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	Array Iterator API
	==================

	.. sectionauthor:: Mark Wiebe

	.. index::
	pair: iterator; C-API
	pair: C-API; iterator

	.. versionadded:: 1.6

	Array Iterator
	--------------

	The array iterator encapsulates many of the key features in ufuncs,
	allowing user code to support features like output parameters,
	preservation of memory layouts, and buffering of data with the wrong
	alignment or type, without requiring difficult coding.

	This page documents the API for the iterator.
	The C-API naming convention chosen is based on the one in the numpy-refactor
	branch, so will integrate naturally into the refactored code base.
	The iterator is named ``NpyIter`` and functions are
	named ``NpyIter_*``.

	There is an :ref:`introductory guide to array iteration <arrays.nditer>`
	which may be of interest for those using this C API. In many instances,
	testing out ideas by creating the iterator in Python is a good idea
	before writing the C iteration code.

	Converting from Previous NumPy Iterators
	----------------------------------------

	The existing iterator API includes functions like PyArrayIter_Check,
	PyArray_Iter* and PyArray_ITER_*. The multi-iterator array includes
	PyArray_MultiIter*, PyArray_Broadcast, and PyArray_RemoveSmallest. The
	new iterator design replaces all of this functionality with a single object
	and associated API. One goal of the new API is that all uses of the
	existing iterator should be replaceable with the new iterator without
	significant effort. In 1.6, the major exception to this is the neighborhood
	iterator, which does not have corresponding features in this iterator.

	Here is a conversion table for which functions to use with the new iterator:

	===================================== =============================================
	Iterator Functions
	:cfunc:`PyArray_IterNew` :cfunc:`NpyIter_New`
	:cfunc:`PyArray_IterAllButAxis` :cfunc:`NpyIter_New` + ``axes`` parameter or
	Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP`
	:cfunc:`PyArray_BroadcastToShape` NOT SUPPORTED (Use the support for
	multiple operands instead.)
	:cfunc:`PyArrayIter_Check` Will need to add this in Python exposure
	:cfunc:`PyArray_ITER_RESET` :cfunc:`NpyIter_Reset`
	:cfunc:`PyArray_ITER_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext`
	:cfunc:`PyArray_ITER_DATA` :cfunc:`NpyIter_GetDataPtrArray`
	:cfunc:`PyArray_ITER_GOTO` :cfunc:`NpyIter_GotoMultiIndex`
	:cfunc:`PyArray_ITER_GOTO1D` :cfunc:`NpyIter_GotoIndex` or
	:cfunc:`NpyIter_GotoIterIndex`
	:cfunc:`PyArray_ITER_NOTDONE` Return value of ``iternext`` function pointer
	Multi-iterator Functions
	:cfunc:`PyArray_MultiIterNew` :cfunc:`NpyIter_MultiNew`
	:cfunc:`PyArray_MultiIter_RESET` :cfunc:`NpyIter_Reset`
	:cfunc:`PyArray_MultiIter_NEXT` Function pointer from :cfunc:`NpyIter_GetIterNext`
	:cfunc:`PyArray_MultiIter_DATA` :cfunc:`NpyIter_GetDataPtrArray`
	:cfunc:`PyArray_MultiIter_NEXTi` NOT SUPPORTED (always lock-step iteration)
	:cfunc:`PyArray_MultiIter_GOTO` :cfunc:`NpyIter_GotoMultiIndex`
	:cfunc:`PyArray_MultiIter_GOTO1D` :cfunc:`NpyIter_GotoIndex` or
	:cfunc:`NpyIter_GotoIterIndex`
	:cfunc:`PyArray_MultiIter_NOTDONE` Return value of ``iternext`` function pointer
	:cfunc:`PyArray_Broadcast` Handled by :cfunc:`NpyIter_MultiNew`
	:cfunc:`PyArray_RemoveSmallest` Iterator flag :cdata:`NPY_ITER_EXTERNAL_LOOP`
	Other Functions
	:cfunc:`PyArray_ConvertToCommonType` Iterator flag :cdata:`NPY_ITER_COMMON_DTYPE`
	===================================== =============================================

	Simple Iteration Example
	------------------------

	The best way to become familiar with the iterator is to look at its
	usage within the NumPy codebase itself. For example, here is a slightly
	tweaked version of the code for :cfunc:`PyArray_CountNonzero`, which counts the
	number of non-zero elements in an array.

	.. code-block:: c

	npy_intp PyArray_CountNonzero(PyArrayObject* self)
	{
	/* Nonzero boolean function */
	PyArray_NonzeroFunc* nonzero = PyArray_DESCR(self)->f->nonzero;

	NpyIter* iter;
	NpyIter_IterNextFunc *iternext;
	char** dataptr;
	npy_intp* strideptr,* innersizeptr;

	/* Handle zero-sized arrays specially */
	if (PyArray_SIZE(self) == 0) {
	return 0;
	}

	/*
	* Create and use an iterator to count the nonzeros.
	* flag NPY_ITER_READONLY
	* - The array is never written to.
	* flag NPY_ITER_EXTERNAL_LOOP
	* - Inner loop is done outside the iterator for efficiency.
	* flag NPY_ITER_NPY_ITER_REFS_OK
	* - Reference types are acceptable.
	* order NPY_KEEPORDER
	* - Visit elements in memory order, regardless of strides.
	* This is good for performance when the specific order
	* elements are visited is unimportant.
	* casting NPY_NO_CASTING
	* - No casting is required for this operation.
	*/
	iter = NpyIter_New(self, NPY_ITER_READONLY\|
	NPY_ITER_EXTERNAL_LOOP\|
	NPY_ITER_REFS_OK,
	NPY_KEEPORDER, NPY_NO_CASTING,
	NULL);
	if (iter == NULL) {
	return -1;
	}

	/*
	* The iternext function gets stored in a local variable
	* so it can be called repeatedly in an efficient manner.
	*/
	iternext = NpyIter_GetIterNext(iter, NULL);
	if (iternext == NULL) {
	NpyIter_Deallocate(iter);
	return -1;
	}
	/* The location of the data pointer which the iterator may update */
	dataptr = NpyIter_GetDataPtrArray(iter);
	/* The location of the stride which the iterator may update */
	strideptr = NpyIter_GetInnerStrideArray(iter);
	/* The location of the inner loop size which the iterator may update */
	innersizeptr = NpyIter_GetInnerLoopSizePtr(iter);

	/* The iteration loop */
	do {
	/* Get the inner loop data/stride/count values */
	char* data = *dataptr;
	npy_intp stride = *strideptr;
	npy_intp count = *innersizeptr;

	/* This is a typical inner loop for NPY_ITER_EXTERNAL_LOOP */
	while (count--) {
	if (nonzero(data, self)) {
	++nonzero_count;
	}
	data += stride;
	}

	/* Increment the iterator to the next inner loop */
	} while(iternext(iter));

	NpyIter_Deallocate(iter);

	return nonzero_count;
	}

	Simple Multi-Iteration Example
	------------------------------

	Here is a simple copy function using the iterator. The ``order`` parameter
	is used to control the memory layout of the allocated result, typically
	:cdata:`NPY_KEEPORDER` is desired.

	.. code-block:: c

	PyObject CopyArray(PyObject arr, NPY_ORDER order)
	{
	NpyIter *iter;
	NpyIter_IterNextFunc *iternext;
	PyObject op[2], ret;
	npy_uint32 flags;
	npy_uint32 op_flags[2];
	npy_intp itemsize, *innersizeptr, innerstride;
	char **dataptrarray;

	/*
	* No inner iteration - inner loop is handled by CopyArray code
	*/
	flags = NPY_ITER_EXTERNAL_LOOP;
	/*
	* Tell the constructor to automatically allocate the output.
	* The data type of the output will match that of the input.
	*/
	op[0] = arr;
	op[1] = NULL;
	op_flags[0] = NPY_ITER_READONLY;
	op_flags[1] = NPY_ITER_WRITEONLY \| NPY_ITER_ALLOCATE;

	/* Construct the iterator */
	iter = NpyIter_MultiNew(2, op, flags, order, NPY_NO_CASTING,
	op_flags, NULL);
	if (iter == NULL) {
	return NULL;
	}

	/*
	* Make a copy of the iternext function pointer and
	* a few other variables the inner loop needs.
	*/
	iternext = NpyIter_GetIterNext(iter, NULL);
	innerstride = NpyIter_GetInnerStrideArray(iter)[0];
	itemsize = NpyIter_GetDescrArray(iter)[0]->elsize;
	/*
	* The inner loop size and data pointers may change during the
	* loop, so just cache the addresses.
	*/
	innersizeptr = NpyIter_GetInnerLoopSizePtr(iter);
	dataptrarray = NpyIter_GetDataPtrArray(iter);

	/*
	* Note that because the iterator allocated the output,
	* it matches the iteration order and is packed tightly,
	* so we don't need to check it like the input.
	*/
	if (innerstride == itemsize) {
	do {
	memcpy(dataptrarray[1], dataptrarray[0],
	itemsize * (*innersizeptr));
	} while (iternext(iter));
	} else {
	/* For efficiency, should specialize this based on item size... */
	npy_intp i;
	do {
	npy_intp size = *innersizeptr;
	char src = dataptrarray[0], dst = dataptrarray[1];
	for(i = 0; i < size; i++, src += innerstride, dst += itemsize) {
	memcpy(dst, src, itemsize);
	}
	} while (iternext(iter));
	}

	/* Get the result from the iterator object array */
	ret = NpyIter_GetOperandArray(iter)[1];
	Py_INCREF(ret);

	if (NpyIter_Deallocate(iter) != NPY_SUCCEED) {
	Py_DECREF(ret);
	return NULL;
	}

	return ret;
	}


	Iterator Data Types
	---------------------

	The iterator layout is an internal detail, and user code only sees
	an incomplete struct.

	.. ctype:: NpyIter

	This is an opaque pointer type for the iterator. Access to its contents
	can only be done through the iterator API.

	.. ctype:: NpyIter_Type

	This is the type which exposes the iterator to Python. Currently, no
	API is exposed which provides access to the values of a Python-created
	iterator. If an iterator is created in Python, it must be used in Python
	and vice versa. Such an API will likely be created in a future version.

	.. ctype:: NpyIter_IterNextFunc

	This is a function pointer for the iteration loop, returned by
	:cfunc:`NpyIter_GetIterNext`.

	.. ctype:: NpyIter_GetMultiIndexFunc

	This is a function pointer for getting the current iterator multi-index,
	returned by :cfunc:`NpyIter_GetGetMultiIndex`.

	Construction and Destruction
	----------------------------

	.. cfunction:: NpyIter* NpyIter_New(PyArrayObject* op, npy_uint32 flags, NPY_ORDER order, NPY_CASTING casting, PyArray_Descr* dtype)

	Creates an iterator for the given numpy array object ``op``.

	Flags that may be passed in ``flags`` are any combination
	of the global and per-operand flags documented in
	:cfunc:`NpyIter_MultiNew`, except for :cdata:`NPY_ITER_ALLOCATE`.

	Any of the :ctype:`NPY_ORDER` enum values may be passed to ``order``. For
	efficient iteration, :ctype:`NPY_KEEPORDER` is the best option, and
	the other orders enforce the particular iteration pattern.

	Any of the :ctype:`NPY_CASTING` enum values may be passed to ``casting``.
	The values include :cdata:`NPY_NO_CASTING`, :cdata:`NPY_EQUIV_CASTING`,
	:cdata:`NPY_SAFE_CASTING`, :cdata:`NPY_SAME_KIND_CASTING`, and
	:cdata:`NPY_UNSAFE_CASTING`. To allow the casts to occur, copying or
	buffering must also be enabled.

	If ``dtype`` isn't ``NULL``, then it requires that data type.
	If copying is allowed, it will make a temporary copy if the data
	is castable. If :cdata:`NPY_ITER_UPDATEIFCOPY` is enabled, it will
	also copy the data back with another cast upon iterator destruction.

	Returns NULL if there is an error, otherwise returns the allocated
	iterator.

	To make an iterator similar to the old iterator, this should work.

	.. code-block:: c

	iter = NpyIter_New(op, NPY_ITER_READWRITE,
	NPY_CORDER, NPY_NO_CASTING, NULL);

	If you want to edit an array with aligned ``double`` code,
	but the order doesn't matter, you would use this.

	.. code-block:: c

	dtype = PyArray_DescrFromType(NPY_DOUBLE);
	iter = NpyIter_New(op, NPY_ITER_READWRITE\|
	NPY_ITER_BUFFERED\|
	NPY_ITER_NBO\|
	NPY_ITER_ALIGNED,
	NPY_KEEPORDER,
	NPY_SAME_KIND_CASTING,
	dtype);
	Py_DECREF(dtype);

	.. cfunction:: NpyIter* NpyIter_MultiNew(npy_intp nop, PyArrayObject** op, npy_uint32 flags, NPY_ORDER order, NPY_CASTING casting, npy_uint32* op_flags, PyArray_Descr** op_dtypes)

	Creates an iterator for broadcasting the ``nop`` array objects provided
	in ``op``, using regular NumPy broadcasting rules.

	Any of the :ctype:`NPY_ORDER` enum values may be passed to ``order``. For
	efficient iteration, :cdata:`NPY_KEEPORDER` is the best option, and the
	other orders enforce the particular iteration pattern. When using
	:cdata:`NPY_KEEPORDER`, if you also want to ensure that the iteration is
	not reversed along an axis, you should pass the flag
	:cdata:`NPY_ITER_DONT_NEGATE_STRIDES`.

	Any of the :ctype:`NPY_CASTING` enum values may be passed to ``casting``.
	The values include :cdata:`NPY_NO_CASTING`, :cdata:`NPY_EQUIV_CASTING`,
	:cdata:`NPY_SAFE_CASTING`, :cdata:`NPY_SAME_KIND_CASTING`, and
	:cdata:`NPY_UNSAFE_CASTING`. To allow the casts to occur, copying or
	buffering must also be enabled.

	If ``op_dtypes`` isn't ``NULL``, it specifies a data type or ``NULL``
	for each ``op[i]``.

	Returns NULL if there is an error, otherwise returns the allocated
	iterator.

	Flags that may be passed in ``flags``, applying to the whole
	iterator, are:

	.. cvar:: NPY_ITER_C_INDEX

	Causes the iterator to track a raveled flat index matching C
	order. This option cannot be used with :cdata:`NPY_ITER_F_INDEX`.

	.. cvar:: NPY_ITER_F_INDEX

	Causes the iterator to track a raveled flat index matching Fortran
	order. This option cannot be used with :cdata:`NPY_ITER_C_INDEX`.

	.. cvar:: NPY_ITER_MULTI_INDEX

	Causes the iterator to track a multi-index.
	This prevents the iterator from coalescing axes to
	produce bigger inner loops. If the loop is also not buffered
	and no index is being tracked (`NpyIter_RemoveAxis` can be called),
	then the iterator size can be ``-1`` to indicate that the iterator
	is too large. This can happen due to complex broadcasting and
	will result in errors being created when the setting the iterator
	range, removing the multi index, or getting the next function.
	However, it is possible to remove axes again and use the iterator
	normally if the size is small enough after removal.

	.. cvar:: NPY_ITER_EXTERNAL_LOOP

	Causes the iterator to skip iteration of the innermost
	loop, requiring the user of the iterator to handle it.

	This flag is incompatible with :cdata:`NPY_ITER_C_INDEX`,
	:cdata:`NPY_ITER_F_INDEX`, and :cdata:`NPY_ITER_MULTI_INDEX`.

	.. cvar:: NPY_ITER_DONT_NEGATE_STRIDES

	This only affects the iterator when :ctype:`NPY_KEEPORDER` is
	specified for the order parameter. By default with
	:ctype:`NPY_KEEPORDER`, the iterator reverses axes which have
	negative strides, so that memory is traversed in a forward
	direction. This disables this step. Use this flag if you
	want to use the underlying memory-ordering of the axes,
	but don't want an axis reversed. This is the behavior of
	``numpy.ravel(a, order='K')``, for instance.

	.. cvar:: NPY_ITER_COMMON_DTYPE

	Causes the iterator to convert all the operands to a common
	data type, calculated based on the ufunc type promotion rules.
	Copying or buffering must be enabled.

	If the common data type is known ahead of time, don't use this
	flag. Instead, set the requested dtype for all the operands.

	.. cvar:: NPY_ITER_REFS_OK

	Indicates that arrays with reference types (object
	arrays or structured arrays containing an object type)
	may be accepted and used in the iterator. If this flag
	is enabled, the caller must be sure to check whether
	:cfunc:`NpyIter_IterationNeedsAPI`(iter) is true, in which case
	it may not release the GIL during iteration.

	.. cvar:: NPY_ITER_ZEROSIZE_OK

	Indicates that arrays with a size of zero should be permitted.
	Since the typical iteration loop does not naturally work with
	zero-sized arrays, you must check that the IterSize is larger
	than zero before entering the iteration loop.
	Currently only the operands are checked, not a forced shape.

	.. cvar:: NPY_ITER_REDUCE_OK

	Permits writeable operands with a dimension with zero
	stride and size greater than one. Note that such operands
	must be read/write.

	When buffering is enabled, this also switches to a special
	buffering mode which reduces the loop length as necessary to
	not trample on values being reduced.

	Note that if you want to do a reduction on an automatically
	allocated output, you must use :cfunc:`NpyIter_GetOperandArray`
	to get its reference, then set every value to the reduction
	unit before doing the iteration loop. In the case of a
	buffered reduction, this means you must also specify the
	flag :cdata:`NPY_ITER_DELAY_BUFALLOC`, then reset the iterator
	after initializing the allocated operand to prepare the
	buffers.

	.. cvar:: NPY_ITER_RANGED

	Enables support for iteration of sub-ranges of the full
	``iterindex`` range ``[0, NpyIter_IterSize(iter))``. Use
	the function :cfunc:`NpyIter_ResetToIterIndexRange` to specify
	a range for iteration.

	This flag can only be used with :cdata:`NPY_ITER_EXTERNAL_LOOP`
	when :cdata:`NPY_ITER_BUFFERED` is enabled. This is because
	without buffering, the inner loop is always the size of the
	innermost iteration dimension, and allowing it to get cut up
	would require special handling, effectively making it more
	like the buffered version.

	.. cvar:: NPY_ITER_BUFFERED

	Causes the iterator to store buffering data, and use buffering
	to satisfy data type, alignment, and byte-order requirements.
	To buffer an operand, do not specify the :cdata:`NPY_ITER_COPY`
	or :cdata:`NPY_ITER_UPDATEIFCOPY` flags, because they will
	override buffering. Buffering is especially useful for Python
	code using the iterator, allowing for larger chunks
	of data at once to amortize the Python interpreter overhead.

	If used with :cdata:`NPY_ITER_EXTERNAL_LOOP`, the inner loop
	for the caller may get larger chunks than would be possible
	without buffering, because of how the strides are laid out.

	Note that if an operand is given the flag :cdata:`NPY_ITER_COPY`
	or :cdata:`NPY_ITER_UPDATEIFCOPY`, a copy will be made in preference
	to buffering. Buffering will still occur when the array was
	broadcast so elements need to be duplicated to get a constant
	stride.

	In normal buffering, the size of each inner loop is equal
	to the buffer size, or possibly larger if
	:cdata:`NPY_ITER_GROWINNER` is specified. If
	:cdata:`NPY_ITER_REDUCE_OK` is enabled and a reduction occurs,
	the inner loops may become smaller depending
	on the structure of the reduction.

	.. cvar:: NPY_ITER_GROWINNER

	When buffering is enabled, this allows the size of the inner
	loop to grow when buffering isn't necessary. This option
	is best used if you're doing a straight pass through all the
	data, rather than anything with small cache-friendly arrays
	of temporary values for each inner loop.

	.. cvar:: NPY_ITER_DELAY_BUFALLOC

	When buffering is enabled, this delays allocation of the
	buffers until :cfunc:`NpyIter_Reset` or another reset function is
	called. This flag exists to avoid wasteful copying of
	buffer data when making multiple copies of a buffered
	iterator for multi-threaded iteration.

	Another use of this flag is for setting up reduction operations.
	After the iterator is created, and a reduction output
	is allocated automatically by the iterator (be sure to use
	READWRITE access), its value may be initialized to the reduction
	unit. Use :cfunc:`NpyIter_GetOperandArray` to get the object.
	Then, call :cfunc:`NpyIter_Reset` to allocate and fill the buffers
	with their initial values.

	Flags that may be passed in ``op_flags[i]``, where ``0 <= i < nop``:

	.. cvar:: NPY_ITER_READWRITE
	.. cvar:: NPY_ITER_READONLY
	.. cvar:: NPY_ITER_WRITEONLY

	Indicate how the user of the iterator will read or write
	to ``op[i]``. Exactly one of these flags must be specified
	per operand.

	.. cvar:: NPY_ITER_COPY

	Allow a copy of ``op[i]`` to be made if it does not
	meet the data type or alignment requirements as specified
	by the constructor flags and parameters.

	.. cvar:: NPY_ITER_UPDATEIFCOPY

	Triggers :cdata:`NPY_ITER_COPY`, and when an array operand
	is flagged for writing and is copied, causes the data
	in a copy to be copied back to ``op[i]`` when the iterator
	is destroyed.

	If the operand is flagged as write-only and a copy is needed,
	an uninitialized temporary array will be created and then copied
	to back to ``op[i]`` on destruction, instead of doing
	the unecessary copy operation.

	.. cvar:: NPY_ITER_NBO
	.. cvar:: NPY_ITER_ALIGNED
	.. cvar:: NPY_ITER_CONTIG

	Causes the iterator to provide data for ``op[i]``
	that is in native byte order, aligned according to
	the dtype requirements, contiguous, or any combination.

	By default, the iterator produces pointers into the
	arrays provided, which may be aligned or unaligned, and
	with any byte order. If copying or buffering is not
	enabled and the operand data doesn't satisfy the constraints,
	an error will be raised.

	The contiguous constraint applies only to the inner loop,
	successive inner loops may have arbitrary pointer changes.

	If the requested data type is in non-native byte order,
	the NBO flag overrides it and the requested data type is
	converted to be in native byte order.

	.. cvar:: NPY_ITER_ALLOCATE

	This is for output arrays, and requires that the flag
	:cdata:`NPY_ITER_WRITEONLY` or :cdata:`NPY_ITER_READWRITE`
	be set. If ``op[i]`` is NULL, creates a new array with
	the final broadcast dimensions, and a layout matching
	the iteration order of the iterator.

	When ``op[i]`` is NULL, the requested data type
	``op_dtypes[i]`` may be NULL as well, in which case it is
	automatically generated from the dtypes of the arrays which
	are flagged as readable. The rules for generating the dtype
	are the same is for UFuncs. Of special note is handling
	of byte order in the selected dtype. If there is exactly
	one input, the input's dtype is used as is. Otherwise,
	if more than one input dtypes are combined together, the
	output will be in native byte order.

	After being allocated with this flag, the caller may retrieve
	the new array by calling :cfunc:`NpyIter_GetOperandArray` and
	getting the i-th object in the returned C array. The caller
	must call Py_INCREF on it to claim a reference to the array.

	.. cvar:: NPY_ITER_NO_SUBTYPE

	For use with :cdata:`NPY_ITER_ALLOCATE`, this flag disables
	allocating an array subtype for the output, forcing
	it to be a straight ndarray.

	TODO: Maybe it would be better to introduce a function
	``NpyIter_GetWrappedOutput`` and remove this flag?

	.. cvar:: NPY_ITER_NO_BROADCAST

	Ensures that the input or output matches the iteration
	dimensions exactly.

	.. cvar:: NPY_ITER_ARRAYMASK

	.. versionadded:: 1.7

	Indicates that this operand is the mask to use for
	selecting elements when writing to operands which have
	the :cdata:`NPY_ITER_WRITEMASKED` flag applied to them.
	Only one operand may have :cdata:`NPY_ITER_ARRAYMASK` flag
	applied to it.

	The data type of an operand with this flag should be either
	:cdata:`NPY_BOOL`, :cdata:`NPY_MASK`, or a struct dtype
	whose fields are all valid mask dtypes. In the latter case,
	it must match up with a struct operand being WRITEMASKED,
	as it is specifying a mask for each field of that array.

	This flag only affects writing from the buffer back to
	the array. This means that if the operand is also
	:cdata:`NPY_ITER_READWRITE` or :cdata:`NPY_ITER_WRITEONLY`,
	code doing iteration can write to this operand to
	control which elements will be untouched and which ones will be
	modified. This is useful when the mask should be a combination
	of input masks, for example. Mask values can be created
	with the :cfunc:`NpyMask_Create` function.

	.. cvar:: NPY_ITER_WRITEMASKED

	.. versionadded:: 1.7

	Indicates that only elements which the operand with
	the ARRAYMASK flag indicates are intended to be modified
	by the iteration. In general, the iterator does not enforce
	this, it is up to the code doing the iteration to follow
	that promise. Code can use the :cfunc:`NpyMask_IsExposed`
	inline function to test whether the mask at a particular
	element allows writing.

	When this flag is used, and this operand is buffered, this
	changes how data is copied from the buffer into the array.
	A masked copying routine is used, which only copies the
	elements in the buffer for which :cfunc:`NpyMask_IsExposed`
	returns true from the corresponding element in the ARRAYMASK
	operand.

	.. cfunction:: NpyIter* NpyIter_AdvancedNew(npy_intp nop, PyArrayObject** op, npy_uint32 flags, NPY_ORDER order, NPY_CASTING casting, npy_uint32* op_flags, PyArray_Descr op_dtypes, int oa_ndim, int op_axes, npy_intp* itershape, npy_intp buffersize)

	Extends :cfunc:`NpyIter_MultiNew` with several advanced options providing
	more control over broadcasting and buffering.

	If -1/NULL values are passed to ``oa_ndim``, ``op_axes``, ``itershape``,
	and ``buffersize``, it is equivalent to :cfunc:`NpyIter_MultiNew`.

	The parameter ``oa_ndim``, when not zero or -1, specifies the number of
	dimensions that will be iterated with customized broadcasting.
	If it is provided, ``op_axes`` must and ``itershape`` can also be provided.
	The ``op_axes`` parameter let you control in detail how the
	axes of the operand arrays get matched together and iterated.
	In ``op_axes``, you must provide an array of ``nop`` pointers
	to ``oa_ndim``-sized arrays of type ``npy_intp``. If an entry
	in ``op_axes`` is NULL, normal broadcasting rules will apply.
	In ``op_axes[j][i]`` is stored either a valid axis of ``op[j]``, or
	-1 which means ``newaxis``. Within each ``op_axes[j]`` array, axes
	may not be repeated. The following example is how normal broadcasting
	applies to a 3-D array, a 2-D array, a 1-D array and a scalar.

	Note: Before NumPy 1.8 ``oa_ndim == 0` was used for signalling that
	that ``op_axes`` and ``itershape`` are unused. This is deprecated and
	should be replaced with -1. Better backward compatibility may be
	achieved by using :cfunc:`NpyIter_MultiNew` for this case.

	.. code-block:: c

	int oa_ndim = 3; /* # iteration axes */
	int op0_axes[] = {0, 1, 2}; /* 3-D operand */
	int op1_axes[] = {-1, 0, 1}; /* 2-D operand */
	int op2_axes[] = {-1, -1, 0}; /* 1-D operand */
	int op3_axes[] = {-1, -1, -1} /* 0-D (scalar) operand */
	int* op_axes[] = {op0_axes, op1_axes, op2_axes, op3_axes};

	The ``itershape`` parameter allows you to force the iterator
	to have a specific iteration shape. It is an array of length
	``oa_ndim``. When an entry is negative, its value is determined
	from the operands. This parameter allows automatically allocated
	outputs to get additional dimensions which don't match up with
	any dimension of an input.

	If ``buffersize`` is zero, a default buffer size is used,
	otherwise it specifies how big of a buffer to use. Buffers
	which are powers of 2 such as 4096 or 8192 are recommended.

	Returns NULL if there is an error, otherwise returns the allocated
	iterator.

	.. cfunction:: NpyIter* NpyIter_Copy(NpyIter* iter)

	Makes a copy of the given iterator. This function is provided
	primarily to enable multi-threaded iteration of the data.

	TODO: Move this to a section about multithreaded iteration.

	The recommended approach to multithreaded iteration is to
	first create an iterator with the flags
	:cdata:`NPY_ITER_EXTERNAL_LOOP`, :cdata:`NPY_ITER_RANGED`,
	:cdata:`NPY_ITER_BUFFERED`, :cdata:`NPY_ITER_DELAY_BUFALLOC`, and
	possibly :cdata:`NPY_ITER_GROWINNER`. Create a copy of this iterator
	for each thread (minus one for the first iterator). Then, take
	the iteration index range ``[0, NpyIter_GetIterSize(iter))`` and
	split it up into tasks, for example using a TBB parallel_for loop.
	When a thread gets a task to execute, it then uses its copy of
	the iterator by calling :cfunc:`NpyIter_ResetToIterIndexRange` and
	iterating over the full range.

	When using the iterator in multi-threaded code or in code not
	holding the Python GIL, care must be taken to only call functions
	which are safe in that context. :cfunc:`NpyIter_Copy` cannot be safely
	called without the Python GIL, because it increments Python
	references. The ``Reset*`` and some other functions may be safely
	called by passing in the ``errmsg`` parameter as non-NULL, so that
	the functions will pass back errors through it instead of setting
	a Python exception.

	.. cfunction:: int NpyIter_RemoveAxis(NpyIter* iter, int axis)``

	Removes an axis from iteration. This requires that
	:cdata:`NPY_ITER_MULTI_INDEX` was set for iterator creation, and does
	not work if buffering is enabled or an index is being tracked. This
	function also resets the iterator to its initial state.

	This is useful for setting up an accumulation loop, for example.
	The iterator can first be created with all the dimensions, including
	the accumulation axis, so that the output gets created correctly.
	Then, the accumulation axis can be removed, and the calculation
	done in a nested fashion.

	WARNING: This function may change the internal memory layout of
	the iterator. Any cached functions or pointers from the iterator
	must be retrieved again! The iterator range will be reset as well.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.


	.. cfunction:: int NpyIter_RemoveMultiIndex(NpyIter* iter)

	If the iterator is tracking a multi-index, this strips support for them,
	and does further iterator optimizations that are possible if multi-indices
	are not needed. This function also resets the iterator to its initial
	state.

	WARNING: This function may change the internal memory layout of
	the iterator. Any cached functions or pointers from the iterator
	must be retrieved again!

	After calling this function, :cfunc:`NpyIter_HasMultiIndex`(iter) will
	return false.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: int NpyIter_EnableExternalLoop(NpyIter* iter)

	If :cfunc:`NpyIter_RemoveMultiIndex` was called, you may want to enable the
	flag :cdata:`NPY_ITER_EXTERNAL_LOOP`. This flag is not permitted
	together with :cdata:`NPY_ITER_MULTI_INDEX`, so this function is provided
	to enable the feature after :cfunc:`NpyIter_RemoveMultiIndex` is called.
	This function also resets the iterator to its initial state.

	WARNING: This function changes the internal logic of the iterator.
	Any cached functions or pointers from the iterator must be retrieved
	again!

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: int NpyIter_Deallocate(NpyIter* iter)

	Deallocates the iterator object. This additionally frees any
	copies made, triggering UPDATEIFCOPY behavior where necessary.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: int NpyIter_Reset(NpyIter* iter, char** errmsg)

	Resets the iterator back to its initial state, at the beginning
	of the iteration range.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``. If errmsg is non-NULL,
	no Python exception is set when ``NPY_FAIL`` is returned.
	Instead, \*errmsg is set to an error message. When errmsg is
	non-NULL, the function may be safely called without holding
	the Python GIL.

	.. cfunction:: int NpyIter_ResetToIterIndexRange(NpyIter* iter, npy_intp istart, npy_intp iend, char** errmsg)

	Resets the iterator and restricts it to the ``iterindex`` range
	``[istart, iend)``. See :cfunc:`NpyIter_Copy` for an explanation of
	how to use this for multi-threaded iteration. This requires that
	the flag :cdata:`NPY_ITER_RANGED` was passed to the iterator constructor.

	If you want to reset both the ``iterindex`` range and the base
	pointers at the same time, you can do the following to avoid
	extra buffer copying (be sure to add the return code error checks
	when you copy this code).

	.. code-block:: c

	/* Set to a trivial empty range */
	NpyIter_ResetToIterIndexRange(iter, 0, 0);
	/* Set the base pointers */
	NpyIter_ResetBasePointers(iter, baseptrs);
	/* Set to the desired range */
	NpyIter_ResetToIterIndexRange(iter, istart, iend);

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``. If errmsg is non-NULL,
	no Python exception is set when ``NPY_FAIL`` is returned.
	Instead, \*errmsg is set to an error message. When errmsg is
	non-NULL, the function may be safely called without holding
	the Python GIL.

	.. cfunction:: int NpyIter_ResetBasePointers(NpyIter iter, char* baseptrs, char** errmsg)

	Resets the iterator back to its initial state, but using the values
	in ``baseptrs`` for the data instead of the pointers from the arrays
	being iterated. This functions is intended to be used, together with
	the ``op_axes`` parameter, by nested iteration code with two or more
	iterators.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``. If errmsg is non-NULL,
	no Python exception is set when ``NPY_FAIL`` is returned.
	Instead, \*errmsg is set to an error message. When errmsg is
	non-NULL, the function may be safely called without holding
	the Python GIL.

	TODO: Move the following into a special section on nested iterators.

	Creating iterators for nested iteration requires some care. All
	the iterator operands must match exactly, or the calls to
	:cfunc:`NpyIter_ResetBasePointers` will be invalid. This means that
	automatic copies and output allocation should not be used haphazardly.
	It is possible to still use the automatic data conversion and casting
	features of the iterator by creating one of the iterators with
	all the conversion parameters enabled, then grabbing the allocated
	operands with the :cfunc:`NpyIter_GetOperandArray` function and passing
	them into the constructors for the rest of the iterators.

	WARNING: When creating iterators for nested iteration,
	the code must not use a dimension more than once in the different
	iterators. If this is done, nested iteration will produce
	out-of-bounds pointers during iteration.

	WARNING: When creating iterators for nested iteration, buffering
	can only be applied to the innermost iterator. If a buffered iterator
	is used as the source for ``baseptrs``, it will point into a small buffer
	instead of the array and the inner iteration will be invalid.

	The pattern for using nested iterators is as follows.

	.. code-block:: c

	NpyIter iter1, iter1;
	NpyIter_IterNextFunc iternext1, iternext2;
	char **dataptrs1;

	/*
	* With the exact same operands, no copies allowed, and
	* no axis in op_axes used both in iter1 and iter2.
	* Buffering may be enabled for iter2, but not for iter1.
	*/
	iter1 = ...; iter2 = ...;

	iternext1 = NpyIter_GetIterNext(iter1);
	iternext2 = NpyIter_GetIterNext(iter2);
	dataptrs1 = NpyIter_GetDataPtrArray(iter1);

	do {
	NpyIter_ResetBasePointers(iter2, dataptrs1);
	do {
	/* Use the iter2 values */
	} while (iternext2(iter2));
	} while (iternext1(iter1));

	.. cfunction:: int NpyIter_GotoMultiIndex(NpyIter* iter, npy_intp* multi_index)

	Adjusts the iterator to point to the ``ndim`` indices
	pointed to by ``multi_index``. Returns an error if a multi-index
	is not being tracked, the indices are out of bounds,
	or inner loop iteration is disabled.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: int NpyIter_GotoIndex(NpyIter* iter, npy_intp index)

	Adjusts the iterator to point to the ``index`` specified.
	If the iterator was constructed with the flag
	:cdata:`NPY_ITER_C_INDEX`, ``index`` is the C-order index,
	and if the iterator was constructed with the flag
	:cdata:`NPY_ITER_F_INDEX`, ``index`` is the Fortran-order
	index. Returns an error if there is no index being tracked,
	the index is out of bounds, or inner loop iteration is disabled.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: npy_intp NpyIter_GetIterSize(NpyIter* iter)

	Returns the number of elements being iterated. This is the product
	of all the dimensions in the shape. When a multi index is being tracked
	(and `NpyIter_RemoveAxis` may be called) the size may be ``-1`` to
	indicate an iterator is too large. Such an iterator is invalid, but
	may become valid after `NpyIter_RemoveAxis` is called. It is not
	necessary to check for this case.

	.. cfunction:: npy_intp NpyIter_GetIterIndex(NpyIter* iter)

	Gets the ``iterindex`` of the iterator, which is an index matching
	the iteration order of the iterator.

	.. cfunction:: void NpyIter_GetIterIndexRange(NpyIter* iter, npy_intp* istart, npy_intp* iend)

	Gets the ``iterindex`` sub-range that is being iterated. If
	:cdata:`NPY_ITER_RANGED` was not specified, this always returns the
	range ``[0, NpyIter_IterSize(iter))``.

	.. cfunction:: int NpyIter_GotoIterIndex(NpyIter* iter, npy_intp iterindex)

	Adjusts the iterator to point to the ``iterindex`` specified.
	The IterIndex is an index matching the iteration order of the iterator.
	Returns an error if the ``iterindex`` is out of bounds,
	buffering is enabled, or inner loop iteration is disabled.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: npy_bool NpyIter_HasDelayedBufAlloc(NpyIter* iter)

	Returns 1 if the flag :cdata:`NPY_ITER_DELAY_BUFALLOC` was passed
	to the iterator constructor, and no call to one of the Reset
	functions has been done yet, 0 otherwise.

	.. cfunction:: npy_bool NpyIter_HasExternalLoop(NpyIter* iter)

	Returns 1 if the caller needs to handle the inner-most 1-dimensional
	loop, or 0 if the iterator handles all looping. This is controlled
	by the constructor flag :cdata:`NPY_ITER_EXTERNAL_LOOP` or
	:cfunc:`NpyIter_EnableExternalLoop`.

	.. cfunction:: npy_bool NpyIter_HasMultiIndex(NpyIter* iter)

	Returns 1 if the iterator was created with the
	:cdata:`NPY_ITER_MULTI_INDEX` flag, 0 otherwise.

	.. cfunction:: npy_bool NpyIter_HasIndex(NpyIter* iter)

	Returns 1 if the iterator was created with the
	:cdata:`NPY_ITER_C_INDEX` or :cdata:`NPY_ITER_F_INDEX`
	flag, 0 otherwise.

	.. cfunction:: npy_bool NpyIter_RequiresBuffering(NpyIter* iter)

	Returns 1 if the iterator requires buffering, which occurs
	when an operand needs conversion or alignment and so cannot
	be used directly.

	.. cfunction:: npy_bool NpyIter_IsBuffered(NpyIter* iter)

	Returns 1 if the iterator was created with the
	:cdata:`NPY_ITER_BUFFERED` flag, 0 otherwise.

	.. cfunction:: npy_bool NpyIter_IsGrowInner(NpyIter* iter)

	Returns 1 if the iterator was created with the
	:cdata:`NPY_ITER_GROWINNER` flag, 0 otherwise.

	.. cfunction:: npy_intp NpyIter_GetBufferSize(NpyIter* iter)

	If the iterator is buffered, returns the size of the buffer
	being used, otherwise returns 0.

	.. cfunction:: int NpyIter_GetNDim(NpyIter* iter)

	Returns the number of dimensions being iterated. If a multi-index
	was not requested in the iterator constructor, this value
	may be smaller than the number of dimensions in the original
	objects.

	.. cfunction:: int NpyIter_GetNOp(NpyIter* iter)

	Returns the number of operands in the iterator.

	When :cdata:`NPY_ITER_USE_MASKNA` is used on an operand, a new
	operand is added to the end of the operand list in the iterator
	to track that operand's NA mask. Thus, this equals the number
	of construction operands plus the number of operands for
	which the flag :cdata:`NPY_ITER_USE_MASKNA` was specified.

	.. cfunction:: int NpyIter_GetFirstMaskNAOp(NpyIter* iter)

	.. versionadded:: 1.7

	Returns the index of the first NA mask operand in the array. This
	value is equal to the number of operands passed into the constructor.

	.. cfunction:: npy_intp* NpyIter_GetAxisStrideArray(NpyIter* iter, int axis)

	Gets the array of strides for the specified axis. Requires that
	the iterator be tracking a multi-index, and that buffering not
	be enabled.

	This may be used when you want to match up operand axes in
	some fashion, then remove them with :cfunc:`NpyIter_RemoveAxis` to
	handle their processing manually. By calling this function
	before removing the axes, you can get the strides for the
	manual processing.

	Returns ``NULL`` on error.

	.. cfunction:: int NpyIter_GetShape(NpyIter* iter, npy_intp* outshape)

	Returns the broadcast shape of the iterator in ``outshape``.
	This can only be called on an iterator which is tracking a multi-index.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: PyArray_Descr** NpyIter_GetDescrArray(NpyIter* iter)

	This gives back a pointer to the ``nop`` data type Descrs for
	the objects being iterated. The result points into ``iter``,
	so the caller does not gain any references to the Descrs.

	This pointer may be cached before the iteration loop, calling
	``iternext`` will not change it.

	.. cfunction:: PyObject** NpyIter_GetOperandArray(NpyIter* iter)

	This gives back a pointer to the ``nop`` operand PyObjects
	that are being iterated. The result points into ``iter``,
	so the caller does not gain any references to the PyObjects.

	.. cfunction:: npy_int8* NpyIter_GetMaskNAIndexArray(NpyIter* iter)

	.. versionadded:: 1.7

	This gives back a pointer to the ``nop`` indices which map
	construction operands with :cdata:`NPY_ITER_USE_MASKNA` flagged
	to their corresponding NA mask operands and vice versa. For
	operands which were not flagged with :cdata:`NPY_ITER_USE_MASKNA`,
	this array contains negative values.

	.. cfunction:: PyObject* NpyIter_GetIterView(NpyIter* iter, npy_intp i)

	This gives back a reference to a new ndarray view, which is a view
	into the i-th object in the array :cfunc:`NpyIter_GetOperandArray`(),
	whose dimensions and strides match the internal optimized
	iteration pattern. A C-order iteration of this view is equivalent
	to the iterator's iteration order.

	For example, if an iterator was created with a single array as its
	input, and it was possible to rearrange all its axes and then
	collapse it into a single strided iteration, this would return
	a view that is a one-dimensional array.

	.. cfunction:: void NpyIter_GetReadFlags(NpyIter* iter, char* outreadflags)

	Fills ``nop`` flags. Sets ``outreadflags[i]`` to 1 if
	``op[i]`` can be read from, and to 0 if not.

	.. cfunction:: void NpyIter_GetWriteFlags(NpyIter* iter, char* outwriteflags)

	Fills ``nop`` flags. Sets ``outwriteflags[i]`` to 1 if
	``op[i]`` can be written to, and to 0 if not.

	.. cfunction:: int NpyIter_CreateCompatibleStrides(NpyIter* iter, npy_intp itemsize, npy_intp* outstrides)

	Builds a set of strides which are the same as the strides of an
	output array created using the :cdata:`NPY_ITER_ALLOCATE` flag, where NULL
	was passed for op_axes. This is for data packed contiguously,
	but not necessarily in C or Fortran order. This should be used
	together with :cfunc:`NpyIter_GetShape` and :cfunc:`NpyIter_GetNDim`
	with the flag :cdata:`NPY_ITER_MULTI_INDEX` passed into the constructor.

	A use case for this function is to match the shape and layout of
	the iterator and tack on one or more dimensions. For example,
	in order to generate a vector per input value for a numerical gradient,
	you pass in ndim*itemsize for itemsize, then add another dimension to
	the end with size ndim and stride itemsize. To do the Hessian matrix,
	you do the same thing but add two dimensions, or take advantage of
	the symmetry and pack it into 1 dimension with a particular encoding.

	This function may only be called if the iterator is tracking a multi-index
	and if :cdata:`NPY_ITER_DONT_NEGATE_STRIDES` was used to prevent an axis
	from being iterated in reverse order.

	If an array is created with this method, simply adding 'itemsize'
	for each iteration will traverse the new array matching the
	iterator.

	Returns ``NPY_SUCCEED`` or ``NPY_FAIL``.

	.. cfunction:: npy_bool NpyIter_IsFirstVisit(NpyIter* iter, int iop)

	.. versionadded:: 1.7

	Checks to see whether this is the first time the elements of the
	specified reduction operand which the iterator points at are being
	seen for the first time. The function returns a reasonable answer
	for reduction operands and when buffering is disabled. The answer
	may be incorrect for buffered non-reduction operands.

	This function is intended to be used in EXTERNAL_LOOP mode only,
	and will produce some wrong answers when that mode is not enabled.

	If this function returns true, the caller should also check the inner
	loop stride of the operand, because if that stride is 0, then only
	the first element of the innermost external loop is being visited
	for the first time.

	WARNING: For performance reasons, 'iop' is not bounds-checked,
	it is not confirmed that 'iop' is actually a reduction operand,
	and it is not confirmed that EXTERNAL_LOOP mode is enabled. These
	checks are the responsibility of the caller, and should be done
	outside of any inner loops.

	Functions For Iteration
	-----------------------

	.. cfunction:: NpyIter_IterNextFunc* NpyIter_GetIterNext(NpyIter* iter, char** errmsg)

	Returns a function pointer for iteration. A specialized version
	of the function pointer may be calculated by this function
	instead of being stored in the iterator structure. Thus, to
	get good performance, it is required that the function pointer
	be saved in a variable rather than retrieved for each loop iteration.

	Returns NULL if there is an error. If errmsg is non-NULL,
	no Python exception is set when ``NPY_FAIL`` is returned.
	Instead, \*errmsg is set to an error message. When errmsg is
	non-NULL, the function may be safely called without holding
	the Python GIL.

	The typical looping construct is as follows.

	.. code-block:: c

	NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter, NULL);
	char** dataptr = NpyIter_GetDataPtrArray(iter);

	do {
	/* use the addresses dataptr[0], ... dataptr[nop-1] */
	} while(iternext(iter));

	When :cdata:`NPY_ITER_EXTERNAL_LOOP` is specified, the typical
	inner loop construct is as follows.

	.. code-block:: c

	NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter, NULL);
	char** dataptr = NpyIter_GetDataPtrArray(iter);
	npy_intp* stride = NpyIter_GetInnerStrideArray(iter);
	npy_intp* size_ptr = NpyIter_GetInnerLoopSizePtr(iter), size;
	npy_intp iop, nop = NpyIter_GetNOp(iter);

	do {
	size = *size_ptr;
	while (size--) {
	/* use the addresses dataptr[0], ... dataptr[nop-1] */
	for (iop = 0; iop < nop; ++iop) {
	dataptr[iop] += stride[iop];
	}
	}
	} while (iternext());

	Observe that we are using the dataptr array inside the iterator, not
	copying the values to a local temporary. This is possible because
	when ``iternext()`` is called, these pointers will be overwritten
	with fresh values, not incrementally updated.

	If a compile-time fixed buffer is being used (both flags
	:cdata:`NPY_ITER_BUFFERED` and :cdata:`NPY_ITER_EXTERNAL_LOOP`), the
	inner size may be used as a signal as well. The size is guaranteed
	to become zero when ``iternext()`` returns false, enabling the
	following loop construct. Note that if you use this construct,
	you should not pass :cdata:`NPY_ITER_GROWINNER` as a flag, because it
	will cause larger sizes under some circumstances.

	.. code-block:: c

	/* The constructor should have buffersize passed as this value */
	#define FIXED_BUFFER_SIZE 1024

	NpyIter_IterNextFunc *iternext = NpyIter_GetIterNext(iter, NULL);
	char **dataptr = NpyIter_GetDataPtrArray(iter);
	npy_intp *stride = NpyIter_GetInnerStrideArray(iter);
	npy_intp *size_ptr = NpyIter_GetInnerLoopSizePtr(iter), size;
	npy_intp i, iop, nop = NpyIter_GetNOp(iter);

	/* One loop with a fixed inner size */
	size = *size_ptr;
	while (size == FIXED_BUFFER_SIZE) {
	/*
	* This loop could be manually unrolled by a factor
	* which divides into FIXED_BUFFER_SIZE
	*/
	for (i = 0; i < FIXED_BUFFER_SIZE; ++i) {
	/* use the addresses dataptr[0], ... dataptr[nop-1] */
	for (iop = 0; iop < nop; ++iop) {
	dataptr[iop] += stride[iop];
	}
	}
	iternext();
	size = *size_ptr;
	}

	/* Finish-up loop with variable inner size */
	if (size > 0) do {
	size = *size_ptr;
	while (size--) {
	/* use the addresses dataptr[0], ... dataptr[nop-1] */
	for (iop = 0; iop < nop; ++iop) {
	dataptr[iop] += stride[iop];
	}
	}
	} while (iternext());

	.. cfunction:: NpyIter_GetMultiIndexFunc NpyIter_GetGetMultiIndex(NpyIter iter, char** errmsg)

	Returns a function pointer for getting the current multi-index
	of the iterator. Returns NULL if the iterator is not tracking
	a multi-index. It is recommended that this function
	pointer be cached in a local variable before the iteration
	loop.

	Returns NULL if there is an error. If errmsg is non-NULL,
	no Python exception is set when ``NPY_FAIL`` is returned.
	Instead, \*errmsg is set to an error message. When errmsg is
	non-NULL, the function may be safely called without holding
	the Python GIL.

	.. cfunction:: char** NpyIter_GetDataPtrArray(NpyIter* iter)

	This gives back a pointer to the ``nop`` data pointers. If
	:cdata:`NPY_ITER_EXTERNAL_LOOP` was not specified, each data
	pointer points to the current data item of the iterator. If
	no inner iteration was specified, it points to the first data
	item of the inner loop.

	This pointer may be cached before the iteration loop, calling
	``iternext`` will not change it. This function may be safely
	called without holding the Python GIL.

	.. cfunction:: char** NpyIter_GetInitialDataPtrArray(NpyIter* iter)

	Gets the array of data pointers directly into the arrays (never
	into the buffers), corresponding to iteration index 0.

	These pointers are different from the pointers accepted by
	``NpyIter_ResetBasePointers``, because the direction along
	some axes may have been reversed.

	This function may be safely called without holding the Python GIL.

	.. cfunction:: npy_intp* NpyIter_GetIndexPtr(NpyIter* iter)

	This gives back a pointer to the index being tracked, or NULL
	if no index is being tracked. It is only useable if one of
	the flags :cdata:`NPY_ITER_C_INDEX` or :cdata:`NPY_ITER_F_INDEX`
	were specified during construction.

	When the flag :cdata:`NPY_ITER_EXTERNAL_LOOP` is used, the code
	needs to know the parameters for doing the inner loop. These
	functions provide that information.

	.. cfunction:: npy_intp* NpyIter_GetInnerStrideArray(NpyIter* iter)

	Returns a pointer to an array of the ``nop`` strides,
	one for each iterated object, to be used by the inner loop.

	This pointer may be cached before the iteration loop, calling
	``iternext`` will not change it. This function may be safely
	called without holding the Python GIL.

	.. cfunction:: npy_intp* NpyIter_GetInnerLoopSizePtr(NpyIter* iter)

	Returns a pointer to the number of iterations the
	inner loop should execute.

	This address may be cached before the iteration loop, calling
	``iternext`` will not change it. The value itself may change during
	iteration, in particular if buffering is enabled. This function
	may be safely called without holding the Python GIL.

	.. cfunction:: void NpyIter_GetInnerFixedStrideArray(NpyIter* iter, npy_intp* out_strides)

	Gets an array of strides which are fixed, or will not change during
	the entire iteration. For strides that may change, the value
	NPY_MAX_INTP is placed in the stride.

	Once the iterator is prepared for iteration (after a reset if
	:cdata:`NPY_DELAY_BUFALLOC` was used), call this to get the strides
	which may be used to select a fast inner loop function. For example,
	if the stride is 0, that means the inner loop can always load its
	value into a variable once, then use the variable throughout the loop,
	or if the stride equals the itemsize, a contiguous version for that
	operand may be used.

	This function may be safely called without holding the Python GIL.

	.. index::
	pair: iterator; C-API