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-Array API
-=========
-
-.. 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
-
-
-Array structure and data access
--------------------------------
-
-These macros all access the :ctype:`PyArrayObject` structure members. The input
-argument, obj, can be any :ctype:`PyObject *` that is directly interpretable
-as a :ctype:`PyArrayObject *` (any instance of the :cdata:`PyArray_Type` and its
-sub-types).
-
-.. cfunction:: void *PyArray_DATA(PyObject *obj)
-
-.. cfunction:: char *PyArray_BYTES(PyObject *obj)
-
-    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.
-
-.. cfunction:: npy_intp *PyArray_DIMS(PyObject *arr)
-
-.. cfunction:: npy_intp *PyArray_STRIDES(PyObject* arr)
-
-.. cfunction:: npy_intp PyArray_DIM(PyObject* arr, int n)
-
-    Return the shape in the *n* :math:`^{\textrm{th}}` dimension.
-
-.. cfunction:: npy_intp PyArray_STRIDE(PyObject* arr, int n)
-
-    Return the stride in the *n* :math:`^{\textrm{th}}` dimension.
-
-.. cfunction:: PyObject *PyArray_BASE(PyObject* arr)
-
-.. cfunction:: PyArray_Descr *PyArray_DESCR(PyObject* arr)
-
-.. cfunction:: int PyArray_FLAGS(PyObject* arr)
-
-.. cfunction:: int PyArray_ITEMSIZE(PyObject* arr)
-
-    Return the itemsize for the elements of this array.
-
-.. cfunction:: int PyArray_TYPE(PyObject* arr)
-
-    Return the (builtin) typenumber for the elements of this array.
-
-.. cfunction:: PyObject *PyArray_GETITEM(PyObject* arr, void* itemptr)
-
-    Get a Python object from the ndarray, *arr*, at the location
-    pointed to by itemptr. Return ``NULL`` on failure.
-
-.. cfunction:: int PyArray_SETITEM(PyObject* 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.
-
-.. cfunction:: npy_intp PyArray_SIZE(PyObject* arr)
-
-    Returns the total size (in number of elements) of the array.
-
-.. cfunction:: npy_intp PyArray_Size(PyObject* obj)
-
-    Returns 0 if *obj* is not a sub-class of bigndarray. Otherwise,
-    returns the total number of elements in the array. Safer version
-    of :cfunc:`PyArray_SIZE` (*obj*).
-
-.. cfunction:: npy_intp PyArray_NBYTES(PyObject* arr)
-
-    Returns the total number of bytes consumed by the array.
-
-
-Data access
-^^^^^^^^^^^
-
-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 :cfunc:`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.
-
-.. cfunction:: 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.
-
-.. cfunction:: void* PyArray_GETPTR1(PyObject* obj, <npy_intp> i)
-
-.. cfunction:: void* PyArray_GETPTR2(PyObject* obj, <npy_intp> i, <npy_intp> j)
-
-.. cfunction:: void* PyArray_GETPTR3(PyObject* obj, <npy_intp> i, <npy_intp> j, <npy_intp> k)
-
-.. cfunction:: void* PyArray_GETPTR4(PyObject* 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.
-
-
-Creating arrays
----------------
-
-
-From scratch
-^^^^^^^^^^^^
-
-.. cfunction:: PyObject* PyArray_NewFromDescr(PyTypeObject* subtype, PyArray_Descr* descr, int nd, npy_intp* dims, npy_intp* strides, void* data, int flags, PyObject* obj)
-
-    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
-    :cdata:`PyArray_Type`. The array has *nd* dimensions, described by
-    *dims*. The data-type descriptor of the new array is *descr*. If
-    *subtype* is not :cdata:`&PyArray_Type` (*e.g.* a Python subclass of
-    the ndarray), then *obj* is the object to pass to the
-    :obj:`__array_finalize__` method of the subclass. If *data* is
-    ``NULL``, then new memory will be allocated and *flags* can be
-    non-zero to indicate a Fortran-style contiguous array. 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 :cdata:`NPY_OWNDATA` and
-    :cdata:`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* &
-    :cdata:`NPY_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.
-
-.. cfunction:: PyObject* PyArray_New(PyTypeObject* subtype, int nd, npy_intp* dims, int type_num, npy_intp* strides, void* data, int itemsize, int flags, PyObject* obj)
-
-    This is similar to :cfunc:`PyArray_DescrNew` (...) 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 :cfunc:`PyArray_NewFromDescr` or :cfunc:`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 :cfunc:`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.
-
-.. cfunction:: PyObject* PyArray_SimpleNew(int nd, npy_intp* dims, int typenum)
-
-    Create a new unitialized array of type, *typenum*, whose size in
-    each of *nd* dimensions is given by the integer array, *dims*.
-    This function cannot be used to create a flexible-type array (no
-    itemsize given).
-
-.. cfunction:: PyObject* PyArray_SimpleNewFromData(int nd, npy_intp* 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*.
-
-.. cfunction:: PyObject* PyArray_SimpleNewFromDescr(int nd, npy_intp* dims, PyArray_Descr* descr)
-
-    Create a new array with the provided data-type descriptor, *descr*
-    , of the shape deteremined by *nd* and *dims*.
-
-.. cfunction:: PyArray_FILLWBYTE(PyObject* obj, int val)
-
-    Fill the array pointed to by *obj* ---which must be a (subclass
-    of) bigndarray---with the contents of *val* (evaluated as a byte).
-
-.. cfunction:: PyObject* PyArray_Zeros(int nd, npy_intp* 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 :ctype:`PyArray_OBJECT` ).
-
-.. cfunction:: PyObject* PyArray_ZEROS(int nd, npy_intp* dims, int type_num, int fortran)
-
-    Macro form of :cfunc:`PyArray_Zeros` which takes a type-number instead
-    of a data-type object.
-
-.. cfunction:: PyObject* PyArray_Empty(int nd, npy_intp* 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 :ctype:`PyArray_OBJECT` in which case the array is
-    filled with :cdata:`Py_None`.
-
-.. cfunction:: PyObject* PyArray_EMPTY(int nd, npy_intp* dims, int typenum, int fortran)
-
-    Macro form of :cfunc:`PyArray_Empty` which takes a type-number,
-    *typenum*, instead of a data-type object.
-
-.. cfunction:: 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).
-
-.. cfunction:: 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`` ).
-
-
-From other objects
-^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: 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 *type*, the
-    minimum (*min_depth*) and maximum (*max_depth*) number of
-    dimensions acceptable, and other *requirements* for the array. The
-    *dtype* argument needs to be a :ctype:`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. If you want to use
-    ``NULL`` for the *dtype* and ensure the array is notswapped then
-    use :cfunc:`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 :cdata:`NPY_DEFAULT` as its flags member. The *context* argument
-    is passed to the :obj:`__array__` method of *op* and is only used if
-    the array is constructed that way.
-
-    .. cvar:: NPY_C_CONTIGUOUS
-
-        Make sure the returned array is C-style contiguous
-    
-    .. cvar:: NPY_F_CONTIGUOUS
-
-        Make sure the returned array is Fortran-style contiguous.
-    
-    .. cvar:: NPY_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.
-    
-    .. cvar:: NPY_WRITEABLE
-
-        Make sure the returned array can be written to.
-    
-    .. cvar:: NPY_ENSURECOPY
-
-        Make sure a copy is made of *op*. If this flag is not
-        present, data is not copied if it can be avoided.
-    
-    .. cvar:: NPY_ENSUREARRAY
-
-        Make sure the result is a base-class ndarray or bigndarray. By
-        default, if *op* is an instance of a subclass of the
-        bigndarray, an instance of that same subclass is returned. If
-        this flag is set, an ndarray object will be returned instead.
-    
-    .. cvar:: NPY_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 reaised.
-    
-    .. cvar:: NPY_UPDATEIFCOPY
-
-        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 :cdata:`NPY_UPDATEIFCOPY` flag is set in the returned
-        copy and *op* is made to be read-only. When the returned copy
-        is deleted (presumably after your calculations are complete),
-        its contents will be copied back into *op* and the *op* array
-        will be made writeable again. If *op* is not writeable to
-        begin with, then an error is raised. If *op* is not already an
-        array, then this flag has no effect.
-    
-    .. cvar:: NPY_BEHAVED
-
-        :cdata:`NPY_ALIGNED` \| :cdata:`NPY_WRITEABLE`
-    
-    .. cvar:: NPY_CARRAY
-
-        :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_BEHAVED`
-    
-    .. cvar:: NPY_CARRAY_RO
-
-        :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_FARRAY
-
-        :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_BEHAVED`
-    
-    .. cvar:: NPY_FARRAY_RO
-
-        :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_DEFAULT
-
-        :cdata:`NPY_CARRAY`
-    
-    .. cvar:: NPY_IN_ARRAY
-
-        :cdata:`NPY_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_IN_FARRAY
-
-        :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_INOUT_ARRAY
-
-        :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_WRITEABLE` \|
-        :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_INOUT_FARRAY
-
-        :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_WRITEABLE` \|
-        :cdata:`NPY_ALIGNED`
-    
-    .. cvar:: NPY_OUT_ARRAY
-
-        :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_WRITEABLE` \|
-        :cdata:`NPY_ALIGNED` \| :cdata:`NPY_UPDATEIFCOPY`
-    
-    .. cvar:: NPY_OUT_FARRAY
-
-        :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_WRITEABLE` \|
-        :cdata:`NPY_ALIGNED` \| :cdata:`UPDATEIFCOPY`
-    
-    
-.. cfunction:: PyObject* PyArray_CheckFromAny(PyObject* op, PyArray_Descr* dtype, int min_depth, int max_depth, int requirements, PyObject* context)
-
-    Nearly identical to :cfunc:`PyArray_FromAny` (...) except
-    *requirements* can contain :cdata:`NPY_NOTSWAPPED` (over-riding the
-    specification in *dtype*) and :cdata:`NPY_ELEMENTSTRIDES` which
-    indicates that the array should be aligned in the sense that the
-    strides are multiples of the element size.
-
-.. cvar:: NPY_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.
-
-.. cvar:: NPY_BEHAVED_NS
-
-    :cdata:`NPY_ALIGNED` \| :cdata:`NPY_WRITEABLE` \| :cdata:`NPY_NOTSWAPPED`
-
-.. cvar:: NPY_ELEMENTSTRIDES
-
-    Make sure the returned array has strides that are multiples of the
-    element size.
-
-.. cfunction:: PyObject* PyArray_FromArray(PyArrayObject* op, PyArray_Descr* newtype, int requirements)
-
-    Special case of :cfunc:`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*.
-
-.. cfunction:: PyObject* PyArray_FromStructInterface(PyObject* op)
-
-    Returns an ndarray object from a Python object that exposes the
-    :obj:`__array_struct__`` method and follows the array interface
-    protocol. If the object does not contain this method then a
-    borrowed reference to :cdata:`Py_NotImplemented` is returned.
-
-.. cfunction:: PyObject* PyArray_FromInterface(PyObject* op)
-
-    Returns an ndarray object from a Python object that exposes the
-    :obj:`__array_shape__` and :obj:`__array_typestr__`
-    methods following
-    the array interface protocol. If the object does not contain one
-    of these method then a borrowed reference to :cdata:`Py_NotImplemented`
-    is returned.
-
-.. cfunction:: PyObject* PyArray_FromArrayAttr(PyObject* op, PyArray_Descr* dtype, PyObject* context)
-
-    Return an ndarray object from a Python object that exposes the
-    :obj:`__array__` method. The :obj:`__array__` method can take 0, 1, or 2
-    arguments ([dtype, context]) where *context* is used to pass
-    information about where the :obj:`__array__` method is being called
-    from (currently only used in ufuncs).
-
-.. cfunction:: 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 :cfunc:`PyArray_FromAny` with
-    requirements set to :cdata:`NPY_DEFAULT` and the type_num member of the
-    type argument set to *typenum*.
-
-.. cfunction:: 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 :cfunc:`PyArray_FromAny` with requirements set to
-    BEHAVED.
-
-.. cfunction:: 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 :cfunc:`PyArray_FromAny` ( ``op``, NULL, 0, 0,
-    :cdata:`NPY_ENSUREARRAY`).
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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 :cdata:`NPY_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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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 :cfunc:`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.
-
-.. cfunction:: PyObject* PyArray_FROM_OF(PyObject* obj, int requirements)
-
-    Similar to :cfunc:`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
-    :cdata:`NPY_CONTIGUOUS`, :cdata:`NPY_F_CONTIGUOUS`,
-    :cdata:`NPY_ALIGNED`, :cdata:`NPY_WRITEABLE`,
-    :cdata:`NPY_NOTSWAPPED`, :cdata:`NPY_ENSURECOPY`,
-    :cdata:`NPY_UPDATEIFCOPY`, :cdata:`NPY_FORCECAST`, and
-    :cdata:`NPY_ENSUREARRAY`. Standard combinations of flags can also
-    be used:
-
-.. cfunction:: PyObject* PyArray_FROM_OT(PyObject* obj, int typenum)
-
-    Similar to :cfunc:`PyArray_FROM_O` except it can take an argument of
-    *typenum* specifying the type-number the returned array.
-
-.. cfunction:: PyObject* PyArray_FROM_OTF(PyObject* obj, int typenum, int requirements)
-
-    Combination of :cfunc:`PyArray_FROM_OF` and :cfunc:`PyArray_FROM_OT`
-    allowing both a *typenum* and a *flags* argument to be provided..
-
-.. cfunction:: PyObject* PyArray_FROMANY(PyObject* obj, int typenum, int min, int max, int requirements)
-
-    Similar to :cfunc:`PyArray_FromAny` except the data-type is
-    specified using a typenumber. :cfunc:`PyArray_DescrFromType`
-    (*typenum*) is passed directly to :cfunc:`PyArray_FromAny`. This
-    macro also adds :cdata:`NPY_DEFAULT` to requirements if
-    :cdata:`NPY_ENSURECOPY` is passed in as requirements.
-
-.. cfunction:: 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``.
-
-
-Dealing with types
-------------------
-
-
-General check of Python Type
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: PyArray_Check(op)
-
-    Evaluates true if *op* is a Python object whose type is a sub-type
-    of :cdata:`PyArray_Type`.
-
-.. cfunction:: PyArray_CheckExact(op)
-
-    Evaluates true if *op* is a Python object with type
-    :cdata:`PyArray_Type`.
-
-.. cfunction:: PyArray_HasArrayInterface(op, 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 :cdata:`Py_NotImplemented` and no error condition is set.
-
-.. cfunction:: PyArray_HasArrayInterfaceType(op, type, context, 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 type and context in the part of
-    the array interface that looks for the :obj:`__array__` attribute.
-
-.. cfunction:: PyArray_IsZeroDim(op)
-
-    Evaluates true if *op* is an instance of (a subclass of)
-    :cdata:`PyArray_Type` and has 0 dimensions.
-
-.. cfunction:: PyArray_IsScalar(op, cls)
-
-    Evaluates true if *op* is an instance of :cdata:`Py{cls}ArrType_Type`.
-
-.. cfunction:: PyArray_CheckScalar(op)
-
-    Evaluates true if *op* is either an array scalar (an instance of a
-    sub-type of :cdata:`PyGenericArr_Type` ), or an instance of (a
-    sub-class of) :cdata:`PyArray_Type` whose dimensionality is 0.
-
-.. cfunction:: PyArray_IsPythonScalar(op)
-
-    Evaluates true if *op* is a builtin Python "scalar" object (int,
-    float, complex, str, unicode, long, bool).
-
-.. cfunction:: PyArray_IsAnyScalar(op)
-
-    Evaluates true if *op* is either a Python scalar or an array
-    scalar (an instance of a sub- type of :cdata:`PyGenericArr_Type` ).
-
-
-Data-type checking
-^^^^^^^^^^^^^^^^^^
-
-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 :ctype:`PyObject *` that can be directly interpreted as a
-:ctype:`PyArrayObject *`.
-
-.. cfunction:: PyTypeNum_ISUNSIGNED(num)
-
-.. cfunction:: PyDataType_ISUNSIGNED(descr)
-
-.. cfunction:: PyArray_ISUNSIGNED(obj)
-
-    Type represents an unsigned integer.
-
-.. cfunction:: PyTypeNum_ISSIGNED(num)
-
-.. cfunction:: PyDataType_ISSIGNED(descr)
-
-.. cfunction:: PyArray_ISSIGNED(obj)
-
-    Type represents a signed integer.
-
-.. cfunction:: PyTypeNum_ISINTEGER(num)
-
-.. cfunction:: PyDataType_ISINTEGER(descr)
-
-.. cfunction:: PyArray_ISINTEGER(obj)
-
-    Type represents any integer.
-
-.. cfunction:: PyTypeNum_ISFLOAT(num)
-
-.. cfunction:: PyDataType_ISFLOAT(descr)
-
-.. cfunction:: PyArray_ISFLOAT(obj)
-
-    Type represents any floating point number.
-
-.. cfunction:: PyTypeNum_ISCOMPLEX(num)
-
-.. cfunction:: PyDataType_ISCOMPLEX(descr)
-
-.. cfunction:: PyArray_ISCOMPLEX(obj)
-
-    Type represents any complex floating point number.
-
-.. cfunction:: PyTypeNum_ISNUMBER(num)
-
-.. cfunction:: PyDataType_ISNUMBER(descr)
-
-.. cfunction:: PyArray_ISNUMBER(obj)
-
-    Type represents any integer, floating point, or complex floating point
-    number.
-
-.. cfunction:: PyTypeNum_ISSTRING(num)
-
-.. cfunction:: PyDataType_ISSTRING(descr)
-
-.. cfunction:: PyArray_ISSTRING(obj)
-
-    Type represents a string data type.
-
-.. cfunction:: PyTypeNum_ISPYTHON(num)
-
-.. cfunction:: PyDataType_ISPYTHON(descr)
-
-.. cfunction:: PyArray_ISPYTHON(obj)
-
-    Type represents an enumerated type corresponding to one of the
-    standard Python scalar (bool, int, float, or complex).
-
-.. cfunction:: PyTypeNum_ISFLEXIBLE(num)
-
-.. cfunction:: PyDataType_ISFLEXIBLE(descr)
-
-.. cfunction:: PyArray_ISFLEXIBLE(obj)
-
-    Type represents one of the flexible array types ( :cdata:`NPY_STRING`,
-    :cdata:`NPY_UNICODE`, or :cdata:`NPY_VOID` ).
-
-.. cfunction:: PyTypeNum_ISUSERDEF(num)
-
-.. cfunction:: PyDataType_ISUSERDEF(descr)
-
-.. cfunction:: PyArray_ISUSERDEF(obj)
-
-    Type represents a user-defined type.
-
-.. cfunction:: PyTypeNum_ISEXTENDED(num)
-
-.. cfunction:: PyDataType_ISEXTENDED(descr)
-
-.. cfunction:: PyArray_ISEXTENDED(obj)
-
-    Type is either flexible or user-defined.
-
-.. cfunction:: PyTypeNum_ISOBJECT(num)
-
-.. cfunction:: PyDataType_ISOBJECT(descr)
-
-.. cfunction:: PyArray_ISOBJECT(obj)
-
-    Type represents object data type.
-
-.. cfunction:: PyTypeNum_ISBOOL(num)
-
-.. cfunction:: PyDataType_ISBOOL(descr)
-
-.. cfunction:: PyArray_ISBOOL(obj)
-
-    Type represents Boolean data type.
-
-.. cfunction:: PyDataType_HASFIELDS(descr)
-
-.. cfunction:: PyArray_HASFIELDS(obj)
-
-    Type has fields associated with it.
-
-.. cfunction:: PyArray_ISNOTSWAPPED(m)
-
-    Evaluates true if the data area of the ndarray *m* is in machine
-    byte-order according to the array's data-type descriptor.
-
-.. cfunction:: PyArray_ISBYTESWAPPED(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.
-
-.. cfunction:: Bool PyArray_EquivTypes(PyArray_Descr* type1, PyArray_Descr* type2)
-
-    Return :cdata:`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,
-    :cdata:`NPY_LONG` and :cdata:`NPY_INT` are equivalent. Otherwise
-    return :cdata:`NPY_FALSE`.
-
-.. cfunction:: Bool PyArray_EquivArrTypes(PyArrayObject* a1, PyArrayObject * a2)
-
-    Return :cdata:`NPY_TRUE` if *a1* and *a2* are arrays with equivalent
-    types for this platform.
-
-.. cfunction:: Bool PyArray_EquivTypenums(int typenum1, int typenum2)
-
-    Special case of :cfunc:`PyArray_EquivTypes` (...) that does not accept
-    flexible data types but may be easier to call.
-
-.. cfunction:: int PyArray_EquivByteorders({byteorder} b1, {byteorder} b2)
-
-    True if byteorder characters ( :cdata:`NPY_LITTLE`,
-    :cdata:`NPY_BIG`, :cdata:`NPY_NATIVE`, :cdata:`NPY_IGNORE` ) are
-    either equal or equivalent as to their specification of a native
-    byte order. Thus, on a little-endian machine :cdata:`NPY_LITTLE`
-    and :cdata:`NPY_NATIVE` are equivalent where they are not
-    equivalent on a big-endian machine.
-
-
-Converting data types
-^^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: int PyArray_CastTo(PyArrayObject* out, PyArrayObject* in)
-
-    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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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
-    explict requests. Flexible array types are not checked according
-    to their lengths with this function.
-
-.. cfunction:: int PyArray_CanCastTo(PyArray_Descr* fromtype, PyArray_Descr* totype)
-
-    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). This is
-    basically a wrapper around :cfunc:`PyArray_CanCastSafely` with
-    additional support for size checking if *fromtype* and *totype*
-    are :cdata:`NPY_STRING` or :cdata:`NPY_UNICODE`.
-
-.. cfunction:: int PyArray_ObjectType(PyObject* op, int mintype)
-
-    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.
-
-.. cfunction:: void PyArray_ArrayType(PyObject* op, PyArray_Descr* mintype, PyArray_Descr* outtype)
-
-    This function works similarly to :cfunc:`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*.
-
-.. cfunction:: PyArrayObject** PyArray_ConvertToCommonType(PyObject* op, int* n)
-
-    Convert a sequence of Python objects contained in *op* to an array
-    of ndarrays each having the same data type. The type is selected
-    based on the typenumber (larger type number is chosen over a
-    smaller one) ignoring objects that are only scalars. The length of
-    the sequence is returned in *n*, and an *n* -length array of
-    :ctype:`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 :cfunc:`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:
-    
-    .. 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}
-
-.. cfunction:: 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 :cfunc:`PyDataMem_FREE` (ret) when it is
-    not needed anymore.
-
-.. cfunction:: 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 :cfunc:`PyDataMem_FREE` (ret) when it
-    is not needed anymore.
-
-.. cfunction:: int PyArray_ValidType(int typenum)
-
-    Returns :cdata:`NPY_TRUE` if *typenum* represents a valid type-number
-    (builtin or user-defined or character code). Otherwise, this
-    function returns :cdata:`NPY_FALSE`.
-
-
-New data types
-^^^^^^^^^^^^^^
-
-.. cfunction:: void PyArray_InitArrFuncs(PyArray_ArrFuncs* f)
-
-    Initialize all function pointers and members to ``NULL``.
-
-.. cfunction:: 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
-    :cfunc:`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.
-
-.. cfunction:: 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.
-
-.. cfunction:: int PyArray_RegisterCanCast(PyArray_Descr* descr, int totype, PyArray_SCALARKIND scalar)
-
-    Register the data-type number, *totype*, as castable from
-    data-type object, *descr*, of the given *scalar* kind. Use
-    *scalar* = :cdata:`NPY_NOSCALAR` to register that an array of data-type
-    *descr* can be cast safely to a data-type whose type_number is
-    *totype*.
-
-
-Special functions for PyArray_OBJECT
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: 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.
-
-.. cfunction:: 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
-    record with an object at any offset, then this will (recursively)
-    increment the reference count of all object-like items in the
-    record.
-
-.. cfunction:: 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.
-
-.. cfunction:: void PyArray_Item_XDECREF(char* ptr, PyArray_Descr* dtype)
-
-    A function to XDECREF all the object-like items at the loacation
-    *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``.
-
-.. cfunction:: 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 :ctype:`PyArray_OBJECT` and be
-    single-segment and uninitialized (no previous objects in
-    position). Use :cfunc:`PyArray_DECREF` (*arr*) if you need to
-    decrement all the items in the object array prior to calling this
-    function.
-
-
-Array flags
------------
-
-
-Basic Array Flags
-^^^^^^^^^^^^^^^^^
-
-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 Fortan-contiguous order. The
-array flags are used to indicate what can be said about data
-associated with an array.
-
-.. cvar:: NPY_C_CONTIGUOUS
-
-    The data area is in C-style contiguous order (last index varies the
-    fastest).
-
-.. cvar:: NPY_F_CONTIGUOUS
-
-    The data area is in Fortran-style contiguous order (first index varies
-    the fastest).
-
-.. cvar:: NPY_OWNDATA
-
-    The data area is owned by this array.
-
-.. cvar:: NPY_ALIGNED
-
-    The data area is aligned appropriately (for all strides).
-
-.. cvar:: NPY_WRITEABLE
-
-    The data area can be written to.
-
-    Notice that the above 3 flags are are defined so that a new, well-
-    behaved array has these flags defined as true.
-
-.. cvar:: NPY_UPDATEIFCOPY
-
-    The data area represents a (well-behaved) copy whose information
-    should be transferred back to the original when this array is deleted.
-
-
-Combinations of array flags
-^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. cvar:: NPY_BEHAVED
-
-    :cdata:`NPY_ALIGNED` \| :cdata:`NPY_WRITEABLE`
-
-.. cvar:: NPY_CARRAY
-
-    :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_BEHAVED`
-
-.. cvar:: NPY_CARRAY_RO
-
-    :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-
-.. cvar:: NPY_FARRAY
-
-    :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_BEHAVED`
-
-.. cvar:: NPY_FARRAY_RO
-
-    :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-
-.. cvar:: NPY_DEFAULT
-
-    :cdata:`NPY_CARRAY`
-
-.. cvar:: NPY_UPDATE_ALL
-
-    :cdata:`NPY_C_CONTIGUOUS` \| :cdata:`NPY_F_CONTIGUOUS` \| :cdata:`NPY_ALIGNED`
-
-
-Flag-like constants
-^^^^^^^^^^^^^^^^^^^
-
-These constants are used in :cfunc:`PyArray_FromAny` (and its macro forms) to
-specify desired properties of the new array. 
-
-.. cvar:: NPY_FORCECAST
-
-    Cast to the desired type, even if it can't be done without losing
-    information.
-
-.. cvar:: NPY_ENSURECOPY
-
-    Make sure the resulting array is a copy of the original.
-
-.. cvar:: NPY_ENSUREARRAY
-
-    Make sure the resulting object is an actual ndarray (or bigndarray),
-    and not a sub-class.
-
-.. cvar:: NPY_NOTSWAPPED
-
-    Only used in :cfunc:`PyArray_CheckFromAny` to over-ride the byteorder
-    of the data-type object passed in.
-
-.. cvar:: NPY_BEHAVED_NS
-
-    :cdata:`NPY_ALIGNED` \| :cdata:`NPY_WRITEABLE` \| :cdata:`NPY_NOTSWAPPED`
-
-
-Flag checking
-^^^^^^^^^^^^^
-
-For all of these macros *arr* must be an instance of a (subclass of)
-:cdata:`PyArray_Type`, but no checking is done.
-
-.. cfunction:: PyArray_CHKFLAGS(arr, 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:
-    :cdata:`NPY_C_CONTIGUOUS`, :cdata:`NPY_F_CONTIGUOUS`,
-    :cdata:`NPY_OWNDATA`, :cdata:`NPY_ALIGNED`,
-    :cdata:`NPY_WRITEABLE`, :cdata:`NPY_UPDATEIFCOPY`.
-
-.. cfunction:: PyArray_ISCONTIGUOUS(arr)
-
-    Evaluates true if *arr* is C-style contiguous.
-
-.. cfunction:: PyArray_ISFORTRAN(arr)
-
-    Evaluates true if *arr* is Fortran-style contiguous.
-
-.. cfunction:: PyArray_ISWRITEABLE(arr)
-
-    Evaluates true if the data area of *arr* can be written to
-
-.. cfunction:: PyArray_ISALIGNED(arr)
-
-    Evaluates true if the data area of *arr* is properly aligned on
-    the machine.
-
-.. cfunction:: PyArray_ISBEHAVED(arr)
-
-    Evalutes true if the data area of *arr* is aligned and writeable
-    and in machine byte-order according to its descriptor.
-
-.. cfunction:: PyArray_ISBEHAVED_RO(arr)
-
-    Evaluates true if the data area of *arr* is aligned and in machine
-    byte-order.
-
-.. cfunction:: PyArray_ISCARRAY(arr)
-
-    Evaluates true if the data area of *arr* is C-style contiguous,
-    and :cfunc:`PyArray_ISBEHAVED` (*arr*) is true.
-
-.. cfunction:: PyArray_ISFARRAY(arr)
-
-    Evaluates true if the data area of *arr* is Fortran-style
-    contiguous and :cfunc:`PyArray_ISBEHAVED` (*arr*) is true.
-
-.. cfunction:: PyArray_ISCARRAY_RO(arr)
-
-    Evaluates true if the data area of *arr* is C-style contiguous,
-    aligned, and in machine byte-order.
-
-.. cfunction:: PyArray_ISFARRAY_RO(arr)
-
-    Evaluates true if the data area of *arr* is Fortran-style
-    contiguous, aligned, and in machine byte-order **.**
-
-.. cfunction:: PyArray_ISONESEGMENT(arr)
-
-    Evaluates true if the data area of *arr* consists of a single
-    (C-style or Fortran-style) contiguous segment.
-
-.. cfunction:: void PyArray_UpdateFlags(PyArrayObject* arr, int flagmask)
-
-    The :cdata:`NPY_C_CONTIGUOUS`, :cdata:`NPY_ALIGNED`, and
-    :cdata:`NPY_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
-    :cfunc:`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.
-
-
-Array method alternative API
-----------------------------
-
-
-Conversion
-^^^^^^^^^^
-
-.. cfunction:: PyObject* PyArray_GetField(PyArrayObject* self, PyArray_Descr* dtype, int offset)
-
-    Equivalent to :meth:`ndarray.getfield` (*self*, *dtype*, *offset*). Return
-    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 record array. But, it can also
-    be used to select specific bytes or groups of bytes from any array
-    type.
-
-.. cfunction:: int PyArray_SetField(PyArrayObject* self, PyArray_Descr* dtype, int offset, PyObject* val)
-
-    Equivalent to :meth:`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*.
-
-.. cfunction:: PyObject* PyArray_Byteswap(PyArrayObject* self, Bool inplace)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_NewCopy(PyArrayObject* old, NPY_ORDER order)
-
-    Equivalent to :meth:`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
-    :cdata:`NPY_CORDER`, then a C-style contiguous array is returned. If
-    *order* is :cdata:`NPY_FORTRANORDER`, then a Fortran-style contiguous
-    array is returned. If *order is* :cdata:`NPY_ANYORDER`, then the array
-    returned is Fortran-style contiguous only if the old one is;
-    otherwise, it is C-style contiguous.
-
-.. cfunction:: PyObject* PyArray_ToList(PyArrayObject* self)
-
-    Equivalent to :meth:`ndarray.tolist` (*self*). Return a nested Python list
-    from *self*.
-
-.. cfunction:: PyObject* PyArray_ToString(PyArrayObject* self, NPY_ORDER order)
-
-    Equivalent to :meth:`ndarray.tostring` (*self*, *order*). Return the bytes
-    of this array in a Python string.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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*).
-
-.. cfunction:: 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).
-
-.. cfunction:: 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.
-
-.. cfunction:: PyObject* PyArray_View(PyArrayObject* self, PyArray_Descr* dtype)
-
-    Equivalent to :meth:`ndarray.view` (*self*, *dtype*). Return a new view of
-    the array *self* as possibly a different data-type, *dtype*. 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.
-
-
-Shape Manipulation
-^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: PyObject* PyArray_Newshape(PyArrayObject* self, PyArray_Dims* newshape)
-
-    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.
-
-.. cfunction:: PyObject* PyArray_Reshape(PyArrayObject* self, PyObject* shape)
-
-    Equivalent to :meth:`ndarray.reshape` (*self*, *shape*) where *shape* is a
-    sequence. Converts *shape* to a :ctype:`PyArray_Dims` structure and
-    calls :cfunc:`PyArray_Newshape` internally.
-
-.. cfunction:: PyObject* PyArray_Squeeze(PyArrayObject* self)
-
-    Equivalent to :meth:`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,
-    :cfunc:`PyArray_Squeeze` has no effect on arrays of matrix sub-class.
-
-.. cfunction:: PyObject* PyArray_SwapAxes(PyArrayObject* self, int a1, int a2)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_Resize(PyArrayObject* self, PyArray_Dims* newshape, int refcheck, NPY_ORDER fortran)
-
-    Equivalent to :meth:`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. A
-    reference to the new array is returned. The fortran argument can
-    be :cdata:`NPY_ANYORDER`, :cdata:`NPY_CORDER`, or
-    :cdata:`NPY_FORTRANORDER`. This argument is used if the number of
-    dimension is (or is being resized to be) greater than 2. 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.
-
-.. cfunction:: PyObject* PyArray_Transpose(PyArrayObject* self, PyArray_Dims* permute)
-
-    Equivalent to :meth:`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.`
-
-.. cfunction:: PyObject* PyArray_Flatten(PyArrayObject* self, NPY_ORDER order)
-
-    Equivalent to :meth:`ndarray.flatten` (*self*, *order*). Return a 1-d copy
-    of the array. If *order* is :cdata:`NPY_FORTRANORDER` the elements are
-    scanned out in Fortran order (first-dimension varies the
-    fastest). If *order* is :cdata:`NPY_CORDER`, the elements of ``self``
-    are scanned in C-order (last dimension varies the fastest). If
-    *order* :cdata:`NPY_ANYORDER`, then the result of
-    :cfunc:`PyArray_ISFORTRAN` (*self*) is used to determine which order
-    to flatten.
-
-.. cfunction:: PyObject* PyArray_Ravel(PyArrayObject* self, NPY_ORDER order)
-
-    Equivalent to *self*.ravel(*order*). Same basic functionality
-    as :cfunc:`PyArray_Flatten` (*self*, *order*) except if *order* is 0
-    and *self* is C-style contiguous, the shape is altered but no copy
-    is performed.
-
-
-Item selection and manipulation
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: PyObject* PyArray_TakeFrom(PyArrayObject* self, PyObject* indices, int axis, PyArrayObject* ret, NPY_CLIPMODE clipmode)
-
-    Equivalent to :meth:`ndarray.take` (*self*, *indices*, *axis*, *ret*,
-    *clipmode*) except *axis* =None in Python is obtained by setting
-    *axis* = :cdata:`NPY_MAXDIMS` in C. Extract the items from self
-    indicated by the integer-valued *indices* along the given *axis.*
-    The clipmode argument can be :cdata:`NPY_RAISE`, :cdata:`NPY_WRAP`, or
-    :cdata:`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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: PyObject* PyArray_Repeat(PyArrayObject* self, PyObject* op, int axis)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_Choose(PyArrayObject* self, PyObject* op, PyArrayObject* ret, NPY_CLIPMODE clipmode)
-
-    Equivalent to :meth:`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*).
-
-    .. cvar:: NPY_RAISE
-    
-        raise a ValueError;
-    
-    .. cvar:: NPY_WRAP
-    
-        wrap values < 0 by adding len(*op*) and values >=len(*op*)
-        by subtracting len(*op*) until they are in range;
-    
-    .. cvar:: NPY_CLIP
-    
-        all values are clipped to the region [0, len(*op*) ).
-    
-    
-.. cfunction:: PyObject* PyArray_Sort(PyArrayObject* self, int axis)
-
-    Equivalent to :meth:`ndarray.sort` (*self*, *axis*). Return an array with
-    the items of *self* sorted along *axis*.
-
-.. cfunction:: PyObject* PyArray_ArgSort(PyArrayObject* self, int axis)
-
-    Equivalent to :meth:`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 record array, create
-    a new data-type with a different order of names and construct a
-    view of the array with that new data-type.
-
-.. cfunction:: 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 :cfunc:`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 record array, then
-    :cfunc:`PyArray_Sort` (...) can also be used to sort the array
-    directly.
-
-.. cfunction:: PyObject* PyArray_SearchSorted(PyArrayObject* self, PyObject* values)
-
-    Equivalent to :meth:`ndarray.searchsorted` (*self*, *values*). Assuming
-    *self* is a 1-d array in ascending order representing bin
-    boundaries then the output is an array the same shape as *values*
-    of bin numbers, giving the bin into which each item in *values*
-    would be placed. No checking is done on whether or not self is in
-    ascending order.
-
-.. cfunction:: PyObject* PyArray_Diagonal(PyArrayObject* self, int offset, int axis1, int axis2)
-
-    Equivalent to :meth:`ndarray.diagonal` (*self*, *offset*, *axis1*, *axis2*
-    ). Return the *offset* diagonals of the 2-d arrays defined by
-    *axis1* and *axis2*.
-
-.. cfunction:: PyObject* PyArray_Nonzero(PyArrayObject* self)
-
-    Equivalent to :meth:`ndarray.nonzero` (*self*). Returns a tuple of index
-    arrays that select elements of *self* that are nonzero. If (nd=
-    :cfunc:`PyArray_NDIM` ( ``self`` ))==1, then a single index array is
-    returned. The index arrays have data type :cdata:`NPY_INTP`. If a
-    tuple is returned (nd :math:`\neq` 1), then its length is nd.
-
-.. cfunction:: PyObject* PyArray_Compress(PyArrayObject* self, PyObject* condition, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`ndarray.compress` (*self*, *condition*, *axis*
-    ). Return the elements along *axis* corresponding to elements of
-    *condition* that are true.
-
-
-Calculation
-^^^^^^^^^^^
-
-.. tip::
-
-    Pass in :cdata:`NPY_MAXDIMS` for axis in order to achieve the same
-    effect that is obtained by passing in *axis* = :const:`None` in Python
-    (treating the array as a 1-d array).
-
-.. cfunction:: PyObject* PyArray_ArgMax(PyArrayObject* self, int axis)
-
-    Equivalent to :meth:`ndarray.argmax` (*self*, *axis*). Return the index of
-    the largest element of *self* along *axis*.
-
-.. cfunction:: PyObject* PyArray_ArgMin(PyArrayObject* self, int axis)
-
-    Equivalent to :meth:`ndarray.argmin` (*self*, *axis*). Return the index of
-    the smallest element of *self* along *axis*.
-
-.. cfunction:: PyObject* PyArray_Max(PyArrayObject* self, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`ndarray.max` (*self*, *axis*). Return the largest
-    element of *self* along the given *axis*.
-
-.. cfunction:: PyObject* PyArray_Min(PyArrayObject* self, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`ndarray.min` (*self*, *axis*). Return the smallest
-    element of *self* along the given *axis*.
-
-.. cfunction:: PyObject* PyArray_Ptp(PyArrayObject* self, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`ndarray.ptp` (*self*, *axis*). Return the difference
-    between the largest element of *self* along *axis* and the
-    smallest element of *self* along *axis*.
-
-
-
-.. 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 :cdata:`NPY_LONG`
-    for the "add" and "multiply" ufuncs (which form the basis for
-    mean, sum, cumsum, prod, and cumprod functions).
-
-.. cfunction:: PyObject* PyArray_Mean(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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 :cdata:`PyArray_NOTYPE` for *rtype*.
-
-.. cfunction:: PyObject* PyArray_Trace(PyArrayObject* self, int offset, int axis1, int axis2, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_Clip(PyArrayObject* self, PyObject* min, PyObject* max)
-
-    Equivalent to :meth:`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*.
-
-.. cfunction:: PyObject* PyArray_Conjugate(PyArrayObject* self)
-
-    Equivalent to :meth:`ndarray.conjugate` (*self*).
-    Return the complex conjugate of *self*. If *self* is not of
-    complex data type, then return *self* with an reference.
-
-.. cfunction:: PyObject* PyArray_Round(PyArrayObject* self, int decimals, PyArrayObject* out)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_Std(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`ndarray.std` (*self*, *axis*, *rtype*). Return the
-    standard deviation using data along *axis* converted to data type
-    *rtype*.
-
-.. cfunction:: PyObject* PyArray_Sum(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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*.
-
-.. cfunction:: PyObject* PyArray_CumSum(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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*.
-
-.. cfunction:: PyObject* PyArray_Prod(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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*.
-
-.. cfunction:: PyObject* PyArray_CumProd(PyArrayObject* self, int axis, int rtype, PyArrayObject* out)
-
-    Equivalent to :meth:`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``.
-
-.. cfunction:: PyObject* PyArray_All(PyArrayObject* self, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`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.
-
-.. cfunction:: PyObject* PyArray_Any(PyArrayObject* self, int axis, PyArrayObject* out)
-
-    Equivalent to :meth:`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.
-
-Functions
----------
-
-
-Array Functions
-^^^^^^^^^^^^^^^
-
-.. cfunction:: 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 specifice 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. 
-
-.. cfunction:: int PyArray_Free(PyObject* op, void* ptr)
-
-    Must be called with the same objects and memory locations returned
-    from :cfunc:`PyArray_AsCArray` (...). This function cleans up memory
-    that otherwise would get leaked.
-
-.. cfunction:: 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.
-
-.. cfunction:: PyObject* PyArray_InnerProduct(PyObject* obj1, PyObject* obj2)
-
-    Compute a product-sum over the last dimensions of *obj1* and
-    *obj2*. Neither array is conjugated.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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*.
-
-.. cfunction:: 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).
-
-.. cfunction:: PyObject* PyArray_Where(PyObject* condition, PyObject* x, PyObject* y)
-
-    If both ``x`` and ``y`` are ``NULL``, then return
-    :cfunc:`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.
-
-
-Other functions
-^^^^^^^^^^^^^^^
-
-.. cfunction:: Bool PyArray_CheckStrides(int elsize, int nd, npy_intp numbytes, npy_intp* dims, npy_intp* 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 :cdata:`NPY_TRUE` if
-    *newstrides* is acceptable, otherwise return :cdata:`NPY_FALSE`.
-
-.. cfunction:: npy_intp PyArray_MultiplyList(npy_intp* seq, int n)
-
-.. cfunction:: int PyArray_MultiplyIntList(int* seq, int n)
-
-    Both of these routines multiply an *n* -length array, *seq*, of
-    integers and return the result. No overflow checking is performed.
-
-.. cfunction:: int PyArray_CompareLists(npy_intp* l1, npy_intp* l2, int n)
-
-    Given two *n* -length arrays of integers, *l1*, and *l2*, return
-    1 if the lists are identical; otherwise, return 0.
-
-
-Array Iterators
----------------
-
-An array iterator is a simple way to access the elements of an
-N-dimensional array quickly and efficiently. Section `2
-<#sec-array-iterator>`__ provides more description and examples of
-this useful approach to looping over an array.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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 :cfunc:`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.
-
-.. cfunction:: PyObject *PyArray_BroadcastToShape(PyObject* arr, npy_intp *dimensions, int nd)
-
-    Return an array iterator that is broadcast to iterate as an array
-    of the shape provided by *dimensions* and *nd*.
-
-.. cfunction:: int PyArrayIter_Check(PyObject* op)
-
-    Evaluates true if *op* is an array iterator (or instance of a
-    subclass of the array iterator type).
-
-.. cfunction:: void PyArray_ITER_RESET(PyObject* iterator)
-
-    Reset an *iterator* to the beginning of the array.
-
-.. cfunction:: 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.
-
-.. cfunction:: void *PyArray_ITER_DATA(PyObject* iterator)
-
-    A pointer to the current element of the array.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-
-Broadcasting (multi-iterators)
-------------------------------
-
-.. cfunction:: PyObject* PyArray_MultiIterNew(int num, ...)
-
-    A simplified interface to broadcasting. This function takes the
-    number of arrays to broadcast and then *num* extra ( :ctype:`PyObject *`
-    ) arguments. These arguments are converted to arrays and iterators
-    are created. :cfunc:`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 :cfunc:`PyArray_MultiIter_NEXT`
-    (..)
-
-.. cfunction:: void PyArray_MultiIter_RESET(PyObject* multi)
-
-    Reset all the iterators to the beginning in a multi-iterator
-    object, *multi*.
-
-.. cfunction:: void PyArray_MultiIter_NEXT(PyObject* multi)
-
-    Advance each iterator in a multi-iterator object, *multi*, to its
-    next (broadcasted) element.
-
-.. cfunction:: void *PyArray_MultiIter_DATA(PyObject* multi, int i)
-
-    Return the data-pointer of the *i* :math:`^{\textrm{th}}` iterator
-    in a multi-iterator object.
-
-.. cfunction:: void PyArray_MultiIter_NEXTi(PyObject* multi, int i)
-
-    Advance the pointer of only the *i* :math:`^{\textrm{th}}` iterator.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-
-Array Scalars
--------------
-
-.. cfunction:: PyObject* PyArray_Return(PyArrayObject* 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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*.
-
-.. cfunction:: 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*.
-
-.. cfunction:: 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).
-
-.. cfunction:: PyObject* PyArray_TypeObjectFromType(int type)
-
-    Returns a scalar type-object from a type-number, *type*
-    . Equivalent to :cfunc:`PyArray_DescrFromType` (*type*)->typeobj
-    except for reference counting and error-checking. Returns a new
-    reference to the typeobject on success or ``NULL`` on failure.
-
-.. cfunction:: NPY_SCALARKIND PyArray_ScalarKind(int typenum, PyArrayObject** arr)
-
-    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
-    :cdata:`NPY_INTNEG_SCALAR` is returned, otherwise
-    :cdata:`NPY_INTPOS_SCALAR` is returned. The possible return values
-    are :cdata:`NPY_{kind}_SCALAR` where ``{kind}`` can be **INTPOS**,
-    **INTNEG**, **FLOAT**, **COMPLEX**, **BOOL**, or **OBJECT**.
-    :cdata:`NPY_NOSCALAR` is also an enumerated value
-    :ctype:`NPY_SCALARKIND` variables can take on.
-
-.. cfunction:: int PyArray_CanCoerceScalar(char thistype, char neededtype, NPY_SCALARKIND scalar)
-
-    Implements the rules for scalar coercion. Scalars are only
-    silently coerced from thistype to neededtype if this function
-    returns nonzero.  If scalar is :cdata:`NPY_NOSCALAR`, then this
-    function is equivalent to :cfunc:`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.
-
-
-Data-type descriptors
----------------------
-
-
-
-.. 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 :ctype:`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.
-
-.. cfunction:: int PyArrayDescr_Check(PyObject* obj)
-
-    Evaluates as true if *obj* is a data-type object ( :ctype:`PyArray_Descr *` ).
-
-.. cfunction:: 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).
-
-.. cfunction:: 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 :ctype:`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.
-
-.. cfunction:: 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). If a byteorder of :cdata:`NPY_IGNORE` is encountered it
-    is left alone. If newendian is :cdata:`NPY_SWAP`, then all byte-orders
-    are swapped. Other valid newendian values are :cdata:`NPY_NATIVE`,
-    :cdata:`NPY_LITTLE`, and :cdata:`NPY_BIG` which all cause the returned
-    data-typed descriptor (and all it's
-    referenced data-type descriptors) to have the corresponding byte-
-    order.
-
-.. cfunction:: 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
-    :cfunc:`PyArray_DescrConverter`. This function essentially looks at
-    all the objects in the (nested) sequence and determines the
-    data-type from the elements it finds.
-
-.. cfunction:: 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
-    :cdata:`NPY_OBJECT` is returned by default.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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 :cdata:`NPY_DEFAULT_TYPE` data-type object. This function can
-    be used with the "O&" character code in :cfunc:`PyArg_ParseTuple`
-    processing.
-
-.. cfunction:: 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.
-
-.. cfunction:: int Pyarray_DescrAlignConverter(PyObject* obj, PyArray_Descr** dtype)
-
-    Like :cfunc:`PyArray_DescrConverter` except it aligns C-struct-like
-    objects on word-boundaries as the compiler would.
-
-.. cfunction:: int Pyarray_DescrAlignConverter2(PyObject* obj, PyArray_Descr** dtype)
-
-    Like :cfunc:`PyArray_DescrConverter2` except it aligns C-struct-like
-    objects on word-boundaries as the compiler would.
-
-.. cfunction:: 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 :ctype:`PyArray_Descr` object.
-
-
-Conversion Utilities
---------------------
-
-
-For use with :cfunc:`PyArg_ParseTuple`
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-All of these functions can be used in :cfunc:`PyArg_ParseTuple` (...) with
-the "O&" format specifier to automatically convert any Python object
-to the required C-object. All of these functions return
-:cdata:`NPY_SUCCEED` if successful and :cdata:`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.
-
-.. cfunction:: int PyArray_Converter(PyObject* obj, PyObject** address)
-
-    Convert any Python object to a :ctype:`PyArrayObject`. If
-    :cfunc:`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 :cfunc:`PyArray_FromAny`
-    . No matter what is returned, you must DECREF the object returned
-    by this routine in *address* when you are done with it.
-
-.. cfunction:: int PyArray_OutputConverter(PyObject* obj, PyArrayObject** address)
-
-    This is a default converter for output arrays given to
-    functions. If *obj* is :cdata:`Py_None` or ``NULL``, then *\*address*
-    will be ``NULL`` but the call will succeed. If :cfunc:`PyArray_Check` (
-    *obj*) is TRUE then it is returned in *\*address* without
-    incrementing its reference count.
-
-.. cfunction:: int PyArray_IntpConverter(PyObject* obj, PyArray_Dims* seq)
-
-    Convert any Python sequence, *obj*, smaller than :cdata:`NPY_MAXDIMS`
-    to a C-array of :ctype:`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 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.
-
-.. cfunction:: 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
-    :ctype:`PyArray_Chunk` structure is binary compatibile 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 :cdata:`NPY_BEHAVED_RO` with
-    the :cdata:`NPY_WRITEABLE` flag set if *obj* has a writeable buffer
-    interface.
-
-.. cfunction:: 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
-    :cdata:`NPY_MAXDIMS` which is interpreted correctly by the C-API
-    functions that take axis arguments.
-
-.. cfunction:: int PyArray_BoolConverter(PyObject* obj, Bool* value)
-
-    Convert any Python object, *obj*, to :cdata:`NPY_TRUE` or
-    :cdata:`NPY_FALSE`, and place the result in *value*.
-
-.. cfunction:: int PyArray_ByteorderConverter(PyObject* obj, char* endian)
-
-    Convert Python strings into the corresponding byte-order
-    character:
-    '>', '<', 's', '=', or '\|'.
-
-.. cfunction:: int PyArray_SortkindConverter(PyObject* obj, NPY_SORTKIND* sort)
-
-    Convert Python strings into one of :cdata:`NPY_QUICKSORT` (starts
-    with 'q' or 'Q') , :cdata:`NPY_HEAPSORT` (starts with 'h' or 'H'),
-    or :cdata:`NPY_MERGESORT` (starts with 'm' or 'M').
-
-.. cfunction:: int PyArray_SearchsideConverter(PyObject* obj, NPY_SEARCHSIDE* side)
-
-    Convert Python strings into one of :cdata:`NPY_SEARCHLEFT` (starts with 'l'
-    or 'L'), or :cdata:`NPY_SEARCHRIGHT` (starts with 'r' or 'R').
-
-Other conversions
-^^^^^^^^^^^^^^^^^
-
-.. cfunction:: 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()
-
-.. cfunction:: 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.
-
-.. cfunction:: 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.
-
-.. cfunction:: 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 :cdata:`NPY_FLOAT32`.
-
-
-Miscellaneous
--------------
-
-
-Importing the API
-^^^^^^^^^^^^^^^^^
-
-In order to make use of the C-API from another extension module, the
-``import_array`` () command must be used. 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.
-
-.. cfunction:: 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.
-
-.. cmacro:: PY_ARRAY_UNIQUE_SYMBOL
-    
-.. cmacro:: 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
-    :cmacro:`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
-    intialization routine you call ``import_array`` (). In addition,
-    in the files that do not have the module initialization
-    sub_routine define :cmacro:`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 PY_ARRAY_UNIQUE_SYMBOL cool_ARRAY_API
-        #define NO_IMPORT_ARRAY
-        #include numpy/arrayobject.h
-
-.. cfunction:: unsigned int PyArray_GetNDArrayCVersion(void)
-
-    This just returns the value :cdata:`NPY_VERSION`. 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.
-
-
-Internal Flexibility
-^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: 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**, **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.
-
-.. cfunction:: PyObject* PyArray_GetNumericOps(void)
-
-    Return a Python dictionary containing the callable Python objects
-    stored in the the internal arithmetic operation table. The keys of
-    this dictionary are given in the explanation for :cfunc:`PyArray_SetNumericOps`.
-
-.. cfunction:: 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.
-
-
-Memory management
-^^^^^^^^^^^^^^^^^
-
-.. cfunction:: char* PyDataMem_NEW(size_t nbytes)
-
-.. cfunction:: PyDataMem_FREE(char* ptr)
-
-.. cfunction:: char* PyDataMem_RENEW(void * ptr, size_t newbytes)
-
-    Macros to allocate, free, and reallocate memory. These macros are used
-    internally to create arrays.
-
-.. cfunction:: npy_intp*  PyDimMem_NEW(nd)
-
-.. cfunction:: PyDimMem_FREE(npy_intp* ptr)
-
-.. cfunction:: npy_intp* PyDimMem_RENEW(npy_intp* ptr, npy_intp newnd)
-
-    Macros to allocate, free, and reallocate dimension and strides memory.
-
-.. cfunction:: PyArray_malloc(nbytes)
-
-.. cfunction:: PyArray_free(ptr)
-
-.. cfunction:: PyArray_realloc(ptr, nbytes)
-
-    These macros use different memory allocators, depending on the
-    constant :cdata:`NPY_USE_PYMEM`. The system malloc is used when
-    :cdata:`NPY_USE_PYMEM` is 0, if :cdata:`NPY_USE_PYMEM` is 1, then
-    the Python memory allocator is used.
-
-
-Threading support
-^^^^^^^^^^^^^^^^^
-
-These macros are only meaningful if :cdata:`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 excecute 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, :cdata:`NPY_ALLOW_THREADS` is defined to the
-python-defined :cdata:`WITH_THREADS` constant unless the environment
-variable :cdata:`NPY_NOSMP` is set in which case
-:cdata:`NPY_ALLOW_THREADS` is defined to be 0.
-
-Group 1
-"""""""
-
-    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.
-
-    .. cmacro:: NPY_BEGIN_ALLOW_THREADS
-    
-        Equivalent to :cmacro:`Py_BEGIN_ALLOW_THREADS` except it uses
-        :cdata:`NPY_ALLOW_THREADS` to determine if the macro if
-        replaced with white-space or not.
-    
-    .. cmacro:: NPY_END_ALLOW_THREADS
-    
-        Equivalent to :cmacro:`Py_END_ALLOW_THREADS` except it uses
-        :cdata:`NPY_ALLOW_THREADS` to determine if the macro if
-        replaced with white-space or not.
-    
-    .. cmacro:: NPY_BEGIN_THREADS_DEF
-    
-        Place in the variable declaration area. This macro sets up the
-        variable needed for storing the Python state.
-    
-    .. cmacro:: 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.
-    
-    .. cmacro:: 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.
-    
-    .. cfunction:: 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. Equivalent to
-    
-    .. cfunction:: 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.
-    
-Group 2
-"""""""
-
-    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.
-
-    .. cmacro:: NPY_ALLOW_C_API_DEF
-    
-        Place in the variable declaration area to set up the necessary
-        variable.
-    
-    .. cmacro:: 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).
-    
-    .. cmacro:: 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. 
-
-
-Priority
-^^^^^^^^
-
-.. cvar:: NPY_PRIOIRTY
-
-    Default priority for arrays.
-
-.. cvar:: NPY_SUBTYPE_PRIORITY
-
-    Default subtype priority.
-
-.. cvar:: NPY_SCALAR_PRIORITY
-
-    Default scalar priority (very small)
-
-.. cfunction:: double PyArray_GetPriority(PyObject* obj, double def)
-
-    Return the :obj:`__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 :cdata:`PyArray_Type`.
-
-
-Default buffers
-^^^^^^^^^^^^^^^
-
-.. cvar:: NPY_BUFSIZE
-
-    Default size of the user-settable internal buffers.
-
-.. cvar:: NPY_MIN_BUFSIZE
-
-    Smallest size of user-settable internal buffers.
-
-.. cvar:: NPY_MAX_BUFSIZE
-
-    Largest size allowed for the user-settable buffers.
-
-
-Other constants
-^^^^^^^^^^^^^^^
-
-.. cvar:: NPY_NUM_FLOATTYPE
-
-    The number of floating-point types
-
-.. cvar:: NPY_MAXDIMS
-
-    The maximum number of dimensions allowed in arrays.
-
-.. cvar:: 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).
-
-.. cvar:: NPY_FALSE
-
-    Defined as 0 for use with Bool.
-
-.. cvar:: NPY_TRUE
-
-    Defined as 1 for use with Bool.
-
-.. cvar:: NPY_FAIL
-
-    The return value of failed converter functions which are called using
-    the "O&" syntax in :cfunc:`PyArg_ParseTuple`-like functions.
-
-.. cvar:: NPY_SUCCEED
-
-    The return value of successful converter functions which are called
-    using the "O&" syntax in :cfunc:`PyArg_ParseTuple`-like functions.
-
-
-Miscellaneous Macros
-^^^^^^^^^^^^^^^^^^^^
-
-.. cfunction:: PyArray_SAMESHAPE(a1, a2)
-
-    Evaluates as True if arrays *a1* and *a2* have the same shape.
-
-.. cfunction:: PyArray_MAX(a,b)
-
-    Returns the maximum of *a* and *b*. If (*a*) or (*b*) are
-    expressions they are evaluated twice.
-
-.. cfunction:: PyArray_MIN(a,b)
-
-    Returns the minimum of *a* and *b*. If (*a*) or (*b*) are
-    expressions they are evaluated twice.
-
-.. cfunction:: PyArray_CLT(a,b)
-
-.. cfunction:: PyArray_CGT(a,b)
-
-.. cfunction:: PyArray_CLE(a,b)
-
-.. cfunction:: PyArray_CGE(a,b)
-
-.. cfunction:: PyArray_CEQ(a,b)
-
-.. cfunction:: 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.
-
-.. cfunction:: PyArray_REFCOUNT(PyObject* op)
-
-    Returns the reference count of any Python object.
-
-.. cfunction:: PyArray_XDECREF_ERR(PyObject \*obj)
-
-    DECREF's an array object which may have the :cdata:`NPY_UPDATEIFCOPY`
-    flag set without causing the contents to be copied back into the
-    original array. Resets the :cdata:`NPY_WRITEABLE` flag on the base
-    object. This is useful for recovering from an error condition when
-    :cdata:`NPY_UPDATEIFCOPY` is used.
-
-
-Enumerated Types
-^^^^^^^^^^^^^^^^
-
-.. ctype:: NPY_SORTKIND
-
-    A special variable-type which can take on the values :cdata:`NPY_{KIND}`
-    where ``{KIND}`` is
-
-        **QUICKSORT**, **HEAPSORT**, **MERGESORT** 
-    
-    .. cvar:: NPY_NSORTS
-    
-       Defined to be the number of sorts.
-
-.. ctype:: 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 :cdata:`NPY_{KIND}` where ``{KIND}`` can be
-
-        **NOSCALAR**, **BOOL_SCALAR**, **INTPOS_SCALAR**,
-        **INTNEG_SCALAR**, **FLOAT_SCALAR**, **COMPLEX_SCALAR**,
-        **OBJECT_SCALAR** 
-    
-    
-    .. cvar:: NPY_NSCALARKINDS
-    
-       Defined to be the number of scalar kinds
-       (not including :cdata:`NPY_NOSCALAR`).
-
-.. ctype:: NPY_ORDER
-
-    A variable type indicating the order that an array should be
-    interpreted in. The value of a variable of this type can be
-    :cdata:`NPY_{ORDER}` where ``{ORDER}`` is
-
-        **ANYORDER**, **CORDER**, **FORTRANORDER** 
-
-.. ctype:: NPY_CLIPMODE
-
-    A variable type indicating the kind of clipping that should be
-    applied in certain functions. The value of a variable of this type
-    can be :cdata:`NPY_{MODE}` where ``{MODE}`` is
-
-        **CLIP**, **WRAP**, **RAISE** 
-    
-.. index::
-   pair: ndarray; C-API