type t = [ `Nditer | `Object ] Obj.tval of_pyobject : Py.Object.t -> tval to_pyobject : [> tag ] Obj.t -> Py.Object.tval create :
?flags:Py.Object.t ->
?op_flags:Py.Object.t ->
?op_dtypes:[ `Tuple_of_dtype_s_ of Py.Object.t | `Dtype of Dtype.t ] ->
?order:[ `C | `F | `A | `K ] ->
?casting:[ `No | `Equiv | `Safe | `Same_kind | `Unsafe ] ->
?op_axes:Py.Object.t ->
?itershape:int list ->
?buffersize:int ->
op:
[ `Ndarray of [> `Ndarray ] Obj.t
| `Sequence_of_array_like of Py.Object.t ] ->
unit ->
tnditer(op, flags=None, op_flags=None, op_dtypes=None, order='K', casting='safe', op_axes=None, itershape=None, buffersize=0)
Efficient multi-dimensional iterator object to iterate over arrays. To get started using this object, see the :ref:`introductory guide to array iteration <arrays.nditer>`.
Parameters ---------- op : ndarray or sequence of array_like The array(s) to iterate over.
flags : sequence of str, optional Flags to control the behavior of the iterator.
* ``buffered`` enables buffering when required. * ``c_index`` causes a C-order index to be tracked. * ``f_index`` causes a Fortran-order index to be tracked. * ``multi_index`` causes a multi-index, or a tuple of indices with one per iteration dimension, to be tracked. * ``common_dtype`` causes all the operands to be converted to a common data type, with copying or buffering as necessary. * ``copy_if_overlap`` causes the iterator to determine if read operands have overlap with write operands, and make temporary copies as necessary to avoid overlap. False positives (needless copying) are possible in some cases. * ``delay_bufalloc`` delays allocation of the buffers until a reset() call is made. Allows ``allocate`` operands to be initialized before their values are copied into the buffers. * ``external_loop`` causes the ``values`` given to be one-dimensional arrays with multiple values instead of zero-dimensional arrays. * ``grow_inner`` allows the ``value`` array sizes to be made larger than the buffer size when both ``buffered`` and ``external_loop`` is used. * ``ranged`` allows the iterator to be restricted to a sub-range of the iterindex values. * ``refs_ok`` enables iteration of reference types, such as object arrays. * ``reduce_ok`` enables iteration of ``readwrite`` operands which are broadcasted, also known as reduction operands. * ``zerosize_ok`` allows `itersize` to be zero. op_flags : list of list of str, optional This is a list of flags for each operand. At minimum, one of ``readonly``, ``readwrite``, or ``writeonly`` must be specified.
* ``readonly`` indicates the operand will only be read from. * ``readwrite`` indicates the operand will be read from and written to. * ``writeonly`` indicates the operand will only be written to. * ``no_broadcast`` prevents the operand from being broadcasted. * ``contig`` forces the operand data to be contiguous. * ``aligned`` forces the operand data to be aligned. * ``nbo`` forces the operand data to be in native byte order. * ``copy`` allows a temporary read-only copy if required. * ``updateifcopy`` allows a temporary read-write copy if required. * ``allocate`` causes the array to be allocated if it is None in the ``op`` parameter. * ``no_subtype`` prevents an ``allocate`` operand from using a subtype. * ``arraymask`` indicates that this operand is the mask to use for selecting elements when writing to operands with the 'writemasked' flag set. The iterator does not enforce this, but when writing from a buffer back to the array, it only copies those elements indicated by this mask. * ``writemasked`` indicates that only elements where the chosen ``arraymask`` operand is True will be written to. * ``overlap_assume_elementwise`` can be used to mark operands that are accessed only in the iterator order, to allow less conservative copying when ``copy_if_overlap`` is present. op_dtypes : dtype or tuple of dtype(s), optional The required data type(s) of the operands. If copying or buffering is enabled, the data will be converted to/from their original types. order : 'C', 'F', 'A', 'K', optional Controls the iteration order. 'C' means C order, 'F' means Fortran order, 'A' means 'F' order if all the arrays are Fortran contiguous, 'C' order otherwise, and 'K' means as close to the order the array elements appear in memory as possible. This also affects the element memory order of ``allocate`` operands, as they are allocated to be compatible with iteration order. Default is 'K'. casting : 'no', 'equiv', 'safe', 'same_kind', 'unsafe', optional Controls what kind of data casting may occur when making a copy or buffering. Setting this to 'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all. * 'equiv' means only byte-order changes are allowed. * 'safe' means only casts which can preserve values are allowed. * 'same_kind' means only safe casts or casts within a kind, like float64 to float32, are allowed. * 'unsafe' means any data conversions may be done. op_axes : list of list of ints, optional If provided, is a list of ints or None for each operands. The list of axes for an operand is a mapping from the dimensions of the iterator to the dimensions of the operand. A value of -1 can be placed for entries, causing that dimension to be treated as `newaxis`. itershape : tuple of ints, optional The desired shape of the iterator. This allows ``allocate`` operands with a dimension mapped by op_axes not corresponding to a dimension of a different operand to get a value not equal to 1 for that dimension. buffersize : int, optional When buffering is enabled, controls the size of the temporary buffers. Set to 0 for the default value.
Attributes ---------- dtypes : tuple of dtype(s) The data types of the values provided in `value`. This may be different from the operand data types if buffering is enabled. Valid only before the iterator is closed. finished : bool Whether the iteration over the operands is finished or not. has_delayed_bufalloc : bool If True, the iterator was created with the ``delay_bufalloc`` flag, and no reset() function was called on it yet. has_index : bool If True, the iterator was created with either the ``c_index`` or the ``f_index`` flag, and the property `index` can be used to retrieve it. has_multi_index : bool If True, the iterator was created with the ``multi_index`` flag, and the property `multi_index` can be used to retrieve it. index When the ``c_index`` or ``f_index`` flag was used, this property provides access to the index. Raises a ValueError if accessed and ``has_index`` is False. iterationneedsapi : bool Whether iteration requires access to the Python API, for example if one of the operands is an object array. iterindex : int An index which matches the order of iteration. itersize : int Size of the iterator. itviews Structured view(s) of `operands` in memory, matching the reordered and optimized iterator access pattern. Valid only before the iterator is closed. multi_index When the ``multi_index`` flag was used, this property provides access to the index. Raises a ValueError if accessed accessed and ``has_multi_index`` is False. ndim : int The dimensions of the iterator. nop : int The number of iterator operands. operands : tuple of operand(s) The array(s) to be iterated over. Valid only before the iterator is closed. shape : tuple of ints Shape tuple, the shape of the iterator. value Value of ``operands`` at current iteration. Normally, this is a tuple of array scalars, but if the flag ``external_loop`` is used, it is a tuple of one dimensional arrays.
Notes ----- `nditer` supersedes `flatiter`. The iterator implementation behind `nditer` is also exposed by the NumPy C API.
The Python exposure supplies two iteration interfaces, one which follows the Python iterator protocol, and another which mirrors the C-style do-while pattern. The native Python approach is better in most cases, but if you need the coordinates or index of an iterator, use the C-style pattern.
Examples -------- Here is how we might write an ``iter_add`` function, using the Python iterator protocol:
>>> def iter_add_py(x, y, out=None): ... addop = np.add ... it = np.nditer(x, y, out, , ... ['readonly'], ['readonly'], ['writeonly','allocate']) ... with it: ... for (a, b, c) in it: ... addop(a, b, out=c) ... return it.operands2
Here is the same function, but following the C-style pattern:
>>> def iter_add(x, y, out=None): ... addop = np.add ... it = np.nditer(x, y, out, , ... ['readonly'], ['readonly'], ['writeonly','allocate']) ... with it: ... while not it.finished: ... addop(it0, it1, out=it2) ... it.iternext() ... return it.operands2
Here is an example outer product function:
>>> def outer_it(x, y, out=None): ... mulop = np.multiply ... it = np.nditer(x, y, out, 'external_loop', ... ['readonly'], ['readonly'], ['writeonly', 'allocate'], ... op_axes=list(range(x.ndim)) + [-1] * y.ndim,
... [-1] * x.ndim + list(range(y.ndim)),
... None) ... with it: ... for (a, b, c) in it: ... mulop(a, b, out=c) ... return it.operands2
>>> a = np.arange(2)+1 >>> b = np.arange(3)+1 >>> outer_it(a,b) array([1, 2, 3],
[2, 4, 6])
Here is an example function which operates like a 'lambda' ufunc:
>>> def luf(lamdaexpr, *args, **kwargs): ... '''luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)''' ... nargs = len(args) ... op = (kwargs.get('out',None),) + args ... it = np.nditer(op, 'buffered','external_loop', ... ['writeonly','allocate','no_broadcast'] + ... ['readonly','nbo','aligned']*nargs, ... order=kwargs.get('order','K'), ... casting=kwargs.get('casting','safe'), ... buffersize=kwargs.get('buffersize',0)) ... while not it.finished: ... it0 = lamdaexpr( *it1:) ... it.iternext() ... return it.operands0
>>> a = np.arange(5) >>> b = np.ones(5) >>> luf(lambda i,j:i*i + j/2, a, b) array( 0.5, 1.5, 4.5, 9.5, 16.5)
If operand flags `'writeonly'` or `'readwrite'` are used the operands may be views into the original data with the `WRITEBACKIFCOPY` flag. In this case `nditer` must be used as a context manager or the `nditer.close` method must be called before using the result. The temporary data will be written back to the original data when the `__exit__` function is called but not before:
>>> a = np.arange(6, dtype='i4')::-2 >>> with np.nditer(a, , ... ['writeonly', 'updateifcopy'], ... casting='unsafe', ... op_dtypes=np.dtype('f4')) as i: ... x = i.operands0 ... x: = -1, -2, -3 ... # a still unchanged here >>> a, x (array(-1, -2, -3, dtype=int32), array(-1., -2., -3., dtype=float32))
It is important to note that once the iterator is exited, dangling references (like `x` in the example) may or may not share data with the original data `a`. If writeback semantics were active, i.e. if `x.base.flags.writebackifcopy` is `True`, then exiting the iterator will sever the connection between `x` and `a`, writing to `x` will no longer write to `a`. If writeback semantics are not active, then `x.data` will still point at some part of `a.data`, and writing to one will affect the other.
Context management and the `close` method appeared in version 1.15.0.
val __getitem__ : key:Py.Object.t -> [> tag ] Obj.t -> Py.Object.tReturn selfkey.
val __iter__ : [> tag ] Obj.t -> Py.Object.tImplement iter(self).
val __setitem__ :
key:Py.Object.t ->
value:Py.Object.t ->
[> tag ] Obj.t ->
Py.Object.tSet selfkey to value.
val dtypes : t -> Py.Object.tAttribute dtypes: get value or raise Not_found if None.
val dtypes_opt : t -> Py.Object.t optionAttribute dtypes: get value as an option.
val finished : t -> boolAttribute finished: get value or raise Not_found if None.
val finished_opt : t -> bool optionAttribute finished: get value as an option.
val has_delayed_bufalloc : t -> boolAttribute has_delayed_bufalloc: get value or raise Not_found if None.
val has_delayed_bufalloc_opt : t -> bool optionAttribute has_delayed_bufalloc: get value as an option.
val has_index : t -> boolAttribute has_index: get value or raise Not_found if None.
val has_index_opt : t -> bool optionAttribute has_index: get value as an option.
val has_multi_index : t -> boolAttribute has_multi_index: get value or raise Not_found if None.
val has_multi_index_opt : t -> bool optionAttribute has_multi_index: get value as an option.
val index : t -> Py.Object.tAttribute index: get value or raise Not_found if None.
val index_opt : t -> Py.Object.t optionAttribute index: get value as an option.
val iterationneedsapi : t -> boolAttribute iterationneedsapi: get value or raise Not_found if None.
val iterationneedsapi_opt : t -> bool optionAttribute iterationneedsapi: get value as an option.
val iterindex : t -> intAttribute iterindex: get value or raise Not_found if None.
val iterindex_opt : t -> int optionAttribute iterindex: get value as an option.
val itersize : t -> intAttribute itersize: get value or raise Not_found if None.
val itersize_opt : t -> int optionAttribute itersize: get value as an option.
val itviews : t -> Py.Object.tAttribute itviews: get value or raise Not_found if None.
val itviews_opt : t -> Py.Object.t optionAttribute itviews: get value as an option.
val multi_index : t -> Py.Object.tAttribute multi_index: get value or raise Not_found if None.
val multi_index_opt : t -> Py.Object.t optionAttribute multi_index: get value as an option.
val ndim : t -> intAttribute ndim: get value or raise Not_found if None.
val ndim_opt : t -> int optionAttribute ndim: get value as an option.
val nop : t -> intAttribute nop: get value or raise Not_found if None.
val nop_opt : t -> int optionAttribute nop: get value as an option.
val operands : t -> Py.Object.tAttribute operands: get value or raise Not_found if None.
val operands_opt : t -> Py.Object.t optionAttribute operands: get value as an option.
val shape : t -> int listAttribute shape: get value or raise Not_found if None.
val shape_opt : t -> int list optionAttribute shape: get value as an option.
val value : t -> Py.Object.tAttribute value: get value or raise Not_found if None.
val value_opt : t -> Py.Object.t optionAttribute value: get value as an option.
val to_string : t -> stringPrint the object to a human-readable representation.
val show : t -> stringPrint the object to a human-readable representation.
val pp : Stdlib.Format.formatter -> t -> unitPretty-print the object to a formatter.