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-rw-r--r--numpy/core/include/numpy/ndarraytypes.h6
-rw-r--r--numpy/core/numeric.py2
-rw-r--r--numpy/core/src/multiarray/multiarraymodule.c2
-rw-r--r--numpy/core/src/multiarray/nditer_api.c11
-rw-r--r--numpy/core/src/multiarray/nditer_constr.c139
-rw-r--r--numpy/core/src/multiarray/nditer_impl.h16
-rw-r--r--numpy/core/tests/test_na.py4
7 files changed, 130 insertions, 50 deletions
diff --git a/numpy/core/include/numpy/ndarraytypes.h b/numpy/core/include/numpy/ndarraytypes.h
index 1d500d777..70f63c079 100644
--- a/numpy/core/include/numpy/ndarraytypes.h
+++ b/numpy/core/include/numpy/ndarraytypes.h
@@ -546,17 +546,17 @@ typedef struct {
*These are inherited for global data-type if any data-types in the
* field have them
*/
-#define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \
+#define NPY_FROM_FIELDS (NPY_NEEDS_INIT | NPY_LIST_PICKLE | \
NPY_ITEM_REFCOUNT | NPY_NEEDS_PYAPI)
-#define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \
+#define NPY_OBJECT_DTYPE_FLAGS (NPY_LIST_PICKLE | NPY_USE_GETITEM | \
NPY_ITEM_IS_POINTER | NPY_ITEM_REFCOUNT | \
NPY_NEEDS_INIT | NPY_NEEDS_PYAPI)
#define PyDataType_FLAGCHK(dtype, flag) \
(((dtype)->flags & (flag)) == (flag))
-#define PyDataType_REFCHK(dtype) \
+#define PyDataType_REFCHK(dtype) \
PyDataType_FLAGCHK(dtype, NPY_ITEM_REFCOUNT)
typedef struct _PyArray_Descr {
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index e073128dd..a28ecdd19 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -1335,7 +1335,7 @@ def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
if arr.flags.maskna:
lst += ", maskna=True"
# If everything is NA, can't skip the type
- if np.all(np.isna(arr)):
+ if skiptype and np.all(np.isna(arr)):
skiptype = False
if skiptype:
diff --git a/numpy/core/src/multiarray/multiarraymodule.c b/numpy/core/src/multiarray/multiarraymodule.c
index 9e9864256..6ee706904 100644
--- a/numpy/core/src/multiarray/multiarraymodule.c
+++ b/numpy/core/src/multiarray/multiarraymodule.c
@@ -1651,7 +1651,7 @@ _array_fromobject(PyObject *NPY_UNUSED(ignored), PyObject *args, PyObject *kws)
if (maskna) {
flags |= NPY_ARRAY_MASKNA;
}
- if (maskna) {
+ if (ownmaskna) {
flags |= NPY_ARRAY_OWNMASKNA;
}
diff --git a/numpy/core/src/multiarray/nditer_api.c b/numpy/core/src/multiarray/nditer_api.c
index ba3f0b677..96715acd8 100644
--- a/numpy/core/src/multiarray/nditer_api.c
+++ b/numpy/core/src/multiarray/nditer_api.c
@@ -1337,9 +1337,20 @@ NpyIter_DebugPrint(NpyIter *iter)
printf("REDUCE ");
if (itflags&NPY_ITFLAG_REUSE_REDUCE_LOOPS)
printf("REUSE_REDUCE_LOOPS ");
+ if (itflags&NPY_ITFLAG_HAS_MASKNA_OP)
+ printf("ITFLAG_HAS_MASKNA_OP ");
+
printf("\n");
printf("| NDim: %d\n", (int)ndim);
printf("| NOp: %d\n", (int)nop);
+ if (itflags&NPY_ITFLAG_HAS_MASKNA_OP) {
+ printf("| First MaskNA Op: %d\n", (int)NIT_FIRST_MASKNA_OP(iter));
+ printf("| MaskNA Indices: ");
+ for (iop = 0; iop < nop; ++iop) {
+ printf("%d ", (int)NIT_MASKNA_INDICES(iter)[iop]);
+ }
+ printf("\n");
+ }
if (NIT_MASKOP(iter) >= 0) {
printf("| MaskOp: %d\n", (int)NIT_MASKOP(iter));
}
diff --git a/numpy/core/src/multiarray/nditer_constr.c b/numpy/core/src/multiarray/nditer_constr.c
index 441f19de0..37842c72d 100644
--- a/numpy/core/src/multiarray/nditer_constr.c
+++ b/numpy/core/src/multiarray/nditer_constr.c
@@ -33,14 +33,16 @@ npyiter_prepare_one_operand(PyArrayObject **op,
npy_uint32 flags,
npy_uint32 op_flags, char *op_itflags);
static int
-npyiter_prepare_operands(int nop, PyArrayObject **op_in,
+npyiter_prepare_operands(int nop, int first_maskna_op,
+ PyArrayObject **op_in,
PyArrayObject **op,
char **op_dataptr,
PyArray_Descr **op_request_dtypes,
PyArray_Descr **op_dtype,
npy_uint32 flags,
npy_uint32 *op_flags, char *op_itflags,
- npy_int8 *out_maskop);
+ npy_int8 *out_maskop,
+ npy_int8 *out_maskna_indices);
static int
npyiter_check_casting(int nop, PyArrayObject **op,
PyArray_Descr **op_dtype,
@@ -68,7 +70,7 @@ npyiter_reverse_axis_ordering(NpyIter *iter);
static void
npyiter_find_best_axis_ordering(NpyIter *iter);
static PyArray_Descr *
-npyiter_get_common_dtype(int nop, PyArrayObject **op,
+npyiter_get_common_dtype(int first_maskna_op, PyArrayObject **op,
char *op_itflags, PyArray_Descr **op_dtype,
PyArray_Descr **op_request_dtypes,
int only_inputs, int output_scalars);
@@ -84,7 +86,7 @@ npyiter_allocate_arrays(NpyIter *iter,
npy_uint32 *op_flags, char *op_itflags,
int **op_axes, int output_scalars);
static void
-npyiter_get_priority_subtype(int nop, PyArrayObject **op,
+npyiter_get_priority_subtype(int first_maskna_op, PyArrayObject **op,
char *op_itflags,
double *subtype_priority, PyTypeObject **subtype);
static int
@@ -105,7 +107,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
{
npy_uint32 itflags = NPY_ITFLAG_IDENTPERM;
int idim, ndim;
- int iop;
+ int iop, maskna_nop, first_maskna_op;
/* The iterator being constructed */
NpyIter *iter;
@@ -179,6 +181,18 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
NPY_IT_TIME_POINT(c_calculate_ndim);
+ /* Calculate maskna_nop */
+ maskna_nop = 0;
+ for (iop = 0; iop < nop; ++iop) {
+ if (op_flags[iop]&NPY_ITER_USE_MASKNA) {
+ itflags |= NPY_ITFLAG_HAS_MASKNA_OP;
+ ++maskna_nop;
+ }
+ }
+ /* Adjust nop to include the masks at the end */
+ first_maskna_op = nop;
+ nop += maskna_nop;
+
/* Allocate memory for the iterator */
iter = (NpyIter*)
PyArray_malloc(NIT_SIZEOF_ITERATOR(itflags, ndim, nop));
@@ -189,6 +203,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
NIT_ITFLAGS(iter) = itflags;
NIT_NDIM(iter) = ndim;
NIT_NOP(iter) = nop;
+ NIT_FIRST_MASKNA_OP(iter) = first_maskna_op;
NIT_MASKOP(iter) = -1;
NIT_ITERINDEX(iter) = 0;
memset(NIT_BASEOFFSETS(iter), 0, (nop+1)*NPY_SIZEOF_INTP);
@@ -199,10 +214,12 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
op_dataptr = NIT_RESETDATAPTR(iter);
/* Prepare all the operands */
- if (!npyiter_prepare_operands(nop, op_in, op, op_dataptr,
+ if (!npyiter_prepare_operands(nop, first_maskna_op, op_in, op, op_dataptr,
op_request_dtypes, op_dtype,
flags,
- op_flags, op_itflags, &NIT_MASKOP(iter))) {
+ op_flags, op_itflags,
+ &NIT_MASKOP(iter),
+ NIT_MASKNA_INDICES(iter))) {
PyArray_free(iter);
return NULL;
}
@@ -273,7 +290,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
NPY_IT_TIME_POINT(c_apply_forced_iteration_order);
/* Set some flags for allocated outputs */
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op[iop] == NULL) {
/* Flag this so later we can avoid flipping axes */
any_allocate = 1;
@@ -312,7 +329,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
NPY_IT_TIME_POINT(c_find_best_axis_ordering);
if (need_subtype) {
- npyiter_get_priority_subtype(nop, op, op_itflags,
+ npyiter_get_priority_subtype(first_maskna_op, op, op_itflags,
&subtype_priority, &subtype);
}
@@ -329,7 +346,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
op = NIT_OPERANDS(iter);
op_dtype = NIT_DTYPES(iter);
- dtype = npyiter_get_common_dtype(nop, op,
+ dtype = npyiter_get_common_dtype(first_maskna_op, op,
op_itflags, op_dtype,
op_request_dtypes,
only_inputs,
@@ -341,7 +358,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
if (flags & NPY_ITER_COMMON_DTYPE) {
NPY_IT_DBG_PRINT("Iterator: Replacing all data types\n");
/* Replace all the data types */
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op_dtype[iop] != dtype) {
Py_XDECREF(op_dtype[iop]);
Py_INCREF(dtype);
@@ -352,7 +369,7 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
else {
NPY_IT_DBG_PRINT("Iterator: Setting unset output data types\n");
/* Replace the NULL data types */
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op_dtype[iop] == NULL) {
Py_INCREF(dtype);
op_dtype[iop] = dtype;
@@ -369,7 +386,8 @@ NpyIter_AdvancedNew(int nop, PyArrayObject **op_in, npy_uint32 flags,
* to check that data type conversions are following the
* casting rules.
*/
- if (!npyiter_check_casting(nop, op, op_dtype, casting, op_itflags)) {
+ if (!npyiter_check_casting(first_maskna_op, op,
+ op_dtype, casting, op_itflags)) {
NpyIter_Deallocate(iter);
return NULL;
}
@@ -933,8 +951,8 @@ npyiter_check_per_op_flags(npy_uint32 op_flags, char *op_itflags)
/*
* Prepares a a constructor operand. Assumes a reference to 'op'
- * is owned, and that 'op' may be replaced. Fills in 'op_dtype'
- * and 'ndim'.
+ * is owned, and that 'op' may be replaced. Fills in 'op_dataptr',
+ * 'op_dtype', and may modify 'op_itflags'.
*
* Returns 1 on success, 0 on failure.
*/
@@ -1111,24 +1129,51 @@ npyiter_prepare_one_operand(PyArrayObject **op,
}
/*
+ * Prepares the maskna virtual operand for one of the constructor
+ * operands. Assumes that 'op' has already been prepared by
+ * npyiter_prepare_one_operand. Fills in 'op_maskna_dataptr',
+ * 'op_maskna_dtype', and 'op_maskna_itflags'.
+ *
+ * This needs to be called after any 'allocate' operands have
+ * been allocated.
+ *
+ * Returns 1 on success, 0 on failure.
+ */
+static int
+npyiter_prepare_one_maskna_operand(PyArrayObject *op,
+ char **op_maskna_dataptr,
+ PyArray_Descr **op_maskna_dtype,
+ npy_uint32 flags,
+ npy_uint32 op_flags, char *op_maskna_itflags)
+{
+ return 1;
+}
+
+/*
* Process all the operands, copying new references so further processing
* can replace the arrays if copying is necessary.
*/
static int
-npyiter_prepare_operands(int nop, PyArrayObject **op_in,
+npyiter_prepare_operands(int nop, int first_maskna_op, PyArrayObject **op_in,
PyArrayObject **op,
char **op_dataptr,
PyArray_Descr **op_request_dtypes,
PyArray_Descr **op_dtype,
npy_uint32 flags,
npy_uint32 *op_flags, char *op_itflags,
- npy_int8 *out_maskop)
+ npy_int8 *out_maskop,
+ npy_int8 *out_maskna_indices)
{
- int iop, i;
+ int iop, iop_maskna = first_maskna_op, i;
npy_int8 maskop = -1;
int any_writemasked_ops = 0;
- for (iop = 0; iop < nop; ++iop) {
+ /*
+ * Here we just prepare the provided operands. The masks for
+ * the maskna operands get prepared later, after any 'allocate'
+ * operands are allocated.
+ */
+ for (iop = 0; iop < first_maskna_op; ++iop) {
op[iop] = op_in[iop];
Py_XINCREF(op[iop]);
op_dtype[iop] = NULL;
@@ -1163,6 +1208,16 @@ npyiter_prepare_operands(int nop, PyArrayObject **op_in,
any_writemasked_ops = 1;
}
+ /* Link the operands to their maskna operands */
+ if (op_flags[iop] & NPY_ITER_USE_MASKNA) {
+ out_maskna_indices[iop] = iop_maskna;
+ out_maskna_indices[iop_maskna] = iop;
+ ++iop_maskna;
+ }
+ else {
+ out_maskna_indices[iop] = -1;
+ }
+
/*
* Prepare the operand. This produces an op_dtype[iop] reference
* on success.
@@ -1181,7 +1236,6 @@ npyiter_prepare_operands(int nop, PyArrayObject **op_in,
}
}
-
/* If all the operands were NULL, it's an error */
if (op[0] == NULL) {
int all_null = 1;
@@ -1289,14 +1343,14 @@ npyiter_shape_string(npy_intp n, npy_intp *vals, char *ending)
}
static int
-npyiter_check_casting(int nop, PyArrayObject **op,
+npyiter_check_casting(int first_maskna_op, PyArrayObject **op,
PyArray_Descr **op_dtype,
NPY_CASTING casting,
char *op_itflags)
{
int iop;
- for(iop = 0; iop < nop; ++iop) {
+ for(iop = 0; iop < first_maskna_op; ++iop) {
NPY_IT_DBG_PRINT1("Iterator: Checking casting for operand %d\n",
(int)iop);
#if NPY_IT_DBG_TRACING
@@ -1439,6 +1493,7 @@ npyiter_fill_axisdata(NpyIter *iter, npy_uint32 flags, char *op_itflags,
npy_uint32 itflags = NIT_ITFLAGS(iter);
int idim, ndim = NIT_NDIM(iter);
int iop, nop = NIT_NOP(iter);
+ int first_maskna_op = NIT_FIRST_MASKNA_OP(iter);
int maskop = NIT_MASKOP(iter);
int ondim;
@@ -1462,7 +1517,7 @@ npyiter_fill_axisdata(NpyIter *iter, npy_uint32 flags, char *op_itflags,
}
}
}
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
op_cur = op[iop];
if (op_cur != NULL) {
npy_intp *shape = PyArray_DIMS(op_cur);
@@ -1543,7 +1598,8 @@ npyiter_fill_axisdata(NpyIter *iter, npy_uint32 flags, char *op_itflags,
NAD_INDEX(axisdata) = 0;
memcpy(NAD_PTRS(axisdata), op_dataptr, NPY_SIZEOF_INTP*nop);
- for (iop = 0; iop < nop; ++iop) {
+ /* Not processing the maskna masks until after allocation */
+ for (iop = 0; iop < first_maskna_op; ++iop) {
op_cur = op[iop];
if (op_axes == NULL || op_axes[iop] == NULL) {
@@ -1555,7 +1611,7 @@ npyiter_fill_axisdata(NpyIter *iter, npy_uint32 flags, char *op_itflags,
if (bshape == 1) {
strides[iop] = 0;
if (idim >= ondim && !output_scalars &&
- (op_flags[iop] & NPY_ITER_NO_BROADCAST)) {
+ (op_flags[iop] & NPY_ITER_NO_BROADCAST)) {
goto operand_different_than_broadcast;
}
}
@@ -1696,7 +1752,7 @@ broadcast_error: {
if (errmsg == NULL) {
return 0;
}
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op[iop] != NULL) {
tmp = npyiter_shape_string(PyArray_NDIM(op[iop]),
PyArray_DIMS(op[iop]),
@@ -1739,7 +1795,7 @@ broadcast_error: {
errmsg = PyUString_FromString("operands could not be broadcast "
"together with remapped shapes "
"[original->remapped]: ");
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op[iop] != NULL) {
int *axes = op_axes[iop];
@@ -2077,6 +2133,7 @@ npyiter_apply_forced_iteration_order(NpyIter *iter, NPY_ORDER order)
/*npy_uint32 itflags = NIT_ITFLAGS(iter);*/
int ndim = NIT_NDIM(iter);
int iop, nop = NIT_NOP(iter);
+ int first_maskna_op = NIT_FIRST_MASKNA_OP(iter);
switch (order) {
case NPY_CORDER:
@@ -2097,7 +2154,7 @@ npyiter_apply_forced_iteration_order(NpyIter *iter, NPY_ORDER order)
int forder = 1;
/* Check that all the array inputs are fortran order */
- for (iop = 0; iop < nop; ++iop, ++op) {
+ for (iop = 0; iop < first_maskna_op; ++iop, ++op) {
if (*op && !PyArray_CHKFLAGS(*op, NPY_ARRAY_F_CONTIGUOUS)) {
forder = 0;
break;
@@ -2127,6 +2184,7 @@ npyiter_flip_negative_strides(NpyIter *iter)
npy_uint32 itflags = NIT_ITFLAGS(iter);
int idim, ndim = NIT_NDIM(iter);
int iop, nop = NIT_NOP(iter);
+ int first_maskna_op = NIT_FIRST_MASKNA_OP(iter);
npy_intp istrides, nstrides = NAD_NSTRIDES();
NpyIter_AxisData *axisdata, *axisdata0;
@@ -2141,10 +2199,9 @@ npyiter_flip_negative_strides(NpyIter *iter)
int any_negative = 0;
/*
- * Check the signs of all the strides, excluding
- * the index stride at the end.
+ * Check the signs of all the operand strides.
*/
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (strides[iop] < 0) {
any_negative = 1;
}
@@ -2153,10 +2210,10 @@ npyiter_flip_negative_strides(NpyIter *iter)
}
}
/*
- * If at least on stride is negative and none are positive,
+ * If at least one stride is negative and none are positive,
* flip all the strides for this dimension.
*/
- if (any_negative && iop == nop) {
+ if (any_negative && iop == first_maskna_op) {
npy_intp shapem1 = NAD_SHAPE(axisdata) - 1;
for (istrides = 0; istrides < nstrides; ++istrides) {
@@ -2250,6 +2307,7 @@ npyiter_find_best_axis_ordering(NpyIter *iter)
npy_uint32 itflags = NIT_ITFLAGS(iter);
int idim, ndim = NIT_NDIM(iter);
int iop, nop = NIT_NOP(iter);
+ int first_maskna_op = NIT_FIRST_MASKNA_OP(iter);
npy_intp ax_i0, ax_i1, ax_ipos;
npy_int8 ax_j0, ax_j1;
@@ -2281,7 +2339,7 @@ npyiter_find_best_axis_ordering(NpyIter *iter)
strides1 = NAD_STRIDES(NIT_INDEX_AXISDATA(axisdata, ax_j1));
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (strides0[iop] != 0 && strides1[iop] != 0) {
if (intp_abs(strides1[iop]) <=
intp_abs(strides0[iop])) {
@@ -2388,7 +2446,7 @@ npyiter_find_best_axis_ordering(NpyIter *iter)
* are not read from out of the calculation.
*/
static PyArray_Descr *
-npyiter_get_common_dtype(int nop, PyArrayObject **op,
+npyiter_get_common_dtype(int first_maskna_op, PyArrayObject **op,
char *op_itflags, PyArray_Descr **op_dtype,
PyArray_Descr **op_request_dtypes,
int only_inputs, int output_scalars)
@@ -2401,7 +2459,7 @@ npyiter_get_common_dtype(int nop, PyArrayObject **op,
NPY_IT_DBG_PRINT("Iterator: Getting a common data type from operands\n");
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op_dtype[iop] != NULL &&
(!only_inputs || (op_itflags[iop] & NPY_OP_ITFLAG_READ))) {
/* If no dtype was requested and the op is a scalar, pass the op */
@@ -2737,6 +2795,7 @@ npyiter_allocate_arrays(NpyIter *iter,
npy_uint32 itflags = NIT_ITFLAGS(iter);
int idim, ndim = NIT_NDIM(iter);
int iop, nop = NIT_NOP(iter);
+ int first_maskna_op = NIT_FIRST_MASKNA_OP(iter);
int check_writemasked_reductions = 0;
@@ -2747,7 +2806,7 @@ npyiter_allocate_arrays(NpyIter *iter,
bufferdata = NIT_BUFFERDATA(iter);
}
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
/*
* Check whether there are any WRITEMASKED REDUCE operands
* which should be validated after all the strides are filled
@@ -2987,7 +3046,7 @@ npyiter_allocate_arrays(NpyIter *iter,
}
if (check_writemasked_reductions) {
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
/*
* Check whether there are any WRITEMASKED REDUCE operands
* which should be validated now that all the strides are filled
@@ -3020,14 +3079,14 @@ npyiter_allocate_arrays(NpyIter *iter,
* subtype of the input array with highest priority.
*/
static void
-npyiter_get_priority_subtype(int nop, PyArrayObject **op,
+npyiter_get_priority_subtype(int first_maskna_op, PyArrayObject **op,
char *op_itflags,
double *subtype_priority,
PyTypeObject **subtype)
{
int iop;
- for (iop = 0; iop < nop; ++iop) {
+ for (iop = 0; iop < first_maskna_op; ++iop) {
if (op[iop] != NULL && op_itflags[iop] & NPY_OP_ITFLAG_READ) {
double priority = PyArray_GetPriority((PyObject *)op[iop], 0.0);
if (priority > *subtype_priority) {
diff --git a/numpy/core/src/multiarray/nditer_impl.h b/numpy/core/src/multiarray/nditer_impl.h
index e5ec487f8..753b8188f 100644
--- a/numpy/core/src/multiarray/nditer_impl.h
+++ b/numpy/core/src/multiarray/nditer_impl.h
@@ -101,6 +101,8 @@
#define NPY_ITFLAG_REDUCE 0x1000
/* Reduce iteration doesn't need to recalculate reduce loops next time */
#define NPY_ITFLAG_REUSE_REDUCE_LOOPS 0x2000
+/* The iterator has one or more operands with NPY_ITER_USE_MASKNA set */
+#define NPY_ITFLAG_HAS_MASKNA_OP 0x4000
/* Internal iterator per-operand iterator flags */
@@ -133,9 +135,8 @@
struct NpyIter_InternalOnly {
/* Initial fixed position data */
npy_uint32 itflags;
- npy_uint8 ndim, nop;
+ npy_uint8 ndim, nop, first_maskna_op;
npy_int8 maskop;
- npy_uint8 unused_padding;
npy_intp itersize, iterstart, iterend;
/* iterindex is only used if RANGED or BUFFERED is set */
npy_intp iterindex;
@@ -149,6 +150,8 @@ typedef struct NpyIter_BD NpyIter_BufferData;
/* Byte sizes of the iterator members */
#define NIT_PERM_SIZEOF(itflags, ndim, nop) \
NPY_INTP_ALIGNED(NPY_MAXDIMS)
+#define NIT_MASKNA_INDICES_SIZEOF(itflags, ndim, nop) \
+ NPY_INTP_ALIGNED(NPY_MAXDIMS)
#define NIT_DTYPES_SIZEOF(itflags, ndim, nop) \
((NPY_SIZEOF_INTP)*(nop))
#define NIT_RESETDATAPTR_SIZEOF(itflags, ndim, nop) \
@@ -165,9 +168,12 @@ typedef struct NpyIter_BD NpyIter_BufferData;
/* Byte offsets of the iterator members starting from iter->iter_flexdata */
#define NIT_PERM_OFFSET() \
(0)
-#define NIT_DTYPES_OFFSET(itflags, ndim, nop) \
+#define NIT_MASKNA_INDICES_OFFSET(itflags, ndim, nop) \
(NIT_PERM_OFFSET() + \
NIT_PERM_SIZEOF(itflags, ndim, nop))
+#define NIT_DTYPES_OFFSET(itflags, ndim, nop) \
+ (NIT_MASKNA_INDICES_OFFSET(itflags, ndim, nop) + \
+ NIT_MASKNA_INDICES_SIZEOF(itflags, ndim, nop))
#define NIT_RESETDATAPTR_OFFSET(itflags, ndim, nop) \
(NIT_DTYPES_OFFSET(itflags, ndim, nop) + \
NIT_DTYPES_SIZEOF(itflags, ndim, nop))
@@ -194,6 +200,8 @@ typedef struct NpyIter_BD NpyIter_BufferData;
((iter)->ndim)
#define NIT_NOP(iter) \
((iter)->nop)
+#define NIT_FIRST_MASKNA_OP(iter) \
+ ((iter)->first_maskna_op)
#define NIT_MASKOP(iter) \
((iter)->maskop)
#define NIT_ITERSIZE(iter) \
@@ -206,6 +214,8 @@ typedef struct NpyIter_BD NpyIter_BufferData;
(iter->iterindex)
#define NIT_PERM(iter) ((npy_int8 *)( \
&(iter)->iter_flexdata + NIT_PERM_OFFSET()))
+#define NIT_MASKNA_INDICES(iter) ((npy_int8 *)( \
+ &(iter)->iter_flexdata + NIT_MASKNA_INDICES_OFFSET(itflags, ndim, nop)))
#define NIT_DTYPES(iter) ((PyArray_Descr **)( \
&(iter)->iter_flexdata + NIT_DTYPES_OFFSET(itflags, ndim, nop)))
#define NIT_RESETDATAPTR(iter) ((char **)( \
diff --git a/numpy/core/tests/test_na.py b/numpy/core/tests/test_na.py
index bc2e98fb3..d1011ef27 100644
--- a/numpy/core/tests/test_na.py
+++ b/numpy/core/tests/test_na.py
@@ -149,7 +149,7 @@ def test_na_other_operations():
assert_equal((np.NA | False).dtype, np.array(False).dtype)
assert_equal((np.NA & True).dtype, np.array(True).dtype)
-def test_na_mask_flag():
+def test_array_maskna_flags():
a = np.arange(3)
assert_(not a.flags.maskna)
assert_(not a.flags.ownmaskna)
@@ -169,7 +169,7 @@ def test_na_mask_flag():
x.ownmaskna = v
assert_raises(ValueError, setownmaskna, a.flags, False)
-def test_maskna_construction():
+def test_array_maskna_construction():
# Construction with NA inputs
a = np.array([1.0, 2.0, np.NA, 7.0], maskna=True)
assert_equal(a.dtype, np.dtype('f8'))
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