Fix AsyncGroup.create_dataset() dtype handling and optimize tests

Author: dhruvak001

changes/3050.bugfix.rst

@@ -0,0 +1 @@
+- Fixed potential error in `AsyncGroup.create_dataset()` where `dtype` argument could be missing when calling `create_array()`

src/zarr/core/group.py

@@ -1155,8 +1155,11 @@ async def create_dataset(
         # create_dataset in zarr 2.x requires shape but not dtype if data is
         # provided. Allow this configuration by inferring dtype from data if
         # necessary and passing it to create_array
-        if "dtype" not in kwargs and data is not None:
-            kwargs["dtype"] = data.dtype
+        if "dtype" not in kwargs:
+            if data is not None:
+                kwargs["dtype"] = data.dtype
+            else:
+                raise ValueError("dtype must be provided if data is None")
         array = await self.create_array(name, shape=shape, **kwargs)
         if data is not None:
             await array.setitem(slice(None), data)
@@ -2544,12 +2547,17 @@ def require_dataset(self, name: str, *, shape: ShapeLike, **kwargs: Any) -> Arra
         ----------
         name : str
             Array name.
-        **kwargs :
-            See :func:`zarr.Group.create_dataset`.
+        shape : int or tuple of ints
+            Array shape.
+        dtype : str or dtype, optional
+            NumPy dtype.
+        exact : bool, optional
+            If True, require `dtype` to match exactly. If false, require
+            `dtype` can be cast from array dtype.
 
         Returns
         -------
-        a : Array
+        a : AsyncArray
         """
         return Array(self._sync(self._async_group.require_array(name, shape=shape, **kwargs)))
 
@@ -2562,12 +2570,17 @@ def require_array(self, name: str, *, shape: ShapeLike, **kwargs: Any) -> Array:
         ----------
         name : str
             Array name.
-        **kwargs :
-            See :func:`zarr.Group.create_array`.
+        shape : int or tuple of ints
+            Array shape.
+        dtype : str or dtype, optional
+            NumPy dtype.
+        exact : bool, optional
+            If True, require `dtype` to match exactly. If false, require
+            `dtype` can be cast from array dtype.
 
         Returns
         -------
-        a : Array
+        a : AsyncArray
         """
         return Array(self._sync(self._async_group.require_array(name, shape=shape, **kwargs)))
 

tests/test_properties.py

@@ -13,7 +13,7 @@
 
 import hypothesis.extra.numpy as npst
 import hypothesis.strategies as st
-from hypothesis import assume, given, settings
+from hypothesis import assume, given, settings, HealthCheck
 
 from zarr.abc.store import Store
 from zarr.core.common import ZARR_JSON, ZARRAY_JSON, ZATTRS_JSON
@@ -75,7 +75,7 @@ def deep_equal(a: Any, b: Any) -> bool:
 
     return a == b
 
-
+@settings(deadline=None)  # Increased from default 200ms to None
 @given(data=st.data(), zarr_format=zarr_formats)
 def test_array_roundtrip(data: st.DataObject, zarr_format: int) -> None:
     nparray = data.draw(numpy_arrays(zarr_formats=st.just(zarr_format)))
@@ -117,10 +117,11 @@ def test_basic_indexing(data: st.DataObject) -> None:
     assert_array_equal(nparray, zarray[:])
 
 
+@settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
 @given(data=st.data())
 def test_oindex(data: st.DataObject) -> None:
     # integer_array_indices can't handle 0-size dimensions.
-    zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=4, min_side=1)))
+    zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=3, min_side=1, max_side=8)))
     nparray = zarray[:]
 
     zindexer, npindexer = data.draw(orthogonal_indices(shape=nparray.shape))
@@ -138,15 +139,17 @@ def test_oindex(data: st.DataObject) -> None:
     assert_array_equal(nparray, zarray[:])
 
 
+@settings(deadline=None, suppress_health_check=[HealthCheck.too_slow])
 @given(data=st.data())
 def test_vindex(data: st.DataObject) -> None:
     # integer_array_indices can't handle 0-size dimensions.
-    zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=4, min_side=1)))
+    zarray = data.draw(simple_arrays(shapes=npst.array_shapes(max_dims=3, min_side=1, max_side=8)))
     nparray = zarray[:]
 
     indexer = data.draw(
         npst.integer_array_indices(
-            shape=nparray.shape, result_shape=npst.array_shapes(min_side=1, max_dims=None)
+            shape=nparray.shape,
+            result_shape=npst.array_shapes(min_side=1, max_dims=2, max_side=8)
         )
     )
     actual = zarray.vindex[indexer]