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-rw-r--r--numpy/core/include/numpy/ndarraytypes.h23
-rw-r--r--numpy/core/src/multiarray/descriptor.c6
-rw-r--r--numpy/core/src/multiarray/dtypemeta.c79
3 files changed, 52 insertions, 56 deletions
diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h
index 63c896302..5662ca7a3 100644
--- a/numpy/core/include/numpy/ndarraytypes.h
+++ b/numpy/core/include/numpy/ndarraytypes.h
@@ -1839,18 +1839,20 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size,
PyHeapTypeObject super;
/*
- * TODO: Update above comment as necessary.
* Most DTypes will have a singleton default instance, for the
* parametric legacy DTypes (bytes, string, void, datetime) this
* may be a pointer to the *prototype* instance?
*/
PyArray_Descr *singleton;
/*
- * Is this DType created using the old API?
- * TODO: For now this mostly exists for possible assertions,
- * we may not need it.
+ * Is this DType created using the old API? This exists mainly to
+ * allow for assertions in paths specific to wrapping legacy types.
*/
- npy_bool is_legacy;
+ npy_bool legacy;
+ /* The values stored by a parametric datatype depend on its instance */
+ npy_bool parametric;
+ /* whether the DType can be instantiated (i.e. np.dtype cannot) */
+ npy_bool abstract;
/*
* The following fields replicate the most important dtype information.
@@ -1865,20 +1867,11 @@ typedef void (PyDataMem_EventHookFunc)(void *inp, void *outp, size_t size,
char type;
/* flags describing data type */
char flags;
- npy_bool is_parametric;
- /* whether the DType can be instantiated (i.e. np.dtype cannot) */
- npy_bool is_abstract;
/* number representing this type */
int type_num;
/*
- * itemsize of this type, -1 if variable. Use npy_intp, even though it
- * is effectively limited to int due to other public API.
- */
- npy_intp itemsize;
- /*
* Point to the original ArrFuncs.
- * TODO: If we wanted to do, we could make a copy to detect changes.
- * However, I doubt that is necessary.
+ * NOTE: We could make a copy to detect changes to `f`.
*/
PyArray_ArrFuncs *f;
} PyArray_DTypeMeta;
diff --git a/numpy/core/src/multiarray/descriptor.c b/numpy/core/src/multiarray/descriptor.c
index 37373451e..b4107f8f3 100644
--- a/numpy/core/src/multiarray/descriptor.c
+++ b/numpy/core/src/multiarray/descriptor.c
@@ -3490,6 +3490,8 @@ NPY_NO_EXPORT PyArray_DTypeMeta PyArrayDescr_TypeFull = {
},},
.type_num = -1,
.kind = '\0',
- .is_abstract = 1,
- .is_parametric = 0,
+ .abstract = 1,
+ .parametric = 0,
+ .singleton = 0,
+ .scalar_type = NULL,
};
diff --git a/numpy/core/src/multiarray/dtypemeta.c b/numpy/core/src/multiarray/dtypemeta.c
index daaf58aa6..76f7b599a 100644
--- a/numpy/core/src/multiarray/dtypemeta.c
+++ b/numpy/core/src/multiarray/dtypemeta.c
@@ -15,17 +15,17 @@
static void
dtypemeta_dealloc(PyArray_DTypeMeta *self) {
- /*
- * PyType_Type asserts Py_TPFLAGS_HEAPTYPE as well. Do not rely on
- * a python debug build though.
- */
+ /* Do not accidentally delete a statically defined DType: */
assert(((PyTypeObject *)self)->tp_flags & Py_TPFLAGS_HEAPTYPE);
+
Py_XDECREF(self->scalar_type);
+ Py_XDECREF(self->singleton);
PyType_Type.tp_dealloc((PyObject *) self);
}
static PyObject *
-dtypemeta_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
+dtypemeta_new(PyTypeObject *NPY_UNUSED(type),
+ PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds))
{
PyErr_SetString(PyExc_TypeError,
"Preliminary-API: Cannot subclass DType.");
@@ -33,10 +33,11 @@ dtypemeta_new(PyTypeObject *type, PyObject *args, PyObject *kwds)
}
static int
-dtypemeta_init(PyTypeObject *type, PyObject *args, PyObject *kwds)
+dtypemeta_init(PyTypeObject *NPY_UNUSED(type),
+ PyObject *NPY_UNUSED(args), PyObject *NPY_UNUSED(kwds))
{
PyErr_SetString(PyExc_TypeError,
- "Preliminary-API: Cannot initialize DType class.");
+ "Preliminary-API: Cannot __init__ DType class.");
return -1;
}
@@ -46,8 +47,8 @@ dtypemeta_init(PyTypeObject *type, PyObject *args, PyObject *kwds)
*
* Python Type objects are either statically created (typical C-Extension type)
* or HeapTypes (typically created in Python).
- * HeapTypes have the Py_TPFLAGS_HEAPTYPE flag, and are garbage collected, our
- * DTypeMeta instances (`np.dtype` and its subclasses) *may* be HeapTypes
+ * HeapTypes have the Py_TPFLAGS_HEAPTYPE flag and are garbage collected.
+ * Our DTypeMeta instances (`np.dtype` and its subclasses) *may* be HeapTypes
* if the Py_TPFLAGS_HEAPTYPE flag is set (they are created from Python).
* They are not for legacy DTypes or np.dtype itself.
*
@@ -67,10 +68,12 @@ dtypemeta_traverse(PyArray_DTypeMeta *type, visitproc visit, void *arg)
/*
* We have to traverse the base class (if it is a HeapType).
* PyType_Type will handle this logic for us.
- * This function is currently not used, but may be necessary in the future
- * when we implement HeapTypes (python/dynamically defined types).
+ * This function is currently not used, but will probably be necessary
+ * in the future when we implement HeapTypes (python/dynamically
+ * defined types). It should be revised at that time.
*/
- assert(!type->is_legacy && (PyTypeObject *)type != &PyArrayDescr_Type);
+ assert(0);
+ assert(!type->legacy && (PyTypeObject *)type != &PyArrayDescr_Type);
Py_VISIT(type->singleton);
Py_VISIT(type->scalar_type);
return PyType_Type.tp_traverse((PyObject *)type, visit, arg);
@@ -82,7 +85,7 @@ legacy_dtype_default_new(PyArray_DTypeMeta *self,
PyObject *args, PyObject *kwargs)
{
/* TODO: This should allow endianess and possibly metadata */
- if (self->is_parametric) {
+ if (self->parametric) {
/* reject parametric ones since we would need to get unit, etc. info */
PyErr_Format(PyExc_TypeError,
"Preliminary-API: Flexible/Parametric legacy DType '%S' can "
@@ -193,8 +196,8 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
.tp_base = &PyArrayDescr_Type,
.tp_new = (newfunc)legacy_dtype_default_new,
},},
- .is_legacy = 1,
- .is_abstract = 0, /* this is a concrete DType */
+ .legacy = 1,
+ .abstract = 0, /* this is a concrete DType */
/* Further fields are not common between DTypes */
};
memcpy(dtype_class, &prototype, sizeof(PyArray_DTypeMeta));
@@ -217,15 +220,13 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
dtype_class->type = descr->type;
dtype_class->f = descr->f;
dtype_class->kind = descr->kind;
- dtype_class->itemsize = descr->elsize;
if (PyTypeNum_ISDATETIME(descr->type_num)) {
/* Datetimes are flexible, but were not considered previously */
- dtype_class->is_parametric = NPY_TRUE;
+ dtype_class->parametric = NPY_TRUE;
}
else if (PyTypeNum_ISFLEXIBLE(descr->type_num)) {
- dtype_class->is_parametric = NPY_TRUE;
- dtype_class->itemsize = -1; /* itemsize is not fixed */
+ dtype_class->parametric = NPY_TRUE;
}
/* Finally, replace the current class of the descr */
@@ -240,29 +241,29 @@ dtypemeta_wrap_legacy_descriptor(PyArray_Descr *descr)
* preliminary (the flags should also return bools).
*/
static PyMemberDef dtypemeta_members[] = {
- {"_abstract",
- T_BYTE, offsetof(PyArray_DTypeMeta, is_abstract), READONLY, NULL},
- {"type",
- T_OBJECT, offsetof(PyArray_DTypeMeta, scalar_type), READONLY, NULL},
- {"_parametric",
- T_BYTE, offsetof(PyArray_DTypeMeta, is_parametric), READONLY, NULL},
- {NULL, 0, 0, 0, NULL},
+ {"_abstract",
+ T_BYTE, offsetof(PyArray_DTypeMeta, abstract), READONLY, NULL},
+ {"type",
+ T_OBJECT, offsetof(PyArray_DTypeMeta, scalar_type), READONLY, NULL},
+ {"_parametric",
+ T_BYTE, offsetof(PyArray_DTypeMeta, parametric), READONLY, NULL},
+ {NULL, 0, 0, 0, NULL},
};
NPY_NO_EXPORT PyTypeObject PyArrayDTypeMeta_Type = {
- PyVarObject_HEAD_INIT(NULL, 0)
- .tp_name = "numpy._DTypeMeta",
- .tp_basicsize = sizeof(PyArray_DTypeMeta),
- .tp_dealloc = (destructor)dtypemeta_dealloc,
- /* Types are garbage collected (see dtypemeta_is_gc documentation) */
- .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
- .tp_doc = "Preliminary NumPy API: The Type of NumPy DTypes (metaclass)",
- .tp_members = dtypemeta_members,
- .tp_base = NULL, /* set to PyType_Type at import time */
- .tp_init = (initproc)dtypemeta_init,
- .tp_new = dtypemeta_new,
- .tp_is_gc = dtypemeta_is_gc,
- .tp_traverse = (traverseproc)dtypemeta_traverse,
+ PyVarObject_HEAD_INIT(NULL, 0)
+ .tp_name = "numpy._DTypeMeta",
+ .tp_basicsize = sizeof(PyArray_DTypeMeta),
+ .tp_dealloc = (destructor)dtypemeta_dealloc,
+ /* Types are garbage collected (see dtypemeta_is_gc documentation) */
+ .tp_flags = Py_TPFLAGS_DEFAULT | Py_TPFLAGS_HAVE_GC,
+ .tp_doc = "Preliminary NumPy API: The Type of NumPy DTypes (metaclass)",
+ .tp_members = dtypemeta_members,
+ .tp_base = NULL, /* set to PyType_Type at import time */
+ .tp_init = (initproc)dtypemeta_init,
+ .tp_new = dtypemeta_new,
+ .tp_is_gc = dtypemeta_is_gc,
+ .tp_traverse = (traverseproc)dtypemeta_traverse,
};
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