.. sectionauthor:: Travis E. Oliphant
The test of a first-rate intelligence is the ability to hold two
opposed ideas in the mind at the same time, and still retain the
ability to function.
--- F. Scott Fitzgerald
For a successful technology, reality must take precedence over public
relations, for Nature cannot be fooled.
--- Richard P. Feynman
.. index:: pair: ndarray; C-API pair: C-API; array
These macros access the :c:type:`PyArrayObject` structure members and are
defined in ndarraytypes.h. The input argument, arr, can be any
:c:expr:`PyObject *` that is directly interpretable as a
:c:expr:`PyArrayObject *` (any instance of the :c:data:`PyArray_Type`
and its sub-types).
.. c:function:: int PyArray_NDIM(PyArrayObject *arr)
The number of dimensions in the array.
.. c:function:: int PyArray_FLAGS(PyArrayObject* arr)
Returns an integer representing the :ref:`array-flags<array-flags>`.
.. c:function:: int PyArray_TYPE(PyArrayObject* arr)
Return the (builtin) typenumber for the elements of this array.
.. c:function:: int PyArray_SETITEM( \
PyArrayObject* arr, void* itemptr, PyObject* obj)
Convert obj and place it in the ndarray, *arr*, at the place
pointed to by itemptr. Return -1 if an error occurs or 0 on
success.
.. c:function:: void PyArray_ENABLEFLAGS(PyArrayObject* arr, int flags)
.. versionadded:: 1.7
Enables the specified array flags. This function does no validation,
and assumes that you know what you're doing.
.. c:function:: void PyArray_CLEARFLAGS(PyArrayObject* arr, int flags)
.. versionadded:: 1.7
Clears the specified array flags. This function does no validation,
and assumes that you know what you're doing.
.. c:function:: void *PyArray_DATA(PyArrayObject *arr)
.. c:function:: char *PyArray_BYTES(PyArrayObject *arr)
These two macros are similar and obtain the pointer to the
data-buffer for the array. The first macro can (and should be)
assigned to a particular pointer where the second is for generic
processing. If you have not guaranteed a contiguous and/or aligned
array then be sure you understand how to access the data in the
array to avoid memory and/or alignment problems.
.. c:function:: npy_intp *PyArray_DIMS(PyArrayObject *arr)
Returns a pointer to the dimensions/shape of the array. The
number of elements matches the number of dimensions
of the array. Can return ``NULL`` for 0-dimensional arrays.
.. c:function:: npy_intp *PyArray_SHAPE(PyArrayObject *arr)
.. versionadded:: 1.7
A synonym for :c:func:`PyArray_DIMS`, named to be consistent with the
`shape <numpy.ndarray.shape>` usage within Python.
.. c:function:: npy_intp *PyArray_STRIDES(PyArrayObject* arr)
Returns a pointer to the strides of the array. The
number of elements matches the number of dimensions
of the array.
.. c:function:: npy_intp PyArray_DIM(PyArrayObject* arr, int n)
Return the shape in the *n* :math:`^{\textrm{th}}` dimension.
.. c:function:: npy_intp PyArray_STRIDE(PyArrayObject* arr, int n)
Return the stride in the *n* :math:`^{\textrm{th}}` dimension.
.. c:function:: npy_intp PyArray_ITEMSIZE(PyArrayObject* arr)
Return the itemsize for the elements of this array.
Note that, in the old API that was deprecated in version 1.7, this function
had the return type ``int``.
.. c:function:: npy_intp PyArray_SIZE(PyArrayObject* arr)
Returns the total size (in number of elements) of the array.
.. c:function:: npy_intp PyArray_Size(PyArrayObject* obj)
Returns 0 if *obj* is not a sub-class of ndarray. Otherwise,
returns the total number of elements in the array. Safer version
of :c:func:`PyArray_SIZE` (*obj*).
.. c:function:: npy_intp PyArray_NBYTES(PyArrayObject* arr)
Returns the total number of bytes consumed by the array.
.. c:function:: PyObject *PyArray_BASE(PyArrayObject* arr)
This returns the base object of the array. In most cases, this
means the object which owns the memory the array is pointing at.
If you are constructing an array using the C API, and specifying
your own memory, you should use the function :c:func:`PyArray_SetBaseObject`
to set the base to an object which owns the memory.
If the (deprecated) :c:data:`NPY_ARRAY_UPDATEIFCOPY` or the
:c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flags are set, it has a different
meaning, namely base is the array into which the current array will
be copied upon copy resolution. This overloading of the base property
for two functions is likely to change in a future version of NumPy.
.. c:function:: PyArray_Descr *PyArray_DESCR(PyArrayObject* arr)
Returns a borrowed reference to the dtype property of the array.
.. c:function:: PyArray_Descr *PyArray_DTYPE(PyArrayObject* arr)
.. versionadded:: 1.7
A synonym for PyArray_DESCR, named to be consistent with the
'dtype' usage within Python.
.. c:function:: PyObject *PyArray_GETITEM(PyArrayObject* arr, void* itemptr)
Get a Python object of a builtin type from the ndarray, *arr*,
at the location pointed to by itemptr. Return ``NULL`` on failure.
`numpy.ndarray.item` is identical to PyArray_GETITEM.
These functions and macros provide easy access to elements of the ndarray from C. These work for all arrays. You may need to take care when accessing the data in the array, however, if it is not in machine byte-order, misaligned, or not writeable. In other words, be sure to respect the state of the flags unless you know what you are doing, or have previously guaranteed an array that is writeable, aligned, and in machine byte-order using :c:func:`PyArray_FromAny`. If you wish to handle all types of arrays, the copyswap function for each type is useful for handling misbehaved arrays. Some platforms (e.g. Solaris) do not like misaligned data and will crash if you de-reference a misaligned pointer. Other platforms (e.g. x86 Linux) will just work more slowly with misaligned data.
.. c:function:: void* PyArray_GetPtr(PyArrayObject* aobj, npy_intp* ind)
Return a pointer to the data of the ndarray, *aobj*, at the
N-dimensional index given by the c-array, *ind*, (which must be
at least *aobj* ->nd in size). You may want to typecast the
returned pointer to the data type of the ndarray.
.. c:function:: void* PyArray_GETPTR1(PyArrayObject* obj, npy_intp i)
.. c:function:: void* PyArray_GETPTR2( \
PyArrayObject* obj, npy_intp i, npy_intp j)
.. c:function:: void* PyArray_GETPTR3( \
PyArrayObject* obj, npy_intp i, npy_intp j, npy_intp k)
.. c:function:: void* PyArray_GETPTR4( \
PyArrayObject* obj, npy_intp i, npy_intp j, npy_intp k, npy_intp l)
Quick, inline access to the element at the given coordinates in
the ndarray, *obj*, which must have respectively 1, 2, 3, or 4
dimensions (this is not checked). The corresponding *i*, *j*,
*k*, and *l* coordinates can be any integer but will be
interpreted as ``npy_intp``. You may want to typecast the
returned pointer to the data type of the ndarray.
.. c:function:: PyObject* PyArray_NewFromDescr( \
PyTypeObject* subtype, PyArray_Descr* descr, int nd, npy_intp const* dims, \
npy_intp const* strides, void* data, int flags, PyObject* obj)
This function steals a reference to *descr*. The easiest way to get one
is using :c:func:`PyArray_DescrFromType`.
This is the main array creation function. Most new arrays are
created with this flexible function.
The returned object is an object of Python-type *subtype*, which
must be a subtype of :c:data:`PyArray_Type`. The array has *nd*
dimensions, described by *dims*. The data-type descriptor of the
new array is *descr*.
If *subtype* is of an array subclass instead of the base
:c:data:`&PyArray_Type<PyArray_Type>`, then *obj* is the object to pass to
the :obj:`~numpy.class.__array_finalize__` method of the subclass.
If *data* is ``NULL``, then new unitinialized memory will be allocated and
*flags* can be non-zero to indicate a Fortran-style contiguous array. Use
:c:func:`PyArray_FILLWBYTE` to initialize the memory.
If *data* is not ``NULL``, then it is assumed to point to the memory
to be used for the array and the *flags* argument is used as the
new flags for the array (except the state of :c:data:`NPY_ARRAY_OWNDATA`,
:c:data:`NPY_ARRAY_WRITEBACKIFCOPY` and :c:data:`NPY_ARRAY_UPDATEIFCOPY`
flags of the new array will be reset).
In addition, if *data* is non-NULL, then *strides* can
also be provided. If *strides* is ``NULL``, then the array strides
are computed as C-style contiguous (default) or Fortran-style
contiguous (*flags* is nonzero for *data* = ``NULL`` or *flags* &
:c:data:`NPY_ARRAY_F_CONTIGUOUS` is nonzero non-NULL *data*). Any
provided *dims* and *strides* are copied into newly allocated
dimension and strides arrays for the new array object.
:c:func:`PyArray_CheckStrides` can help verify non- ``NULL`` stride
information.
If ``data`` is provided, it must stay alive for the life of the array. One
way to manage this is through :c:func:`PyArray_SetBaseObject`
.. c:function:: PyObject* PyArray_NewLikeArray( \
PyArrayObject* prototype, NPY_ORDER order, PyArray_Descr* descr, \
int subok)
.. versionadded:: 1.6
This function steals a reference to *descr* if it is not NULL.
This array creation routine allows for the convenient creation of
a new array matching an existing array's shapes and memory layout,
possibly changing the layout and/or data type.
When *order* is :c:data:`NPY_ANYORDER`, the result order is
:c:data:`NPY_FORTRANORDER` if *prototype* is a fortran array,
:c:data:`NPY_CORDER` otherwise. When *order* is
:c:data:`NPY_KEEPORDER`, the result order matches that of *prototype*, even
when the axes of *prototype* aren't in C or Fortran order.
If *descr* is NULL, the data type of *prototype* is used.
If *subok* is 1, the newly created array will use the sub-type of
*prototype* to create the new array, otherwise it will create a
base-class array.
.. c:function:: PyObject* PyArray_New( \
PyTypeObject* subtype, int nd, npy_intp const* dims, int type_num, \
npy_intp const* strides, void* data, int itemsize, int flags, \
PyObject* obj)
This is similar to :c:func:`PyArray_NewFromDescr` (...) except you
specify the data-type descriptor with *type_num* and *itemsize*,
where *type_num* corresponds to a builtin (or user-defined)
type. If the type always has the same number of bytes, then
itemsize is ignored. Otherwise, itemsize specifies the particular
size of this array.
Warning
If data is passed to :c:func:`PyArray_NewFromDescr` or :c:func:`PyArray_New`, this memory must not be deallocated until the new array is deleted. If this data came from another Python object, this can be accomplished using :c:func:`Py_INCREF` on that object and setting the base member of the new array to point to that object. If strides are passed in they must be consistent with the dimensions, the itemsize, and the data of the array.
.. c:function:: PyObject* PyArray_SimpleNew(int nd, npy_intp const* dims, int typenum)
Create a new uninitialized array of type, *typenum*, whose size in
each of *nd* dimensions is given by the integer array, *dims*.The memory
for the array is uninitialized (unless typenum is :c:data:`NPY_OBJECT`
in which case each element in the array is set to NULL). The
*typenum* argument allows specification of any of the builtin
data-types such as :c:data:`NPY_FLOAT` or :c:data:`NPY_LONG`. The
memory for the array can be set to zero if desired using
:c:func:`PyArray_FILLWBYTE` (return_object, 0).This function cannot be
used to create a flexible-type array (no itemsize given).
.. c:function:: PyObject* PyArray_SimpleNewFromData( \
int nd, npy_intp const* dims, int typenum, void* data)
Create an array wrapper around *data* pointed to by the given
pointer. The array flags will have a default that the data area is
well-behaved and C-style contiguous. The shape of the array is
given by the *dims* c-array of length *nd*. The data-type of the
array is indicated by *typenum*. If data comes from another
reference-counted Python object, the reference count on this object
should be increased after the pointer is passed in, and the base member
of the returned ndarray should point to the Python object that owns
the data. This will ensure that the provided memory is not
freed while the returned array is in existence. To free memory as soon
as the ndarray is deallocated, set the OWNDATA flag on the returned ndarray.
.. c:function:: PyObject* PyArray_SimpleNewFromDescr( \
int nd, npy_int const* dims, PyArray_Descr* descr)
This function steals a reference to *descr*.
Create a new array with the provided data-type descriptor, *descr*,
of the shape determined by *nd* and *dims*.
.. c:function:: void PyArray_FILLWBYTE(PyObject* obj, int val)
Fill the array pointed to by *obj* ---which must be a (subclass
of) ndarray---with the contents of *val* (evaluated as a byte).
This macro calls memset, so obj must be contiguous.
.. c:function:: PyObject* PyArray_Zeros( \
int nd, npy_intp const* dims, PyArray_Descr* dtype, int fortran)
Construct a new *nd* -dimensional array with shape given by *dims*
and data type given by *dtype*. If *fortran* is non-zero, then a
Fortran-order array is created, otherwise a C-order array is
created. Fill the memory with zeros (or the 0 object if *dtype*
corresponds to :c:type:`NPY_OBJECT` ).
.. c:function:: PyObject* PyArray_ZEROS( \
int nd, npy_intp const* dims, int type_num, int fortran)
Macro form of :c:func:`PyArray_Zeros` which takes a type-number instead
of a data-type object.
.. c:function:: PyObject* PyArray_Empty( \
int nd, npy_intp const* dims, PyArray_Descr* dtype, int fortran)
Construct a new *nd* -dimensional array with shape given by *dims*
and data type given by *dtype*. If *fortran* is non-zero, then a
Fortran-order array is created, otherwise a C-order array is
created. The array is uninitialized unless the data type
corresponds to :c:type:`NPY_OBJECT` in which case the array is
filled with :c:data:`Py_None`.
.. c:function:: PyObject* PyArray_EMPTY( \
int nd, npy_intp const* dims, int typenum, int fortran)
Macro form of :c:func:`PyArray_Empty` which takes a type-number,
*typenum*, instead of a data-type object.
.. c:function:: PyObject* PyArray_Arange( \
double start, double stop, double step, int typenum)
Construct a new 1-dimensional array of data-type, *typenum*, that
ranges from *start* to *stop* (exclusive) in increments of *step*
. Equivalent to **arange** (*start*, *stop*, *step*, dtype).
.. c:function:: PyObject* PyArray_ArangeObj( \
PyObject* start, PyObject* stop, PyObject* step, PyArray_Descr* descr)
Construct a new 1-dimensional array of data-type determined by
``descr``, that ranges from ``start`` to ``stop`` (exclusive) in
increments of ``step``. Equivalent to arange( ``start``,
``stop``, ``step``, ``typenum`` ).
.. c:function:: int PyArray_SetBaseObject(PyArrayObject* arr, PyObject* obj)
.. versionadded:: 1.7
This function **steals a reference** to ``obj`` and sets it as the
base property of ``arr``.
If you construct an array by passing in your own memory buffer as
a parameter, you need to set the array's `base` property to ensure
the lifetime of the memory buffer is appropriate.
The return value is 0 on success, -1 on failure.
If the object provided is an array, this function traverses the
chain of `base` pointers so that each array points to the owner
of the memory directly. Once the base is set, it may not be changed
to another value.
.. c:function:: PyObject* PyArray_FromAny( \
PyObject* op, PyArray_Descr* dtype, int min_depth, int max_depth, \
int requirements, PyObject* context)
This is the main function used to obtain an array from any nested
sequence, or object that exposes the array interface, *op*. The
parameters allow specification of the required *dtype*, the
minimum (*min_depth*) and maximum (*max_depth*) number of
dimensions acceptable, and other *requirements* for the array. This
function **steals a reference** to the dtype argument, which needs
to be a :c:type:`PyArray_Descr` structure
indicating the desired data-type (including required
byteorder). The *dtype* argument may be ``NULL``, indicating that any
data-type (and byteorder) is acceptable. Unless
:c:data:`NPY_ARRAY_FORCECAST` is present in ``flags``,
this call will generate an error if the data
type cannot be safely obtained from the object. If you want to use
``NULL`` for the *dtype* and ensure the array is notswapped then
use :c:func:`PyArray_CheckFromAny`. A value of 0 for either of the
depth parameters causes the parameter to be ignored. Any of the
following array flags can be added (*e.g.* using \|) to get the
*requirements* argument. If your code can handle general (*e.g.*
strided, byte-swapped, or unaligned arrays) then *requirements*
may be 0. Also, if *op* is not already an array (or does not
expose the array interface), then a new array will be created (and
filled from *op* using the sequence protocol). The new array will
have :c:data:`NPY_ARRAY_DEFAULT` as its flags member. The *context*
argument is unused.
.. c:macro:: NPY_ARRAY_C_CONTIGUOUS
Make sure the returned array is C-style contiguous
.. c:macro:: NPY_ARRAY_F_CONTIGUOUS
Make sure the returned array is Fortran-style contiguous.
.. c:macro:: NPY_ARRAY_ALIGNED
Make sure the returned array is aligned on proper boundaries for its
data type. An aligned array has the data pointer and every strides
factor as a multiple of the alignment factor for the data-type-
descriptor.
.. c:macro:: NPY_ARRAY_WRITEABLE
Make sure the returned array can be written to.
.. c:macro:: NPY_ARRAY_ENSURECOPY
Make sure a copy is made of *op*. If this flag is not
present, data is not copied if it can be avoided.
.. c:macro:: NPY_ARRAY_ENSUREARRAY
Make sure the result is a base-class ndarray. By
default, if *op* is an instance of a subclass of
ndarray, an instance of that same subclass is returned. If
this flag is set, an ndarray object will be returned instead.
.. c:macro:: NPY_ARRAY_FORCECAST
Force a cast to the output type even if it cannot be done
safely. Without this flag, a data cast will occur only if it
can be done safely, otherwise an error is raised.
.. c:macro:: NPY_ARRAY_WRITEBACKIFCOPY
If *op* is already an array, but does not satisfy the
requirements, then a copy is made (which will satisfy the
requirements). If this flag is present and a copy (of an object
that is already an array) must be made, then the corresponding
:c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag is set in the returned
copy and *op* is made to be read-only. You must be sure to call
:c:func:`PyArray_ResolveWritebackIfCopy` to copy the contents
back into *op* and the *op* array
will be made writeable again. If *op* is not writeable to begin
with, or if it is not already an array, then an error is raised.
.. c:macro:: NPY_ARRAY_UPDATEIFCOPY
Deprecated. Use :c:data:`NPY_ARRAY_WRITEBACKIFCOPY`, which is similar.
This flag "automatically" copies the data back when the returned
array is deallocated, which is not supported in all python
implementations.
.. c:macro:: NPY_ARRAY_BEHAVED
:c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEABLE`
.. c:macro:: NPY_ARRAY_CARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_BEHAVED`
.. c:macro:: NPY_ARRAY_CARRAY_RO
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_FARRAY
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_BEHAVED`
.. c:macro:: NPY_ARRAY_FARRAY_RO
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_DEFAULT
:c:data:`NPY_ARRAY_CARRAY`
.. c:macro:: NPY_ARRAY_IN_ARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_IN_FARRAY
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_OUT_ARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \|
:c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_OUT_ARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED` \|
:c:data:`NPY_ARRAY_WRITEABLE`
.. c:macro:: NPY_ARRAY_OUT_FARRAY
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \|
:c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_INOUT_ARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \|
:c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` \|
:c:data:`NPY_ARRAY_UPDATEIFCOPY`
.. c:macro:: NPY_ARRAY_INOUT_FARRAY
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_WRITEABLE` \|
:c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` \|
:c:data:`NPY_ARRAY_UPDATEIFCOPY`
.. c:function:: int PyArray_GetArrayParamsFromObject( \
PyObject* op, PyArray_Descr* requested_dtype, npy_bool writeable, \
PyArray_Descr** out_dtype, int* out_ndim, npy_intp* out_dims, \
PyArrayObject** out_arr, PyObject* context)
.. deprecated:: NumPy 1.19
Unless NumPy is made aware of an issue with this, this function
is scheduled for rapid removal without replacement.
.. versionchanged:: NumPy 1.19
`context` is never used. Its use results in an error.
.. versionadded:: 1.6
.. c:function:: PyObject* PyArray_CheckFromAny( \
PyObject* op, PyArray_Descr* dtype, int min_depth, int max_depth, \
int requirements, PyObject* context)
Nearly identical to :c:func:`PyArray_FromAny` (...) except
*requirements* can contain :c:data:`NPY_ARRAY_NOTSWAPPED` (over-riding the
specification in *dtype*) and :c:data:`NPY_ARRAY_ELEMENTSTRIDES` which
indicates that the array should be aligned in the sense that the
strides are multiples of the element size.
In versions 1.6 and earlier of NumPy, the following flags
did not have the _ARRAY_ macro namespace in them. That form
of the constant names is deprecated in 1.7.
.. c:macro:: NPY_ARRAY_NOTSWAPPED
Make sure the returned array has a data-type descriptor that is in
machine byte-order, over-riding any specification in the *dtype*
argument. Normally, the byte-order requirement is determined by
the *dtype* argument. If this flag is set and the dtype argument
does not indicate a machine byte-order descriptor (or is NULL and
the object is already an array with a data-type descriptor that is
not in machine byte- order), then a new data-type descriptor is
created and used with its byte-order field set to native.
.. c:macro:: NPY_ARRAY_BEHAVED_NS
:c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEABLE` \| :c:data:`NPY_ARRAY_NOTSWAPPED`
.. c:macro:: NPY_ARRAY_ELEMENTSTRIDES
Make sure the returned array has strides that are multiples of the
element size.
.. c:function:: PyObject* PyArray_FromArray( \
PyArrayObject* op, PyArray_Descr* newtype, int requirements)
Special case of :c:func:`PyArray_FromAny` for when *op* is already an
array but it needs to be of a specific *newtype* (including
byte-order) or has certain *requirements*.
.. c:function:: PyObject* PyArray_FromStructInterface(PyObject* op)
Returns an ndarray object from a Python object that exposes the
:obj:`~object.__array_struct__` attribute and follows the array interface
protocol. If the object does not contain this attribute then a
borrowed reference to :c:data:`Py_NotImplemented` is returned.
.. c:function:: PyObject* PyArray_FromInterface(PyObject* op)
Returns an ndarray object from a Python object that exposes the
:obj:`~object.__array_interface__` attribute following the array interface
protocol. If the object does not contain this attribute then a
borrowed reference to :c:data:`Py_NotImplemented` is returned.
.. c:function:: PyObject* PyArray_FromArrayAttr( \
PyObject* op, PyArray_Descr* dtype, PyObject* context)
Return an ndarray object from a Python object that exposes the
:obj:`~numpy.class.__array__` method. The :obj:`~numpy.class.__array__`
method can take 0, or 1 argument ``([dtype])``. ``context`` is unused.
.. c:function:: PyObject* PyArray_ContiguousFromAny( \
PyObject* op, int typenum, int min_depth, int max_depth)
This function returns a (C-style) contiguous and behaved function
array from any nested sequence or array interface exporting
object, *op*, of (non-flexible) type given by the enumerated
*typenum*, of minimum depth *min_depth*, and of maximum depth
*max_depth*. Equivalent to a call to :c:func:`PyArray_FromAny` with
requirements set to :c:data:`NPY_ARRAY_DEFAULT` and the type_num member of the
type argument set to *typenum*.
.. c:function:: PyObject* PyArray_ContiguousFromObject( \
PyObject* op, int typenum, int min_depth, int max_depth)
This function returns a well-behaved C-style contiguous array from any nested
sequence or array-interface exporting object. The minimum number of dimensions
the array can have is given by `min_depth` while the maximum is `max_depth`.
This is equivalent to call :c:func:`PyArray_FromAny` with requirements
:c:data:`NPY_ARRAY_DEFAULT` and :c:data:`NPY_ARRAY_ENSUREARRAY`.
.. c:function:: PyObject* PyArray_FromObject( \
PyObject* op, int typenum, int min_depth, int max_depth)
Return an aligned and in native-byteorder array from any nested
sequence or array-interface exporting object, op, of a type given by
the enumerated typenum. The minimum number of dimensions the array can
have is given by min_depth while the maximum is max_depth. This is
equivalent to a call to :c:func:`PyArray_FromAny` with requirements set to
BEHAVED.
.. c:function:: PyObject* PyArray_EnsureArray(PyObject* op)
This function **steals a reference** to ``op`` and makes sure that
``op`` is a base-class ndarray. It special cases array scalars,
but otherwise calls :c:func:`PyArray_FromAny` ( ``op``, NULL, 0, 0,
:c:data:`NPY_ARRAY_ENSUREARRAY`, NULL).
.. c:function:: PyObject* PyArray_FromString( \
char* string, npy_intp slen, PyArray_Descr* dtype, npy_intp num, \
char* sep)
Construct a one-dimensional ndarray of a single type from a binary
or (ASCII) text ``string`` of length ``slen``. The data-type of
the array to-be-created is given by ``dtype``. If num is -1, then
**copy** the entire string and return an appropriately sized
array, otherwise, ``num`` is the number of items to **copy** from
the string. If ``sep`` is NULL (or ""), then interpret the string
as bytes of binary data, otherwise convert the sub-strings
separated by ``sep`` to items of data-type ``dtype``. Some
data-types may not be readable in text mode and an error will be
raised if that occurs. All errors return NULL.
.. c:function:: PyObject* PyArray_FromFile( \
FILE* fp, PyArray_Descr* dtype, npy_intp num, char* sep)
Construct a one-dimensional ndarray of a single type from a binary
or text file. The open file pointer is ``fp``, the data-type of
the array to be created is given by ``dtype``. This must match
the data in the file. If ``num`` is -1, then read until the end of
the file and return an appropriately sized array, otherwise,
``num`` is the number of items to read. If ``sep`` is NULL (or
""), then read from the file in binary mode, otherwise read from
the file in text mode with ``sep`` providing the item
separator. Some array types cannot be read in text mode in which
case an error is raised.
.. c:function:: PyObject* PyArray_FromBuffer( \
PyObject* buf, PyArray_Descr* dtype, npy_intp count, npy_intp offset)
Construct a one-dimensional ndarray of a single type from an
object, ``buf``, that exports the (single-segment) buffer protocol
(or has an attribute __buffer\__ that returns an object that
exports the buffer protocol). A writeable buffer will be tried
first followed by a read- only buffer. The :c:data:`NPY_ARRAY_WRITEABLE`
flag of the returned array will reflect which one was
successful. The data is assumed to start at ``offset`` bytes from
the start of the memory location for the object. The type of the
data in the buffer will be interpreted depending on the data- type
descriptor, ``dtype.`` If ``count`` is negative then it will be
determined from the size of the buffer and the requested itemsize,
otherwise, ``count`` represents how many elements should be
converted from the buffer.
.. c:function:: int PyArray_CopyInto(PyArrayObject* dest, PyArrayObject* src)
Copy from the source array, ``src``, into the destination array,
``dest``, performing a data-type conversion if necessary. If an
error occurs return -1 (otherwise 0). The shape of ``src`` must be
broadcastable to the shape of ``dest``. The data areas of dest
and src must not overlap.
.. c:function:: int PyArray_MoveInto(PyArrayObject* dest, PyArrayObject* src)
Move data from the source array, ``src``, into the destination
array, ``dest``, performing a data-type conversion if
necessary. If an error occurs return -1 (otherwise 0). The shape
of ``src`` must be broadcastable to the shape of ``dest``. The
data areas of dest and src may overlap.
.. c:function:: PyArrayObject* PyArray_GETCONTIGUOUS(PyObject* op)
If ``op`` is already (C-style) contiguous and well-behaved then
just return a reference, otherwise return a (contiguous and
well-behaved) copy of the array. The parameter op must be a
(sub-class of an) ndarray and no checking for that is done.
.. c:function:: PyObject* PyArray_FROM_O(PyObject* obj)
Convert ``obj`` to an ndarray. The argument can be any nested
sequence or object that exports the array interface. This is a
macro form of :c:func:`PyArray_FromAny` using ``NULL``, 0, 0, 0 for the
other arguments. Your code must be able to handle any data-type
descriptor and any combination of data-flags to use this macro.
.. c:function:: PyObject* PyArray_FROM_OF(PyObject* obj, int requirements)
Similar to :c:func:`PyArray_FROM_O` except it can take an argument
of *requirements* indicating properties the resulting array must
have. Available requirements that can be enforced are
:c:data:`NPY_ARRAY_C_CONTIGUOUS`, :c:data:`NPY_ARRAY_F_CONTIGUOUS`,
:c:data:`NPY_ARRAY_ALIGNED`, :c:data:`NPY_ARRAY_WRITEABLE`,
:c:data:`NPY_ARRAY_NOTSWAPPED`, :c:data:`NPY_ARRAY_ENSURECOPY`,
:c:data:`NPY_ARRAY_WRITEBACKIFCOPY`, :c:data:`NPY_ARRAY_UPDATEIFCOPY`,
:c:data:`NPY_ARRAY_FORCECAST`, and
:c:data:`NPY_ARRAY_ENSUREARRAY`. Standard combinations of flags can also
be used:
.. c:function:: PyObject* PyArray_FROM_OT(PyObject* obj, int typenum)
Similar to :c:func:`PyArray_FROM_O` except it can take an argument of
*typenum* specifying the type-number the returned array.
.. c:function:: PyObject* PyArray_FROM_OTF( \
PyObject* obj, int typenum, int requirements)
Combination of :c:func:`PyArray_FROM_OF` and :c:func:`PyArray_FROM_OT`
allowing both a *typenum* and a *flags* argument to be provided.
.. c:function:: PyObject* PyArray_FROMANY( \
PyObject* obj, int typenum, int min, int max, int requirements)
Similar to :c:func:`PyArray_FromAny` except the data-type is
specified using a typenumber. :c:func:`PyArray_DescrFromType`
(*typenum*) is passed directly to :c:func:`PyArray_FromAny`. This
macro also adds :c:data:`NPY_ARRAY_DEFAULT` to requirements if
:c:data:`NPY_ARRAY_ENSURECOPY` is passed in as requirements.
.. c:function:: PyObject *PyArray_CheckAxis( \
PyObject* obj, int* axis, int requirements)
Encapsulate the functionality of functions and methods that take
the axis= keyword and work properly with None as the axis
argument. The input array is ``obj``, while ``*axis`` is a
converted integer (so that >=MAXDIMS is the None value), and
``requirements`` gives the needed properties of ``obj``. The
output is a converted version of the input so that requirements
are met and if needed a flattening has occurred. On output
negative values of ``*axis`` are converted and the new value is
checked to ensure consistency with the shape of ``obj``.
.. c:function:: int PyArray_Check(PyObject *op)
Evaluates true if *op* is a Python object whose type is a sub-type
of :c:data:`PyArray_Type`.
.. c:function:: int PyArray_CheckExact(PyObject *op)
Evaluates true if *op* is a Python object with type
:c:data:`PyArray_Type`.
.. c:function:: int PyArray_HasArrayInterface(PyObject *op, PyObject *out)
If ``op`` implements any part of the array interface, then ``out``
will contain a new reference to the newly created ndarray using
the interface or ``out`` will contain ``NULL`` if an error during
conversion occurs. Otherwise, out will contain a borrowed
reference to :c:data:`Py_NotImplemented` and no error condition is set.
.. c:function:: int PyArray_HasArrayInterfaceType(\
PyObject *op, PyArray_Descr *dtype, PyObject *context, PyObject *out)
If ``op`` implements any part of the array interface, then ``out``
will contain a new reference to the newly created ndarray using
the interface or ``out`` will contain ``NULL`` if an error during
conversion occurs. Otherwise, out will contain a borrowed
reference to Py_NotImplemented and no error condition is set.
This version allows setting of the dtype in the part of the array interface
that looks for the :obj:`~numpy.class.__array__` attribute. `context` is
unused.
.. c:function:: int PyArray_IsZeroDim(PyObject *op)
Evaluates true if *op* is an instance of (a subclass of)
:c:data:`PyArray_Type` and has 0 dimensions.
.. c:macro:: PyArray_IsScalar(op, cls)
Evaluates true if *op* is an instance of ``Py{cls}ArrType_Type``.
.. c:function:: int PyArray_CheckScalar(PyObject *op)
Evaluates true if *op* is either an array scalar (an instance of a
sub-type of :c:data:`PyGenericArr_Type` ), or an instance of (a
sub-class of) :c:data:`PyArray_Type` whose dimensionality is 0.
.. c:function:: int PyArray_IsPythonNumber(PyObject *op)
Evaluates true if *op* is an instance of a builtin numeric type (int,
float, complex, long, bool)
.. c:function:: int PyArray_IsPythonScalar(PyObject *op)
Evaluates true if *op* is a builtin Python scalar object (int,
float, complex, bytes, str, long, bool).
.. c:function:: int PyArray_IsAnyScalar(PyObject *op)
Evaluates true if *op* is either a Python scalar object (see
:c:func:`PyArray_IsPythonScalar`) or an array scalar (an instance of a sub-
type of :c:data:`PyGenericArr_Type` ).
.. c:function:: int PyArray_CheckAnyScalar(PyObject *op)
Evaluates true if *op* is a Python scalar object (see
:c:func:`PyArray_IsPythonScalar`), an array scalar (an instance of a
sub-type of :c:data:`PyGenericArr_Type`) or an instance of a sub-type of
:c:data:`PyArray_Type` whose dimensionality is 0.
For the typenum macros, the argument is an integer representing an enumerated array data type. For the array type checking macros the argument must be a :c:expr:`PyObject *` that can be directly interpreted as a :c:expr:`PyArrayObject *`.
.. c:function:: int PyTypeNum_ISUNSIGNED(int num)
.. c:function:: int PyDataType_ISUNSIGNED(PyArray_Descr *descr)
.. c:function:: int PyArray_ISUNSIGNED(PyArrayObject *obj)
Type represents an unsigned integer.
.. c:function:: int PyTypeNum_ISSIGNED(int num)
.. c:function:: int PyDataType_ISSIGNED(PyArray_Descr *descr)
.. c:function:: int PyArray_ISSIGNED(PyArrayObject *obj)
Type represents a signed integer.
.. c:function:: int PyTypeNum_ISINTEGER(int num)
.. c:function:: int PyDataType_ISINTEGER(PyArray_Descr* descr)
.. c:function:: int PyArray_ISINTEGER(PyArrayObject *obj)
Type represents any integer.
.. c:function:: int PyTypeNum_ISFLOAT(int num)
.. c:function:: int PyDataType_ISFLOAT(PyArray_Descr* descr)
.. c:function:: int PyArray_ISFLOAT(PyArrayObject *obj)
Type represents any floating point number.
.. c:function:: int PyTypeNum_ISCOMPLEX(int num)
.. c:function:: int PyDataType_ISCOMPLEX(PyArray_Descr* descr)
.. c:function:: int PyArray_ISCOMPLEX(PyArrayObject *obj)
Type represents any complex floating point number.
.. c:function:: int PyTypeNum_ISNUMBER(int num)
.. c:function:: int PyDataType_ISNUMBER(PyArray_Descr* descr)
.. c:function:: int PyArray_ISNUMBER(PyArrayObject *obj)
Type represents any integer, floating point, or complex floating point
number.
.. c:function:: int PyTypeNum_ISSTRING(int num)
.. c:function:: int PyDataType_ISSTRING(PyArray_Descr* descr)
.. c:function:: int PyArray_ISSTRING(PyArrayObject *obj)
Type represents a string data type.
.. c:function:: int PyTypeNum_ISPYTHON(int num)
.. c:function:: int PyDataType_ISPYTHON(PyArray_Descr* descr)
.. c:function:: int PyArray_ISPYTHON(PyArrayObject *obj)
Type represents an enumerated type corresponding to one of the
standard Python scalar (bool, int, float, or complex).
.. c:function:: int PyTypeNum_ISFLEXIBLE(int num)
.. c:function:: int PyDataType_ISFLEXIBLE(PyArray_Descr* descr)
.. c:function:: int PyArray_ISFLEXIBLE(PyArrayObject *obj)
Type represents one of the flexible array types ( :c:data:`NPY_STRING`,
:c:data:`NPY_UNICODE`, or :c:data:`NPY_VOID` ).
.. c:function:: int PyDataType_ISUNSIZED(PyArray_Descr* descr)
Type has no size information attached, and can be resized. Should only be
called on flexible dtypes. Types that are attached to an array will always
be sized, hence the array form of this macro not existing.
.. versionchanged:: 1.18
For structured datatypes with no fields this function now returns False.
.. c:function:: int PyTypeNum_ISUSERDEF(int num)
.. c:function:: int PyDataType_ISUSERDEF(PyArray_Descr* descr)
.. c:function:: int PyArray_ISUSERDEF(PyArrayObject *obj)
Type represents a user-defined type.
.. c:function:: int PyTypeNum_ISEXTENDED(int num)
.. c:function:: int PyDataType_ISEXTENDED(PyArray_Descr* descr)
.. c:function:: int PyArray_ISEXTENDED(PyArrayObject *obj)
Type is either flexible or user-defined.
.. c:function:: int PyTypeNum_ISOBJECT(int num)
.. c:function:: int PyDataType_ISOBJECT(PyArray_Descr* descr)
.. c:function:: int PyArray_ISOBJECT(PyArrayObject *obj)
Type represents object data type.
.. c:function:: int PyTypeNum_ISBOOL(int num)
.. c:function:: int PyDataType_ISBOOL(PyArray_Descr* descr)
.. c:function:: int PyArray_ISBOOL(PyArrayObject *obj)
Type represents Boolean data type.
.. c:function:: int PyDataType_HASFIELDS(PyArray_Descr* descr)
.. c:function:: int PyArray_HASFIELDS(PyArrayObject *obj)
Type has fields associated with it.
.. c:function:: int PyArray_ISNOTSWAPPED(PyArrayObject *m)
Evaluates true if the data area of the ndarray *m* is in machine
byte-order according to the array's data-type descriptor.
.. c:function:: int PyArray_ISBYTESWAPPED(PyArrayObject *m)
Evaluates true if the data area of the ndarray *m* is **not** in
machine byte-order according to the array's data-type descriptor.
.. c:function:: npy_bool PyArray_EquivTypes( \
PyArray_Descr* type1, PyArray_Descr* type2)
Return :c:data:`NPY_TRUE` if *type1* and *type2* actually represent
equivalent types for this platform (the fortran member of each
type is ignored). For example, on 32-bit platforms,
:c:data:`NPY_LONG` and :c:data:`NPY_INT` are equivalent. Otherwise
return :c:data:`NPY_FALSE`.
.. c:function:: npy_bool PyArray_EquivArrTypes( \
PyArrayObject* a1, PyArrayObject * a2)
Return :c:data:`NPY_TRUE` if *a1* and *a2* are arrays with equivalent
types for this platform.
.. c:function:: npy_bool PyArray_EquivTypenums(int typenum1, int typenum2)
Special case of :c:func:`PyArray_EquivTypes` (...) that does not accept
flexible data types but may be easier to call.
.. c:function:: int PyArray_EquivByteorders(int b1, int b2)
True if byteorder characters *b1* and *b2* ( :c:data:`NPY_LITTLE`,
:c:data:`NPY_BIG`, :c:data:`NPY_NATIVE`, :c:data:`NPY_IGNORE` ) are
either equal or equivalent as to their specification of a native
byte order. Thus, on a little-endian machine :c:data:`NPY_LITTLE`
and :c:data:`NPY_NATIVE` are equivalent where they are not
equivalent on a big-endian machine.
.. c:function:: PyObject* PyArray_Cast(PyArrayObject* arr, int typenum)
Mainly for backwards compatibility to the Numeric C-API and for
simple casts to non-flexible types. Return a new array object with
the elements of *arr* cast to the data-type *typenum* which must
be one of the enumerated types and not a flexible type.
.. c:function:: PyObject* PyArray_CastToType( \
PyArrayObject* arr, PyArray_Descr* type, int fortran)
Return a new array of the *type* specified, casting the elements
of *arr* as appropriate. The fortran argument specifies the
ordering of the output array.
.. c:function:: int PyArray_CastTo(PyArrayObject* out, PyArrayObject* in)
As of 1.6, this function simply calls :c:func:`PyArray_CopyInto`,
which handles the casting.
Cast the elements of the array *in* into the array *out*. The
output array should be writeable, have an integer-multiple of the
number of elements in the input array (more than one copy can be
placed in out), and have a data type that is one of the builtin
types. Returns 0 on success and -1 if an error occurs.
.. c:function:: PyArray_VectorUnaryFunc* PyArray_GetCastFunc( \
PyArray_Descr* from, int totype)
Return the low-level casting function to cast from the given
descriptor to the builtin type number. If no casting function
exists return ``NULL`` and set an error. Using this function
instead of direct access to *from* ->f->cast will allow support of
any user-defined casting functions added to a descriptors casting
dictionary.
.. c:function:: int PyArray_CanCastSafely(int fromtype, int totype)
Returns non-zero if an array of data type *fromtype* can be cast
to an array of data type *totype* without losing information. An
exception is that 64-bit integers are allowed to be cast to 64-bit
floating point values even though this can lose precision on large
integers so as not to proliferate the use of long doubles without
explicit requests. Flexible array types are not checked according
to their lengths with this function.
.. c:function:: int PyArray_CanCastTo( \
PyArray_Descr* fromtype, PyArray_Descr* totype)
:c:func:`PyArray_CanCastTypeTo` supersedes this function in
NumPy 1.6 and later.
Equivalent to PyArray_CanCastTypeTo(fromtype, totype, NPY_SAFE_CASTING).
.. c:function:: int PyArray_CanCastTypeTo( \
PyArray_Descr* fromtype, PyArray_Descr* totype, NPY_CASTING casting)
.. versionadded:: 1.6
Returns non-zero if an array of data type *fromtype* (which can
include flexible types) can be cast safely to an array of data
type *totype* (which can include flexible types) according to
the casting rule *casting*. For simple types with :c:data:`NPY_SAFE_CASTING`,
this is basically a wrapper around :c:func:`PyArray_CanCastSafely`, but
for flexible types such as strings or unicode, it produces results
taking into account their sizes. Integer and float types can only be cast
to a string or unicode type using :c:data:`NPY_SAFE_CASTING` if the string
or unicode type is big enough to hold the max value of the integer/float
type being cast from.
.. c:function:: int PyArray_CanCastArrayTo( \
PyArrayObject* arr, PyArray_Descr* totype, NPY_CASTING casting)
.. versionadded:: 1.6
Returns non-zero if *arr* can be cast to *totype* according
to the casting rule given in *casting*. If *arr* is an array
scalar, its value is taken into account, and non-zero is also
returned when the value will not overflow or be truncated to
an integer when converting to a smaller type.
This is almost the same as the result of
PyArray_CanCastTypeTo(PyArray_MinScalarType(arr), totype, casting),
but it also handles a special case arising because the set
of uint values is not a subset of the int values for types with the
same number of bits.
.. c:function:: PyArray_Descr* PyArray_MinScalarType(PyArrayObject* arr)
.. versionadded:: 1.6
If *arr* is an array, returns its data type descriptor, but if
*arr* is an array scalar (has 0 dimensions), it finds the data type
of smallest size to which the value may be converted
without overflow or truncation to an integer.
This function will not demote complex to float or anything to
boolean, but will demote a signed integer to an unsigned integer
when the scalar value is positive.
.. c:function:: PyArray_Descr* PyArray_PromoteTypes( \
PyArray_Descr* type1, PyArray_Descr* type2)
.. versionadded:: 1.6
Finds the data type of smallest size and kind to which *type1* and
*type2* may be safely converted. This function is symmetric and
associative. A string or unicode result will be the proper size for
storing the max value of the input types converted to a string or unicode.
.. c:function:: PyArray_Descr* PyArray_ResultType( \
npy_intp narrs, PyArrayObject **arrs, npy_intp ndtypes, \
PyArray_Descr **dtypes)
.. versionadded:: 1.6
This applies type promotion to all the inputs,
using the NumPy rules for combining scalars and arrays, to
determine the output type of a set of operands. This is the
same result type that ufuncs produce. The specific algorithm
used is as follows.
Categories are determined by first checking which of boolean,
integer (int/uint), or floating point (float/complex) the maximum
kind of all the arrays and the scalars are.
If there are only scalars or the maximum category of the scalars
is higher than the maximum category of the arrays,
the data types are combined with :c:func:`PyArray_PromoteTypes`
to produce the return value.
Otherwise, PyArray_MinScalarType is called on each array, and
the resulting data types are all combined with
:c:func:`PyArray_PromoteTypes` to produce the return value.
The set of int values is not a subset of the uint values for types
with the same number of bits, something not reflected in
:c:func:`PyArray_MinScalarType`, but handled as a special case in
PyArray_ResultType.
.. c:function:: int PyArray_ObjectType(PyObject* op, int mintype)
This function is superseded by :c:func:`PyArray_MinScalarType` and/or
:c:func:`PyArray_ResultType`.
This function is useful for determining a common type that two or
more arrays can be converted to. It only works for non-flexible
array types as no itemsize information is passed. The *mintype*
argument represents the minimum type acceptable, and *op*
represents the object that will be converted to an array. The
return value is the enumerated typenumber that represents the
data-type that *op* should have.
.. c:function:: void PyArray_ArrayType( \
PyObject* op, PyArray_Descr* mintype, PyArray_Descr* outtype)
This function is superseded by :c:func:`PyArray_ResultType`.
This function works similarly to :c:func:`PyArray_ObjectType` (...)
except it handles flexible arrays. The *mintype* argument can have
an itemsize member and the *outtype* argument will have an
itemsize member at least as big but perhaps bigger depending on
the object *op*.
.. c:function:: PyArrayObject** PyArray_ConvertToCommonType( \
PyObject* op, int* n)
The functionality this provides is largely superseded by iterator
:c:type:`NpyIter` introduced in 1.6, with flag
:c:data:`NPY_ITER_COMMON_DTYPE` or with the same dtype parameter for
all operands.
Convert a sequence of Python objects contained in *op* to an array
of ndarrays each having the same data type. The type is selected
in the same way as `PyArray_ResultType`. The length of the sequence is
returned in *n*, and an *n* -length array of :c:type:`PyArrayObject`
pointers is the return value (or ``NULL`` if an error occurs).
The returned array must be freed by the caller of this routine
(using :c:func:`PyDataMem_FREE` ) and all the array objects in it
``DECREF`` 'd or a memory-leak will occur. The example template-code
below shows a typically usage:
.. versionchanged:: 1.18.0
A mix of scalars and zero-dimensional arrays now produces a type
capable of holding the scalar value.
Previously priority was given to the dtype of the arrays.
.. code-block:: c
mps = PyArray_ConvertToCommonType(obj, &n);
if (mps==NULL) return NULL;
{code}
<before return>
for (i=0; i<n; i++) Py_DECREF(mps[i]);
PyDataMem_FREE(mps);
{return}
.. c:function:: char* PyArray_Zero(PyArrayObject* arr)
A pointer to newly created memory of size *arr* ->itemsize that
holds the representation of 0 for that type. The returned pointer,
*ret*, **must be freed** using :c:func:`PyDataMem_FREE` (ret) when it is
not needed anymore.
.. c:function:: char* PyArray_One(PyArrayObject* arr)
A pointer to newly created memory of size *arr* ->itemsize that
holds the representation of 1 for that type. The returned pointer,
*ret*, **must be freed** using :c:func:`PyDataMem_FREE` (ret) when it
is not needed anymore.
.. c:function:: int PyArray_ValidType(int typenum)
Returns :c:data:`NPY_TRUE` if *typenum* represents a valid type-number
(builtin or user-defined or character code). Otherwise, this
function returns :c:data:`NPY_FALSE`.
.. c:function:: void PyArray_InitArrFuncs(PyArray_ArrFuncs* f)
Initialize all function pointers and members to ``NULL``.
.. c:function:: int PyArray_RegisterDataType(PyArray_Descr* dtype)
Register a data-type as a new user-defined data type for
arrays. The type must have most of its entries filled in. This is
not always checked and errors can produce segfaults. In
particular, the typeobj member of the ``dtype`` structure must be
filled with a Python type that has a fixed-size element-size that
corresponds to the elsize member of *dtype*. Also the ``f``
member must have the required functions: nonzero, copyswap,
copyswapn, getitem, setitem, and cast (some of the cast functions
may be ``NULL`` if no support is desired). To avoid confusion, you
should choose a unique character typecode but this is not enforced
and not relied on internally.
A user-defined type number is returned that uniquely identifies
the type. A pointer to the new structure can then be obtained from
:c:func:`PyArray_DescrFromType` using the returned type number. A -1 is
returned if an error occurs. If this *dtype* has already been
registered (checked only by the address of the pointer), then
return the previously-assigned type-number.
.. c:function:: int PyArray_RegisterCastFunc( \
PyArray_Descr* descr, int totype, PyArray_VectorUnaryFunc* castfunc)
Register a low-level casting function, *castfunc*, to convert
from the data-type, *descr*, to the given data-type number,
*totype*. Any old casting function is over-written. A ``0`` is
returned on success or a ``-1`` on failure.
.. c:function:: int PyArray_RegisterCanCast( \
PyArray_Descr* descr, int totype, NPY_SCALARKIND scalar)
Register the data-type number, *totype*, as castable from
data-type object, *descr*, of the given *scalar* kind. Use
*scalar* = :c:data:`NPY_NOSCALAR` to register that an array of data-type
*descr* can be cast safely to a data-type whose type_number is
*totype*.
.. c:function:: int PyArray_INCREF(PyArrayObject* op)
Used for an array, *op*, that contains any Python objects. It
increments the reference count of every object in the array
according to the data-type of *op*. A -1 is returned if an error
occurs, otherwise 0 is returned.
.. c:function:: void PyArray_Item_INCREF(char* ptr, PyArray_Descr* dtype)
A function to INCREF all the objects at the location *ptr*
according to the data-type *dtype*. If *ptr* is the start of a
structured type with an object at any offset, then this will (recursively)
increment the reference count of all object-like items in the
structured type.
.. c:function:: int PyArray_XDECREF(PyArrayObject* op)
Used for an array, *op*, that contains any Python objects. It
decrements the reference count of every object in the array
according to the data-type of *op*. Normal return value is 0. A
-1 is returned if an error occurs.
.. c:function:: void PyArray_Item_XDECREF(char* ptr, PyArray_Descr* dtype)
A function to XDECREF all the object-like items at the location
*ptr* as recorded in the data-type, *dtype*. This works
recursively so that if ``dtype`` itself has fields with data-types
that contain object-like items, all the object-like fields will be
XDECREF ``'d``.
.. c:function:: void PyArray_FillObjectArray(PyArrayObject* arr, PyObject* obj)
Fill a newly created array with a single value obj at all
locations in the structure with object data-types. No checking is
performed but *arr* must be of data-type :c:type:`NPY_OBJECT` and be
single-segment and uninitialized (no previous objects in
position). Use :c:func:`PyArray_XDECREF` (*arr*) if you need to
decrement all the items in the object array prior to calling this
function.
.. c:function:: int PyArray_SetUpdateIfCopyBase(PyArrayObject* arr, PyArrayObject* base)
Precondition: ``arr`` is a copy of ``base`` (though possibly with different
strides, ordering, etc.) Set the UPDATEIFCOPY flag and ``arr->base`` so
that when ``arr`` is destructed, it will copy any changes back to ``base``.
DEPRECATED, use :c:func:`PyArray_SetWritebackIfCopyBase`.
Returns 0 for success, -1 for failure.
.. c:function:: int PyArray_SetWritebackIfCopyBase(PyArrayObject* arr, PyArrayObject* base)
Precondition: ``arr`` is a copy of ``base`` (though possibly with different
strides, ordering, etc.) Sets the :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` flag
and ``arr->base``, and set ``base`` to READONLY. Call
:c:func:`PyArray_ResolveWritebackIfCopy` before calling
`Py_DECREF` in order copy any changes back to ``base`` and
reset the READONLY flag.
Returns 0 for success, -1 for failure.
The flags attribute of the PyArrayObject structure contains
important information about the memory used by the array (pointed to
by the data member) This flag information must be kept accurate or
strange results and even segfaults may result.
There are 6 (binary) flags that describe the memory area used by the
data buffer. These constants are defined in arrayobject.h and
determine the bit-position of the flag. Python exposes a nice
attribute- based interface as well as a dictionary-like interface for
getting (and, if appropriate, setting) these flags.
Memory areas of all kinds can be pointed to by an ndarray, necessitating
these flags. If you get an arbitrary PyArrayObject in C-code, you
need to be aware of the flags that are set. If you need to guarantee
a certain kind of array (like :c:data:`NPY_ARRAY_C_CONTIGUOUS` and
:c:data:`NPY_ARRAY_BEHAVED`), then pass these requirements into the
PyArray_FromAny function.
An ndarray can have a data segment that is not a simple contiguous chunk of well-behaved memory you can manipulate. It may not be aligned with word boundaries (very important on some platforms). It might have its data in a different byte-order than the machine recognizes. It might not be writeable. It might be in Fortran-contiguous order. The array flags are used to indicate what can be said about data associated with an array.
In versions 1.6 and earlier of NumPy, the following flags did not have the _ARRAY_ macro namespace in them. That form of the constant names is deprecated in 1.7.
.. c:macro:: NPY_ARRAY_C_CONTIGUOUS
The data area is in C-style contiguous order (last index varies the
fastest).
.. c:macro:: NPY_ARRAY_F_CONTIGUOUS
The data area is in Fortran-style contiguous order (first index varies
the fastest).
Note
Arrays can be both C-style and Fortran-style contiguous simultaneously. This is clear for 1-dimensional arrays, but can also be true for higher dimensional arrays.
Even for contiguous arrays a stride for a given dimension
arr.strides[dim] may be arbitrary if arr.shape[dim] == 1
or the array has no elements.
It does not generally hold that self.strides[-1] == self.itemsize
for C-style contiguous arrays or self.strides[0] == self.itemsize for
Fortran-style contiguous arrays is true. The correct way to access the
itemsize of an array from the C API is PyArray_ITEMSIZE(arr).
.. seealso:: :ref:`Internal memory layout of an ndarray <arrays.ndarray>`
.. c:macro:: NPY_ARRAY_OWNDATA
The data area is owned by this array.
.. c:macro:: NPY_ARRAY_ALIGNED
The data area and all array elements are aligned appropriately.
.. c:macro:: NPY_ARRAY_WRITEABLE
The data area can be written to.
Notice that the above 3 flags are defined so that a new, well-
behaved array has these flags defined as true.
.. c:macro:: NPY_ARRAY_WRITEBACKIFCOPY
The data area represents a (well-behaved) copy whose information
should be transferred back to the original when
:c:func:`PyArray_ResolveWritebackIfCopy` is called.
This is a special flag that is set if this array represents a copy
made because a user required certain flags in
:c:func:`PyArray_FromAny` and a copy had to be made of some other
array (and the user asked for this flag to be set in such a
situation). The base attribute then points to the "misbehaved"
array (which is set read_only). :c:func`PyArray_ResolveWritebackIfCopy`
will copy its contents back to the "misbehaved"
array (casting if necessary) and will reset the "misbehaved" array
to :c:data:`NPY_ARRAY_WRITEABLE`. If the "misbehaved" array was not
:c:data:`NPY_ARRAY_WRITEABLE` to begin with then :c:func:`PyArray_FromAny`
would have returned an error because :c:data:`NPY_ARRAY_WRITEBACKIFCOPY`
would not have been possible.
.. c:macro:: NPY_ARRAY_UPDATEIFCOPY
A deprecated version of :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` which
depends upon ``dealloc`` to trigger the writeback. For backwards
compatibility, :c:func:`PyArray_ResolveWritebackIfCopy` is called at
``dealloc`` but relying
on that behavior is deprecated and not supported in PyPy.
:c:func:`PyArray_UpdateFlags` (obj, flags) will update the obj->flags
for flags which can be any of :c:data:`NPY_ARRAY_C_CONTIGUOUS`,
:c:data:`NPY_ARRAY_F_CONTIGUOUS`, :c:data:`NPY_ARRAY_ALIGNED`, or
:c:data:`NPY_ARRAY_WRITEABLE`.
.. c:macro:: NPY_ARRAY_BEHAVED
:c:data:`NPY_ARRAY_ALIGNED` \| :c:data:`NPY_ARRAY_WRITEABLE`
.. c:macro:: NPY_ARRAY_CARRAY
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_BEHAVED`
.. c:macro:: NPY_ARRAY_CARRAY_RO
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_FARRAY
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_BEHAVED`
.. c:macro:: NPY_ARRAY_FARRAY_RO
:c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
.. c:macro:: NPY_ARRAY_DEFAULT
:c:data:`NPY_ARRAY_CARRAY`
.. c:macro:: NPY_ARRAY_UPDATE_ALL
:c:data:`NPY_ARRAY_C_CONTIGUOUS` \| :c:data:`NPY_ARRAY_F_CONTIGUOUS` \| :c:data:`NPY_ARRAY_ALIGNED`
These constants are used in :c:func:`PyArray_FromAny` (and its macro forms) to specify desired properties of the new array.
.. c:macro:: NPY_ARRAY_FORCECAST
Cast to the desired type, even if it can't be done without losing
information.
.. c:macro:: NPY_ARRAY_ENSURECOPY
Make sure the resulting array is a copy of the original.
.. c:macro:: NPY_ARRAY_ENSUREARRAY
Make sure the resulting object is an actual ndarray, and not a sub-class.
For all of these macros arr must be an instance of a (subclass of) :c:data:`PyArray_Type`.
.. c:function:: int PyArray_CHKFLAGS(PyObject *arr, int flags)
The first parameter, arr, must be an ndarray or subclass. The
parameter, *flags*, should be an integer consisting of bitwise
combinations of the possible flags an array can have:
:c:data:`NPY_ARRAY_C_CONTIGUOUS`, :c:data:`NPY_ARRAY_F_CONTIGUOUS`,
:c:data:`NPY_ARRAY_OWNDATA`, :c:data:`NPY_ARRAY_ALIGNED`,
:c:data:`NPY_ARRAY_WRITEABLE`, :c:data:`NPY_ARRAY_WRITEBACKIFCOPY`,
:c:data:`NPY_ARRAY_UPDATEIFCOPY`.
.. c:function:: int PyArray_IS_C_CONTIGUOUS(PyObject *arr)
Evaluates true if *arr* is C-style contiguous.
.. c:function:: int PyArray_IS_F_CONTIGUOUS(PyObject *arr)
Evaluates true if *arr* is Fortran-style contiguous.
.. c:function:: int PyArray_ISFORTRAN(PyObject *arr)
Evaluates true if *arr* is Fortran-style contiguous and *not*
C-style contiguous. :c:func:`PyArray_IS_F_CONTIGUOUS`
is the correct way to test for Fortran-style contiguity.
.. c:function:: int PyArray_ISWRITEABLE(PyObject *arr)
Evaluates true if the data area of *arr* can be written to
.. c:function:: int PyArray_ISALIGNED(PyObject *arr)
Evaluates true if the data area of *arr* is properly aligned on
the machine.
.. c:function:: int PyArray_ISBEHAVED(PyObject *arr)
Evaluates true if the data area of *arr* is aligned and writeable
and in machine byte-order according to its descriptor.
.. c:function:: int PyArray_ISBEHAVED_RO(PyObject *arr)
Evaluates true if the data area of *arr* is aligned and in machine
byte-order.
.. c:function:: int PyArray_ISCARRAY(PyObject *arr)
Evaluates true if the data area of *arr* is C-style contiguous,
and :c:func:`PyArray_ISBEHAVED` (*arr*) is true.
.. c:function:: int PyArray_ISFARRAY(PyObject *arr)
Evaluates true if the data area of *arr* is Fortran-style
contiguous and :c:func:`PyArray_ISBEHAVED` (*arr*) is true.
.. c:function:: int PyArray_ISCARRAY_RO(PyObject *arr)
Evaluates true if the data area of *arr* is C-style contiguous,
aligned, and in machine byte-order.
.. c:function:: int PyArray_ISFARRAY_RO(PyObject *arr)
Evaluates true if the data area of *arr* is Fortran-style
contiguous, aligned, and in machine byte-order **.**
.. c:function:: int PyArray_ISONESEGMENT(PyObject *arr)
Evaluates true if the data area of *arr* consists of a single
(C-style or Fortran-style) contiguous segment.
.. c:function:: void PyArray_UpdateFlags(PyArrayObject* arr, int flagmask)
The :c:data:`NPY_ARRAY_C_CONTIGUOUS`, :c:data:`NPY_ARRAY_ALIGNED`, and
:c:data:`NPY_ARRAY_F_CONTIGUOUS` array flags can be "calculated" from the
array object itself. This routine updates one or more of these
flags of *arr* as specified in *flagmask* by performing the
required calculation.
Warning
It is important to keep the flags updated (using :c:func:`PyArray_UpdateFlags` can help) whenever a manipulation with an array is performed that might cause them to change. Later calculations in NumPy that rely on the state of these flags do not repeat the calculation to update them.
.. c:function:: PyObject* PyArray_GetField( \
PyArrayObject* self, PyArray_Descr* dtype, int offset)
Equivalent to :meth:`ndarray.getfield<numpy.ndarray.getfield>`
(*self*, *dtype*, *offset*). This function `steals a reference
<https://docs.python.org/3/c-api/intro.html?reference-count-details>`_
to `PyArray_Descr` and returns a new array of the given `dtype` using
the data in the current array at a specified `offset` in bytes. The
`offset` plus the itemsize of the new array type must be less than ``self
->descr->elsize`` or an error is raised. The same shape and strides
as the original array are used. Therefore, this function has the
effect of returning a field from a structured array. But, it can also
be used to select specific bytes or groups of bytes from any array
type.
.. c:function:: int PyArray_SetField( \
PyArrayObject* self, PyArray_Descr* dtype, int offset, PyObject* val)
Equivalent to :meth:`ndarray.setfield<numpy.ndarray.setfield>` (*self*, *val*, *dtype*, *offset*
). Set the field starting at *offset* in bytes and of the given
*dtype* to *val*. The *offset* plus *dtype* ->elsize must be less
than *self* ->descr->elsize or an error is raised. Otherwise, the
*val* argument is converted to an array and copied into the field
pointed to. If necessary, the elements of *val* are repeated to
fill the destination array, But, the number of elements in the
destination must be an integer multiple of the number of elements
in *val*.
.. c:function:: PyObject* PyArray_Byteswap(PyArrayObject* self, npy_bool inplace)
Equivalent to :meth:`ndarray.byteswap<numpy.ndarray.byteswap>` (*self*, *inplace*). Return an array
whose data area is byteswapped. If *inplace* is non-zero, then do
the byteswap inplace and return a reference to self. Otherwise,
create a byteswapped copy and leave self unchanged.
.. c:function:: PyObject* PyArray_NewCopy(PyArrayObject* old, NPY_ORDER order)
Equivalent to :meth:`ndarray.copy<numpy.ndarray.copy>` (*self*, *fortran*). Make a copy of the
*old* array. The returned array is always aligned and writeable
with data interpreted the same as the old array. If *order* is
:c:data:`NPY_CORDER`, then a C-style contiguous array is returned. If
*order* is :c:data:`NPY_FORTRANORDER`, then a Fortran-style contiguous
array is returned. If *order is* :c:data:`NPY_ANYORDER`, then the array
returned is Fortran-style contiguous only if the old one is;
otherwise, it is C-style contiguous.
.. c:function:: PyObject* PyArray_ToList(PyArrayObject* self)
Equivalent to :meth:`ndarray.tolist<numpy.ndarray.tolist>` (*self*). Return a nested Python list
from *self*.
.. c:function:: PyObject* PyArray_ToString(PyArrayObject* self, NPY_ORDER order)
Equivalent to :meth:`ndarray.tobytes<numpy.ndarray.tobytes>` (*self*, *order*). Return the bytes
of this array in a Python string.
.. c:function:: PyObject* PyArray_ToFile( \
PyArrayObject* self, FILE* fp, char* sep, char* format)
Write the contents of *self* to the file pointer *fp* in C-style
contiguous fashion. Write the data as binary bytes if *sep* is the
string ""or ``NULL``. Otherwise, write the contents of *self* as
text using the *sep* string as the item separator. Each item will
be printed to the file. If the *format* string is not ``NULL`` or
"", then it is a Python print statement format string showing how
the items are to be written.
.. c:function:: int PyArray_Dump(PyObject* self, PyObject* file, int protocol)
Pickle the object in *self* to the given *file* (either a string
or a Python file object). If *file* is a Python string it is
considered to be the name of a file which is then opened in binary
mode. The given *protocol* is used (if *protocol* is negative, or
the highest available is used). This is a simple wrapper around
cPickle.dump(*self*, *file*, *protocol*).
.. c:function:: PyObject* PyArray_Dumps(PyObject* self, int protocol)
Pickle the object in *self* to a Python string and return it. Use
the Pickle *protocol* provided (or the highest available if
*protocol* is negative).
.. c:function:: int PyArray_FillWithScalar(PyArrayObject* arr, PyObject* obj)
Fill the array, *arr*, with the given scalar object, *obj*. The
object is first converted to the data type of *arr*, and then
copied into every location. A -1 is returned if an error occurs,
otherwise 0 is returned.
.. c:function:: PyObject* PyArray_View( \
PyArrayObject* self, PyArray_Descr* dtype, PyTypeObject *ptype)
Equivalent to :meth:`ndarray.view<numpy.ndarray.view>` (*self*, *dtype*). Return a new
view of the array *self* as possibly a different data-type, *dtype*,
and different array subclass *ptype*.
If *dtype* is ``NULL``, then the returned array will have the same
data type as *self*. The new data-type must be consistent with the
size of *self*. Either the itemsizes must be identical, or *self* must
be single-segment and the total number of bytes must be the same.
In the latter case the dimensions of the returned array will be
altered in the last (or first for Fortran-style contiguous arrays)
dimension. The data area of the returned array and self is exactly
the same.
.. c:function:: PyObject* PyArray_Newshape( \
PyArrayObject* self, PyArray_Dims* newshape, NPY_ORDER order)
Result will be a new array (pointing to the same memory location
as *self* if possible), but having a shape given by *newshape*.
If the new shape is not compatible with the strides of *self*,
then a copy of the array with the new specified shape will be
returned.
.. c:function:: PyObject* PyArray_Reshape(PyArrayObject* self, PyObject* shape)
Equivalent to :meth:`ndarray.reshape<numpy.ndarray.reshape>` (*self*, *shape*) where *shape* is a
sequence. Converts *shape* to a :c:type:`PyArray_Dims` structure and
calls :c:func:`PyArray_Newshape` internally.
For back-ward compatibility -- Not recommended
.. c:function:: PyObject* PyArray_Squeeze(PyArrayObject* self)
Equivalent to :meth:`ndarray.squeeze<numpy.ndarray.squeeze>` (*self*). Return a new view of *self*
with all of the dimensions of length 1 removed from the shape.
Warning
matrix objects are always 2-dimensional. Therefore, :c:func:`PyArray_Squeeze` has no effect on arrays of matrix sub-class.
.. c:function:: PyObject* PyArray_SwapAxes(PyArrayObject* self, int a1, int a2)
Equivalent to :meth:`ndarray.swapaxes<numpy.ndarray.swapaxes>` (*self*, *a1*, *a2*). The returned
array is a new view of the data in *self* with the given axes,
*a1* and *a2*, swapped.
.. c:function:: PyObject* PyArray_Resize( \
PyArrayObject* self, PyArray_Dims* newshape, int refcheck, \
NPY_ORDER fortran)
Equivalent to :meth:`ndarray.resize<numpy.ndarray.resize>` (*self*, *newshape*, refcheck
``=`` *refcheck*, order= fortran ). This function only works on
single-segment arrays. It changes the shape of *self* inplace and
will reallocate the memory for *self* if *newshape* has a
different total number of elements then the old shape. If
reallocation is necessary, then *self* must own its data, have
*self* - ``>base==NULL``, have *self* - ``>weakrefs==NULL``, and
(unless refcheck is 0) not be referenced by any other array.
The fortran argument can be :c:data:`NPY_ANYORDER`, :c:data:`NPY_CORDER`,
or :c:data:`NPY_FORTRANORDER`. It currently has no effect. Eventually
it could be used to determine how the resize operation should view
the data when constructing a differently-dimensioned array.
Returns None on success and NULL on error.
.. c:function:: PyObject* PyArray_Transpose( \
PyArrayObject* self, PyArray_Dims* permute)
Equivalent to :meth:`ndarray.transpose<numpy.ndarray.transpose>` (*self*, *permute*). Permute the
axes of the ndarray object *self* according to the data structure
*permute* and return the result. If *permute* is ``NULL``, then
the resulting array has its axes reversed. For example if *self*
has shape :math:`10\times20\times30`, and *permute* ``.ptr`` is
(0,2,1) the shape of the result is :math:`10\times30\times20.` If
*permute* is ``NULL``, the shape of the result is
:math:`30\times20\times10.`
.. c:function:: PyObject* PyArray_Flatten(PyArrayObject* self, NPY_ORDER order)
Equivalent to :meth:`ndarray.flatten<numpy.ndarray.flatten>` (*self*, *order*). Return a 1-d copy
of the array. If *order* is :c:data:`NPY_FORTRANORDER` the elements are
scanned out in Fortran order (first-dimension varies the
fastest). If *order* is :c:data:`NPY_CORDER`, the elements of ``self``
are scanned in C-order (last dimension varies the fastest). If
*order* :c:data:`NPY_ANYORDER`, then the result of
:c:func:`PyArray_ISFORTRAN` (*self*) is used to determine which order
to flatten.
.. c:function:: PyObject* PyArray_Ravel(PyArrayObject* self, NPY_ORDER order)
Equivalent to *self*.ravel(*order*). Same basic functionality
as :c:func:`PyArray_Flatten` (*self*, *order*) except if *order* is 0
and *self* is C-style contiguous, the shape is altered but no copy
is performed.
.. c:function:: PyObject* PyArray_TakeFrom( \
PyArrayObject* self, PyObject* indices, int axis, PyArrayObject* ret, \
NPY_CLIPMODE clipmode)
Equivalent to :meth:`ndarray.take<numpy.ndarray.take>` (*self*, *indices*, *axis*, *ret*,
*clipmode*) except *axis* =None in Python is obtained by setting
*axis* = :c:data:`NPY_MAXDIMS` in C. Extract the items from self
indicated by the integer-valued *indices* along the given *axis.*
The clipmode argument can be :c:data:`NPY_RAISE`, :c:data:`NPY_WRAP`, or
:c:data:`NPY_CLIP` to indicate what to do with out-of-bound indices. The
*ret* argument can specify an output array rather than having one
created internally.
.. c:function:: PyObject* PyArray_PutTo( \
PyArrayObject* self, PyObject* values, PyObject* indices, \
NPY_CLIPMODE clipmode)
Equivalent to *self*.put(*values*, *indices*, *clipmode*
). Put *values* into *self* at the corresponding (flattened)
*indices*. If *values* is too small it will be repeated as
necessary.
.. c:function:: PyObject* PyArray_PutMask( \
PyArrayObject* self, PyObject* values, PyObject* mask)
Place the *values* in *self* wherever corresponding positions
(using a flattened context) in *mask* are true. The *mask* and
*self* arrays must have the same total number of elements. If
*values* is too small, it will be repeated as necessary.
.. c:function:: PyObject* PyArray_Repeat( \
PyArrayObject* self, PyObject* op, int axis)
Equivalent to :meth:`ndarray.repeat<numpy.ndarray.repeat>` (*self*, *op*, *axis*). Copy the
elements of *self*, *op* times along the given *axis*. Either
*op* is a scalar integer or a sequence of length *self*
->dimensions[ *axis* ] indicating how many times to repeat each
item along the axis.
.. c:function:: PyObject* PyArray_Choose( \
PyArrayObject* self, PyObject* op, PyArrayObject* ret, \
NPY_CLIPMODE clipmode)
Equivalent to :meth:`ndarray.choose<numpy.ndarray.choose>` (*self*, *op*, *ret*, *clipmode*).
Create a new array by selecting elements from the sequence of
arrays in *op* based on the integer values in *self*. The arrays
must all be broadcastable to the same shape and the entries in
*self* should be between 0 and len(*op*). The output is placed
in *ret* unless it is ``NULL`` in which case a new output is
created. The *clipmode* argument determines behavior for when
entries in *self* are not between 0 and len(*op*).
.. c:macro:: NPY_RAISE
raise a ValueError;
.. c:macro:: NPY_WRAP
wrap values < 0 by adding len(*op*) and values >=len(*op*)
by subtracting len(*op*) until they are in range;
.. c:macro:: NPY_CLIP
all values are clipped to the region [0, len(*op*) ).
.. c:function:: PyObject* PyArray_Sort(PyArrayObject* self, int axis, NPY_SORTKIND kind)
Equivalent to :meth:`ndarray.sort<numpy.ndarray.sort>` (*self*, *axis*, *kind*).
Return an array with the items of *self* sorted along *axis*. The array
is sorted using the algorithm denoted by *kind*, which is an integer/enum pointing
to the type of sorting algorithms used.
.. c:function:: PyObject* PyArray_ArgSort(PyArrayObject* self, int axis)
Equivalent to :meth:`ndarray.argsort<numpy.ndarray.argsort>` (*self*, *axis*).
Return an array of indices such that selection of these indices
along the given ``axis`` would return a sorted version of *self*. If *self* ->descr
is a data-type with fields defined, then self->descr->names is used
to determine the sort order. A comparison where the first field is equal
will use the second field and so on. To alter the sort order of a
structured array, create a new data-type with a different order of names
and construct a view of the array with that new data-type.
.. c:function:: PyObject* PyArray_LexSort(PyObject* sort_keys, int axis)
Given a sequence of arrays (*sort_keys*) of the same shape,
return an array of indices (similar to :c:func:`PyArray_ArgSort` (...))
that would sort the arrays lexicographically. A lexicographic sort
specifies that when two keys are found to be equal, the order is
based on comparison of subsequent keys. A merge sort (which leaves
equal entries unmoved) is required to be defined for the
types. The sort is accomplished by sorting the indices first using
the first *sort_key* and then using the second *sort_key* and so
forth. This is equivalent to the lexsort(*sort_keys*, *axis*)
Python command. Because of the way the merge-sort works, be sure
to understand the order the *sort_keys* must be in (reversed from
the order you would use when comparing two elements).
If these arrays are all collected in a structured array, then
:c:func:`PyArray_Sort` (...) can also be used to sort the array
directly.
.. c:function:: PyObject* PyArray_SearchSorted( \
PyArrayObject* self, PyObject* values, NPY_SEARCHSIDE side, \
PyObject* perm)
Equivalent to :meth:`ndarray.searchsorted<numpy.ndarray.searchsorted>` (*self*, *values*, *side*,
*perm*). Assuming *self* is a 1-d array in ascending order, then the
output is an array of indices the same shape as *values* such that, if
the elements in *values* were inserted before the indices, the order of
*self* would be preserved. No checking is done on whether or not self is
in ascending order.
The *side* argument indicates whether the index returned should be that of
the first suitable location (if :c:data:`NPY_SEARCHLEFT`) or of the last
(if :c:data:`NPY_SEARCHRIGHT`).
The *sorter* argument, if not ``NULL``, must be a 1D array of integer
indices the same length as *self*, that sorts it into ascending order.
This is typically the result of a call to :c:func:`PyArray_ArgSort` (...)
Binary search is used to find the required insertion points.
.. c:function:: int PyArray_Partition( \
PyArrayObject *self, PyArrayObject * ktharray, int axis, \
NPY_SELECTKIND which)
Equivalent to :meth:`ndarray.partition<numpy.ndarray.partition>` (*self*, *ktharray*, *axis*,
*kind*). Partitions the array so that the values of the element indexed by
*ktharray* are in the positions they would be if the array is fully sorted
and places all elements smaller than the kth before and all elements equal
or greater after the kth element. The ordering of all elements within the
partitions is undefined.
If *self*->descr is a data-type with fields defined, then
self->descr->names is used to determine the sort order. A comparison where
the first field is equal will use the second field and so on. To alter the
sort order of a structured array, create a new data-type with a different
order of names and construct a view of the array with that new data-type.
Returns zero on success and -1 on failure.
.. c:function:: PyObject* PyArray_ArgPartition( \
PyArrayObject *op, PyArrayObject * ktharray, int axis, \
NPY_SELECTKIND which)
Equivalent to :meth:`ndarray.argpartition<numpy.ndarray.argpartition>` (*self*, *ktharray*, *axis*,
*kind*). Return an array of indices such that selection of these indices
along the given ``axis`` would return a partitioned version of *self*.
.. c:function:: PyObject* PyArray_Diagonal( \
PyArrayObject* self, int offset, int axis1, int axis2)
Equivalent to :meth:`ndarray.diagonal<numpy.ndarray.diagonal>` (*self*, *offset*, *axis1*, *axis2*
). Return the *offset* diagonals of the 2-d arrays defined by
*axis1* and *axis2*.
.. c:function:: npy_intp PyArray_CountNonzero(PyArrayObject* self)
.. versionadded:: 1.6
Counts the number of non-zero elements in the array object *self*.
.. c:function:: PyObject* PyArray_Nonzero(PyArrayObject* self)
Equivalent to :meth:`ndarray.nonzero<numpy.ndarray.nonzero>` (*self*). Returns a tuple of index
arrays that select elements of *self* that are nonzero. If (nd=
:c:func:`PyArray_NDIM` ( ``self`` ))==1, then a single index array is
returned. The index arrays have data type :c:data:`NPY_INTP`. If a
tuple is returned (nd :math:`\neq` 1), then its length is nd.
.. c:function:: PyObject* PyArray_Compress( \
PyArrayObject* self, PyObject* condition, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.compress<numpy.ndarray.compress>` (*self*, *condition*, *axis*
). Return the elements along *axis* corresponding to elements of
*condition* that are true.
Tip
Pass in :c:data:`NPY_MAXDIMS` for axis in order to achieve the same
effect that is obtained by passing in axis=None in Python
(treating the array as a 1-d array).
Note
The out argument specifies where to place the result. If out is
NULL, then the output array is created, otherwise the output is
placed in out which must be the correct size and type. A new
reference to the output array is always returned even when out
is not NULL. The caller of the routine has the responsibility
to Py_DECREF out if not NULL or a memory-leak will occur.
.. c:function:: PyObject* PyArray_ArgMax( \
PyArrayObject* self, int axis, PyArrayObject* out, \
int keepdims)
Equivalent to :meth:`ndarray.argmax<numpy.ndarray.argmax>` (*self*, *axis*). Return the index of
the largest element of *self* along *axis*.
.. c:function:: PyObject* PyArray_ArgMin( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.argmin<numpy.ndarray.argmin>` (*self*, *axis*). Return the index of
the smallest element of *self* along *axis*.
.. c:function:: PyObject* PyArray_Max( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.max<numpy.ndarray.max>` (*self*, *axis*). Returns the largest
element of *self* along the given *axis*. When the result is a single
element, returns a numpy scalar instead of an ndarray.
.. c:function:: PyObject* PyArray_Min( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.min<numpy.ndarray.min>` (*self*, *axis*). Return the smallest
element of *self* along the given *axis*. When the result is a single
element, returns a numpy scalar instead of an ndarray.
.. c:function:: PyObject* PyArray_Ptp( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.ptp<numpy.ndarray.ptp>` (*self*, *axis*). Return the difference
between the largest element of *self* along *axis* and the
smallest element of *self* along *axis*. When the result is a single
element, returns a numpy scalar instead of an ndarray.
Note
The rtype argument specifies the data-type the reduction should take place over. This is important if the data-type of the array is not "large" enough to handle the output. By default, all integer data-types are made at least as large as :c:data:`NPY_LONG` for the "add" and "multiply" ufuncs (which form the basis for mean, sum, cumsum, prod, and cumprod functions).
.. c:function:: PyObject* PyArray_Mean( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.mean<numpy.ndarray.mean>` (*self*, *axis*, *rtype*). Returns the
mean of the elements along the given *axis*, using the enumerated
type *rtype* as the data type to sum in. Default sum behavior is
obtained using :c:data:`NPY_NOTYPE` for *rtype*.
.. c:function:: PyObject* PyArray_Trace( \
PyArrayObject* self, int offset, int axis1, int axis2, int rtype, \
PyArrayObject* out)
Equivalent to :meth:`ndarray.trace<numpy.ndarray.trace>` (*self*, *offset*, *axis1*, *axis2*,
*rtype*). Return the sum (using *rtype* as the data type of
summation) over the *offset* diagonal elements of the 2-d arrays
defined by *axis1* and *axis2* variables. A positive offset
chooses diagonals above the main diagonal. A negative offset
selects diagonals below the main diagonal.
.. c:function:: PyObject* PyArray_Clip( \
PyArrayObject* self, PyObject* min, PyObject* max)
Equivalent to :meth:`ndarray.clip<numpy.ndarray.clip>` (*self*, *min*, *max*). Clip an array,
*self*, so that values larger than *max* are fixed to *max* and
values less than *min* are fixed to *min*.
.. c:function:: PyObject* PyArray_Conjugate(PyArrayObject* self)
Equivalent to :meth:`ndarray.conjugate<numpy.ndarray.conjugate>` (*self*).
Return the complex conjugate of *self*. If *self* is not of
complex data type, then return *self* with a reference.
.. c:function:: PyObject* PyArray_Round( \
PyArrayObject* self, int decimals, PyArrayObject* out)
Equivalent to :meth:`ndarray.round<numpy.ndarray.round>` (*self*, *decimals*, *out*). Returns
the array with elements rounded to the nearest decimal place. The
decimal place is defined as the :math:`10^{-\textrm{decimals}}`
digit so that negative *decimals* cause rounding to the nearest 10's, 100's, etc. If out is ``NULL``, then the output array is created, otherwise the output is placed in *out* which must be the correct size and type.
.. c:function:: PyObject* PyArray_Std( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.std<numpy.ndarray.std>` (*self*, *axis*, *rtype*). Return the
standard deviation using data along *axis* converted to data type
*rtype*.
.. c:function:: PyObject* PyArray_Sum( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.sum<numpy.ndarray.sum>` (*self*, *axis*, *rtype*). Return 1-d
vector sums of elements in *self* along *axis*. Perform the sum
after converting data to data type *rtype*.
.. c:function:: PyObject* PyArray_CumSum( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.cumsum<numpy.ndarray.cumsum>` (*self*, *axis*, *rtype*). Return
cumulative 1-d sums of elements in *self* along *axis*. Perform
the sum after converting data to data type *rtype*.
.. c:function:: PyObject* PyArray_Prod( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.prod<numpy.ndarray.prod>` (*self*, *axis*, *rtype*). Return 1-d
products of elements in *self* along *axis*. Perform the product
after converting data to data type *rtype*.
.. c:function:: PyObject* PyArray_CumProd( \
PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
Equivalent to :meth:`ndarray.cumprod<numpy.ndarray.cumprod>` (*self*, *axis*, *rtype*). Return
1-d cumulative products of elements in ``self`` along ``axis``.
Perform the product after converting data to data type ``rtype``.
.. c:function:: PyObject* PyArray_All( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.all<numpy.ndarray.all>` (*self*, *axis*). Return an array with
True elements for every 1-d sub-array of ``self`` defined by
``axis`` in which all the elements are True.
.. c:function:: PyObject* PyArray_Any( \
PyArrayObject* self, int axis, PyArrayObject* out)
Equivalent to :meth:`ndarray.any<numpy.ndarray.any>` (*self*, *axis*). Return an array with
True elements for every 1-d sub-array of *self* defined by *axis*
in which any of the elements are True.
.. c:function:: int PyArray_AsCArray( \
PyObject** op, void* ptr, npy_intp* dims, int nd, int typenum, \
int itemsize)
Sometimes it is useful to access a multidimensional array as a
C-style multi-dimensional array so that algorithms can be
implemented using C's a[i][j][k] syntax. This routine returns a
pointer, *ptr*, that simulates this kind of C-style array, for
1-, 2-, and 3-d ndarrays.
:param op:
The address to any Python object. This Python object will be replaced
with an equivalent well-behaved, C-style contiguous, ndarray of the
given data type specified by the last two arguments. Be sure that
stealing a reference in this way to the input object is justified.
:param ptr:
The address to a (ctype* for 1-d, ctype** for 2-d or ctype*** for 3-d)
variable where ctype is the equivalent C-type for the data type. On
return, *ptr* will be addressable as a 1-d, 2-d, or 3-d array.
:param dims:
An output array that contains the shape of the array object. This
array gives boundaries on any looping that will take place.
:param nd:
The dimensionality of the array (1, 2, or 3).
:param typenum:
The expected data type of the array.
:param itemsize:
This argument is only needed when *typenum* represents a
flexible array. Otherwise it should be 0.
Note
The simulation of a C-style array is not complete for 2-d and 3-d arrays. For example, the simulated arrays of pointers cannot be passed to subroutines expecting specific, statically-defined 2-d and 3-d arrays. To pass to functions requiring those kind of inputs, you must statically define the required array and copy data.
.. c:function:: int PyArray_Free(PyObject* op, void* ptr)
Must be called with the same objects and memory locations returned
from :c:func:`PyArray_AsCArray` (...). This function cleans up memory
that otherwise would get leaked.
.. c:function:: PyObject* PyArray_Concatenate(PyObject* obj, int axis)
Join the sequence of objects in *obj* together along *axis* into a
single array. If the dimensions or types are not compatible an
error is raised.
.. c:function:: PyObject* PyArray_InnerProduct(PyObject* obj1, PyObject* obj2)
Compute a product-sum over the last dimensions of *obj1* and
*obj2*. Neither array is conjugated.
.. c:function:: PyObject* PyArray_MatrixProduct(PyObject* obj1, PyObject* obj)
Compute a product-sum over the last dimension of *obj1* and the
second-to-last dimension of *obj2*. For 2-d arrays this is a
matrix-product. Neither array is conjugated.
.. c:function:: PyObject* PyArray_MatrixProduct2( \
PyObject* obj1, PyObject* obj, PyArrayObject* out)
.. versionadded:: 1.6
Same as PyArray_MatrixProduct, but store the result in *out*. The
output array must have the correct shape, type, and be
C-contiguous, or an exception is raised.
.. c:function:: PyObject* PyArray_EinsteinSum( \
char* subscripts, npy_intp nop, PyArrayObject** op_in, \
PyArray_Descr* dtype, NPY_ORDER order, NPY_CASTING casting, \
PyArrayObject* out)
.. versionadded:: 1.6
Applies the Einstein summation convention to the array operands
provided, returning a new array or placing the result in *out*.
The string in *subscripts* is a comma separated list of index
letters. The number of operands is in *nop*, and *op_in* is an
array containing those operands. The data type of the output can
be forced with *dtype*, the output order can be forced with *order*
(:c:data:`NPY_KEEPORDER` is recommended), and when *dtype* is specified,
*casting* indicates how permissive the data conversion should be.
See the :func:`~numpy.einsum` function for more details.
.. c:function:: PyObject* PyArray_CopyAndTranspose(PyObject * op)
A specialized copy and transpose function that works only for 2-d
arrays. The returned array is a transposed copy of *op*.
.. c:function:: PyObject* PyArray_Correlate( \
PyObject* op1, PyObject* op2, int mode)
Compute the 1-d correlation of the 1-d arrays *op1* and *op2*
. The correlation is computed at each output point by multiplying
*op1* by a shifted version of *op2* and summing the result. As a
result of the shift, needed values outside of the defined range of
*op1* and *op2* are interpreted as zero. The mode determines how
many shifts to return: 0 - return only shifts that did not need to
assume zero- values; 1 - return an object that is the same size as
*op1*, 2 - return all possible shifts (any overlap at all is
accepted).
.. rubric:: Notes
This does not compute the usual correlation: if op2 is larger than op1, the
arguments are swapped, and the conjugate is never taken for complex arrays.
See PyArray_Correlate2 for the usual signal processing correlation.
.. c:function:: PyObject* PyArray_Correlate2( \
PyObject* op1, PyObject* op2, int mode)
Updated version of PyArray_Correlate, which uses the usual definition of
correlation for 1d arrays. The correlation is computed at each output point
by multiplying *op1* by a shifted version of *op2* and summing the result.
As a result of the shift, needed values outside of the defined range of
*op1* and *op2* are interpreted as zero. The mode determines how many
shifts to return: 0 - return only shifts that did not need to assume zero-
values; 1 - return an object that is the same size as *op1*, 2 - return all
possible shifts (any overlap at all is accepted).
.. rubric:: Notes
Compute z as follows::
z[k] = sum_n op1[n] * conj(op2[n+k])
.. c:function:: PyObject* PyArray_Where( \
PyObject* condition, PyObject* x, PyObject* y)
If both ``x`` and ``y`` are ``NULL``, then return
:c:func:`PyArray_Nonzero` (*condition*). Otherwise, both *x* and *y*
must be given and the object returned is shaped like *condition*
and has elements of *x* and *y* where *condition* is respectively
True or False.
.. c:function:: npy_bool PyArray_CheckStrides( \
int elsize, int nd, npy_intp numbytes, npy_intp const* dims, \
npy_intp const* newstrides)
Determine if *newstrides* is a strides array consistent with the
memory of an *nd* -dimensional array with shape ``dims`` and
element-size, *elsize*. The *newstrides* array is checked to see
if jumping by the provided number of bytes in each direction will
ever mean jumping more than *numbytes* which is the assumed size
of the available memory segment. If *numbytes* is 0, then an
equivalent *numbytes* is computed assuming *nd*, *dims*, and
*elsize* refer to a single-segment array. Return :c:data:`NPY_TRUE` if
*newstrides* is acceptable, otherwise return :c:data:`NPY_FALSE`.
.. c:function:: npy_intp PyArray_MultiplyList(npy_intp const* seq, int n)
.. c:function:: int PyArray_MultiplyIntList(int const* seq, int n)
Both of these routines multiply an *n* -length array, *seq*, of
integers and return the result. No overflow checking is performed.
.. c:function:: int PyArray_CompareLists(npy_intp const* l1, npy_intp const* l2, int n)
Given two *n* -length arrays of integers, *l1*, and *l2*, return
1 if the lists are identical; otherwise, return 0.
.. versionadded:: 1.7.0
.. c:type:: NpyAuxData
When working with more complex dtypes which are composed of other dtypes, such as the struct dtype, creating inner loops that manipulate the dtypes requires carrying along additional data. NumPy supports this idea through a struct :c:type:`NpyAuxData`, mandating a few conventions so that it is possible to do this.
Defining an :c:type:`NpyAuxData` is similar to defining a class in C++, but the object semantics have to be tracked manually since the API is in C. Here's an example for a function which doubles up an element using an element copier function as a primitive.
typedef struct {
NpyAuxData base;
ElementCopier_Func *func;
NpyAuxData *funcdata;
} eldoubler_aux_data;
void free_element_doubler_aux_data(NpyAuxData *data)
{
eldoubler_aux_data *d = (eldoubler_aux_data *)data;
/* Free the memory owned by this auxdata */
NPY_AUXDATA_FREE(d->funcdata);
PyArray_free(d);
}
NpyAuxData *clone_element_doubler_aux_data(NpyAuxData *data)
{
eldoubler_aux_data *ret = PyArray_malloc(sizeof(eldoubler_aux_data));
if (ret == NULL) {
return NULL;
}
/* Raw copy of all data */
memcpy(ret, data, sizeof(eldoubler_aux_data));
/* Fix up the owned auxdata so we have our own copy */
ret->funcdata = NPY_AUXDATA_CLONE(ret->funcdata);
if (ret->funcdata == NULL) {
PyArray_free(ret);
return NULL;
}
return (NpyAuxData *)ret;
}
NpyAuxData *create_element_doubler_aux_data(
ElementCopier_Func *func,
NpyAuxData *funcdata)
{
eldoubler_aux_data *ret = PyArray_malloc(sizeof(eldoubler_aux_data));
if (ret == NULL) {
PyErr_NoMemory();
return NULL;
}
memset(&ret, 0, sizeof(eldoubler_aux_data));
ret->base->free = &free_element_doubler_aux_data;
ret->base->clone = &clone_element_doubler_aux_data;
ret->func = func;
ret->funcdata = funcdata;
return (NpyAuxData *)ret;
}.. c:type:: NpyAuxData_FreeFunc
The function pointer type for NpyAuxData free functions.
.. c:type:: NpyAuxData_CloneFunc
The function pointer type for NpyAuxData clone functions. These
functions should never set the Python exception on error, because
they may be called from a multi-threaded context.
.. c:function:: void NPY_AUXDATA_FREE(NpyAuxData *auxdata)
A macro which calls the auxdata's free function appropriately,
does nothing if auxdata is NULL.
.. c:function:: NpyAuxData *NPY_AUXDATA_CLONE(NpyAuxData *auxdata)
A macro which calls the auxdata's clone function appropriately,
returning a deep copy of the auxiliary data.
As of NumPy 1.6.0, these array iterators are superseded by the new array iterator, :c:type:`NpyIter`.
An array iterator is a simple way to access the elements of an N-dimensional array quickly and efficiently. Section 2 provides more description and examples of this useful approach to looping over an array.
.. c:function:: PyObject* PyArray_IterNew(PyObject* arr)
Return an array iterator object from the array, *arr*. This is
equivalent to *arr*. **flat**. The array iterator object makes
it easy to loop over an N-dimensional non-contiguous array in
C-style contiguous fashion.
.. c:function:: PyObject* PyArray_IterAllButAxis(PyObject* arr, int* axis)
Return an array iterator that will iterate over all axes but the
one provided in *\*axis*. The returned iterator cannot be used
with :c:func:`PyArray_ITER_GOTO1D`. This iterator could be used to
write something similar to what ufuncs do wherein the loop over
the largest axis is done by a separate sub-routine. If *\*axis* is
negative then *\*axis* will be set to the axis having the smallest
stride and that axis will be used.
.. c:function:: PyObject *PyArray_BroadcastToShape( \
PyObject* arr, npy_intp const *dimensions, int nd)
Return an array iterator that is broadcast to iterate as an array
of the shape provided by *dimensions* and *nd*.
.. c:function:: int PyArrayIter_Check(PyObject* op)
Evaluates true if *op* is an array iterator (or instance of a
subclass of the array iterator type).
.. c:function:: void PyArray_ITER_RESET(PyObject* iterator)
Reset an *iterator* to the beginning of the array.
.. c:function:: void PyArray_ITER_NEXT(PyObject* iterator)
Incremement the index and the dataptr members of the *iterator* to
point to the next element of the array. If the array is not
(C-style) contiguous, also increment the N-dimensional coordinates
array.
.. c:function:: void *PyArray_ITER_DATA(PyObject* iterator)
A pointer to the current element of the array.
.. c:function:: void PyArray_ITER_GOTO( \
PyObject* iterator, npy_intp* destination)
Set the *iterator* index, dataptr, and coordinates members to the
location in the array indicated by the N-dimensional c-array,
*destination*, which must have size at least *iterator*
->nd_m1+1.
.. c:function:: void PyArray_ITER_GOTO1D(PyObject* iterator, npy_intp index)
Set the *iterator* index and dataptr to the location in the array
indicated by the integer *index* which points to an element in the
C-styled flattened array.
.. c:function:: int PyArray_ITER_NOTDONE(PyObject* iterator)
Evaluates TRUE as long as the iterator has not looped through all of
the elements, otherwise it evaluates FALSE.
.. c:function:: PyObject* PyArray_MultiIterNew(int num, ...)
A simplified interface to broadcasting. This function takes the
number of arrays to broadcast and then *num* extra ( :c:type:`PyObject *<PyObject>`
) arguments. These arguments are converted to arrays and iterators
are created. :c:func:`PyArray_Broadcast` is then called on the resulting
multi-iterator object. The resulting, broadcasted mult-iterator
object is then returned. A broadcasted operation can then be
performed using a single loop and using :c:func:`PyArray_MultiIter_NEXT`
(..)
.. c:function:: void PyArray_MultiIter_RESET(PyObject* multi)
Reset all the iterators to the beginning in a multi-iterator
object, *multi*.
.. c:function:: void PyArray_MultiIter_NEXT(PyObject* multi)
Advance each iterator in a multi-iterator object, *multi*, to its
next (broadcasted) element.
.. c:function:: void *PyArray_MultiIter_DATA(PyObject* multi, int i)
Return the data-pointer of the *i* :math:`^{\textrm{th}}` iterator
in a multi-iterator object.
.. c:function:: void PyArray_MultiIter_NEXTi(PyObject* multi, int i)
Advance the pointer of only the *i* :math:`^{\textrm{th}}` iterator.
.. c:function:: void PyArray_MultiIter_GOTO( \
PyObject* multi, npy_intp* destination)
Advance each iterator in a multi-iterator object, *multi*, to the
given :math:`N` -dimensional *destination* where :math:`N` is the
number of dimensions in the broadcasted array.
.. c:function:: void PyArray_MultiIter_GOTO1D(PyObject* multi, npy_intp index)
Advance each iterator in a multi-iterator object, *multi*, to the
corresponding location of the *index* into the flattened
broadcasted array.
.. c:function:: int PyArray_MultiIter_NOTDONE(PyObject* multi)
Evaluates TRUE as long as the multi-iterator has not looped
through all of the elements (of the broadcasted result), otherwise
it evaluates FALSE.
.. c:function:: int PyArray_Broadcast(PyArrayMultiIterObject* mit)
This function encapsulates the broadcasting rules. The *mit*
container should already contain iterators for all the arrays that
need to be broadcast. On return, these iterators will be adjusted
so that iteration over each simultaneously will accomplish the
broadcasting. A negative number is returned if an error occurs.
.. c:function:: int PyArray_RemoveSmallest(PyArrayMultiIterObject* mit)
This function takes a multi-iterator object that has been
previously "broadcasted," finds the dimension with the smallest
"sum of strides" in the broadcasted result and adapts all the
iterators so as not to iterate over that dimension (by effectively
making them of length-1 in that dimension). The corresponding
dimension is returned unless *mit* ->nd is 0, then -1 is
returned. This function is useful for constructing ufunc-like
routines that broadcast their inputs correctly and then call a
strided 1-d version of the routine as the inner-loop. This 1-d
version is usually optimized for speed and for this reason the
loop should be performed over the axis that won't require large
stride jumps.
.. versionadded:: 1.4.0
Neighborhood iterators are subclasses of the iterator object, and can be used to iter over a neighborhood of a point. For example, you may want to iterate over every voxel of a 3d image, and for every such voxel, iterate over an hypercube. Neighborhood iterator automatically handle boundaries, thus making this kind of code much easier to write than manual boundaries handling, at the cost of a slight overhead.
.. c:function:: PyObject* PyArray_NeighborhoodIterNew( \
PyArrayIterObject* iter, npy_intp bounds, int mode, \
PyArrayObject* fill_value)
This function creates a new neighborhood iterator from an existing
iterator. The neighborhood will be computed relatively to the position
currently pointed by *iter*, the bounds define the shape of the
neighborhood iterator, and the mode argument the boundaries handling mode.
The *bounds* argument is expected to be a (2 * iter->ao->nd) arrays, such
as the range bound[2*i]->bounds[2*i+1] defines the range where to walk for
dimension i (both bounds are included in the walked coordinates). The
bounds should be ordered for each dimension (bounds[2*i] <= bounds[2*i+1]).
The mode should be one of:
.. c:macro:: NPY_NEIGHBORHOOD_ITER_ZERO_PADDING
Zero padding. Outside bounds values will be 0.
.. c:macro:: NPY_NEIGHBORHOOD_ITER_ONE_PADDING
One padding, Outside bounds values will be 1.
.. c:macro:: NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING
Constant padding. Outside bounds values will be the
same as the first item in fill_value.
.. c:macro:: NPY_NEIGHBORHOOD_ITER_MIRROR_PADDING
Mirror padding. Outside bounds values will be as if the
array items were mirrored. For example, for the array [1, 2, 3, 4],
x[-2] will be 2, x[-2] will be 1, x[4] will be 4, x[5] will be 1,
etc...
.. c:macro:: NPY_NEIGHBORHOOD_ITER_CIRCULAR_PADDING
Circular padding. Outside bounds values will be as if the array
was repeated. For example, for the array [1, 2, 3, 4], x[-2] will
be 3, x[-2] will be 4, x[4] will be 1, x[5] will be 2, etc...
If the mode is constant filling (`NPY_NEIGHBORHOOD_ITER_CONSTANT_PADDING`),
fill_value should point to an array object which holds the filling value
(the first item will be the filling value if the array contains more than
one item). For other cases, fill_value may be NULL.
- The iterator holds a reference to iter
- Return NULL on failure (in which case the reference count of iter is not
changed)
- iter itself can be a Neighborhood iterator: this can be useful for .e.g
automatic boundaries handling
- the object returned by this function should be safe to use as a normal
iterator
- If the position of iter is changed, any subsequent call to
PyArrayNeighborhoodIter_Next is undefined behavior, and
PyArrayNeighborhoodIter_Reset must be called.
- If the position of iter is not the beginning of the data and the
underlying data for iter is contiguous, the iterator will point to the
start of the data instead of position pointed by iter.
To avoid this situation, iter should be moved to the required position
only after the creation of iterator, and PyArrayNeighborhoodIter_Reset
must be called.
.. code-block:: c
PyArrayIterObject *iter;
PyArrayNeighborhoodIterObject *neigh_iter;
iter = PyArray_IterNew(x);
/*For a 3x3 kernel */
bounds = {-1, 1, -1, 1};
neigh_iter = (PyArrayNeighborhoodIterObject*)PyArray_NeighborhoodIterNew(
iter, bounds, NPY_NEIGHBORHOOD_ITER_ZERO_PADDING, NULL);
for(i = 0; i < iter->size; ++i) {
for (j = 0; j < neigh_iter->size; ++j) {
/* Walk around the item currently pointed by iter->dataptr */
PyArrayNeighborhoodIter_Next(neigh_iter);
}
/* Move to the next point of iter */
PyArrayIter_Next(iter);
PyArrayNeighborhoodIter_Reset(neigh_iter);
}
.. c:function:: int PyArrayNeighborhoodIter_Reset( \
PyArrayNeighborhoodIterObject* iter)
Reset the iterator position to the first point of the neighborhood. This
should be called whenever the iter argument given at
PyArray_NeighborhoodIterObject is changed (see example)
.. c:function:: int PyArrayNeighborhoodIter_Next( \
PyArrayNeighborhoodIterObject* iter)
After this call, iter->dataptr points to the next point of the
neighborhood. Calling this function after every point of the
neighborhood has been visited is undefined.
.. c:function:: PyObject* PyArray_Return(PyArrayObject* arr)
This function steals a reference to *arr*.
This function checks to see if *arr* is a 0-dimensional array and,
if so, returns the appropriate array scalar. It should be used
whenever 0-dimensional arrays could be returned to Python.
.. c:function:: PyObject* PyArray_Scalar( \
void* data, PyArray_Descr* dtype, PyObject* itemsize)
Return an array scalar object of the given enumerated *typenum*
and *itemsize* by **copying** from memory pointed to by *data*
. If *swap* is nonzero then this function will byteswap the data
if appropriate to the data-type because array scalars are always
in correct machine-byte order.
.. c:function:: PyObject* PyArray_ToScalar(void* data, PyArrayObject* arr)
Return an array scalar object of the type and itemsize indicated
by the array object *arr* copied from the memory pointed to by
*data* and swapping if the data in *arr* is not in machine
byte-order.
.. c:function:: PyObject* PyArray_FromScalar( \
PyObject* scalar, PyArray_Descr* outcode)
Return a 0-dimensional array of type determined by *outcode* from
*scalar* which should be an array-scalar object. If *outcode* is
NULL, then the type is determined from *scalar*.
.. c:function:: void PyArray_ScalarAsCtype(PyObject* scalar, void* ctypeptr)
Return in *ctypeptr* a pointer to the actual value in an array
scalar. There is no error checking so *scalar* must be an
array-scalar object, and ctypeptr must have enough space to hold
the correct type. For flexible-sized types, a pointer to the data
is copied into the memory of *ctypeptr*, for all other types, the
actual data is copied into the address pointed to by *ctypeptr*.
.. c:function:: void PyArray_CastScalarToCtype( \
PyObject* scalar, void* ctypeptr, PyArray_Descr* outcode)
Return the data (cast to the data type indicated by *outcode*)
from the array-scalar, *scalar*, into the memory pointed to by
*ctypeptr* (which must be large enough to handle the incoming
memory).
.. c:function:: PyObject* PyArray_TypeObjectFromType(int type)
Returns a scalar type-object from a type-number, *type*
. Equivalent to :c:func:`PyArray_DescrFromType` (*type*)->typeobj
except for reference counting and error-checking. Returns a new
reference to the typeobject on success or ``NULL`` on failure.
.. c:function:: NPY_SCALARKIND PyArray_ScalarKind( \
int typenum, PyArrayObject** arr)
See the function :c:func:`PyArray_MinScalarType` for an alternative
mechanism introduced in NumPy 1.6.0.
Return the kind of scalar represented by *typenum* and the array
in *\*arr* (if *arr* is not ``NULL`` ). The array is assumed to be
rank-0 and only used if *typenum* represents a signed integer. If
*arr* is not ``NULL`` and the first element is negative then
:c:data:`NPY_INTNEG_SCALAR` is returned, otherwise
:c:data:`NPY_INTPOS_SCALAR` is returned. The possible return values
are the enumerated values in :c:type:`NPY_SCALARKIND`.
.. c:function:: int PyArray_CanCoerceScalar( \
char thistype, char neededtype, NPY_SCALARKIND scalar)
See the function :c:func:`PyArray_ResultType` for details of
NumPy type promotion, updated in NumPy 1.6.0.
Implements the rules for scalar coercion. Scalars are only
silently coerced from thistype to neededtype if this function
returns nonzero. If scalar is :c:data:`NPY_NOSCALAR`, then this
function is equivalent to :c:func:`PyArray_CanCastSafely`. The rule is
that scalars of the same KIND can be coerced into arrays of the
same KIND. This rule means that high-precision scalars will never
cause low-precision arrays of the same KIND to be upcast.
Warning
Data-type objects must be reference counted so be aware of the action on the data-type reference of different C-API calls. The standard rule is that when a data-type object is returned it is a new reference. Functions that take :c:expr:`PyArray_Descr *` objects and return arrays steal references to the data-type their inputs unless otherwise noted. Therefore, you must own a reference to any data-type object used as input to such a function.
.. c:function:: int PyArray_DescrCheck(PyObject* obj)
Evaluates as true if *obj* is a data-type object ( :c:expr:`PyArray_Descr *` ).
.. c:function:: PyArray_Descr* PyArray_DescrNew(PyArray_Descr* obj)
Return a new data-type object copied from *obj* (the fields
reference is just updated so that the new object points to the
same fields dictionary if any).
.. c:function:: PyArray_Descr* PyArray_DescrNewFromType(int typenum)
Create a new data-type object from the built-in (or
user-registered) data-type indicated by *typenum*. All builtin
types should not have any of their fields changed. This creates a
new copy of the :c:type:`PyArray_Descr` structure so that you can fill
it in as appropriate. This function is especially needed for
flexible data-types which need to have a new elsize member in
order to be meaningful in array construction.
.. c:function:: PyArray_Descr* PyArray_DescrNewByteorder( \
PyArray_Descr* obj, char newendian)
Create a new data-type object with the byteorder set according to
*newendian*. All referenced data-type objects (in subdescr and
fields members of the data-type object) are also changed
(recursively).
The value of *newendian* is one of these macros:
.. c:macro:: NPY_IGNORE
NPY_SWAP
NPY_NATIVE
NPY_LITTLE
NPY_BIG
If a byteorder of :c:data:`NPY_IGNORE` is encountered it
is left alone. If newendian is :c:data:`NPY_SWAP`, then all byte-orders
are swapped. Other valid newendian values are :c:data:`NPY_NATIVE`,
:c:data:`NPY_LITTLE`, and :c:data:`NPY_BIG` which all cause
the returned data-typed descriptor (and all it's
referenced data-type descriptors) to have the corresponding byte-
order.
.. c:function:: PyArray_Descr* PyArray_DescrFromObject( \
PyObject* op, PyArray_Descr* mintype)
Determine an appropriate data-type object from the object *op*
(which should be a "nested" sequence object) and the minimum
data-type descriptor mintype (which can be ``NULL`` ). Similar in
behavior to array(*op*).dtype. Don't confuse this function with
:c:func:`PyArray_DescrConverter`. This function essentially looks at
all the objects in the (nested) sequence and determines the
data-type from the elements it finds.
.. c:function:: PyArray_Descr* PyArray_DescrFromScalar(PyObject* scalar)
Return a data-type object from an array-scalar object. No checking
is done to be sure that *scalar* is an array scalar. If no
suitable data-type can be determined, then a data-type of
:c:data:`NPY_OBJECT` is returned by default.
.. c:function:: PyArray_Descr* PyArray_DescrFromType(int typenum)
Returns a data-type object corresponding to *typenum*. The
*typenum* can be one of the enumerated types, a character code for
one of the enumerated types, or a user-defined type. If you want to use a
flexible size array, then you need to ``flexible typenum`` and set the
results ``elsize`` parameter to the desired size. The typenum is one of the
:c:data:`NPY_TYPES`.
.. c:function:: int PyArray_DescrConverter(PyObject* obj, PyArray_Descr** dtype)
Convert any compatible Python object, *obj*, to a data-type object
in *dtype*. A large number of Python objects can be converted to
data-type objects. See :ref:`arrays.dtypes` for a complete
description. This version of the converter converts None objects
to a :c:data:`NPY_DEFAULT_TYPE` data-type object. This function can
be used with the "O&" character code in :c:func:`PyArg_ParseTuple`
processing.
.. c:function:: int PyArray_DescrConverter2( \
PyObject* obj, PyArray_Descr** dtype)
Convert any compatible Python object, *obj*, to a data-type
object in *dtype*. This version of the converter converts None
objects so that the returned data-type is ``NULL``. This function
can also be used with the "O&" character in PyArg_ParseTuple
processing.
.. c:function:: int Pyarray_DescrAlignConverter( \
PyObject* obj, PyArray_Descr** dtype)
Like :c:func:`PyArray_DescrConverter` except it aligns C-struct-like
objects on word-boundaries as the compiler would.
.. c:function:: int Pyarray_DescrAlignConverter2( \
PyObject* obj, PyArray_Descr** dtype)
Like :c:func:`PyArray_DescrConverter2` except it aligns C-struct-like
objects on word-boundaries as the compiler would.
.. c:function:: PyObject *PyArray_FieldNames(PyObject* dict)
Take the fields dictionary, *dict*, such as the one attached to a
data-type object and construct an ordered-list of field names such
as is stored in the names field of the :c:type:`PyArray_Descr` object.
For use with :c:func:`PyArg_ParseTuple`
All of these functions can be used in :c:func:`PyArg_ParseTuple` (...) with the "O&" format specifier to automatically convert any Python object to the required C-object. All of these functions return :c:data:`NPY_SUCCEED` if successful and :c:data:`NPY_FAIL` if not. The first argument to all of these function is a Python object. The second argument is the address of the C-type to convert the Python object to.
Warning
Be sure to understand what steps you should take to manage the memory when using these conversion functions. These functions can require freeing memory, and/or altering the reference counts of specific objects based on your use.
.. c:function:: int PyArray_Converter(PyObject* obj, PyObject** address)
Convert any Python object to a :c:type:`PyArrayObject`. If
:c:func:`PyArray_Check` (*obj*) is TRUE then its reference count is
incremented and a reference placed in *address*. If *obj* is not
an array, then convert it to an array using :c:func:`PyArray_FromAny`
. No matter what is returned, you must DECREF the object returned
by this routine in *address* when you are done with it.
.. c:function:: int PyArray_OutputConverter( \
PyObject* obj, PyArrayObject** address)
This is a default converter for output arrays given to
functions. If *obj* is :c:data:`Py_None` or ``NULL``, then *\*address*
will be ``NULL`` but the call will succeed. If :c:func:`PyArray_Check` (
*obj*) is TRUE then it is returned in *\*address* without
incrementing its reference count.
.. c:function:: int PyArray_IntpConverter(PyObject* obj, PyArray_Dims* seq)
Convert any Python sequence, *obj*, smaller than :c:data:`NPY_MAXDIMS`
to a C-array of :c:type:`npy_intp`. The Python object could also be a
single number. The *seq* variable is a pointer to a structure with
members ptr and len. On successful return, *seq* ->ptr contains a
pointer to memory that must be freed, by calling :c:func:`PyDimMem_FREE`,
to avoid a memory leak. The restriction on memory size allows this
converter to be conveniently used for sequences intended to be
interpreted as array shapes.
.. c:function:: int PyArray_BufferConverter(PyObject* obj, PyArray_Chunk* buf)
Convert any Python object, *obj*, with a (single-segment) buffer
interface to a variable with members that detail the object's use
of its chunk of memory. The *buf* variable is a pointer to a
structure with base, ptr, len, and flags members. The
:c:type:`PyArray_Chunk` structure is binary compatible with the
Python's buffer object (through its len member on 32-bit platforms
and its ptr member on 64-bit platforms or in Python 2.5). On
return, the base member is set to *obj* (or its base if *obj* is
already a buffer object pointing to another object). If you need
to hold on to the memory be sure to INCREF the base member. The
chunk of memory is pointed to by *buf* ->ptr member and has length
*buf* ->len. The flags member of *buf* is :c:data:`NPY_ARRAY_ALIGNED`
with the :c:data:`NPY_ARRAY_WRITEABLE` flag set if *obj* has
a writeable buffer interface.
.. c:function:: int PyArray_AxisConverter(PyObject* obj, int* axis)
Convert a Python object, *obj*, representing an axis argument to
the proper value for passing to the functions that take an integer
axis. Specifically, if *obj* is None, *axis* is set to
:c:data:`NPY_MAXDIMS` which is interpreted correctly by the C-API
functions that take axis arguments.
.. c:function:: int PyArray_BoolConverter(PyObject* obj, npy_bool* value)
Convert any Python object, *obj*, to :c:data:`NPY_TRUE` or
:c:data:`NPY_FALSE`, and place the result in *value*.
.. c:function:: int PyArray_ByteorderConverter(PyObject* obj, char* endian)
Convert Python strings into the corresponding byte-order
character:
'>', '<', 's', '=', or '\|'.
.. c:function:: int PyArray_SortkindConverter(PyObject* obj, NPY_SORTKIND* sort)
Convert Python strings into one of :c:data:`NPY_QUICKSORT` (starts
with 'q' or 'Q'), :c:data:`NPY_HEAPSORT` (starts with 'h' or 'H'),
:c:data:`NPY_MERGESORT` (starts with 'm' or 'M') or :c:data:`NPY_STABLESORT`
(starts with 't' or 'T'). :c:data:`NPY_MERGESORT` and :c:data:`NPY_STABLESORT`
are aliased to each other for backwards compatibility and may refer to one
of several stable sorting algorithms depending on the data type.
.. c:function:: int PyArray_SearchsideConverter( \
PyObject* obj, NPY_SEARCHSIDE* side)
Convert Python strings into one of :c:data:`NPY_SEARCHLEFT` (starts with 'l'
or 'L'), or :c:data:`NPY_SEARCHRIGHT` (starts with 'r' or 'R').
.. c:function:: int PyArray_OrderConverter(PyObject* obj, NPY_ORDER* order) Convert the Python strings 'C', 'F', 'A', and 'K' into the :c:type:`NPY_ORDER` enumeration :c:data:`NPY_CORDER`, :c:data:`NPY_FORTRANORDER`, :c:data:`NPY_ANYORDER`, and :c:data:`NPY_KEEPORDER`.
.. c:function:: int PyArray_CastingConverter( \
PyObject* obj, NPY_CASTING* casting)
Convert the Python strings 'no', 'equiv', 'safe', 'same_kind', and
'unsafe' into the :c:type:`NPY_CASTING` enumeration :c:data:`NPY_NO_CASTING`,
:c:data:`NPY_EQUIV_CASTING`, :c:data:`NPY_SAFE_CASTING`,
:c:data:`NPY_SAME_KIND_CASTING`, and :c:data:`NPY_UNSAFE_CASTING`.
.. c:function:: int PyArray_ClipmodeConverter( \
PyObject* object, NPY_CLIPMODE* val)
Convert the Python strings 'clip', 'wrap', and 'raise' into the
:c:type:`NPY_CLIPMODE` enumeration :c:data:`NPY_CLIP`, :c:data:`NPY_WRAP`,
and :c:data:`NPY_RAISE`.
.. c:function:: int PyArray_ConvertClipmodeSequence( \
PyObject* object, NPY_CLIPMODE* modes, int n)
Converts either a sequence of clipmodes or a single clipmode into
a C array of :c:type:`NPY_CLIPMODE` values. The number of clipmodes *n*
must be known before calling this function. This function is provided
to help functions allow a different clipmode for each dimension.
.. c:function:: int PyArray_PyIntAsInt(PyObject* op)
Convert all kinds of Python objects (including arrays and array
scalars) to a standard integer. On error, -1 is returned and an
exception set. You may find useful the macro:
.. code-block:: c
#define error_converting(x) (((x) == -1) && PyErr_Occurred())
.. c:function:: npy_intp PyArray_PyIntAsIntp(PyObject* op)
Convert all kinds of Python objects (including arrays and array
scalars) to a (platform-pointer-sized) integer. On error, -1 is
returned and an exception set.
.. c:function:: int PyArray_IntpFromSequence( \
PyObject* seq, npy_intp* vals, int maxvals)
Convert any Python sequence (or single Python number) passed in as
*seq* to (up to) *maxvals* pointer-sized integers and place them
in the *vals* array. The sequence can be smaller then *maxvals* as
the number of converted objects is returned.
.. c:function:: int PyArray_TypestrConvert(int itemsize, int gentype)
Convert typestring characters (with *itemsize*) to basic
enumerated data types. The typestring character corresponding to
signed and unsigned integers, floating point numbers, and
complex-floating point numbers are recognized and converted. Other
values of gentype are returned. This function can be used to
convert, for example, the string 'f4' to :c:data:`NPY_FLOAT32`.
In order to make use of the C-API from another extension module, the :c:func:`import_array` function must be called. If the extension module is self-contained in a single .c file, then that is all that needs to be done. If, however, the extension module involves multiple files where the C-API is needed then some additional steps must be taken.
.. c:function:: void import_array(void)
This function must be called in the initialization section of a
module that will make use of the C-API. It imports the module
where the function-pointer table is stored and points the correct
variable to it.
.. c:macro:: PY_ARRAY_UNIQUE_SYMBOL
.. c:macro:: NO_IMPORT_ARRAY
Using these #defines you can use the C-API in multiple files for a
single extension module. In each file you must define
:c:macro:`PY_ARRAY_UNIQUE_SYMBOL` to some name that will hold the
C-API (*e.g.* myextension_ARRAY_API). This must be done **before**
including the numpy/arrayobject.h file. In the module
initialization routine you call :c:func:`import_array`. In addition,
in the files that do not have the module initialization
sub_routine define :c:macro:`NO_IMPORT_ARRAY` prior to including
numpy/arrayobject.h.
Suppose I have two files coolmodule.c and coolhelper.c which need
to be compiled and linked into a single extension module. Suppose
coolmodule.c contains the required initcool module initialization
function (with the import_array() function called). Then,
coolmodule.c would have at the top:
.. code-block:: c
#define PY_ARRAY_UNIQUE_SYMBOL cool_ARRAY_API
#include numpy/arrayobject.h
On the other hand, coolhelper.c would contain at the top:
.. code-block:: c
#define NO_IMPORT_ARRAY
#define PY_ARRAY_UNIQUE_SYMBOL cool_ARRAY_API
#include numpy/arrayobject.h
You can also put the common two last lines into an extension-local
header file as long as you make sure that NO_IMPORT_ARRAY is
#defined before #including that file.
Internally, these #defines work as follows:
* If neither is defined, the C-API is declared to be
``static void**``, so it is only visible within the
compilation unit that #includes numpy/arrayobject.h.
* If :c:macro:`PY_ARRAY_UNIQUE_SYMBOL` is #defined, but
:c:macro:`NO_IMPORT_ARRAY` is not, the C-API is declared to
be ``void**``, so that it will also be visible to other
compilation units.
* If :c:macro:`NO_IMPORT_ARRAY` is #defined, regardless of
whether :c:macro:`PY_ARRAY_UNIQUE_SYMBOL` is, the C-API is
declared to be ``extern void**``, so it is expected to
be defined in another compilation unit.
* Whenever :c:macro:`PY_ARRAY_UNIQUE_SYMBOL` is #defined, it
also changes the name of the variable holding the C-API, which
defaults to ``PyArray_API``, to whatever the macro is
#defined to.
Because python extensions are not used in the same way as usual libraries on most platforms, some errors cannot be automatically detected at build time or even runtime. For example, if you build an extension using a function available only for numpy >= 1.3.0, and you import the extension later with numpy 1.2, you will not get an import error (but almost certainly a segmentation fault when calling the function). That's why several functions are provided to check for numpy versions. The macros :c:data:`NPY_VERSION` and :c:data:`NPY_FEATURE_VERSION` corresponds to the numpy version used to build the extension, whereas the versions returned by the functions :c:func:`PyArray_GetNDArrayCVersion` and :c:func:`PyArray_GetNDArrayCFeatureVersion` corresponds to the runtime numpy's version.
The rules for ABI and API compatibilities can be summarized as follows:
- Whenever :c:data:`NPY_VERSION` !=
PyArray_GetNDArrayCVersion(), the extension has to be recompiled (ABI incompatibility).- :c:data:`NPY_VERSION` ==
PyArray_GetNDArrayCVersion()and :c:data:`NPY_FEATURE_VERSION` <=PyArray_GetNDArrayCFeatureVersion()means backward compatible changes.
ABI incompatibility is automatically detected in every numpy's version. API incompatibility detection was added in numpy 1.4.0. If you want to supported many different numpy versions with one extension binary, you have to build your extension with the lowest :c:data:`NPY_FEATURE_VERSION` as possible.
.. c:macro:: NPY_VERSION
The current version of the ndarray object (check to see if this
variable is defined to guarantee the ``numpy/arrayobject.h`` header is
being used).
.. c:macro:: NPY_FEATURE_VERSION
The current version of the C-API.
.. c:function:: unsigned int PyArray_GetNDArrayCVersion(void)
This just returns the value :c:data:`NPY_VERSION`. :c:data:`NPY_VERSION`
changes whenever a backward incompatible change at the ABI level. Because
it is in the C-API, however, comparing the output of this function from the
value defined in the current header gives a way to test if the C-API has
changed thus requiring a re-compilation of extension modules that use the
C-API. This is automatically checked in the function :c:func:`import_array`.
.. c:function:: unsigned int PyArray_GetNDArrayCFeatureVersion(void)
.. versionadded:: 1.4.0
This just returns the value :c:data:`NPY_FEATURE_VERSION`.
:c:data:`NPY_FEATURE_VERSION` changes whenever the API changes (e.g. a
function is added). A changed value does not always require a recompile.
.. c:function:: int PyArray_SetNumericOps(PyObject* dict)
NumPy stores an internal table of Python callable objects that are
used to implement arithmetic operations for arrays as well as
certain array calculation methods. This function allows the user
to replace any or all of these Python objects with their own
versions. The keys of the dictionary, *dict*, are the named
functions to replace and the paired value is the Python callable
object to use. Care should be taken that the function used to
replace an internal array operation does not itself call back to
that internal array operation (unless you have designed the
function to handle that), or an unchecked infinite recursion can
result (possibly causing program crash). The key names that
represent operations that can be replaced are:
**add**, **subtract**, **multiply**, **divide**,
**remainder**, **power**, **square**, **reciprocal**,
**ones_like**, **sqrt**, **negative**, **positive**,
**absolute**, **invert**, **left_shift**, **right_shift**,
**bitwise_and**, **bitwise_xor**, **bitwise_or**,
**less**, **less_equal**, **equal**, **not_equal**,
**greater**, **greater_equal**, **floor_divide**,
**true_divide**, **logical_or**, **logical_and**,
**floor**, **ceil**, **maximum**, **minimum**, **rint**.
These functions are included here because they are used at least once
in the array object's methods. The function returns -1 (without
setting a Python Error) if one of the objects being assigned is not
callable.
.. deprecated:: 1.16
.. c:function:: PyObject* PyArray_GetNumericOps(void)
Return a Python dictionary containing the callable Python objects
stored in the internal arithmetic operation table. The keys of
this dictionary are given in the explanation for :c:func:`PyArray_SetNumericOps`.
.. deprecated:: 1.16
.. c:function:: void PyArray_SetStringFunction(PyObject* op, int repr)
This function allows you to alter the tp_str and tp_repr methods
of the array object to any Python function. Thus you can alter
what happens for all arrays when str(arr) or repr(arr) is called
from Python. The function to be called is passed in as *op*. If
*repr* is non-zero, then this function will be called in response
to repr(arr), otherwise the function will be called in response to
str(arr). No check on whether or not *op* is callable is
performed. The callable passed in to *op* should expect an array
argument and should return a string to be printed.
.. c:function:: char* PyDataMem_NEW(size_t nbytes)
.. c:function:: void PyDataMem_FREE(char* ptr)
.. c:function:: char* PyDataMem_RENEW(void * ptr, size_t newbytes)
Macros to allocate, free, and reallocate memory. These macros are used
internally to create arrays.
.. c:function:: npy_intp* PyDimMem_NEW(int nd)
.. c:function:: void PyDimMem_FREE(char* ptr)
.. c:function:: npy_intp* PyDimMem_RENEW(void* ptr, size_t newnd)
Macros to allocate, free, and reallocate dimension and strides memory.
.. c:function:: void* PyArray_malloc(size_t nbytes)
.. c:function:: void PyArray_free(void* ptr)
.. c:function:: void* PyArray_realloc(npy_intp* ptr, size_t nbytes)
These macros use different memory allocators, depending on the
constant :c:data:`NPY_USE_PYMEM`. The system malloc is used when
:c:data:`NPY_USE_PYMEM` is 0, if :c:data:`NPY_USE_PYMEM` is 1, then
the Python memory allocator is used.
.. c:macro:: NPY_USE_PYMEM
.. c:function:: int PyArray_ResolveWritebackIfCopy(PyArrayObject* obj)
If ``obj.flags`` has :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` or (deprecated)
:c:data:`NPY_ARRAY_UPDATEIFCOPY`, this function clears the flags, `DECREF` s
`obj->base` and makes it writeable, and sets ``obj->base`` to NULL. It then
copies ``obj->data`` to `obj->base->data`, and returns the error state of
the copy operation. This is the opposite of
:c:func:`PyArray_SetWritebackIfCopyBase`. Usually this is called once
you are finished with ``obj``, just before ``Py_DECREF(obj)``. It may be called
multiple times, or with ``NULL`` input. See also
:c:func:`PyArray_DiscardWritebackIfCopy`.
Returns 0 if nothing was done, -1 on error, and 1 if action was taken.
These macros are only meaningful if :c:data:`NPY_ALLOW_THREADS`
evaluates True during compilation of the extension module. Otherwise,
these macros are equivalent to whitespace. Python uses a single Global
Interpreter Lock (GIL) for each Python process so that only a single
thread may execute at a time (even on multi-cpu machines). When
calling out to a compiled function that may take time to compute (and
does not have side-effects for other threads like updated global
variables), the GIL should be released so that other Python threads
can run while the time-consuming calculations are performed. This can
be accomplished using two groups of macros. Typically, if one macro in
a group is used in a code block, all of them must be used in the same
code block. Currently, :c:data:`NPY_ALLOW_THREADS` is defined to the
python-defined :c:data:`WITH_THREADS` constant unless the environment
variable NPY_NOSMP is set in which case
:c:data:`NPY_ALLOW_THREADS` is defined to be 0.
.. c:macro:: NPY_ALLOW_THREADS
.. c:macro:: WITH_THREADS
This group is used to call code that may take some time but does not use any Python C-API calls. Thus, the GIL should be released during its calculation.
.. c:macro:: NPY_BEGIN_ALLOW_THREADS Equivalent to :c:macro:`Py_BEGIN_ALLOW_THREADS` except it uses :c:data:`NPY_ALLOW_THREADS` to determine if the macro if replaced with white-space or not... c:macro:: NPY_END_ALLOW_THREADS Equivalent to :c:macro:`Py_END_ALLOW_THREADS` except it uses :c:data:`NPY_ALLOW_THREADS` to determine if the macro if replaced with white-space or not... c:macro:: NPY_BEGIN_THREADS_DEF Place in the variable declaration area. This macro sets up the variable needed for storing the Python state... c:macro:: NPY_BEGIN_THREADS Place right before code that does not need the Python interpreter (no Python C-API calls). This macro saves the Python state and releases the GIL... c:macro:: NPY_END_THREADS Place right after code that does not need the Python interpreter. This macro acquires the GIL and restores the Python state from the saved variable... c:function:: void NPY_BEGIN_THREADS_DESCR(PyArray_Descr *dtype) Useful to release the GIL only if *dtype* does not contain arbitrary Python objects which may need the Python interpreter during execution of the loop... c:function:: void NPY_END_THREADS_DESCR(PyArray_Descr *dtype) Useful to regain the GIL in situations where it was released using the BEGIN form of this macro... c:function:: void NPY_BEGIN_THREADS_THRESHOLDED(int loop_size) Useful to release the GIL only if *loop_size* exceeds a minimum threshold, currently set to 500. Should be matched with a :c:macro:`NPY_END_THREADS` to regain the GIL.
This group is used to re-acquire the Python GIL after it has been released. For example, suppose the GIL has been released (using the previous calls), and then some path in the code (perhaps in a different subroutine) requires use of the Python C-API, then these macros are useful to acquire the GIL. These macros accomplish essentially a reverse of the previous three (acquire the LOCK saving what state it had) and then re-release it with the saved state.
.. c:macro:: NPY_ALLOW_C_API_DEF Place in the variable declaration area to set up the necessary variable... c:macro:: NPY_ALLOW_C_API Place before code that needs to call the Python C-API (when it is known that the GIL has already been released)... c:macro:: NPY_DISABLE_C_API Place after code that needs to call the Python C-API (to re-release the GIL).
Tip
Never use semicolons after the threading support macros.
.. c:macro:: NPY_PRIORITY
Default priority for arrays.
.. c:macro:: NPY_SUBTYPE_PRIORITY
Default subtype priority.
.. c:macro:: NPY_SCALAR_PRIORITY
Default scalar priority (very small)
.. c:function:: double PyArray_GetPriority(PyObject* obj, double def)
Return the :obj:`~numpy.class.__array_priority__` attribute (converted to a
double) of *obj* or *def* if no attribute of that name
exists. Fast returns that avoid the attribute lookup are provided
for objects of type :c:data:`PyArray_Type`.
.. c:macro:: NPY_BUFSIZE
Default size of the user-settable internal buffers.
.. c:macro:: NPY_MIN_BUFSIZE
Smallest size of user-settable internal buffers.
.. c:macro:: NPY_MAX_BUFSIZE
Largest size allowed for the user-settable buffers.
.. c:macro:: NPY_NUM_FLOATTYPE
The number of floating-point types
.. c:macro:: NPY_MAXDIMS
The maximum number of dimensions allowed in arrays.
.. c:macro:: NPY_MAXARGS
The maximum number of array arguments that can be used in functions.
.. c:macro:: NPY_FALSE
Defined as 0 for use with Bool.
.. c:macro:: NPY_TRUE
Defined as 1 for use with Bool.
.. c:macro:: NPY_FAIL
The return value of failed converter functions which are called using
the "O&" syntax in :c:func:`PyArg_ParseTuple`-like functions.
.. c:macro:: NPY_SUCCEED
The return value of successful converter functions which are called
using the "O&" syntax in :c:func:`PyArg_ParseTuple`-like functions.
.. c:function:: int PyArray_SAMESHAPE(PyArrayObject *a1, PyArrayObject *a2)
Evaluates as True if arrays *a1* and *a2* have the same shape.
.. c:macro:: PyArray_MAX(a,b)
Returns the maximum of *a* and *b*. If (*a*) or (*b*) are
expressions they are evaluated twice.
.. c:macro:: PyArray_MIN(a,b)
Returns the minimum of *a* and *b*. If (*a*) or (*b*) are
expressions they are evaluated twice.
.. c:macro:: PyArray_CLT(a,b)
.. c:macro:: PyArray_CGT(a,b)
.. c:macro:: PyArray_CLE(a,b)
.. c:macro:: PyArray_CGE(a,b)
.. c:macro:: PyArray_CEQ(a,b)
.. c:macro:: PyArray_CNE(a,b)
Implements the complex comparisons between two complex numbers
(structures with a real and imag member) using NumPy's definition
of the ordering which is lexicographic: comparing the real parts
first and then the complex parts if the real parts are equal.
.. c:function:: npy_intp PyArray_REFCOUNT(PyObject* op)
Returns the reference count of any Python object.
.. c:function:: void PyArray_DiscardWritebackIfCopy(PyObject* obj)
If ``obj.flags`` has :c:data:`NPY_ARRAY_WRITEBACKIFCOPY` or (deprecated)
:c:data:`NPY_ARRAY_UPDATEIFCOPY`, this function clears the flags, `DECREF` s
`obj->base` and makes it writeable, and sets ``obj->base`` to NULL. In
contrast to :c:func:`PyArray_DiscardWritebackIfCopy` it makes no attempt
to copy the data from `obj->base` This undoes
:c:func:`PyArray_SetWritebackIfCopyBase`. Usually this is called after an
error when you are finished with ``obj``, just before ``Py_DECREF(obj)``.
It may be called multiple times, or with ``NULL`` input.
.. c:function:: void PyArray_XDECREF_ERR(PyObject* obj)
Deprecated in 1.14, use :c:func:`PyArray_DiscardWritebackIfCopy`
followed by ``Py_XDECREF``
DECREF's an array object which may have the (deprecated)
:c:data:`NPY_ARRAY_UPDATEIFCOPY` or :c:data:`NPY_ARRAY_WRITEBACKIFCOPY`
flag set without causing the contents to be copied back into the
original array. Resets the :c:data:`NPY_ARRAY_WRITEABLE` flag on the base
object. This is useful for recovering from an error condition when
writeback semantics are used, but will lead to wrong results.
.. c:enum:: NPY_SORTKIND
A special variable-type which can take on different values to indicate
the sorting algorithm being used.
.. c:enumerator:: NPY_QUICKSORT
.. c:enumerator:: NPY_HEAPSORT
.. c:enumerator:: NPY_MERGESORT
.. c:enumerator:: NPY_STABLESORT
Used as an alias of :c:data:`NPY_MERGESORT` and vica versa.
.. c:enumerator:: NPY_NSORTS
Defined to be the number of sorts. It is fixed at three by the need for
backwards compatibility, and consequently :c:data:`NPY_MERGESORT` and
:c:data:`NPY_STABLESORT` are aliased to each other and may refer to one
of several stable sorting algorithms depending on the data type.
.. c:enum:: NPY_SCALARKIND
A special variable type indicating the number of "kinds" of
scalars distinguished in determining scalar-coercion rules. This
variable can take on the values:
.. c:enumerator:: NPY_NOSCALAR
.. c:enumerator:: NPY_BOOL_SCALAR
.. c:enumerator:: NPY_INTPOS_SCALAR
.. c:enumerator:: NPY_INTNEG_SCALAR
.. c:enumerator:: NPY_FLOAT_SCALAR
.. c:enumerator:: NPY_COMPLEX_SCALAR
.. c:enumerator:: NPY_OBJECT_SCALAR
.. c:enumerator:: NPY_NSCALARKINDS
Defined to be the number of scalar kinds
(not including :c:data:`NPY_NOSCALAR`).
.. c:enum:: NPY_ORDER
An enumeration type indicating the element order that an array should be
interpreted in. When a brand new array is created, generally
only **NPY_CORDER** and **NPY_FORTRANORDER** are used, whereas
when one or more inputs are provided, the order can be based on them.
.. c:enumerator:: NPY_ANYORDER
Fortran order if all the inputs are Fortran, C otherwise.
.. c:enumerator:: NPY_CORDER
C order.
.. c:enumerator:: NPY_FORTRANORDER
Fortran order.
.. c:enumerator:: NPY_KEEPORDER
An order as close to the order of the inputs as possible, even
if the input is in neither C nor Fortran order.
.. c:enum:: NPY_CLIPMODE
A variable type indicating the kind of clipping that should be
applied in certain functions.
.. c:enumerator:: NPY_RAISE
The default for most operations, raises an exception if an index
is out of bounds.
.. c:enumerator:: NPY_CLIP
Clips an index to the valid range if it is out of bounds.
.. c:enumerator:: NPY_WRAP
Wraps an index to the valid range if it is out of bounds.
.. c:enum:: NPY_SEARCHSIDE
A variable type indicating whether the index returned should be that of
the first suitable location (if :c:data:`NPY_SEARCHLEFT`) or of the last
(if :c:data:`NPY_SEARCHRIGHT`).
.. c:enumerator:: NPY_SEARCHLEFT
.. c:enumerator:: NPY_SEARCHRIGHT
.. c:enum:: NPY_SELECTKIND
A variable type indicating the selection algorithm being used.
.. c:enumerator:: NPY_INTROSELECT
.. c:enum:: NPY_CASTING
.. versionadded:: 1.6
An enumeration type indicating how permissive data conversions should
be. This is used by the iterator added in NumPy 1.6, and is intended
to be used more broadly in a future version.
.. c:enumerator:: NPY_NO_CASTING
Only allow identical types.
.. c:enumerator:: NPY_EQUIV_CASTING
Allow identical and casts involving byte swapping.
.. c:enumerator:: NPY_SAFE_CASTING
Only allow casts which will not cause values to be rounded,
truncated, or otherwise changed.
.. c:enumerator:: NPY_SAME_KIND_CASTING
Allow any safe casts, and casts between types of the same kind.
For example, float64 -> float32 is permitted with this rule.
.. c:enumerator:: NPY_UNSAFE_CASTING
Allow any cast, no matter what kind of data loss may occur.
.. index:: pair: ndarray; C-API