Replace more usages of np.any with duck array ops equivalent

xarray/coding/times.py

@@ -22,7 +22,7 @@
 )
 from xarray.core import indexing
 from xarray.core.common import contains_cftime_datetimes, is_np_datetime_like
-from xarray.core.duck_array_ops import array_all, asarray, ravel, reshape
+from xarray.core.duck_array_ops import array_all, array_any, asarray, ravel, reshape
 from xarray.core.formatting import first_n_items, format_timestamp, last_item
 from xarray.core.pdcompat import nanosecond_precision_timestamp, timestamp_as_unit
 from xarray.core.utils import attempt_import, emit_user_level_warning
@@ -824,7 +824,7 @@ def _cast_to_dtype_if_safe(num: np.ndarray, dtype: np.dtype) -> np.ndarray:
                     "a larger integer dtype."
                 )
     else:
-        if np.isinf(cast_num).any():
+        if array_any(np.isinf(cast_num)):
             raise OverflowError(
                 f"Not possible to cast encoded times from {num.dtype!r} to "
                 f"{dtype!r} without overflow.  Consider removing the dtype "

xarray/core/formatting.py

@@ -18,7 +18,7 @@
 from pandas.errors import OutOfBoundsDatetime
 
 from xarray.core.datatree_render import RenderDataTree
-from xarray.core.duck_array_ops import array_all, array_equiv, astype
+from xarray.core.duck_array_ops import array_all, array_any, array_equiv, astype
 from xarray.core.indexing import MemoryCachedArray
 from xarray.core.options import OPTIONS, _get_boolean_with_default
 from xarray.core.treenode import group_subtrees
@@ -232,7 +232,7 @@ def format_array_flat(array, max_width: int):
 
     cum_len = np.cumsum([len(s) + 1 for s in relevant_items]) - 1
     if (array.size > 2) and (
-        (max_possibly_relevant < array.size) or (cum_len > max_width).any()
+        (max_possibly_relevant < array.size) or array_any(cum_len > max_width)
     ):
         padding = " ... "
         max_len = max(int(np.argmax(cum_len + len(padding) - 1 > max_width)), 2)

xarray/core/nanops.py

@@ -45,7 +45,7 @@ def _nan_argminmax_object(func, fill_value, value, axis=None, **kwargs):
     data = getattr(np, func)(value, axis=axis, **kwargs)
 
     # TODO This will evaluate dask arrays and might be costly.
-    if (valid_count == 0).any():
+    if duck_array_ops.array_any(valid_count == 0):
         raise ValueError("All-NaN slice encountered")
 
     return data