Fixup

Author: TomAugspurger

src/zarr/codecs/pipeline.py

@@ -250,8 +250,12 @@ async def read_batch(
                 else:
                     fill_value = chunk_spec.fill_value
 
-                    # this should maybe be version specific?
                     if fill_value is None:
+                        # Zarr V2 allowed `fill_value` to be null in the metadata.
+                        # Zarr V3 requires it to be set. This has already been
+                        # validated when decoding the metadata, but we support reading
+                        # Zarr V2 data and need to support the case where fill_value
+                        # is None.
                         fill_value = default_fill_value(dtype=chunk_spec.dtype)
 
                     out[out_selection] = fill_value

src/zarr/core/metadata/v2.py

@@ -62,10 +62,7 @@ def __init__(
         order_parsed = parse_indexing_order(order)
         dimension_separator_parsed = parse_separator(dimension_separator)
         filters_parsed = parse_filters(filters)
-        if fill_value is None:
-            fill_value_parsed = None
-        else:
-            fill_value_parsed = parse_fill_value(fill_value, dtype=data_type_parsed)
+        fill_value_parsed = parse_fill_value(fill_value, dtype=data_type_parsed)
         attributes_parsed = parse_attributes(attributes)
 
         object.__setattr__(self, "shape", shape_parsed)
@@ -290,4 +287,16 @@ def parse_fill_value(fill_value: object, dtype: np.dtype[Any]) -> Any:
 
 
 def default_fill_value(dtype: np.dtype[Any]) -> Any:
+    """
+    Get the default fill value for a type.
+
+    Notes
+    -----
+    This differs from :func:`parse_fill_value`, which parses a fill value
+    stored in the Array metadata into an in-memory value. This only gives
+    the default fill value for some type.
+
+    This is useful for reading Zarr V2 arrays, which allow the fill
+    value to be unspecified.
+    """
     return dtype.type(0)

tests/v3/test_v2.py

@@ -55,5 +55,3 @@ def test_codec_pipeline() -> None:
     result = array[:]
     expected = np.ones(1)
     np.testing.assert_array_equal(result, expected)
-
-