Search for the mem_handler name of the data owner

numpy · seberg · Oct 25, 2021 · Oct 11, 2020 · Oct 16, 2020 · Oct 18, 2020
commit 3d56aa08453388d92950b2e1de280d70a3dda73f
diff --git a/doc/source/reference/c-api/data_memory.rst b/doc/source/reference/c-api/data_memory.rst
@@ -64,7 +64,7 @@ reallocate or free the data memory of the instance.
             PyDataMemAllocator allocator;
         } PyDataMem_Handler;
 
-    where the allocator structure is:
+    where the allocator structure is
 
     .. code-block:: c
 
@@ -88,7 +88,9 @@ reallocate or free the data memory of the instance.
 .. c:function:: const char * PyDataMem_GetHandlerName(PyArrayObject *obj)
 
    Return the const char name of the `PyDataMem_Handler` used by the
-   ``PyArrayObject``. If ``NULL``, return the name of the current global policy
+   ``PyArrayObject`` or its base. If neither the ``PyArrayObject`` owns its own
+   data nor its base is a ``PyArrayObject`` which owns its own data return an
+   empty string. If ``NULL``, return the name of the current global policy
    that will be used to allocate data for the next ``PyArrayObject``.
 
 For an example of setting up and using the PyDataMem_Handler, see the test in

diff --git a/numpy/core/src/multiarray/alloc.c b/numpy/core/src/multiarray/alloc.c
@@ -467,16 +467,30 @@ PyDataMem_SetHandler(PyDataMem_Handler *handler)
 
 /*NUMPY_API
  * Return the const char name of the PyDataMem_Handler used by the
- * PyArrayObject. If NULL, return the name of the current global policy that
- * will be used to allocate data for the next PyArrayObject
+ * PyArrayObject or its base. If neither the PyArrayObject owns its own data
+ * nor its base is a PyArrayObject which owns its own data return an empty string.
+ * If NULL, return the name of the current global policy that
+ * will be used to allocate data for the next PyArrayObject.
  */
 NPY_NO_EXPORT const char *
 PyDataMem_GetHandlerName(PyArrayObject *obj)
 {
     if (obj == NULL) {
         return current_handler->name;
     }
-    return PyArray_HANDLER(obj)->name;
+    PyDataMem_Handler *handler;
+    handler = PyArray_HANDLER(obj);
+    if (handler != NULL) {
+        return handler->name;
+    }
+    PyObject *base = PyArray_BASE(obj);
+    if (base != NULL && PyArray_Check(base)) {
+        handler = PyArray_HANDLER((PyArrayObject *) base);
+        if (handler != NULL) {
+             return handler->name;
+        }
+    }
+    return "";
 }
 
 NPY_NO_EXPORT PyObject *
@@ -494,5 +508,8 @@ get_handler_name(PyObject *NPY_UNUSED(self), PyObject *args)
     if (name == NULL) {
         return NULL;
     }
+    else if (strlen(name) == 0) {
+        Py_RETURN_NONE;
+    }
     return PyUnicode_FromString(name);
 }
diff --git a/numpy/core/tests/test_mem_policy.py b/numpy/core/tests/test_mem_policy.py
@@ -152,7 +152,7 @@ def test_new_policy(get_module):
     a = np.arange(10)
     orig_policy = get_module.test_prefix(a)
     assert get_module.set_new_policy() == orig_policy
-    b = np.arange(10)
+    b = np.arange(10).reshape((2, 5))
     assert get_module.test_prefix(b) == 'secret_data_allocator'
 
     # test array manipulation. This is slow