Merge branch 'main' of https://github.com/pydata/xarray

Author: TomNicholas

doc/api.rst

@@ -1576,6 +1576,7 @@ Custom Indexes
 
    CFTimeIndex
    indexes.RangeIndex
+   indexes.CoordinateTransformIndex
 
 Creating custom indexes
 -----------------------
@@ -1588,6 +1589,13 @@ Creating custom indexes
    indexes.RangeIndex.arange
    indexes.RangeIndex.linspace
 
+Building custom indexes
+-----------------------
+.. autosummary::
+   :toctree: generated/
+
+   indexes.CoordinateTransform
+
 Tutorial
 ========
 

doc/user-guide/ecosystem.rst

@@ -14,6 +14,7 @@ Geosciences
 
 - `aospy <https://aospy.readthedocs.io>`_: Automated analysis and management of gridded climate data.
 - `argopy <https://github.com/euroargodev/argopy>`_: xarray-based Argo data access, manipulation and visualisation for standard users as well as Argo experts.
+- `cf_xarray <https://cf-xarray.readthedocs.io/en/latest/>`_: Provides an accessor (DataArray.cf or Dataset.cf) that allows you to interpret Climate and Forecast metadata convention attributes present on xarray objects.
 - `climpred <https://climpred.readthedocs.io>`_: Analysis of ensemble forecast models for climate prediction.
 - `geocube <https://corteva.github.io/geocube>`_: Tool to convert geopandas vector data into rasterized xarray data.
 - `GeoWombat <https://github.com/jgrss/geowombat>`_: Utilities for analysis of remotely sensed and gridded raster data at scale (easily tame Landsat, Sentinel, Quickbird, and PlanetScope).

doc/whats-new.rst

@@ -12,6 +12,8 @@ v2025.07.0 (unreleased)
 
 New Features
 ~~~~~~~~~~~~
+- Expose :py:class:`~xarray.indexes.RangeIndex`, and :py:class:`~xarray.indexes.CoordinateTransformIndex` as public api
+  under the ``xarray.indexes`` namespace. By `Deepak Cherian <https://github.com/dcherian>`_.
 
 
 Breaking changes

xarray/__init__.py

@@ -1,6 +1,6 @@
 from importlib.metadata import version as _version
 
-from xarray import coders, groupers, testing, tutorial, ufuncs
+from xarray import coders, groupers, indexes, testing, tutorial, ufuncs
 from xarray.backends.api import (
     load_dataarray,
     load_dataset,
@@ -70,6 +70,7 @@
     # Sub-packages
     "coders",
     "groupers",
+    "indexes",
     "testing",
     "tutorial",
     "ufuncs",

xarray/backends/api.py

@@ -11,6 +11,7 @@
 )
 from functools import partial
 from io import BytesIO
+from itertools import starmap
 from numbers import Number
 from typing import (
     TYPE_CHECKING,
@@ -2109,10 +2110,9 @@ def save_mfdataset(
         import dask
 
         return dask.delayed(
-            [
-                dask.delayed(_finalize_store)(w, s)
-                for w, s in zip(writes, stores, strict=True)
-            ]
+            list(
+                starmap(dask.delayed(_finalize_store), zip(writes, stores, strict=True))
+            )
         )
 
 

xarray/coding/cftimeindex.py

@@ -43,7 +43,7 @@
 
 import math
 from datetime import timedelta
-from typing import TYPE_CHECKING, Any, Optional
+from typing import TYPE_CHECKING, Any
 
 import numpy as np
 import pandas as pd
@@ -549,7 +549,7 @@ def __rsub__(self, other):
             ) from err
 
     def to_datetimeindex(
-        self, unsafe: bool = False, time_unit: Optional[PDDatetimeUnitOptions] = None
+        self, unsafe: bool = False, time_unit: PDDatetimeUnitOptions | None = None
     ) -> pd.DatetimeIndex:
         """If possible, convert this index to a pandas.DatetimeIndex.
 

xarray/computation/apply_ufunc.py

@@ -529,8 +529,10 @@ def apply_dataset_vfunc(
     out: Dataset | tuple[Dataset, ...]
     if signature.num_outputs > 1:
         out = tuple(
-            _fast_dataset(*args)
-            for args in zip(result_vars, list_of_coords, list_of_indexes, strict=True)
+            itertools.starmap(
+                _fast_dataset,
+                zip(result_vars, list_of_coords, list_of_indexes, strict=True),
+            )
         )
     else:
         (coord_vars,) = list_of_coords
@@ -600,9 +602,7 @@ def apply_groupby_func(func, *args):
             iterator = itertools.repeat(arg)
         iterators.append(iterator)
 
-    applied: Iterator = (
-        func(*zipped_args) for zipped_args in zip(*iterators, strict=False)
-    )
+    applied: Iterator = itertools.starmap(func, zip(*iterators, strict=False))
     applied_example, applied = peek_at(applied)
     combine = first_groupby._combine  # type: ignore[attr-defined]
     if isinstance(applied_example, tuple):

xarray/computation/computation.py

@@ -258,7 +258,7 @@ def _cov_corr(
     weights: T_DataArray | None = None,
     dim: Dims = None,
     ddof: int = 0,
-    method: Literal["cov", "corr", None] = None,
+    method: Literal["cov", "corr"] | None = None,
 ) -> T_DataArray:
     """
     Internal method for xr.cov() and xr.corr() so only have to

xarray/core/dataset.py

@@ -2334,9 +2334,10 @@ def info(self, buf: IO | None = None) -> None:
         if buf is None:  # pragma: no cover
             buf = sys.stdout
 
-        lines = []
-        lines.append("xarray.Dataset {")
-        lines.append("dimensions:")
+        lines = [
+            "xarray.Dataset {",
+            "dimensions:",
+        ]
         for name, size in self.sizes.items():
             lines.append(f"\t{name} = {size} ;")
         lines.append("\nvariables:")
@@ -9708,7 +9709,7 @@ def convert_calendar(
         self,
         calendar: CFCalendar,
         dim: Hashable = "time",
-        align_on: Literal["date", "year", None] = None,
+        align_on: Literal["date", "year"] | None = None,
         missing: Any | None = None,
         use_cftime: bool | None = None,
     ) -> Self:

xarray/core/formatting.py

@@ -190,7 +190,7 @@ def format_item(x, timedelta_format=None, quote_strings=True):
         if hasattr(x, "dtype"):
             x = x.item()
         return repr(x) if quote_strings else x
-    elif hasattr(x, "dtype") and np.issubdtype(x.dtype, np.floating):
+    elif hasattr(x, "dtype") and np.issubdtype(x.dtype, np.floating) and x.shape == ():
         return f"{x.item():.4}"
     else:
         return str(x)