make _STRING_DTYPE private to try to make sphinx happy

Author: rabernat

src/zarr/core/metadata/v3.py

@@ -28,7 +28,7 @@
 from zarr.core.common import ZARR_JSON, parse_named_configuration, parse_shapelike
 from zarr.core.config import config
 from zarr.core.metadata.common import ArrayMetadata, parse_attributes
-from zarr.core.strings import STRING_DTYPE as STRING_NP_DTYPE
+from zarr.core.strings import _STRING_DTYPE as STRING_NP_DTYPE
 from zarr.registry import get_codec_class
 
 DEFAULT_DTYPE = "float64"

src/zarr/core/strings.py

@@ -7,12 +7,12 @@
 
 import numpy as np
 
-# STRING_DTYPE is the in-memory datatype that will be used for V3 string arrays
+# _STRING_DTYPE is the in-memory datatype that will be used for V3 string arrays
 # when reading data back from Zarr.
 # Any valid string-like datatype should be fine for *setting* data.
 
-STRING_DTYPE: Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]
-NUMPY_SUPPORTS_VLEN_STRING: bool
+_STRING_DTYPE: Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]
+_NUMPY_SUPPORTS_VLEN_STRING: bool
 
 
 def cast_array(
@@ -23,24 +23,24 @@ def cast_array(
 
 try:
     # this new vlen string dtype was added in NumPy 2.0
-    STRING_DTYPE = np.dtypes.StringDType()
-    NUMPY_SUPPORTS_VLEN_STRING = True
+    _STRING_DTYPE = np.dtypes.StringDType()
+    _NUMPY_SUPPORTS_VLEN_STRING = True
 
     def cast_array(
         data: np.ndarray[Any, np.dtype[Any]],
     ) -> np.ndarray[Any, np.dtypes.StringDType | np.dtypes.ObjectDType]:
-        out = data.astype(STRING_DTYPE, copy=False)
+        out = data.astype(_STRING_DTYPE, copy=False)
         return cast(np.ndarray[Any, np.dtypes.StringDType], out)
 
 except AttributeError:
     # if not available, we fall back on an object array of strings, as in Zarr < 3
-    STRING_DTYPE = np.dtypes.ObjectDType()
-    NUMPY_SUPPORTS_VLEN_STRING = False
+    _STRING_DTYPE = np.dtypes.ObjectDType()
+    _NUMPY_SUPPORTS_VLEN_STRING = False
 
     def cast_array(
         data: np.ndarray[Any, np.dtype[Any]],
     ) -> np.ndarray[Any, Union["np.dtypes.StringDType", "np.dtypes.ObjectDType"]]:
-        out = data.astype(STRING_DTYPE, copy=False)
+        out = data.astype(_STRING_DTYPE, copy=False)
         return cast(np.ndarray[Any, np.dtypes.ObjectDType], out)
 
 
@@ -61,13 +61,13 @@ def cast_to_string_dtype(
         return cast_array(data)
         # out = data.astype(STRING_DTYPE, copy=False)
         # return cast(np.ndarray[Any, np.dtypes.StringDType | np.dtypes.ObjectDType], out)
-    if NUMPY_SUPPORTS_VLEN_STRING:
-        if np.issubdtype(data.dtype, STRING_DTYPE):
+    if _NUMPY_SUPPORTS_VLEN_STRING:
+        if np.issubdtype(data.dtype, _STRING_DTYPE):
             # already a valid string variable length string dtype
             return cast_array(data)
     if np.issubdtype(data.dtype, np.object_):
         # object arrays require more careful handling
-        if NUMPY_SUPPORTS_VLEN_STRING:
+        if _NUMPY_SUPPORTS_VLEN_STRING:
             try:
                 # cast to variable-length string dtype, fail if object contains non-string data
                 # mypy says "error: Unexpected keyword argument "coerce" for "StringDType"  [call-arg]"

tests/test_strings.py

@@ -3,33 +3,33 @@
 import numpy as np
 import pytest
 
-from zarr.core.strings import NUMPY_SUPPORTS_VLEN_STRING, STRING_DTYPE, cast_to_string_dtype
+from zarr.core.strings import _NUMPY_SUPPORTS_VLEN_STRING, _STRING_DTYPE, cast_to_string_dtype
 
 
 def test_string_defaults() -> None:
-    if NUMPY_SUPPORTS_VLEN_STRING:
-        assert STRING_DTYPE == np.dtypes.StringDType()
+    if _NUMPY_SUPPORTS_VLEN_STRING:
+        assert _STRING_DTYPE == np.dtypes.StringDType()
     else:
-        assert STRING_DTYPE == np.dtypes.ObjectDType()
+        assert _STRING_DTYPE == np.dtypes.ObjectDType()
 
 
 def test_cast_to_string_dtype() -> None:
     d1 = np.array(["a", "b", "c"])
     assert d1.dtype == np.dtype("<U1")
     d1s = cast_to_string_dtype(d1)
-    assert d1s.dtype == STRING_DTYPE
+    assert d1s.dtype == _STRING_DTYPE
 
     with pytest.raises(ValueError, match="Cannot cast dtype |S1"):
         cast_to_string_dtype(d1.astype("|S1"))
 
-    if NUMPY_SUPPORTS_VLEN_STRING:
-        assert cast_to_string_dtype(d1.astype("T")).dtype == STRING_DTYPE
-        assert cast_to_string_dtype(d1.astype("O")).dtype == STRING_DTYPE
+    if _NUMPY_SUPPORTS_VLEN_STRING:
+        assert cast_to_string_dtype(d1.astype("T")).dtype == _STRING_DTYPE
+        assert cast_to_string_dtype(d1.astype("O")).dtype == _STRING_DTYPE
         with pytest.raises(ValueError, match="Cannot cast object dtype to string dtype"):
             cast_to_string_dtype(np.array([1, "b", "c"], dtype="O"))
     else:
         with pytest.warns():
-            assert cast_to_string_dtype(d1.astype("O")).dtype == STRING_DTYPE
+            assert cast_to_string_dtype(d1.astype("O")).dtype == _STRING_DTYPE
         with pytest.warns():
-            assert cast_to_string_dtype(np.array([1, "b", "c"], dtype="O")).dtype == STRING_DTYPE
-        assert cast_to_string_dtype(d1.astype("O"), safe=True).dtype == STRING_DTYPE
+            assert cast_to_string_dtype(np.array([1, "b", "c"], dtype="O")).dtype == _STRING_DTYPE
+        assert cast_to_string_dtype(d1.astype("O"), safe=True).dtype == _STRING_DTYPE

tests/v3/test_codecs/test_vlen.py

@@ -8,12 +8,12 @@
 from zarr.abc.store import Store
 from zarr.codecs import VLenBytesCodec, VLenUTF8Codec, ZstdCodec
 from zarr.core.metadata.v3 import ArrayV3Metadata, DataType
-from zarr.core.strings import NUMPY_SUPPORTS_VLEN_STRING
+from zarr.core.strings import _NUMPY_SUPPORTS_VLEN_STRING
 from zarr.storage.common import StorePath
 
 numpy_str_dtypes: list[type | None] = [None, str, np.dtypes.StrDType]
 expected_zarr_string_dtype: np.dtype[Any]
-if NUMPY_SUPPORTS_VLEN_STRING:
+if _NUMPY_SUPPORTS_VLEN_STRING:
     numpy_str_dtypes.append(np.dtypes.StringDType)
     expected_zarr_string_dtype = np.dtypes.StringDType()
 else: