Implement virtual array indexing using ndindex

cubed/core/ops.py

@@ -16,7 +16,6 @@
     OrthogonalIndexer,
     SliceDimIndexer,
     is_integer_list,
-    is_slice,
     replace_ellipsis,
 )
 
@@ -408,7 +407,7 @@ def index(x, key):
         key = (key,)
 
     # No op case
-    if all(is_slice(ind) and ind == slice(None) for ind in key):
+    if all(isinstance(ind, slice) and ind == slice(None) for ind in key):
         return x
 
     # Remove None values, to be filled in with expand_dims at end
@@ -435,7 +434,9 @@ def index(x, key):
     selection = replace_ellipsis(selection, x.shape)
 
     # Check selection is supported
-    if any(s.step is not None and s.step < 1 for s in selection if is_slice(s)):
+    if any(
+        s.step is not None and s.step < 1 for s in selection if isinstance(s, slice)
+    ):
         raise NotImplementedError(f"Slice step must be >= 1: {key}")
     assert all(isinstance(s, (slice, list, Integral)) for s in selection)
     where_list = [i for i, ind in enumerate(selection) if is_integer_list(ind)]
@@ -489,7 +490,6 @@ def merged_chunk_len_for_indexer(s):
         extra_projected_mem=extra_projected_mem,
         target_chunks=target_chunks,
         selection=selection,
-        advanced_indexing=len(where_list) > 0,
     )
 
     # merge chunks for any dims with step > 1 so they are
@@ -515,13 +515,14 @@ def _read_index_chunk(
     *arrays,
     target_chunks=None,
     selection=None,
-    advanced_indexing=None,
     block_id=None,
 ):
     array = arrays[0].zarray
-    if advanced_indexing:
-        array = array.oindex
     idx = block_id
+    # Note that since we only have a maximum of one integer array index
+    # we don't need to use Zarr orthogonal indexing, since it is
+    # "available directly on the array" according to
+    # https://zarr.readthedocs.io/en/stable/tutorial.html#orthogonal-indexing
     out = array[_target_chunk_selection(target_chunks, idx, selection)]
     out = numpy_array_to_backend_array(out)
     return out
@@ -534,7 +535,7 @@ def _target_chunk_selection(target_chunks, idx, selection):
     sel = []
     i = 0  # index into target_chunks and idx
     for s in selection:
-        if is_slice(s):
+        if isinstance(s, slice):
             offset = s.start or 0
             step = s.step if s.step is not None else 1
             start = tuple(

cubed/runtime/executors/modal.py

@@ -21,6 +21,7 @@
             "donfig",
             "fsspec",
             "mypy_extensions",  # for rechunker
+            "ndindex",
             "networkx",
             "pytest-mock",  # TODO: only needed for tests
             "s3fs",
@@ -35,6 +36,7 @@
             "donfig",
             "fsspec",
             "mypy_extensions",  # for rechunker
+            "ndindex",
             "networkx",
             "pytest-mock",  # TODO: only needed for tests
             "gcsfs",

cubed/storage/virtual.py

@@ -2,10 +2,8 @@
 from typing import Any
 
 import numpy as np
-import zarr
-from zarr.indexing import BasicIndexer, is_slice
+from ndindex import ndindex
 
-from cubed.backend_array_api import backend_array_to_numpy_array
 from cubed.backend_array_api import namespace as nxp
 from cubed.backend_array_api import numpy_array_to_backend_array
 from cubed.types import T_DType, T_RegularChunks, T_Shape
@@ -21,25 +19,15 @@ def __init__(
         dtype: T_DType,
         chunks: T_RegularChunks,
     ):
-        # use an empty in-memory Zarr array as a template since it normalizes its properties
-        template = zarr.empty(
-            shape, dtype=dtype, chunks=chunks, store=zarr.storage.MemoryStore()
-        )
-        self.shape = template.shape
-        self.dtype = template.dtype
-        self.chunks = template.chunks
-        self.template = template
+        self.shape = shape
+        self.dtype = np.dtype(dtype)
+        self.chunks = chunks
 
     def __getitem__(self, key):
-        if not isinstance(key, tuple):
-            key = (key,)
-        indexer = BasicIndexer(key, self.template)
+        idx = ndindex[key]
+        newshape = idx.newshape(self.shape)
         # use broadcast trick so array chunks only occupy a single value in memory
-        return broadcast_trick(nxp.empty)(indexer.shape, dtype=self.dtype)
-
-    @property
-    def oindex(self):
-        return self.template.oindex
+        return broadcast_trick(nxp.empty)(newshape, dtype=self.dtype)
 
 
 class VirtualFullArray:
@@ -52,48 +40,29 @@ def __init__(
         chunks: T_RegularChunks,
         fill_value: Any = None,
     ):
-        # use an empty in-memory Zarr array as a template since it normalizes its properties
-        template = zarr.full(
-            shape,
-            fill_value,
-            dtype=dtype,
-            chunks=chunks,
-            store=zarr.storage.MemoryStore(),
-        )
-        self.shape = template.shape
-        self.dtype = template.dtype
-        self.chunks = template.chunks
-        self.template = template
+        self.shape = shape
+        self.dtype = np.dtype(dtype)
+        self.chunks = chunks
         self.fill_value = fill_value
 
     def __getitem__(self, key):
-        if not isinstance(key, tuple):
-            key = (key,)
-        indexer = BasicIndexer(key, self.template)
+        idx = ndindex[key]
+        newshape = idx.newshape(self.shape)
         # use broadcast trick so array chunks only occupy a single value in memory
         return broadcast_trick(nxp.full)(
-            indexer.shape, fill_value=self.fill_value, dtype=self.dtype
+            newshape, fill_value=self.fill_value, dtype=self.dtype
         )
 
-    @property
-    def oindex(self):
-        return self.template.oindex
-
 
 class VirtualOffsetsArray:
     """An array that is never materialized (in memory or on disk) and contains sequentially incrementing integers."""
 
     def __init__(self, shape: T_Shape):
         dtype = nxp.int32
         chunks = (1,) * len(shape)
-        # use an empty in-memory Zarr array as a template since it normalizes its properties
-        template = zarr.empty(
-            shape, dtype=dtype, chunks=chunks, store=zarr.storage.MemoryStore()
-        )
-        self.shape = template.shape
-        self.dtype = template.dtype
-        self.chunks = template.chunks
-        self.ndim = template.ndim
+        self.shape = shape
+        self.dtype = np.dtype(dtype)
+        self.chunks = chunks
 
     def __getitem__(self, key):
         if key == () and self.shape == ():
@@ -117,28 +86,13 @@ def __init__(
                 f"Size of in memory array is {memory_repr(array.nbytes)} which exceeds maximum of {memory_repr(max_nbytes)}. Consider loading the array from storage using `from_array`."
             )
         self.array = array
-        # use an in-memory Zarr array as a template since it normalizes its properties
-        # and is needed for oindex
-        template = zarr.empty(
-            array.shape,
-            dtype=array.dtype,
-            chunks=chunks,
-            store=zarr.storage.MemoryStore(),
-        )
-        self.shape = template.shape
-        self.dtype = template.dtype
-        self.chunks = template.chunks
-        self.template = template
-        if array.size > 0:
-            template[...] = backend_array_to_numpy_array(array)
+        self.shape = array.shape
+        self.dtype = array.dtype
+        self.chunks = chunks
 
     def __getitem__(self, key):
         return self.array.__getitem__(key)
 
-    @property
-    def oindex(self):
-        return self.template.oindex
-
 
 def _key_to_index_tuple(selection):
     if isinstance(selection, slice):
@@ -148,7 +102,11 @@ def _key_to_index_tuple(selection):
     for s in selection:
         if isinstance(s, Integral):
             sel.append(s)
-        elif is_slice(s) and s.stop == s.start + 1 and (s.step is None or s.step == 1):
+        elif (
+            isinstance(s, slice)
+            and s.stop == s.start + 1
+            and (s.step is None or s.step == 1)
+        ):
             sel.append(s.start)
         else:
             raise NotImplementedError(f"Offset selection not supported: {selection}")

cubed/storage/zarr.py

@@ -1,6 +1,9 @@
+from operator import mul
 from typing import Optional, Union
 
+import numpy as np
 import zarr
+from toolz import reduce
 
 from cubed.types import T_DType, T_RegularChunks, T_Shape, T_Store
 
@@ -23,18 +26,23 @@ def __init__(
         **kwargs,
     ):
         """Create a Zarr array lazily in memory."""
-        # use an empty in-memory Zarr array as a template since it normalizes its properties
-        template = zarr.empty(
-            shape, dtype=dtype, chunks=chunks, store=zarr.storage.MemoryStore()
-        )
-        self.shape = template.shape
-        self.dtype = template.dtype
-        self.chunks = template.chunks
-        self.nbytes = template.nbytes
+        self.shape = shape
+        self.dtype = np.dtype(dtype)
+        self.chunks = chunks
         self.store = store
         self.path = path
         self.kwargs = kwargs
 
+    @property
+    def size(self):
+        """Number of elements in the array."""
+        return reduce(mul, self.shape, 1)
+
+    @property
+    def nbytes(self) -> int:
+        """Number of bytes in array"""
+        return self.size * self.dtype.itemsize
+
     def create(self, mode: str = "w-") -> zarr.Array:
         """Create the Zarr array in storage.
 

cubed/tests/runtime/test_modal_async.py

@@ -23,6 +23,7 @@
         "donfig",
         "fsspec",
         "mypy_extensions",  # for rechunker
+        "ndindex",
         "networkx",
         "pytest-mock",  # TODO: only needed for tests
         "s3fs",

cubed/tests/test_indexing.py

@@ -23,8 +23,9 @@ def spec(tmp_path):
     ],
 )
 def test_int_array_index_1d(spec, ind):
-    a = xp.arange(12, chunks=(4,), spec=spec)
-    assert_array_equal(a[ind].compute(), np.arange(12)[ind])
+    a = xp.arange(12, chunks=(3,), spec=spec)
+    b = a.rechunk((4,))  # force materialization to test indexing against zarr
+    assert_array_equal(b[ind].compute(), np.arange(12)[ind])
 
 
 @pytest.mark.parametrize(
@@ -40,11 +41,12 @@ def test_int_array_index_1d(spec, ind):
 def test_int_array_index_2d(spec, ind):
     a = xp.asarray(
         [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]],
-        chunks=(2, 2),
+        chunks=(3, 3),
         spec=spec,
     )
+    b = a.rechunk((2, 2))  # force materialization to test indexing against zarr
     x = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12], [13, 14, 15, 16]])
-    assert_array_equal(a[ind].compute(), x[ind])
+    assert_array_equal(b[ind].compute(), x[ind])
 
 
 def test_multiple_int_array_indexes(spec):

pyproject.toml

@@ -27,6 +27,7 @@ dependencies = [
     "donfig",
     "fsspec",
     "mypy_extensions", # for rechunker
+    "ndindex",
     "networkx < 2.8.3",
     "numpy >= 1.22",
     "tenacity",

requirements.txt

@@ -6,6 +6,7 @@ aiostream
 array-api-compat
 fsspec
 mypy_extensions # for rechunker
+ndindex
 networkx < 2.8.3
 numpy >= 1.22
 tenacity

setup.cfg

@@ -44,6 +44,8 @@ ignore_missing_imports = True
 ignore_missing_imports = True
 [mypy-matplotlib.*]
 ignore_missing_imports = True
+[mypy-ndindex.*]
+ignore_missing_imports = True
 [mypy-networkx.*]
 ignore_missing_imports = True
 [mypy-numpy.*]