Merge branch 'main' into feat/global-concurrency-limit

Author: d-v-b

changes/3464.doc.md

@@ -0,0 +1 @@
+Add documentation example for creating uncompressed arrays in the Compression section of the user guide.

docs/overrides/stylesheets/extra.css

@@ -52,14 +52,14 @@
   color: white;
 }
 
-/* Search box styling */
-.md-search__input {
+/* Search box styling in the header */
+.md-header .md-search__input {
   background-color: rgba(255, 255, 255, 0.15);
   border: 1px solid rgba(255, 255, 255, 0.2);
   color: white;
 }
 
-.md-search__input::placeholder {
+.md-header .md-search__input::placeholder {
   color: rgba(255, 255, 255, 0.7);
 }
 

docs/user-guide/arrays.md

@@ -224,6 +224,13 @@ print(z.info_complete())
 If you don't specify a compressor, by default Zarr uses the Zstandard
 compressor.
 
+To create an array without any compression, set `compressors=None`:
+
+```python exec="true" session="arrays" source="above" result="ansi"
+z_no_compress = zarr.create_array(store='data/example-uncompressed.zarr', shape=(10000, 10000), chunks=(1000, 1000), dtype='int32', compressors=None)
+print(f"Compressors: {z_no_compress.compressors}")
+```
+
 In addition to Blosc and Zstandard, other compression libraries can also be used. For example,
 here is an array using Gzip compression, level 1:
 

src/zarr/core/array.py

@@ -1234,24 +1234,28 @@ def basename(self) -> str:
     @property
     def cdata_shape(self) -> tuple[int, ...]:
         """
-        The shape of the chunk grid for this array.
+        The number of chunks along each dimension.
+
+        When sharding is used, this counts inner chunks (not shards) per dimension.
 
         Returns
         -------
         tuple[int, ...]
-            The shape of the chunk grid for this array.
+            The number of chunks along each dimension.
         """
         return self._chunk_grid_shape
 
     @property
     def _chunk_grid_shape(self) -> tuple[int, ...]:
         """
-        The shape of the chunk grid for this array.
+        The number of chunks along each dimension.
+
+        When sharding is used, this counts inner chunks (not shards) per dimension.
 
         Returns
         -------
         tuple[int, ...]
-            The shape of the chunk grid for this array.
+            The number of chunks along each dimension.
         """
         return tuple(starmap(ceildiv, zip(self.shape, self.chunks, strict=True)))
 
@@ -2398,14 +2402,23 @@ def compressors(self) -> tuple[Numcodec, ...] | tuple[BytesBytesCodec, ...]:
     @property
     def cdata_shape(self) -> tuple[int, ...]:
         """
-        The shape of the chunk grid for this array.
+        The number of chunks along each dimension.
+
+        When sharding is used, this counts inner chunks (not shards) per dimension.
         """
         return self.async_array._chunk_grid_shape
 
     @property
     def _chunk_grid_shape(self) -> tuple[int, ...]:
         """
-        The shape of the chunk grid for this array.
+        The number of chunks along each dimension.
+
+        When sharding is used, this counts inner chunks (not shards) per dimension.
+
+        Returns
+        -------
+        tuple[int, ...]
+            The number of chunks along each dimension.
         """
         return self.async_array._chunk_grid_shape
 

src/zarr/core/dtype/npy/float.py

@@ -201,6 +201,16 @@ def _check_scalar(self, data: object) -> TypeGuard[FloatLike]:
         TypeGuard[FloatLike]
             True if the input is a valid scalar value, False otherwise.
         """
+        if isinstance(data, str):
+            # Only accept strings that are valid float representations (e.g. "NaN", "inf").
+            # Plain strings that cannot be converted should return False so that cast_scalar
+            # raises TypeError rather than a confusing ValueError.
+            try:
+                self.to_native_dtype().type(data)
+            except (ValueError, OverflowError):
+                return False
+            else:
+                return True
         return isinstance(data, FloatLike)
 
     def _cast_scalar_unchecked(self, data: FloatLike) -> TFloatScalar_co:

tests/test_dtype/test_npy/test_float.py

@@ -65,7 +65,10 @@ class TestFloat16(_BaseTestFloat):
         (Float16(), -1.0, np.float16(-1.0)),
         (Float16(), "NaN", np.float16("NaN")),
     )
-    invalid_scalar_params = ((Float16(), {"set!"}),)
+    invalid_scalar_params = (
+        (Float16(), {"set!"}),
+        (Float16(), "not_a_float"),
+    )
     hex_string_params = (("0x7fc0", np.nan), ("0x7fc1", np.nan), ("0x3c00", 1.0))
     item_size_params = (Float16(),)
 
@@ -113,7 +116,10 @@ class TestFloat32(_BaseTestFloat):
         (Float32(), -1.0, np.float32(-1.0)),
         (Float32(), "NaN", np.float32("NaN")),
     )
-    invalid_scalar_params = ((Float32(), {"set!"}),)
+    invalid_scalar_params = (
+        (Float32(), {"set!"}),
+        (Float32(), "not_a_float"),
+    )
     hex_string_params = (("0x7fc00000", np.nan), ("0x7fc00001", np.nan), ("0x3f800000", 1.0))
     item_size_params = (Float32(),)
 
@@ -160,7 +166,10 @@ class TestFloat64(_BaseTestFloat):
         (Float64(), -1.0, np.float64(-1.0)),
         (Float64(), "NaN", np.float64("NaN")),
     )
-    invalid_scalar_params = ((Float64(), {"set!"}),)
+    invalid_scalar_params = (
+        (Float64(), {"set!"}),
+        (Float64(), "not_a_float"),
+    )
     hex_string_params = (
         ("0x7ff8000000000000", np.nan),
         ("0x7ff8000000000001", np.nan),