute of the `GroupMetadata` object.
@@ -49,7 +49,7 @@ print(result)
 ```
 
 If we open that group, the Group's metadata has a `zarr.core.group.ConsolidatedMetadata`
-that can be used.:
+that can be used:
 
 ```python exec="true" session="consolidated_metadata" source="above" result="ansi"
 from pprint import pprint
@@ -58,19 +58,19 @@ import io
 consolidated = zarr.open_group(store="memory://consolidated-metadata-demo")
 consolidated_metadata = consolidated.metadata.consolidated_metadata.metadata
 
-# Note: pprint can be users without capturing the output regularly
+# Note: pprint can be used without capturing the output regularly
 output = io.StringIO()
 pprint(dict(sorted(consolidated_metadata.items())), stream=output, width=60)
 print(output.getvalue())
 ```
 
-Operations on the group to get children automatically use the consolidated metadata.:
+Operations on the group to get children automatically use the consolidated metadata:
 
 ```python exec="true" session="consolidated_metadata" source="above" result="ansi"
 print(consolidated['a'])  # no read / HTTP request to the Store is required
 ```
 
-With nested groups, the consolidated metadata is available on the children, recursively.:
+With nested groups, the consolidated metadata is available on the children, recursively:
 
 ```python exec="true" session="consolidated_metadata" source="above" result="ansi"
 child = group.create_group('child', attributes={'kind': 'child'})

docs/user-guide/data_types.md

@@ -262,7 +262,7 @@ here, it's possible to create it yourself: see [Adding New Data Types](#adding-n
 
 ### Example Usage
 
-This section will demonstrates the basic usage of Zarr data types.
+This section will demonstrate the basic usage of Zarr data types.
 
 Create a `ZDType` from a native data type:
 

docs/user-guide/experimental.md

@@ -4,7 +4,7 @@ This section contains documentation for experimental Zarr Python features. The f
 
 ## `CacheStore`
 
-Zarr Python 3.1.4 adds [`zarr.experimental.cache_store.CacheStore`][] provides a dual-store caching implementation
+Zarr Python 3.1.4 adds [`zarr.experimental.cache_store.CacheStore`][], which provides a dual-store caching implementation
 that can be wrapped around any Zarr store to improve performance for repeated data access.
 This is particularly useful when working with remote stores (e.g., S3, HTTP) where network
 latency can significantly impact data access speed.

docs/user-guide/extending.md

@@ -78,11 +78,11 @@ Coming soon.
 
 ## Custom array buffers
 
-Zarr-python provides control over where and how arrays stored in memory through
+Zarr-python provides control over where and how arrays are stored in memory through
 [`zarr.abc.buffer.Buffer`][]. Currently both CPU (the default) and GPU implementations are
 provided (see [Using GPUs with Zarr](gpu.md) for more information). You can implement your own buffer
 classes by implementing the interface defined in [`zarr.abc.buffer.BufferPrototype`][].
 
 ## Other extensions
 
-In the future, Zarr will support writing custom custom data types and chunk grids.
+In the future, Zarr will support writing custom data types and chunk grids.

docs/user-guide/gpu.md

@@ -7,7 +7,7 @@ Zarr can use GPUs to accelerate your workload by running `zarr.Config.enable_gpu
     memory as the final stage of the codec pipeline. Data will still be read into
     or copied to host (CPU) memory for encoding and decoding.
 
-    In the future, codecs will be available compressing and decompressing data on
+    In the future, codecs will be available for compressing and decompressing data on
     the GPU, avoiding the need to move data between the host and device for
     compression and decompression.
 

docs/user-guide/groups.md

@@ -69,7 +69,7 @@ For more information on groups see the [`zarr.Group` API docs](../api/zarr/group
 ## Batch Group Creation
 
 You can also create multiple groups concurrently with a single function call. [`zarr.create_hierarchy`][] takes
-a [`zarr Storage instance`](../api/zarr/storage.md) instance and a dict of `key : metadata` pairs, parses that dict, and
+a [`zarr Storage`](../api/zarr/storage.md) instance and a dict of `key : metadata` pairs, parses that dict, and
 writes metadata documents to storage:
 
 ```python exec="true" session="groups" source="above" result="ansi"

docs/user-guide/performance.md

@@ -296,7 +296,7 @@ Zarr arrays and groups can be pickled, as long as the underlying store object ca
 pickled. With the exception of the `zarr.storage.MemoryStore`, any of the
 storage classes provided in the `zarr.storage` module can be pickled.
 
-If an array or group is backed by a persistent store such as the a `zarr.storage.LocalStore`,
+If an array or group is backed by a persistent store such as a `zarr.storage.LocalStore`,
 `zarr.storage.ZipStore` or `zarr.storage.FsspecStore` then the store data
 **are not** pickled. The only thing that is pickled is the necessary parameters to allow the store
 to re-open any underlying files or databases upon being unpickled.