typing for numpy 1.21

xarray/core/accessor_str.py

@@ -270,7 +270,10 @@ def _re_compile(
 
         if getattr(pat, "dtype", None) != np.object_:
             pat = self._stringify(pat)
-        func = lambda x: re.compile(x, flags=flags)
+
+        def func(x):
+            return re.compile(x, flags=flags)
+
         if isinstance(pat, np.ndarray):
             # apply_ufunc doesn't work for numpy arrays with output object dtypes
             func = np.vectorize(func)

xarray/core/computation.py

@@ -597,7 +597,7 @@ def broadcast_compat_data(
         data = duck_array_ops.transpose(data, order)
 
     if new_dims != reordered_dims:
-        key_parts = []
+        key_parts: List[Optional[slice]] = []
         for dim in new_dims:
             if dim in set_old_dims:
                 key_parts.append(SLICE_NONE)

xarray/core/formatting.py

@@ -190,7 +190,7 @@ def format_array_flat(array, max_width: int):
         (max_possibly_relevant < array.size) or (cum_len > max_width).any()
     ):
         padding = " ... "
-        max_len = max(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)  # type: ignore[type-var]
         count = min(array.size, max_len)
     else:
         count = array.size

xarray/core/indexes.py

@@ -163,7 +163,7 @@ def __init__(
             else:
                 dtype_ = array.dtype
         else:
-            dtype_ = np.dtype(dtype)
+            dtype_ = np.dtype(dtype)  # type: ignore[assignment]
         self._dtype = dtype_
 
     def to_pandas_index(self) -> pd.Index: