Add support for custom metadata. Tested :) (#19)

Add support for custom metadata. Tested :)

funlib/persistence/arrays/array.py

@@ -1,10 +1,11 @@
 import logging
 from functools import reduce
-from typing import Optional, Sequence, Union
+from typing import Optional, Sequence, Union, Any
 
 import dask.array as da
 import numpy as np
 from dask.array.optimization import fuse_slice
+from zarr import Array as ZarrArray
 
 from funlib.geometry import Coordinate, Roi
 
@@ -83,6 +84,10 @@ def __init__(
             shape=self._source_data.shape,
         )
 
+        # used for custom metadata unrelated to indexing with physical units
+        # only used if not reading from zarr and there is no built in `.attrs`
+        self._attrs: dict[str, Any] = {}
+
         if lazy_op is not None:
             self.apply_lazy_ops(lazy_op)
 
@@ -93,6 +98,18 @@ def __init__(
 
         self.validate()
 
+    @property
+    def attrs(self) -> dict:
+        """
+        Return dict that can be used to store custom metadata. Will be persistent
+        for zarr arrays. If reading from zarr, any existing metadata (such as
+        voxel_size, axis_names, etc.) will also be exposed here.
+        """
+        if isinstance(self._source_data, ZarrArray):
+            return self._source_data.attrs
+        else:
+            return self._attrs
+
     @property
     def chunk_shape(self) -> Coordinate:
         return Coordinate(self.data.chunksize)

funlib/persistence/arrays/datasets.py

@@ -1,5 +1,5 @@
 import logging
-from typing import Optional, Sequence, Union
+from typing import Any, Optional, Sequence, Union
 
 import numpy as np
 import zarr
@@ -128,6 +128,7 @@ def prepare_ds(
     chunk_shape: Optional[Sequence[int]] = None,
     dtype: DTypeLike = np.float32,
     mode: str = "a",
+    custom_metadata: dict[str, Any] | None = None,
     **kwargs,
 ) -> Array:
     """Prepare a Zarr or N5 dataset.
@@ -179,6 +180,11 @@ def prepare_ds(
             The mode to open the dataset in.
             See https://zarr.readthedocs.io/en/stable/api/creation.html#zarr.creation.open_array
 
+        custom_metadata:
+
+            A dictionary of custom metadata to add to the dataset. This will be written to the
+            zarr .attrs object.
+
         kwargs:
 
             See additional arguments available here:
@@ -319,14 +325,18 @@ def prepare_ds(
         raise ArrayNotFoundError(f"Nothing found at path {store}")
 
     default_metadata_format = get_default_metadata_format()
-    ds.attrs.put(
-        {
-            default_metadata_format.axis_names_attr: combined_metadata.axis_names,
-            default_metadata_format.units_attr: combined_metadata.units,
-            default_metadata_format.voxel_size_attr: combined_metadata.voxel_size,
-            default_metadata_format.offset_attr: combined_metadata.offset,
-        }
-    )
+    our_metadata = {
+        default_metadata_format.axis_names_attr: combined_metadata.axis_names,
+        default_metadata_format.units_attr: combined_metadata.units,
+        default_metadata_format.voxel_size_attr: combined_metadata.voxel_size,
+        default_metadata_format.offset_attr: combined_metadata.offset,
+    }
+    # check keys don't conflict
+    if custom_metadata is not None:
+        assert set(our_metadata.keys()).isdisjoint(custom_metadata.keys())
+        our_metadata.update(custom_metadata)
+
+    ds.attrs.put(our_metadata)
 
     # open array
     array = Array(ds, offset, voxel_size, axis_names, units)

tests/test_datasets.py

@@ -15,6 +15,23 @@
 }
 
 
+@pytest.mark.parametrize("store", stores.keys())
+def test_metadata(tmpdir, store):
+    store = tmpdir / store
+
+    # test prepare_ds creates array if it does not exist and mode is write
+    array = prepare_ds(
+        store,
+        (10, 10),
+        mode="w",
+        custom_metadata={"custom": "metadata"},
+    )
+    assert array.attrs["custom"] == "metadata"
+    array.attrs["custom2"] = "new metadata"
+
+    assert open_ds(store).attrs["custom2"] == "new metadata"
+
+
 @pytest.mark.parametrize("store", stores.keys())
 @pytest.mark.parametrize("dtype", [np.float32, np.uint8, np.uint64])
 def test_helpers(tmpdir, store, dtype):