Update test and nicer code

lib/iris/_lazy_data.py

@@ -10,7 +10,7 @@
 
 from functools import lru_cache, wraps
 from types import ModuleType
-from typing import Any, Sequence
+from typing import Sequence
 
 import dask
 import dask.array as da
@@ -229,24 +229,24 @@ def _optimum_chunksize(
 
 
 class LRUCache:
-    def __init__(self, maxsize: int):
-        self._cache: dict[str, Any] = {}
+    def __init__(self, maxsize: int) -> None:
+        self._cache: dict[str, da.Array] = {}
         self.maxsize = maxsize
 
-    def __getitem__(self, key):
+    def __getitem__(self, key: str) -> da.Array:
         value = self._cache.pop(key)
         self._cache[key] = value
         return value
 
-    def __setitem__(self, key, value):
+    def __setitem__(self, key: str, value: da.Array) -> None:
         self._cache[key] = value
         if len(self._cache) > self.maxsize:
             self._cache.pop(next(iter(self._cache)))
 
-    def __contains__(self, key):
+    def __contains__(self, key: str) -> bool:
         return key in self._cache
 
-    def __repr__(self):
+    def __repr__(self) -> str:
         return (
             f"<{self.__class__.__name__} maxsize={self.maxsize} cache={self._cache!r} >"
         )
@@ -331,12 +331,10 @@ def as_lazy_data(data, chunks=None, asarray=False, meta=None, dims_fixed=None):
 
     if is_lazy_data(data):
         result = data
-    elif key in CACHE:
-        result = CACHE[key].copy()
     else:
-        result = da.from_array(data, chunks=chunks, asarray=asarray, meta=meta)
-        if key is not None:
-            CACHE[key] = result.copy()
+        if key not in CACHE:
+            CACHE[key] = da.from_array(data, chunks=chunks, asarray=asarray, meta=meta)
+        result = CACHE[key].copy()
 
     return result
 

lib/iris/tests/unit/lazy_data/test_as_lazy_data.py

@@ -161,7 +161,7 @@ def test_default_chunks_limiting(self, mocker):
         as_lazy_data(data)
         assert limitcall_patch.call_args_list == [
             mock.call(
-                list(test_shape),
+                tuple(test_shape),
                 shape=test_shape,
                 dtype=np.dtype("f4"),
                 dims_fixed=None,