avoid a couple of warnings in polyfit (pydata#8939)

* use `numpy.finfo` instead of `numpy.core.finfo`

* add `copy` to the signature of `__array__`

* don't pass `copy` to `asarray`

The `copy` kwarg appears to only exist in `numpy>=2`.

* try making the typing the same for all overloads

* change the typing for `NamedArray.__array__`

* fix the signature of `__array__`

* revert the change to the signature of `__array__`

* ignore all deprecation warnings about `__array__` gaining a new kwarg

I don't know enough about typing to ignore typing issues within a
protocol, so that was the only way I could figure out how to get this
PR unstuck. Once we know what to do here, we can remove the ignore in
a new PR.

* add back the `copy` kwarg if the protocol does not have type hints

* Update xarray/core/dataset.py

---------

Co-authored-by: Deepak Cherian <dcherian@users.noreply.github.com>

pyproject.toml

@@ -330,6 +330,8 @@ filterwarnings = [
   "default:Using a non-tuple sequence for multidimensional indexing is deprecated:FutureWarning",
   "default:Duplicate dimension names present:UserWarning:xarray.namedarray.core",
   "default:::xarray.tests.test_strategies",
+  # TODO: remove once we know how to deal with a changed signature in protocols
+  "ignore:__array__ implementation doesn't accept a copy keyword, so passing copy=False failed.",
 ]
 
 log_cli_level = "INFO"

xarray/core/dataset.py

@@ -1524,7 +1524,7 @@ def __iter__(self) -> Iterator[Hashable]:
 
     else:
 
-        def __array__(self, dtype=None):
+        def __array__(self, dtype=None, copy=None):
             raise TypeError(
                 "cannot directly convert an xarray.Dataset into a "
                 "numpy array. Instead, create an xarray.DataArray "
@@ -8937,9 +8937,7 @@ def polyfit(
         lhs = np.vander(x, order)
 
         if rcond is None:
-            rcond = (
-                x.shape[0] * np.core.finfo(x.dtype).eps  # type: ignore[attr-defined]
-            )
+            rcond = x.shape[0] * np.finfo(x.dtype).eps
 
         # Weights:
         if w is not None:

xarray/core/datatree.py

@@ -624,7 +624,7 @@ def __bool__(self) -> bool:
     def __iter__(self) -> Iterator[Hashable]:
         return itertools.chain(self.ds.data_vars, self.children)
 
-    def __array__(self, dtype=None):
+    def __array__(self, dtype=None, copy=None):
         raise TypeError(
             "cannot directly convert a DataTree into a "
             "numpy array. Instead, create an xarray.DataArray "

xarray/tests/test_assertions.py

@@ -149,7 +149,7 @@ def dims(self):
             warnings.warn("warning in test")
             return super().dims
 
-        def __array__(self):
+        def __array__(self, dtype=None, copy=None):
             warnings.warn("warning in test")
             return super().__array__()
 

xarray/tests/test_formatting.py

@@ -877,7 +877,7 @@ def test_lazy_array_wont_compute() -> None:
     from xarray.core.indexing import LazilyIndexedArray
 
     class LazilyIndexedArrayNotComputable(LazilyIndexedArray):
-        def __array__(self, dtype=None):
+        def __array__(self, dtype=None, copy=None):
             raise NotImplementedError("Computing this array is not possible.")
 
     arr = LazilyIndexedArrayNotComputable(np.array([1, 2]))