Deprecate tuples of chunks? (#8341)

* Deprecate tuples of chunks?

(I was planning on putting an issue in, but then thought it wasn't much more difficult to make the PR. But it's totally fine if we don't think this is a good idea...)

Allowing a tuple of dims means we're reliant on dimension order, which we really try and not be reliant on. It also makes the type signature even more complicated.

So are we OK to encourage a dict of `dim: chunksizes`, rather than a tuple of chunksizes?

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* Update xarray/core/dataarray.py

---------

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

doc/whats-new.rst

@@ -30,6 +30,13 @@ Breaking changes
 Deprecations
 ~~~~~~~~~~~~
 
+- Supplying dimension-ordered sequences to :py:meth:`DataArray.chunk` &
+  :py:meth:`Dataset.chunk` is deprecated in favor of supplying a dictionary of
+  dimensions, or a single ``int`` or ``"auto"`` argument covering all
+  dimensions. Xarray favors using dimensions names rather than positions, and
+  this was one place in the API where dimension positions were used.
+  (:pull:`8341`)
+  By `Maximilian Roos <https://github.com/max-sixty>`_.
 
 Bug fixes
 ~~~~~~~~~

xarray/core/dataarray.py

@@ -1371,6 +1371,11 @@ def chunk(
             # ignoring type; unclear why it won't accept a Literal into the value.
             chunks = dict.fromkeys(self.dims, chunks)
         elif isinstance(chunks, (tuple, list)):
+            utils.emit_user_level_warning(
+                "Supplying chunks as dimension-order tuples is deprecated. "
+                "It will raise an error in the future. Instead use a dict with dimension names as keys.",
+                category=DeprecationWarning,
+            )
             chunks = dict(zip(self.dims, chunks))
         else:
             chunks = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk")

xarray/core/dataset.py

@@ -2648,11 +2648,17 @@ def chunk(
             warnings.warn(
                 "None value for 'chunks' is deprecated. "
                 "It will raise an error in the future. Use instead '{}'",
-                category=FutureWarning,
+                category=DeprecationWarning,
             )
             chunks = {}
         chunks_mapping: Mapping[Any, Any]
         if not isinstance(chunks, Mapping) and chunks is not None:
+            if isinstance(chunks, (tuple, list)):
+                utils.emit_user_level_warning(
+                    "Supplying chunks as dimension-order tuples is deprecated. "
+                    "It will raise an error in the future. Instead use a dict with dimensions as keys.",
+                    category=DeprecationWarning,
+                )
             chunks_mapping = dict.fromkeys(self.dims, chunks)
         else:
             chunks_mapping = either_dict_or_kwargs(chunks, chunks_kwargs, "chunk")

xarray/core/types.py

@@ -187,9 +187,7 @@ def copy(
 # FYI in some cases we don't allow `None`, which this doesn't take account of.
 T_ChunkDim: TypeAlias = Union[int, Literal["auto"], None, tuple[int, ...]]
 # We allow the tuple form of this (though arguably we could transition to named dims only)
-T_Chunks: TypeAlias = Union[
-    T_ChunkDim, Mapping[Any, T_ChunkDim], tuple[T_ChunkDim, ...]
-]
+T_Chunks: TypeAlias = Union[T_ChunkDim, Mapping[Any, T_ChunkDim]]
 T_NormalizedChunks = tuple[tuple[int, ...], ...]
 
 DataVars = Mapping[Any, Any]

xarray/core/variable.py

@@ -8,7 +8,7 @@
 from collections.abc import Hashable, Mapping, Sequence
 from datetime import timedelta
 from functools import partial
-from typing import TYPE_CHECKING, Any, Callable, Literal, NoReturn, cast
+from typing import TYPE_CHECKING, Any, Callable, NoReturn, cast
 
 import numpy as np
 import pandas as pd
@@ -34,6 +34,7 @@
     is_duck_dask_array,
     to_numpy,
 )
+from xarray.core.types import T_Chunks
 from xarray.core.utils import (
     OrderedSet,
     _default,
@@ -965,13 +966,7 @@ def _replace(
 
     def chunk(
         self,
-        chunks: (
-            int
-            | Literal["auto"]
-            | tuple[int, ...]
-            | tuple[tuple[int, ...], ...]
-            | Mapping[Any, None | int | tuple[int, ...]]
-        ) = {},
+        chunks: T_Chunks = {},
         name: str | None = None,
         lock: bool | None = None,
         inline_array: bool | None = None,

xarray/tests/test_backends.py

@@ -2798,7 +2798,7 @@ def test_write_empty(self, write_empty: bool) -> None:
         )
 
         if has_dask:
-            ds["test"] = ds["test"].chunk((1, 1, 1))
+            ds["test"] = ds["test"].chunk(1)
             encoding = None
         else:
             encoding = {"test": {"chunks": (1, 1, 1)}}

xarray/tests/test_dataarray.py

@@ -879,13 +879,14 @@ def test_chunk(self) -> None:
         assert blocked.chunks == ((3,), (4,))
         first_dask_name = blocked.data.name
 
-        blocked = unblocked.chunk(chunks=((2, 1), (2, 2)))
-        assert blocked.chunks == ((2, 1), (2, 2))
-        assert blocked.data.name != first_dask_name
+        with pytest.warns(DeprecationWarning):
+            blocked = unblocked.chunk(chunks=((2, 1), (2, 2)))  # type: ignore
+            assert blocked.chunks == ((2, 1), (2, 2))
+            assert blocked.data.name != first_dask_name
 
-        blocked = unblocked.chunk(chunks=(3, 3))
-        assert blocked.chunks == ((3,), (3, 1))
-        assert blocked.data.name != first_dask_name
+            blocked = unblocked.chunk(chunks=(3, 3))
+            assert blocked.chunks == ((3,), (3, 1))
+            assert blocked.data.name != first_dask_name
 
         # name doesn't change when rechunking by same amount
         # this fails if ReprObject doesn't have __dask_tokenize__ defined

xarray/tests/test_variable.py

@@ -2663,7 +2663,7 @@ def test_full_like(self) -> None:
     def test_full_like_dask(self) -> None:
         orig = Variable(
             dims=("x", "y"), data=[[1.5, 2.0], [3.1, 4.3]], attrs={"foo": "bar"}
-        ).chunk(((1, 1), (2,)))
+        ).chunk(dict(x=(1, 1), y=(2,)))
 
         def check(actual, expect_dtype, expect_values):
             assert actual.dtype == expect_dtype