Merge branch 'main' into groupby-aggs-using-numpy-groupies

Author: andersy005

.pre-commit-config.yaml

@@ -45,6 +45,7 @@ repos:
             types-PyYAML,
             types-pytz,
             typing-extensions==3.10.0.0,
+            numpy,
           ]
   # run this occasionally, ref discussion https://github.com/pydata/xarray/pull/3194
   # - repo: https://github.com/asottile/pyupgrade

doc/whats-new.rst

@@ -46,6 +46,8 @@ Deprecations
 
 Bug fixes
 ~~~~~~~~~
+- Preserve chunks when creating a :py:class:`DataArray` from another :py:class:`DataArray`
+  (:pull:`5984`). By `Fabian Hofmann <https://github.com/FabianHofmann>`_.
 - Properly support :py:meth:`DataArray.ffill`, :py:meth:`DataArray.bfill`, :py:meth:`Dataset.ffill` and :py:meth:`Dataset.bfill` along chunked dimensions (:issue:`6112`).
   By `Joseph Nowak <https://github.com/josephnowak>`_.
 

xarray/backends/file_manager.py

@@ -2,7 +2,7 @@
 import io
 import threading
 import warnings
-from typing import Any, Dict, cast
+from typing import Any, Dict
 
 from ..core import utils
 from ..core.options import OPTIONS
@@ -11,7 +11,7 @@
 
 # Global cache for storing open files.
 FILE_CACHE: LRUCache[str, io.IOBase] = LRUCache(
-    maxsize=cast(int, OPTIONS["file_cache_maxsize"]), on_evict=lambda k, v: v.close()
+    maxsize=OPTIONS["file_cache_maxsize"], on_evict=lambda k, v: v.close()
 )
 assert FILE_CACHE.maxsize, "file cache must be at least size one"
 

xarray/core/accessor_str.py

@@ -276,8 +276,8 @@ def func(x):
 
         if isinstance(pat, np.ndarray):
             # apply_ufunc doesn't work for numpy arrays with output object dtypes
-            func = np.vectorize(func)
-            return func(pat)
+            func_ = np.vectorize(func)
+            return func_(pat)
         else:
             return _apply_str_ufunc(func=func, obj=pat, dtype=np.object_)
 

xarray/core/dataset.py

@@ -6920,9 +6920,9 @@ def polyfit(
         if full:
             rank = xr.DataArray(rank, name=xname + "matrix_rank")
             variables[rank.name] = rank
-            sing = np.linalg.svd(lhs, compute_uv=False)
+            _sing = np.linalg.svd(lhs, compute_uv=False)
             sing = xr.DataArray(
-                sing,
+                _sing,
                 dims=(degree_dim,),
                 coords={degree_dim: np.arange(rank - 1, -1, -1)},
                 name=xname + "singular_values",

xarray/core/duck_array_ops.py

@@ -16,7 +16,7 @@
 from numpy import zeros_like  # noqa
 from numpy import around, broadcast_to  # noqa
 from numpy import concatenate as _concatenate
-from numpy import einsum, isclose, isin, isnan, isnat, pad  # noqa
+from numpy import einsum, isclose, isin, isnan, isnat  # noqa
 from numpy import stack as _stack
 from numpy import take, tensordot, transpose, unravel_index  # noqa
 from numpy import where as _where
@@ -168,7 +168,7 @@ def cumulative_trapezoid(y, x, axis):
 
     # Pad so that 'axis' has same length in result as it did in y
     pads = [(1, 0) if i == axis else (0, 0) for i in range(y.ndim)]
-    integrand = pad(integrand, pads, mode="constant", constant_values=0.0)
+    integrand = np.pad(integrand, pads, mode="constant", constant_values=0.0)
 
     return cumsum(integrand, axis=axis, skipna=False)
 

xarray/core/formatting.py

@@ -200,7 +200,7 @@ def format_array_flat(array, max_width: int):
         (max_possibly_relevant < array.size) or (cum_len > max_width).any()
     ):
         padding = " ... "
-        max_len = max(int(np.argmax(cum_len + len(padding) - 1 > max_width)), 2)  # type: ignore[type-var]
+        max_len = max(int(np.argmax(cum_len + len(padding) - 1 > max_width)), 2)
         count = min(array.size, max_len)
     else:
         count = array.size

xarray/core/indexing.py

@@ -872,7 +872,7 @@ def _decompose_outer_indexer(
     backend_indexer: List[Any] = []
     np_indexer = []
     # make indexer positive
-    pos_indexer = []
+    pos_indexer: list[np.ndarray | int | np.number] = []
     for k, s in zip(indexer.tuple, shape):
         if isinstance(k, np.ndarray):
             pos_indexer.append(np.where(k < 0, k + s, k))

xarray/core/utils.py

@@ -652,7 +652,7 @@ def read_magic_number_from_file(filename_or_obj, count=8) -> bytes:
                 "file-like object read/write pointer not at the start of the file, "
                 "please close and reopen, or use a context manager"
             )
-        magic_number = filename_or_obj.read(count)  # type: ignore
+        magic_number = filename_or_obj.read(count)
         filename_or_obj.seek(0)
     else:
         raise TypeError(f"cannot read the magic number form {type(filename_or_obj)}")

xarray/core/variable.py

@@ -198,11 +198,13 @@ def as_compatible_data(data, fastpath=False):
 
     Finally, wrap it up with an adapter if necessary.
     """
+    from .dataarray import DataArray
+
     if fastpath and getattr(data, "ndim", 0) > 0:
         # can't use fastpath (yet) for scalars
         return _maybe_wrap_data(data)
 
-    if isinstance(data, Variable):
+    if isinstance(data, (Variable, DataArray)):
         return data.data
 
     if isinstance(data, NON_NUMPY_SUPPORTED_ARRAY_TYPES):
@@ -1236,7 +1238,7 @@ def _shift_one_dim(self, dim, count, fill_value=dtypes.NA):
         dim_pad = (width, 0) if count >= 0 else (0, width)
         pads = [(0, 0) if d != dim else dim_pad for d in self.dims]
 
-        data = duck_array_ops.pad(
+        data = np.pad(
             trimmed_data.astype(dtype),
             pads,
             mode="constant",
@@ -1376,7 +1378,7 @@ def pad(
         if reflect_type is not None:
             pad_option_kwargs["reflect_type"] = reflect_type  # type: ignore[assignment]
 
-        array = duck_array_ops.pad(
+        array = np.pad(  # type: ignore[call-overload]
             self.data.astype(dtype, copy=False),
             pad_width_by_index,
             mode=mode,