Merge branch 'main' into fix_deepsource.io_issues

.deepsource.toml

@@ -5,6 +5,3 @@ test_patterns = ["zarr/tests/test_*.py"]
 [[analyzers]]
 name = "python"
 enabled = true
-
-  [analyzers.meta]
-  runtime_version = "3.x.x"

docs/release.rst

@@ -6,10 +6,16 @@ Release notes
     # to document your changes. On releases it will be
     # re-indented so that it does not show up in the notes.
 
-    .. _unreleased:
+.. _unreleased:
+
+Unreleased
+----------
+
+Documentation
+~~~~~~~~~~~~~
+
+* Typo fixes to close quotes. By :user:`Pavithra Eswaramoorthy <pavithraes>`
 
-    Unreleased
-    ----------
 
 .. _release_2.12.0:
 

docs/tutorial.rst

@@ -1404,15 +1404,15 @@ access patterns and incur a substantial performance hit when using
 file based stores. One of the most pathological examples is
 switching from column-based chunking to row-based chunking e.g. ::
 
-    >>> a = zarr.zeros((10000,10000), chunks=(10000, 1), dtype='uint16, store='a.zarr')
+    >>> a = zarr.zeros((10000,10000), chunks=(10000, 1), dtype='uint16', store='a.zarr')
     >>> b = zarr.array(a, chunks=(1,10000), store='b.zarr')
 
 which will require every chunk in the input data set to be repeatedly read when creating
 each output chunk. If the entire array will fit within memory, this is simply resolved
 by forcing the entire input array into memory as a numpy array before converting
 back to zarr with the desired chunking. ::
 
-    >>> a = zarr.zeros((10000,10000), chunks=(10000, 1), dtype='uint16, store='a.zarr')
+    >>> a = zarr.zeros((10000,10000), chunks=(10000, 1), dtype='uint16', store='a.zarr')
     >>> b = a[...]
     >>> c = zarr.array(b, chunks=(1,10000), store='c.zarr')