ENH: Add RFC 2 support

.github/workflows/test.yml

@@ -32,6 +32,13 @@ jobs:
           python -m pip install --upgrade pip
           python -m pip install -e ".[tensorstore]"
 
+      - name: Install Zarr Python 3 for OME-Zarr 0.5
+        if:
+          ${{ matrix.python-version != '3.9' && matrix.python-version != '3.10'
+          }}
+        run: |
+          python -m pip install --upgrade --pre "zarr==3.0.0b2"
+
       - name: Test with pytest
         run: |
           pytest --junitxml=junit/test-results.xml

ngff_zarr/__init__.py

@@ -18,7 +18,7 @@
 from .to_ngff_image import to_ngff_image
 from .to_ngff_zarr import to_ngff_zarr
 from .validate import validate
-from .zarr_metadata import (
+from .v04.zarr_metadata import (
     AxesType,
     SpatialDims,
     SupportedDims,

ngff_zarr/_zarr_kwargs.py

@@ -0,0 +1,8 @@
+import zarr
+from packaging import version
+
+zarr_version = version.parse(zarr.__version__)
+if zarr_version >= version.parse("3.0.0b1"):
+    zarr_kwargs = {}
+else:
+    zarr_kwargs = {"dimension_separator": "/"}

ngff_zarr/_zarr_open_array.py

@@ -0,0 +1,9 @@
+import zarr
+from packaging import version
+
+zarr_version = version.parse(zarr.__version__)
+if zarr_version >= version.parse("3.0.0b1"):
+    from zarr.api.synchronous import open_array
+else:
+    from zarr.creation import open_array
+open_array = open_array

ngff_zarr/cli.py

@@ -41,7 +41,8 @@
 from .to_multiscales import to_multiscales
 from .to_ngff_image import to_ngff_image
 from .to_ngff_zarr import to_ngff_zarr
-from .zarr_metadata import is_unit_supported
+from .v04.zarr_metadata import is_unit_supported
+from ._zarr_kwargs import zarr_kwargs
 
 
 def _multiscales_to_ngff_zarr(
@@ -235,9 +236,7 @@ def main():
         cache_dir = Path(args.cache_dir).resolve()
         if not cache_dir.exists():
             Path.makedirs(cache_dir, parents=True)
-        config.cache_store = zarr.storage.DirectoryStore(
-            cache_dir, dimension_separator="/"
-        )
+        config.cache_store = zarr.storage.DirectoryStore(cache_dir, **zarr_kwargs)
 
     console = Console()
     progress = RichProgress(
@@ -304,7 +303,7 @@ def shutdown_client(sig_id, frame):  # noqa: ARG001
         )
     output_store = None
     if args.output and output_backend is ConversionBackend.NGFF_ZARR:
-        output_store = DirectoryStore(args.output, dimension_separator="/")
+        output_store = DirectoryStore(args.output, **zarr_kwargs)
 
     subtitle = "[red]generation"
     if not args.output:

ngff_zarr/config.py

@@ -2,9 +2,9 @@
 from pathlib import Path
 
 import dask.config
-import zarr
 from platformdirs import user_cache_dir
 from zarr.storage import StoreLike
+from ._zarr_kwargs import zarr_kwargs
 
 if dask.config.get("temporary-directory") is not None:
     _store_dir = dask.config.get("temporary-directory")
@@ -13,7 +13,14 @@
 
 
 def default_store_factory():
-    return zarr.storage.DirectoryStore(_store_dir, dimension_separator="/")
+    try:
+        from zarr.storage import DirectoryStore
+
+        return DirectoryStore(_store_dir, **zarr_kwargs)
+    except ImportError:
+        from zarr.storage import LocalStore
+
+        return LocalStore(_store_dir)
 
 
 try:

ngff_zarr/from_ngff_zarr.py

@@ -1,41 +1,70 @@
 from collections.abc import MutableMapping
 from pathlib import Path
-from typing import Union
+from typing import Union, Optional
+from packaging import version
 
 import dask.array
 import zarr
-from zarr.storage import BaseStore
+import zarr.storage
+
+# Zarr Python 3
+if hasattr(zarr.storage, "StoreLike"):
+    StoreLike = zarr.storage.StoreLike
+else:
+    StoreLike = Union[MutableMapping, str, Path, zarr.storage.BaseStore]
 
 from .ngff_image import NgffImage
 from .to_multiscales import Multiscales
-from .zarr_metadata import Axis, Dataset, Metadata, Scale, Translation
+from .v04.zarr_metadata import Axis, Dataset, Scale, Translation
 from .validate import validate as validate_ngff
 
+zarr_version = version.parse(zarr.__version__)
+zarr_version_major = zarr_version.major
+
 
 def from_ngff_zarr(
-    store: Union[MutableMapping, str, Path, BaseStore],
+    store: StoreLike,
     validate: bool = False,
+    version: Optional[str] = None,
 ) -> Multiscales:
     """
     Read an OME-Zarr NGFF Multiscales data structure from a Zarr store.
 
-    store : MutableMapping, str or Path, zarr.storage.BaseStore
+    store : StoreLike
         Store or path to directory in file system.
 
     validate : bool
         If True, validate the NGFF metadata against the schema.
 
+    version : string, optional
+        OME-Zarr version, if known.
+
     Returns
     -------
 
     multiscales: multiscale ngff image with dask-chunked arrays for data
 
     """
 
-    root = zarr.open_group(store, mode="r")
+    format_kwargs = {}
+    if version and zarr_version_major >= 3:
+        format_kwargs = {"zarr_format": 2} if version == "0.4" else {"zarr_format": 3}
+    root = zarr.open_group(store, mode="r", **format_kwargs)
+    root_attrs = root.attrs.asdict()
+
+    if not version:
+        if "ome" in root_attrs:
+            version = root_attrs["ome"]["version"]
+        else:
+            version = root_attrs["multiscales"][0].get("version", "0.4")
+
     if validate:
-        validate_ngff(root.attrs.asdict())
-    metadata = root.attrs["multiscales"][0]
+        validate_ngff(root_attrs, version=version)
+
+    if "ome" in root_attrs:
+        metadata = root.attrs["ome"]["multiscales"][0]
+    else:
+        metadata = root.attrs["multiscales"][0]
 
     dims = [a["name"] for a in metadata["axes"]]
 
@@ -82,12 +111,24 @@ def from_ngff_zarr(
     coordinateTransformations = None
     if "coordinateTransformations" in metadata:
         coordinateTransformations = metadata["coordinateTransformations"]
-    metadata = Metadata(
-        axes=axes,
-        datasets=datasets,
-        name=name,
-        version=metadata["version"],
-        coordinateTransformations=coordinateTransformations,
-    )
+    if version == "0.5":
+        from .v05.zarr_metadata import Metadata
+
+        metadata = Metadata(
+            axes=axes,
+            datasets=datasets,
+            name=name,
+            coordinateTransformations=coordinateTransformations,
+        )
+    else:
+        from .v04.zarr_metadata import Metadata
+
+        metadata = Metadata(
+            axes=axes,
+            datasets=datasets,
+            name=name,
+            version=metadata["version"],
+            coordinateTransformations=coordinateTransformations,
+        )
 
     return Multiscales(images, metadata)

ngff_zarr/multiscales.py

@@ -3,7 +3,7 @@
 
 from .methods import Methods
 from .ngff_image import NgffImage
-from .zarr_metadata import Metadata
+from .v04.zarr_metadata import Metadata
 
 
 @dataclass

ngff_zarr/ngff_image.py

@@ -3,7 +3,7 @@
 
 from dask.array.core import Array as DaskArray
 
-from .zarr_metadata import Units
+from .v04.zarr_metadata import Units
 
 ComputedCallback = Callable[[], None]
 

ngff_zarr/to_multiscales.py

@@ -11,7 +11,14 @@
 import zarr
 from dask.array.core import Array as DaskArray
 from numpy.typing import ArrayLike
-from zarr.core import Array as ZarrArray
+
+try:
+    from zarr.core import Array as ZarrArray
+except ImportError:
+    from zarr.core.array import Array as ZarrArray
+from ._zarr_kwargs import zarr_kwargs
+from ._zarr_open_array import open_array
+import zarr.storage
 
 from .config import config
 from .memory_usage import memory_usage
@@ -30,7 +37,7 @@
 from .ngff_image import NgffImage
 from .rich_dask_progress import NgffProgress, NgffProgressCallback
 from .to_ngff_image import to_ngff_image
-from .zarr_metadata import Axis, Dataset, Metadata, Scale, Translation
+from .v04.zarr_metadata import Axis, Dataset, Metadata, Scale, Translation
 
 
 def _ngff_image_scale_factors(ngff_image, min_length, out_chunks):
@@ -82,10 +89,18 @@ def _large_image_serialization(
     def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
         nonlocal base_path_removed
         if not base_path_removed:
-            if isinstance(cache_store, zarr.storage.DirectoryStore):
+            if hasattr(zarr.storage, "DirectoryStore") and isinstance(
+                cache_store, zarr.storage.DirectoryStore
+            ):
                 full_path = Path(cache_store.dir_path()) / base_path
                 if full_path.exists():
                     shutil.rmtree(full_path, ignore_errors=True)
+            elif hasattr(zarr.storage, "LocalStore") and isinstance(
+                cache_store, zarr.storage.LocalStore
+            ):
+                full_path = Path(cache_store.root) / base_path
+                if full_path.exists():
+                    shutil.rmtree(full_path, ignore_errors=True)
             else:
                 zarr.storage.rmdir(cache_store, base_path)
             base_path_removed = True
@@ -129,14 +144,14 @@ def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
             slabs.chunks,
             meta=slabs,
         )
-        zarr_array = zarr.creation.open_array(
+        zarr_array = open_array(
             shape=data.shape,
             chunks=chunks,
             dtype=data.dtype,
             store=cache_store,
             path=path,
             mode="a",
-            dimension_separator="/",
+            **zarr_kwargs,
         )
 
         n_slabs = int(np.ceil(data.shape[z_index] / slab_slices))
@@ -164,7 +179,7 @@ def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
                 overwrite=False,
                 compute=True,
                 return_stored=False,
-                dimension_separator="/",
+                **zarr_kwargs,
             )
         data = dask.array.from_zarr(cache_store, component=path)
         if optimized_chunks < data.shape[z_index] and slab_slices < optimized_chunks:
@@ -173,14 +188,14 @@ def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
             path = f"{base_path}/optimized_chunks"
             chunks = tuple([c[0] for c in optimized.chunks])
             data = data.rechunk(chunks)
-            zarr_array = zarr.creation.open_array(
+            zarr_array = open_array(
                 shape=data.shape,
                 chunks=chunks,
                 dtype=data.dtype,
                 store=cache_store,
                 path=path,
                 mode="a",
-                dimension_separator="/",
+                **zarr_kwargs,
             )
             n_slabs = int(np.ceil(data.shape[z_index] / optimized_chunks))
             for slab_index in range(n_slabs):
@@ -205,7 +220,7 @@ def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
                     overwrite=False,
                     compute=True,
                     return_stored=False,
-                    dimension_separator="/",
+                    **zarr_kwargs,
                 )
             data = dask.array.from_zarr(cache_store, component=path)
         else:
@@ -223,7 +238,7 @@ def remove_from_cache_store(sig_id, frame):  # noqa: ARG001
             overwrite=False,
             compute=True,
             return_stored=False,
-            dimension_separator="/",
+            **zarr_kwargs,
         )
         data = dask.array.from_zarr(cache_store, component=path)
 

ngff_zarr/to_ngff_image.py

@@ -4,11 +4,15 @@
 import dask
 from dask.array.core import Array as DaskArray
 from numpy.typing import ArrayLike
-from zarr.core import Array as ZarrArray
+
+try:
+    from zarr.core import Array as ZarrArray
+except ImportError:
+    from zarr.core.array import Array as ZarrArray
 
 from .methods._support import _spatial_dims
 from .ngff_image import NgffImage
-from .zarr_metadata import SupportedDims, Units
+from .v04.zarr_metadata import SupportedDims, Units
 
 
 def to_ngff_image(