Fix fill_value handling for complex dtypes (#2200)

* Fix fill_value handling for complex & datetime dtypes

* cleanup

* more cleanup

* more cleanup

* Fix default fill_value

* Fixes

* Add booleans

* Add v2, v3 specific dtypes

* Add version.py to gitignore

* cleanpu

* style: pre-commit fixes

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: dcherian

.gitignore

@@ -84,3 +84,5 @@ fixture/
 .DS_Store
 tests/.hypothesis
 .hypothesis/
+
+zarr/version.py

src/zarr/core/array.py

@@ -252,12 +252,6 @@ async def _create_v3(
         shape = parse_shapelike(shape)
         codecs = list(codecs) if codecs is not None else [BytesCodec()]
 
-        if fill_value is None:
-            if dtype == np.dtype("bool"):
-                fill_value = False
-            else:
-                fill_value = 0
-
         if chunk_key_encoding is None:
             chunk_key_encoding = ("default", "/")
         assert chunk_key_encoding is not None
@@ -281,7 +275,6 @@ async def _create_v3(
         )
 
         array = cls(metadata=metadata, store_path=store_path)
-
         await array._save_metadata(metadata)
         return array
 

src/zarr/core/buffer/core.py

@@ -464,9 +464,14 @@ def __repr__(self) -> str:
 
     def all_equal(self, other: Any, equal_nan: bool = True) -> bool:
         """Compare to `other` using np.array_equal."""
+        if other is None:
+            # Handle None fill_value for Zarr V2
+            return False
         # use array_equal to obtain equal_nan=True functionality
         data, other = np.broadcast_arrays(self._data, other)
-        result = np.array_equal(self._data, other, equal_nan=equal_nan)
+        result = np.array_equal(
+            self._data, other, equal_nan=equal_nan if self._data.dtype.kind not in "US" else False
+        )
         return result
 
     def fill(self, value: Any) -> None:

src/zarr/core/metadata/v3.py

@@ -360,7 +360,7 @@ def parse_fill_value(
     if fill_value is None:
         return dtype.type(0)
     if isinstance(fill_value, Sequence) and not isinstance(fill_value, str):
-        if dtype in (np.complex64, np.complex128):
+        if dtype.type in (np.complex64, np.complex128):
             dtype = cast(COMPLEX_DTYPE, dtype)
             if len(fill_value) == 2:
                 # complex datatypes serialize to JSON arrays with two elements
@@ -391,7 +391,7 @@ def parse_fill_value(
         pass
     elif fill_value in ["Infinity", "-Infinity"] and not np.isfinite(casted_value):
         pass
-    elif dtype.kind == "f":
+    elif dtype.kind in "cf":
         # float comparison is not exact, especially when dtype <float64
         # so we us np.isclose for this comparison.
         # this also allows us to compare nan fill_values

src/zarr/testing/strategies.py

@@ -1,5 +1,4 @@
-import re
-from typing import Any
+from typing import Any, Literal
 
 import hypothesis.extra.numpy as npst
 import hypothesis.strategies as st
@@ -19,6 +18,35 @@
     max_leaves=3,
 )
 
+
+def v3_dtypes() -> st.SearchStrategy[np.dtype]:
+    return (
+        npst.boolean_dtypes()
+        | npst.integer_dtypes(endianness="=")
+        | npst.unsigned_integer_dtypes(endianness="=")
+        | npst.floating_dtypes(endianness="=")
+        | npst.complex_number_dtypes(endianness="=")
+        # | npst.byte_string_dtypes(endianness="=")
+        # | npst.unicode_string_dtypes()
+        # | npst.datetime64_dtypes()
+        # | npst.timedelta64_dtypes()
+    )
+
+
+def v2_dtypes() -> st.SearchStrategy[np.dtype]:
+    return (
+        npst.boolean_dtypes()
+        | npst.integer_dtypes(endianness="=")
+        | npst.unsigned_integer_dtypes(endianness="=")
+        | npst.floating_dtypes(endianness="=")
+        | npst.complex_number_dtypes(endianness="=")
+        | npst.byte_string_dtypes(endianness="=")
+        | npst.unicode_string_dtypes(endianness="=")
+        | npst.datetime64_dtypes()
+        # | npst.timedelta64_dtypes()
+    )
+
+
 # From https://zarr-specs.readthedocs.io/en/latest/v3/core/v3.0.html#node-names
 # 1. must not be the empty string ("")
 # 2. must not include the character "/"
@@ -33,21 +61,29 @@
 array_names = node_names
 attrs = st.none() | st.dictionaries(_attr_keys, _attr_values)
 paths = st.lists(node_names, min_size=1).map(lambda x: "/".join(x)) | st.just("/")
-np_arrays = npst.arrays(
-    # TODO: re-enable timedeltas once they are supported
-    dtype=npst.scalar_dtypes().filter(
-        lambda x: (x.kind not in ["m", "M"]) and (x.byteorder not in [">"])
-    ),
-    shape=npst.array_shapes(max_dims=4),
-)
 stores = st.builds(MemoryStore, st.just({}), mode=st.just("w"))
 compressors = st.sampled_from([None, "default"])
-format = st.sampled_from([2, 3])
+zarr_formats: st.SearchStrategy[Literal[2, 3]] = st.sampled_from([2, 3])
+array_shapes = npst.array_shapes(max_dims=4)
+
+
+@st.composite  # type: ignore[misc]
+def numpy_arrays(
+    draw: st.DrawFn,
+    *,
+    shapes: st.SearchStrategy[tuple[int, ...]] = array_shapes,
+    zarr_formats: st.SearchStrategy[Literal[2, 3]] = zarr_formats,
+) -> Any:
+    """
+    Generate numpy arrays that can be saved in the provided Zarr format.
+    """
+    zarr_format = draw(zarr_formats)
+    return draw(npst.arrays(dtype=v3_dtypes() if zarr_format == 3 else v2_dtypes(), shape=shapes))
 
 
 @st.composite  # type: ignore[misc]
 def np_array_and_chunks(
-    draw: st.DrawFn, *, arrays: st.SearchStrategy[np.ndarray] = np_arrays
+    draw: st.DrawFn, *, arrays: st.SearchStrategy[np.ndarray] = numpy_arrays
 ) -> tuple[np.ndarray, tuple[int]]:  # type: ignore[type-arg]
     """A hypothesis strategy to generate small sized random arrays.
 
@@ -66,73 +102,49 @@ def np_array_and_chunks(
 def arrays(
     draw: st.DrawFn,
     *,
+    shapes: st.SearchStrategy[tuple[int, ...]] = array_shapes,
     compressors: st.SearchStrategy = compressors,
     stores: st.SearchStrategy[StoreLike] = stores,
-    arrays: st.SearchStrategy[np.ndarray] = np_arrays,
     paths: st.SearchStrategy[None | str] = paths,
     array_names: st.SearchStrategy = array_names,
+    arrays: st.SearchStrategy | None = None,
     attrs: st.SearchStrategy = attrs,
-    format: st.SearchStrategy = format,
+    zarr_formats: st.SearchStrategy = zarr_formats,
 ) -> Array:
     store = draw(stores)
-    nparray, chunks = draw(np_array_and_chunks(arrays=arrays))
     path = draw(paths)
     name = draw(array_names)
     attributes = draw(attrs)
-    zarr_format = draw(format)
+    zarr_format = draw(zarr_formats)
+    if arrays is None:
+        arrays = numpy_arrays(shapes=shapes, zarr_formats=st.just(zarr_format))
+    nparray, chunks = draw(np_array_and_chunks(arrays=arrays))
+    # test that None works too.
+    fill_value = draw(st.one_of([st.none(), npst.from_dtype(nparray.dtype)]))
     # compressor = draw(compressors)
 
-    # TODO: clean this up
-    # if path is None and name is None:
-    #     array_path = None
-    #     array_name = None
-    # elif path is None and name is not None:
-    #     array_path = f"{name}"
-    #     array_name = f"/{name}"
-    # elif path is not None and name is None:
-    #     array_path = path
-    #     array_name = None
-    # elif path == "/":
-    #     assert name is not None
-    #     array_path = name
-    #     array_name = "/" + name
-    # else:
-    #     assert name is not None
-    #     array_path = f"{path}/{name}"
-    #     array_name = "/" + array_path
-
     expected_attrs = {} if attributes is None else attributes
 
     array_path = path + ("/" if not path.endswith("/") else "") + name
     root = Group.from_store(store, zarr_format=zarr_format)
-    fill_value_args: tuple[Any, ...] = tuple()
-    if nparray.dtype.kind == "M":
-        m = re.search(r"\[(.+)\]", nparray.dtype.str)
-        if not m:
-            raise ValueError(f"Couldn't find precision for dtype '{nparray.dtype}.")
-
-        fill_value_args = (
-            # e.g. ns, D
-            m.groups()[0],
-        )
 
     a = root.create_array(
         array_path,
         shape=nparray.shape,
         chunks=chunks,
-        dtype=nparray.dtype.str,
+        dtype=nparray.dtype,
         attributes=attributes,
-        # compressor=compressor,  # TODO: FIXME
-        fill_value=nparray.dtype.type(0, *fill_value_args),
+        # compressor=compressor,  # FIXME
+        fill_value=fill_value,
     )
 
     assert isinstance(a, Array)
+    assert a.fill_value is not None
+    assert isinstance(root[array_path], Array)
     assert nparray.shape == a.shape
     assert chunks == a.chunks
     assert array_path == a.path, (path, name, array_path, a.name, a.path)
-    # assert array_path == a.name, (path, name, array_path, a.name, a.path)
-    # assert a.basename is None  # TODO
-    # assert a.store == normalize_store_arg(store)
+    assert a.basename == name, (a.basename, name)
     assert dict(a.attrs) == expected_attrs
 
     a[:] = nparray

tests/v3/test_properties.py

@@ -8,13 +8,13 @@
 import hypothesis.strategies as st  # noqa: E402
 from hypothesis import given  # noqa: E402
 
-from zarr.testing.strategies import arrays, basic_indices, np_arrays  # noqa: E402
+from zarr.testing.strategies import arrays, basic_indices, numpy_arrays, zarr_formats  # noqa: E402
 
 
-@given(st.data())
-def test_roundtrip(data: st.DataObject) -> None:
-    nparray = data.draw(np_arrays)
-    zarray = data.draw(arrays(arrays=st.just(nparray)))
+@given(data=st.data(), zarr_format=zarr_formats)
+def test_roundtrip(data: st.DataObject, zarr_format: int) -> None:
+    nparray = data.draw(numpy_arrays(zarr_formats=st.just(zarr_format)))
+    zarray = data.draw(arrays(arrays=st.just(nparray), zarr_formats=st.just(zarr_format)))
     assert_array_equal(nparray, zarray[:])