AnonymousFox

//opt/alt/python35/lib64/python3.5/site-packages/numpy/add_newdocs.py

"""
This is only meant to add docs to objects defined in C-extension modules.
The purpose is to allow easier editing of the docstrings without
requiring a re-compile.

NOTE: Many of the methods of ndarray have corresponding functions.
      If you update these docstrings, please keep also the ones in
      core/fromnumeric.py, core/defmatrix.py up-to-date.

"""
from __future__ import division, absolute_import, print_function

from numpy.lib import add_newdoc

###############################################################################
#
# flatiter
#
# flatiter needs a toplevel description
#
###############################################################################

add_newdoc('numpy.core', 'flatiter',
    """
    Flat iterator object to iterate over arrays.

A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
    It allows iterating over the array as if it were a 1-D array,
    either in a for-loop or by calling its `next` method.

Iteration is done in row-major, C-style order (the last
    index varying the fastest). The iterator can also be indexed using
    basic slicing or advanced indexing.

See Also
    --------
    ndarray.flat : Return a flat iterator over an array.
    ndarray.flatten : Returns a flattened copy of an array.

Notes
    -----
    A `flatiter` iterator can not be constructed directly from Python code
    by calling the `flatiter` constructor.

Examples
    --------
    >>> x = np.arange(6).reshape(2, 3)
    >>> fl = x.flat
    >>> type(fl)
    <type 'numpy.flatiter'>
    >>> for item in fl:
    ...     print(item)
    ...
    0
    1
    2
    3
    4
    5

>>> fl[2:4]
    array([2, 3])

""")

# flatiter attributes

add_newdoc('numpy.core', 'flatiter', ('base',
    """
    A reference to the array that is iterated over.

Examples
    --------
    >>> x = np.arange(5)
    >>> fl = x.flat
    >>> fl.base is x
    True

"""))

add_newdoc('numpy.core', 'flatiter', ('coords',
    """
    An N-dimensional tuple of current coordinates.

Examples
    --------
    >>> x = np.arange(6).reshape(2, 3)
    >>> fl = x.flat
    >>> fl.coords
    (0, 0)
    >>> fl.next()
    0
    >>> fl.coords
    (0, 1)

"""))

add_newdoc('numpy.core', 'flatiter', ('index',
    """
    Current flat index into the array.

Examples
    --------
    >>> x = np.arange(6).reshape(2, 3)
    >>> fl = x.flat
    >>> fl.index
    0
    >>> fl.next()
    0
    >>> fl.index
    1

"""))

# flatiter functions

add_newdoc('numpy.core', 'flatiter', ('__array__',
    """__array__(type=None) Get array from iterator

"""))

add_newdoc('numpy.core', 'flatiter', ('copy',
    """
    copy()

Get a copy of the iterator as a 1-D array.

Examples
    --------
    >>> x = np.arange(6).reshape(2, 3)
    >>> x
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> fl = x.flat
    >>> fl.copy()
    array([0, 1, 2, 3, 4, 5])

"""))

###############################################################################
#
# nditer
#
###############################################################################

add_newdoc('numpy.core', 'nditer',
    """
    Efficient multi-dimensional iterator object to iterate over arrays.
    To get started using this object, see the
    :ref:`introductory guide to array iteration <arrays.nditer>`.

Parameters
    ----------
    op : ndarray or sequence of array_like
        The array(s) to iterate over.
    flags : sequence of str, optional
        Flags to control the behavior of the iterator.

* "buffered" enables buffering when required.
          * "c_index" causes a C-order index to be tracked.
          * "f_index" causes a Fortran-order index to be tracked.
          * "multi_index" causes a multi-index, or a tuple of indices
            with one per iteration dimension, to be tracked.
          * "common_dtype" causes all the operands to be converted to
            a common data type, with copying or buffering as necessary.
          * "delay_bufalloc" delays allocation of the buffers until
            a reset() call is made. Allows "allocate" operands to
            be initialized before their values are copied into the buffers.
          * "external_loop" causes the `values` given to be
            one-dimensional arrays with multiple values instead of
            zero-dimensional arrays.
          * "grow_inner" allows the `value` array sizes to be made
            larger than the buffer size when both "buffered" and
            "external_loop" is used.
          * "ranged" allows the iterator to be restricted to a sub-range
            of the iterindex values.
          * "refs_ok" enables iteration of reference types, such as
            object arrays.
          * "reduce_ok" enables iteration of "readwrite" operands
            which are broadcasted, also known as reduction operands.
          * "zerosize_ok" allows `itersize` to be zero.
    op_flags : list of list of str, optional
        This is a list of flags for each operand. At minimum, one of
        "readonly", "readwrite", or "writeonly" must be specified.

* "readonly" indicates the operand will only be read from.
          * "readwrite" indicates the operand will be read from and written to.
          * "writeonly" indicates the operand will only be written to.
          * "no_broadcast" prevents the operand from being broadcasted.
          * "contig" forces the operand data to be contiguous.
          * "aligned" forces the operand data to be aligned.
          * "nbo" forces the operand data to be in native byte order.
          * "copy" allows a temporary read-only copy if required.
          * "updateifcopy" allows a temporary read-write copy if required.
          * "allocate" causes the array to be allocated if it is None
            in the `op` parameter.
          * "no_subtype" prevents an "allocate" operand from using a subtype.
          * "arraymask" indicates that this operand is the mask to use
            for selecting elements when writing to operands with the
            'writemasked' flag set. The iterator does not enforce this,
            but when writing from a buffer back to the array, it only
            copies those elements indicated by this mask.
          * 'writemasked' indicates that only elements where the chosen
            'arraymask' operand is True will be written to.
    op_dtypes : dtype or tuple of dtype(s), optional
        The required data type(s) of the operands. If copying or buffering
        is enabled, the data will be converted to/from their original types.
    order : {'C', 'F', 'A', 'K'}, optional
        Controls the iteration order. 'C' means C order, 'F' means
        Fortran order, 'A' means 'F' order if all the arrays are Fortran
        contiguous, 'C' order otherwise, and 'K' means as close to the
        order the array elements appear in memory as possible. This also
        affects the element memory order of "allocate" operands, as they
        are allocated to be compatible with iteration order.
        Default is 'K'.
    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        Controls what kind of data casting may occur when making a copy
        or buffering.  Setting this to 'unsafe' is not recommended,
        as it can adversely affect accumulations.

* 'no' means the data types should not be cast at all.
          * 'equiv' means only byte-order changes are allowed.
          * 'safe' means only casts which can preserve values are allowed.
          * 'same_kind' means only safe casts or casts within a kind,
            like float64 to float32, are allowed.
          * 'unsafe' means any data conversions may be done.
    op_axes : list of list of ints, optional
        If provided, is a list of ints or None for each operands.
        The list of axes for an operand is a mapping from the dimensions
        of the iterator to the dimensions of the operand. A value of
        -1 can be placed for entries, causing that dimension to be
        treated as "newaxis".
    itershape : tuple of ints, optional
        The desired shape of the iterator. This allows "allocate" operands
        with a dimension mapped by op_axes not corresponding to a dimension
        of a different operand to get a value not equal to 1 for that
        dimension.
    buffersize : int, optional
        When buffering is enabled, controls the size of the temporary
        buffers. Set to 0 for the default value.

Attributes
    ----------
    dtypes : tuple of dtype(s)
        The data types of the values provided in `value`. This may be
        different from the operand data types if buffering is enabled.
    finished : bool
        Whether the iteration over the operands is finished or not.
    has_delayed_bufalloc : bool
        If True, the iterator was created with the "delay_bufalloc" flag,
        and no reset() function was called on it yet.
    has_index : bool
        If True, the iterator was created with either the "c_index" or
        the "f_index" flag, and the property `index` can be used to
        retrieve it.
    has_multi_index : bool
        If True, the iterator was created with the "multi_index" flag,
        and the property `multi_index` can be used to retrieve it.
    index :
        When the "c_index" or "f_index" flag was used, this property
        provides access to the index. Raises a ValueError if accessed
        and `has_index` is False.
    iterationneedsapi : bool
        Whether iteration requires access to the Python API, for example
        if one of the operands is an object array.
    iterindex : int
        An index which matches the order of iteration.
    itersize : int
        Size of the iterator.
    itviews :
        Structured view(s) of `operands` in memory, matching the reordered
        and optimized iterator access pattern.
    multi_index :
        When the "multi_index" flag was used, this property
        provides access to the index. Raises a ValueError if accessed
        accessed and `has_multi_index` is False.
    ndim : int
        The iterator's dimension.
    nop : int
        The number of iterator operands.
    operands : tuple of operand(s)
        The array(s) to be iterated over.
    shape : tuple of ints
        Shape tuple, the shape of the iterator.
    value :
        Value of `operands` at current iteration. Normally, this is a
        tuple of array scalars, but if the flag "external_loop" is used,
        it is a tuple of one dimensional arrays.

Notes
    -----
    `nditer` supersedes `flatiter`.  The iterator implementation behind
    `nditer` is also exposed by the Numpy C API.

The Python exposure supplies two iteration interfaces, one which follows
    the Python iterator protocol, and another which mirrors the C-style
    do-while pattern.  The native Python approach is better in most cases, but
    if you need the iterator's coordinates or index, use the C-style pattern.

Examples
    --------
    Here is how we might write an ``iter_add`` function, using the
    Python iterator protocol::

def iter_add_py(x, y, out=None):
            addop = np.add
            it = np.nditer([x, y, out], [],
                        [['readonly'], ['readonly'], ['writeonly','allocate']])
            for (a, b, c) in it:
                addop(a, b, out=c)
            return it.operands[2]

Here is the same function, but following the C-style pattern::

def iter_add(x, y, out=None):
            addop = np.add

it = np.nditer([x, y, out], [],
                        [['readonly'], ['readonly'], ['writeonly','allocate']])

while not it.finished:
                addop(it[0], it[1], out=it[2])
                it.iternext()

return it.operands[2]

Here is an example outer product function::

def outer_it(x, y, out=None):
            mulop = np.multiply

it = np.nditer([x, y, out], ['external_loop'],
                    [['readonly'], ['readonly'], ['writeonly', 'allocate']],
                    op_axes=[range(x.ndim)+[-1]*y.ndim,
                             [-1]*x.ndim+range(y.ndim),
                             None])

for (a, b, c) in it:
                mulop(a, b, out=c)

return it.operands[2]

>>> a = np.arange(2)+1
        >>> b = np.arange(3)+1
        >>> outer_it(a,b)
        array([[1, 2, 3],
               [2, 4, 6]])

Here is an example function which operates like a "lambda" ufunc::

def luf(lamdaexpr, *args, **kwargs):
            "luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)"
            nargs = len(args)
            op = (kwargs.get('out',None),) + args
            it = np.nditer(op, ['buffered','external_loop'],
                    [['writeonly','allocate','no_broadcast']] +
                                    [['readonly','nbo','aligned']]*nargs,
                    order=kwargs.get('order','K'),
                    casting=kwargs.get('casting','safe'),
                    buffersize=kwargs.get('buffersize',0))
            while not it.finished:
                it[0] = lamdaexpr(*it[1:])
                it.iternext()
            return it.operands[0]

>>> a = np.arange(5)
        >>> b = np.ones(5)
        >>> luf(lambda i,j:i*i + j/2, a, b)
        array([  0.5,   1.5,   4.5,   9.5,  16.5])

""")

# nditer methods

add_newdoc('numpy.core', 'nditer', ('copy',
    """
    copy()

Get a copy of the iterator in its current state.

Examples
    --------
    >>> x = np.arange(10)
    >>> y = x + 1
    >>> it = np.nditer([x, y])
    >>> it.next()
    (array(0), array(1))
    >>> it2 = it.copy()
    >>> it2.next()
    (array(1), array(2))

"""))

add_newdoc('numpy.core', 'nditer', ('debug_print',
    """
    debug_print()

Print the current state of the `nditer` instance and debug info to stdout.

"""))

add_newdoc('numpy.core', 'nditer', ('enable_external_loop',
    """
    enable_external_loop()

When the "external_loop" was not used during construction, but
    is desired, this modifies the iterator to behave as if the flag
    was specified.

"""))

add_newdoc('numpy.core', 'nditer', ('iternext',
    """
    iternext()

Check whether iterations are left, and perform a single internal iteration
    without returning the result.  Used in the C-style pattern do-while
    pattern.  For an example, see `nditer`.

Returns
    -------
    iternext : bool
        Whether or not there are iterations left.

"""))

add_newdoc('numpy.core', 'nditer', ('remove_axis',
    """
    remove_axis(i)

Removes axis `i` from the iterator. Requires that the flag "multi_index"
    be enabled.

"""))

add_newdoc('numpy.core', 'nditer', ('remove_multi_index',
    """
    remove_multi_index()

When the "multi_index" flag was specified, this removes it, allowing
    the internal iteration structure to be optimized further.

"""))

add_newdoc('numpy.core', 'nditer', ('reset',
    """
    reset()

Reset the iterator to its initial state.

"""))

###############################################################################
#
# broadcast
#
###############################################################################

add_newdoc('numpy.core', 'broadcast',
    """
    Produce an object that mimics broadcasting.

Parameters
    ----------
    in1, in2, ... : array_like
        Input parameters.

Returns
    -------
    b : broadcast object
        Broadcast the input parameters against one another, and
        return an object that encapsulates the result.
        Amongst others, it has ``shape`` and ``nd`` properties, and
        may be used as an iterator.

Examples
    --------
    Manually adding two vectors, using broadcasting:

>>> x = np.array([[1], [2], [3]])
    >>> y = np.array([4, 5, 6])
    >>> b = np.broadcast(x, y)

>>> out = np.empty(b.shape)
    >>> out.flat = [u+v for (u,v) in b]
    >>> out
    array([[ 5.,  6.,  7.],
           [ 6.,  7.,  8.],
           [ 7.,  8.,  9.]])

Compare against built-in broadcasting:

>>> x + y
    array([[5, 6, 7],
           [6, 7, 8],
           [7, 8, 9]])

""")

# attributes

add_newdoc('numpy.core', 'broadcast', ('index',
    """
    current index in broadcasted result

Examples
    --------
    >>> x = np.array([[1], [2], [3]])
    >>> y = np.array([4, 5, 6])
    >>> b = np.broadcast(x, y)
    >>> b.index
    0
    >>> b.next(), b.next(), b.next()
    ((1, 4), (1, 5), (1, 6))
    >>> b.index
    3

"""))

add_newdoc('numpy.core', 'broadcast', ('iters',
    """
    tuple of iterators along ``self``'s "components."

Returns a tuple of `numpy.flatiter` objects, one for each "component"
    of ``self``.

See Also
    --------
    numpy.flatiter

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]])
    >>> b = np.broadcast(x, y)
    >>> row, col = b.iters
    >>> row.next(), col.next()
    (1, 4)

"""))

add_newdoc('numpy.core', 'broadcast', ('nd',
    """
    Number of dimensions of broadcasted result.

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]])
    >>> b = np.broadcast(x, y)
    >>> b.nd
    2

"""))

add_newdoc('numpy.core', 'broadcast', ('numiter',
    """
    Number of iterators possessed by the broadcasted result.

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]])
    >>> b = np.broadcast(x, y)
    >>> b.numiter
    2

"""))

add_newdoc('numpy.core', 'broadcast', ('shape',
    """
    Shape of broadcasted result.

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]])
    >>> b = np.broadcast(x, y)
    >>> b.shape
    (3, 3)

"""))

add_newdoc('numpy.core', 'broadcast', ('size',
    """
    Total size of broadcasted result.

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]])
    >>> b = np.broadcast(x, y)
    >>> b.size
    9

"""))

add_newdoc('numpy.core', 'broadcast', ('reset',
    """
    reset()

Reset the broadcasted result's iterator(s).

Parameters
    ----------
    None

Returns
    -------
    None

Examples
    --------
    >>> x = np.array([1, 2, 3])
    >>> y = np.array([[4], [5], [6]]
    >>> b = np.broadcast(x, y)
    >>> b.index
    0
    >>> b.next(), b.next(), b.next()
    ((1, 4), (2, 4), (3, 4))
    >>> b.index
    3
    >>> b.reset()
    >>> b.index
    0

"""))

###############################################################################
#
# numpy functions
#
###############################################################################

add_newdoc('numpy.core.multiarray', 'array',
    """
    array(object, dtype=None, copy=True, order=None, subok=False, ndmin=0)

Create an array.

Parameters
    ----------
    object : array_like
        An array, any object exposing the array interface, an
        object whose __array__ method returns an array, or any
        (nested) sequence.
    dtype : data-type, optional
        The desired data-type for the array.  If not given, then
        the type will be determined as the minimum type required
        to hold the objects in the sequence.  This argument can only
        be used to 'upcast' the array.  For downcasting, use the
        .astype(t) method.
    copy : bool, optional
        If true (default), then the object is copied.  Otherwise, a copy
        will only be made if __array__ returns a copy, if obj is a
        nested sequence, or if a copy is needed to satisfy any of the other
        requirements (`dtype`, `order`, etc.).
    order : {'C', 'F', 'A'}, optional
        Specify the order of the array.  If order is 'C', then the array
        will be in C-contiguous order (last-index varies the fastest).
        If order is 'F', then the returned array will be in
        Fortran-contiguous order (first-index varies the fastest).
        If order is 'A' (default), then the returned array may be
        in any order (either C-, Fortran-contiguous, or even discontiguous),
        unless a copy is required, in which case it will be C-contiguous.
    subok : bool, optional
        If True, then sub-classes will be passed-through, otherwise
        the returned array will be forced to be a base-class array (default).
    ndmin : int, optional
        Specifies the minimum number of dimensions that the resulting
        array should have.  Ones will be pre-pended to the shape as
        needed to meet this requirement.

Returns
    -------
    out : ndarray
        An array object satisfying the specified requirements.

See Also
    --------
    empty, empty_like, zeros, zeros_like, ones, ones_like, fill

Examples
    --------
    >>> np.array([1, 2, 3])
    array([1, 2, 3])

Upcasting:

>>> np.array([1, 2, 3.0])
    array([ 1.,  2.,  3.])

More than one dimension:

>>> np.array([[1, 2], [3, 4]])
    array([[1, 2],
           [3, 4]])

Minimum dimensions 2:

>>> np.array([1, 2, 3], ndmin=2)
    array([[1, 2, 3]])

Type provided:

>>> np.array([1, 2, 3], dtype=complex)
    array([ 1.+0.j,  2.+0.j,  3.+0.j])

Data-type consisting of more than one element:

>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
    >>> x['a']
    array([1, 3])

Creating an array from sub-classes:

>>> np.array(np.mat('1 2; 3 4'))
    array([[1, 2],
           [3, 4]])

>>> np.array(np.mat('1 2; 3 4'), subok=True)
    matrix([[1, 2],
            [3, 4]])

""")

add_newdoc('numpy.core.multiarray', 'empty',
    """
    empty(shape, dtype=float, order='C')

Return a new array of given shape and type, without initializing entries.

Parameters
    ----------
    shape : int or tuple of int
        Shape of the empty array
    dtype : data-type, optional
        Desired output data-type.
    order : {'C', 'F'}, optional
        Whether to store multi-dimensional data in row-major
        (C-style) or column-major (Fortran-style) order in
        memory.

Returns
    -------
    out : ndarray
        Array of uninitialized (arbitrary) data of the given shape, dtype, and
        order.  Object arrays will be initialized to None.

See Also
    --------
    empty_like, zeros, ones

Notes
    -----
    `empty`, unlike `zeros`, does not set the array values to zero,
    and may therefore be marginally faster.  On the other hand, it requires
    the user to manually set all the values in the array, and should be
    used with caution.

Examples
    --------
    >>> np.empty([2, 2])
    array([[ -9.74499359e+001,   6.69583040e-309],
           [  2.13182611e-314,   3.06959433e-309]])         #random

>>> np.empty([2, 2], dtype=int)
    array([[-1073741821, -1067949133],
           [  496041986,    19249760]])                     #random

""")

add_newdoc('numpy.core.multiarray', 'empty_like',
    """
    empty_like(a, dtype=None, order='K', subok=True)

Return a new array with the same shape and type as a given array.

Parameters
    ----------
    a : array_like
        The shape and data-type of `a` define these same attributes of the
        returned array.
    dtype : data-type, optional
        Overrides the data type of the result.

.. versionadded:: 1.6.0
    order : {'C', 'F', 'A', or 'K'}, optional
        Overrides the memory layout of the result. 'C' means C-order,
        'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,
        'C' otherwise. 'K' means match the layout of ``a`` as closely
        as possible.

.. versionadded:: 1.6.0
    subok : bool, optional.
        If True, then the newly created array will use the sub-class
        type of 'a', otherwise it will be a base-class array. Defaults
        to True.

Returns
    -------
    out : ndarray
        Array of uninitialized (arbitrary) data with the same
        shape and type as `a`.

See Also
    --------
    ones_like : Return an array of ones with shape and type of input.
    zeros_like : Return an array of zeros with shape and type of input.
    empty : Return a new uninitialized array.
    ones : Return a new array setting values to one.
    zeros : Return a new array setting values to zero.

Notes
    -----
    This function does *not* initialize the returned array; to do that use
    `zeros_like` or `ones_like` instead.  It may be marginally faster than
    the functions that do set the array values.

Examples
    --------
    >>> a = ([1,2,3], [4,5,6])                         # a is array-like
    >>> np.empty_like(a)
    array([[-1073741821, -1073741821,           3],    #random
           [          0,           0, -1073741821]])
    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
    >>> np.empty_like(a)
    array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
           [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])

""")

add_newdoc('numpy.core.multiarray', 'scalar',
    """
    scalar(dtype, obj)

Return a new scalar array of the given type initialized with obj.

This function is meant mainly for pickle support. `dtype` must be a
    valid data-type descriptor. If `dtype` corresponds to an object
    descriptor, then `obj` can be any object, otherwise `obj` must be a
    string. If `obj` is not given, it will be interpreted as None for object
    type and as zeros for all other types.

""")

add_newdoc('numpy.core.multiarray', 'zeros',
    """
    zeros(shape, dtype=float, order='C')

Return a new array of given shape and type, filled with zeros.

Parameters
    ----------
    shape : int or sequence of ints
        Shape of the new array, e.g., ``(2, 3)`` or ``2``.
    dtype : data-type, optional
        The desired data-type for the array, e.g., `numpy.int8`.  Default is
        `numpy.float64`.
    order : {'C', 'F'}, optional
        Whether to store multidimensional data in C- or Fortran-contiguous
        (row- or column-wise) order in memory.

Returns
    -------
    out : ndarray
        Array of zeros with the given shape, dtype, and order.

See Also
    --------
    zeros_like : Return an array of zeros with shape and type of input.
    ones_like : Return an array of ones with shape and type of input.
    empty_like : Return an empty array with shape and type of input.
    ones : Return a new array setting values to one.
    empty : Return a new uninitialized array.

Examples
    --------
    >>> np.zeros(5)
    array([ 0.,  0.,  0.,  0.,  0.])

>>> np.zeros((5,), dtype=np.int)
    array([0, 0, 0, 0, 0])

>>> np.zeros((2, 1))
    array([[ 0.],
           [ 0.]])

>>> s = (2,2)
    >>> np.zeros(s)
    array([[ 0.,  0.],
           [ 0.,  0.]])

>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
    array([(0, 0), (0, 0)],
          dtype=[('x', '<i4'), ('y', '<i4')])

""")

add_newdoc('numpy.core.multiarray', 'count_nonzero',
    """
    count_nonzero(a)

Counts the number of non-zero values in the array ``a``.

Parameters
    ----------
    a : array_like
        The array for which to count non-zeros.

Returns
    -------
    count : int or array of int
        Number of non-zero values in the array.

See Also
    --------
    nonzero : Return the coordinates of all the non-zero values.

Examples
    --------
    >>> np.count_nonzero(np.eye(4))
    4
    >>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])
    5
    """)

add_newdoc('numpy.core.multiarray', 'set_typeDict',
    """set_typeDict(dict)

Set the internal dictionary that can look up an array type using a
    registered code.

""")

add_newdoc('numpy.core.multiarray', 'fromstring',
    """
    fromstring(string, dtype=float, count=-1, sep='')

A new 1-D array initialized from raw binary or text data in a string.

Parameters
    ----------
    string : str
        A string containing the data.
    dtype : data-type, optional
        The data type of the array; default: float.  For binary input data,
        the data must be in exactly this format.
    count : int, optional
        Read this number of `dtype` elements from the data.  If this is
        negative (the default), the count will be determined from the
        length of the data.
    sep : str, optional
        If not provided or, equivalently, the empty string, the data will
        be interpreted as binary data; otherwise, as ASCII text with
        decimal numbers.  Also in this latter case, this argument is
        interpreted as the string separating numbers in the data; extra
        whitespace between elements is also ignored.

Returns
    -------
    arr : ndarray
        The constructed array.

Raises
    ------
    ValueError
        If the string is not the correct size to satisfy the requested
        `dtype` and `count`.

See Also
    --------
    frombuffer, fromfile, fromiter

Examples
    --------
    >>> np.fromstring('\\x01\\x02', dtype=np.uint8)
    array([1, 2], dtype=uint8)
    >>> np.fromstring('1 2', dtype=int, sep=' ')
    array([1, 2])
    >>> np.fromstring('1, 2', dtype=int, sep=',')
    array([1, 2])
    >>> np.fromstring('\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)
    array([1, 2, 3], dtype=uint8)

""")

add_newdoc('numpy.core.multiarray', 'fromiter',
    """
    fromiter(iterable, dtype, count=-1)

Create a new 1-dimensional array from an iterable object.

Parameters
    ----------
    iterable : iterable object
        An iterable object providing data for the array.
    dtype : data-type
        The data-type of the returned array.
    count : int, optional
        The number of items to read from *iterable*.  The default is -1,
        which means all data is read.

Returns
    -------
    out : ndarray
        The output array.

Notes
    -----
    Specify `count` to improve performance.  It allows ``fromiter`` to
    pre-allocate the output array, instead of resizing it on demand.

Examples
    --------
    >>> iterable = (x*x for x in range(5))
    >>> np.fromiter(iterable, np.float)
    array([  0.,   1.,   4.,   9.,  16.])

""")

add_newdoc('numpy.core.multiarray', 'fromfile',
    """
    fromfile(file, dtype=float, count=-1, sep='')

Construct an array from data in a text or binary file.

A highly efficient way of reading binary data with a known data-type,
    as well as parsing simply formatted text files.  Data written using the
    `tofile` method can be read using this function.

Parameters
    ----------
    file : file or str
        Open file object or filename.
    dtype : data-type
        Data type of the returned array.
        For binary files, it is used to determine the size and byte-order
        of the items in the file.
    count : int
        Number of items to read. ``-1`` means all items (i.e., the complete
        file).
    sep : str
        Separator between items if file is a text file.
        Empty ("") separator means the file should be treated as binary.
        Spaces (" ") in the separator match zero or more whitespace characters.
        A separator consisting only of spaces must match at least one
        whitespace.

See also
    --------
    load, save
    ndarray.tofile
    loadtxt : More flexible way of loading data from a text file.

Notes
    -----
    Do not rely on the combination of `tofile` and `fromfile` for
    data storage, as the binary files generated are are not platform
    independent.  In particular, no byte-order or data-type information is
    saved.  Data can be stored in the platform independent ``.npy`` format
    using `save` and `load` instead.

Examples
    --------
    Construct an ndarray:

>>> dt = np.dtype([('time', [('min', int), ('sec', int)]),
    ...                ('temp', float)])
    >>> x = np.zeros((1,), dtype=dt)
    >>> x['time']['min'] = 10; x['temp'] = 98.25
    >>> x
    array([((10, 0), 98.25)],
          dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

Save the raw data to disk:

>>> import os
    >>> fname = os.tmpnam()
    >>> x.tofile(fname)

Read the raw data from disk:

>>> np.fromfile(fname, dtype=dt)
    array([((10, 0), 98.25)],
          dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

The recommended way to store and load data:

>>> np.save(fname, x)
    >>> np.load(fname + '.npy')
    array([((10, 0), 98.25)],
          dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])

""")

add_newdoc('numpy.core.multiarray', 'frombuffer',
    """
    frombuffer(buffer, dtype=float, count=-1, offset=0)

Interpret a buffer as a 1-dimensional array.

Parameters
    ----------
    buffer : buffer_like
        An object that exposes the buffer interface.
    dtype : data-type, optional
        Data-type of the returned array; default: float.
    count : int, optional
        Number of items to read. ``-1`` means all data in the buffer.
    offset : int, optional
        Start reading the buffer from this offset; default: 0.

Notes
    -----
    If the buffer has data that is not in machine byte-order, this should
    be specified as part of the data-type, e.g.::

>>> dt = np.dtype(int)
      >>> dt = dt.newbyteorder('>')
      >>> np.frombuffer(buf, dtype=dt)

The data of the resulting array will not be byteswapped, but will be
    interpreted correctly.

Examples
    --------
    >>> s = 'hello world'
    >>> np.frombuffer(s, dtype='S1', count=5, offset=6)
    array(['w', 'o', 'r', 'l', 'd'],
          dtype='|S1')

""")

add_newdoc('numpy.core.multiarray', 'concatenate',
    """
    concatenate((a1, a2, ...), axis=0)

Join a sequence of arrays along an existing axis.

Parameters
    ----------
    a1, a2, ... : sequence of array_like
        The arrays must have the same shape, except in the dimension
        corresponding to `axis` (the first, by default).
    axis : int, optional
        The axis along which the arrays will be joined.  Default is 0.

Returns
    -------
    res : ndarray
        The concatenated array.

See Also
    --------
    ma.concatenate : Concatenate function that preserves input masks.
    array_split : Split an array into multiple sub-arrays of equal or
                  near-equal size.
    split : Split array into a list of multiple sub-arrays of equal size.
    hsplit : Split array into multiple sub-arrays horizontally (column wise)
    vsplit : Split array into multiple sub-arrays vertically (row wise)
    dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
    stack : Stack a sequence of arrays along a new axis.
    hstack : Stack arrays in sequence horizontally (column wise)
    vstack : Stack arrays in sequence vertically (row wise)
    dstack : Stack arrays in sequence depth wise (along third dimension)

Notes
    -----
    When one or more of the arrays to be concatenated is a MaskedArray,
    this function will return a MaskedArray object instead of an ndarray,
    but the input masks are *not* preserved. In cases where a MaskedArray
    is expected as input, use the ma.concatenate function from the masked
    array module instead.

Examples
    --------
    >>> a = np.array([[1, 2], [3, 4]])
    >>> b = np.array([[5, 6]])
    >>> np.concatenate((a, b), axis=0)
    array([[1, 2],
           [3, 4],
           [5, 6]])
    >>> np.concatenate((a, b.T), axis=1)
    array([[1, 2, 5],
           [3, 4, 6]])

This function will not preserve masking of MaskedArray inputs.

>>> a = np.ma.arange(3)
    >>> a[1] = np.ma.masked
    >>> b = np.arange(2, 5)
    >>> a
    masked_array(data = [0 -- 2],
                 mask = [False  True False],
           fill_value = 999999)
    >>> b
    array([2, 3, 4])
    >>> np.concatenate([a, b])
    masked_array(data = [0 1 2 2 3 4],
                 mask = False,
           fill_value = 999999)
    >>> np.ma.concatenate([a, b])
    masked_array(data = [0 -- 2 2 3 4],
                 mask = [False  True False False False False],
           fill_value = 999999)

""")

add_newdoc('numpy.core', 'inner',
    """
    inner(a, b)

Inner product of two arrays.

Ordinary inner product of vectors for 1-D arrays (without complex
    conjugation), in higher dimensions a sum product over the last axes.

Parameters
    ----------
    a, b : array_like
        If `a` and `b` are nonscalar, their last dimensions must match.

Returns
    -------
    out : ndarray
        `out.shape = a.shape[:-1] + b.shape[:-1]`

Raises
    ------
    ValueError
        If the last dimension of `a` and `b` has different size.

See Also
    --------
    tensordot : Sum products over arbitrary axes.
    dot : Generalised matrix product, using second last dimension of `b`.
    einsum : Einstein summation convention.

Notes
    -----
    For vectors (1-D arrays) it computes the ordinary inner-product::

np.inner(a, b) = sum(a[:]*b[:])

More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::

np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

or explicitly::

np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
             = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])

In addition `a` or `b` may be scalars, in which case::

np.inner(a,b) = a*b

Examples
    --------
    Ordinary inner product for vectors:

>>> a = np.array([1,2,3])
    >>> b = np.array([0,1,0])
    >>> np.inner(a, b)
    2

A multidimensional example:

>>> a = np.arange(24).reshape((2,3,4))
    >>> b = np.arange(4)
    >>> np.inner(a, b)
    array([[ 14,  38,  62],
           [ 86, 110, 134]])

An example where `b` is a scalar:

>>> np.inner(np.eye(2), 7)
    array([[ 7.,  0.],
           [ 0.,  7.]])

""")

add_newdoc('numpy.core', 'fastCopyAndTranspose',
    """_fastCopyAndTranspose(a)""")

add_newdoc('numpy.core.multiarray', 'correlate',
    """cross_correlate(a,v, mode=0)""")

add_newdoc('numpy.core.multiarray', 'arange',
    """
    arange([start,] stop[, step,], dtype=None)

Return evenly spaced values within a given interval.

Values are generated within the half-open interval ``[start, stop)``
    (in other words, the interval including `start` but excluding `stop`).
    For integer arguments the function is equivalent to the Python built-in
    `range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
    but returns an ndarray rather than a list.

When using a non-integer step, such as 0.1, the results will often not
    be consistent.  It is better to use ``linspace`` for these cases.

Parameters
    ----------
    start : number, optional
        Start of interval.  The interval includes this value.  The default
        start value is 0.
    stop : number
        End of interval.  The interval does not include this value, except
        in some cases where `step` is not an integer and floating point
        round-off affects the length of `out`.
    step : number, optional
        Spacing between values.  For any output `out`, this is the distance
        between two adjacent values, ``out[i+1] - out[i]``.  The default
        step size is 1.  If `step` is specified, `start` must also be given.
    dtype : dtype
        The type of the output array.  If `dtype` is not given, infer the data
        type from the other input arguments.

Returns
    -------
    arange : ndarray
        Array of evenly spaced values.

For floating point arguments, the length of the result is
        ``ceil((stop - start)/step)``.  Because of floating point overflow,
        this rule may result in the last element of `out` being greater
        than `stop`.

See Also
    --------
    linspace : Evenly spaced numbers with careful handling of endpoints.
    ogrid: Arrays of evenly spaced numbers in N-dimensions.
    mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.

Examples
    --------
    >>> np.arange(3)
    array([0, 1, 2])
    >>> np.arange(3.0)
    array([ 0.,  1.,  2.])
    >>> np.arange(3,7)
    array([3, 4, 5, 6])
    >>> np.arange(3,7,2)
    array([3, 5])

""")

add_newdoc('numpy.core.multiarray', '_get_ndarray_c_version',
    """_get_ndarray_c_version()

Return the compile time NDARRAY_VERSION number.

""")

add_newdoc('numpy.core.multiarray', '_reconstruct',
    """_reconstruct(subtype, shape, dtype)

Construct an empty array. Used by Pickles.

""")

add_newdoc('numpy.core.multiarray', 'set_string_function',
    """
    set_string_function(f, repr=1)

Internal method to set a function to be used when pretty printing arrays.

""")

add_newdoc('numpy.core.multiarray', 'set_numeric_ops',
    """
    set_numeric_ops(op1=func1, op2=func2, ...)

Set numerical operators for array objects.

Parameters
    ----------
    op1, op2, ... : callable
        Each ``op = func`` pair describes an operator to be replaced.
        For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
        addition by modulus 5 addition.

Returns
    -------
    saved_ops : list of callables
        A list of all operators, stored before making replacements.

Notes
    -----
    .. WARNING::
       Use with care!  Incorrect usage may lead to memory errors.

A function replacing an operator cannot make use of that operator.
    For example, when replacing add, you may not use ``+``.  Instead,
    directly call ufuncs.

Examples
    --------
    >>> def add_mod5(x, y):
    ...     return np.add(x, y) % 5
    ...
    >>> old_funcs = np.set_numeric_ops(add=add_mod5)

>>> x = np.arange(12).reshape((3, 4))
    >>> x + x
    array([[0, 2, 4, 1],
           [3, 0, 2, 4],
           [1, 3, 0, 2]])

>>> ignore = np.set_numeric_ops(**old_funcs) # restore operators

""")

add_newdoc('numpy.core.multiarray', 'where',
    """
    where(condition, [x, y])

Return elements, either from `x` or `y`, depending on `condition`.

If only `condition` is given, return ``condition.nonzero()``.

Parameters
    ----------
    condition : array_like, bool
        When True, yield `x`, otherwise yield `y`.
    x, y : array_like, optional
        Values from which to choose. `x` and `y` need to have the same
        shape as `condition`.

Returns
    -------
    out : ndarray or tuple of ndarrays
        If both `x` and `y` are specified, the output array contains
        elements of `x` where `condition` is True, and elements from
        `y` elsewhere.

If only `condition` is given, return the tuple
        ``condition.nonzero()``, the indices where `condition` is True.

See Also
    --------
    nonzero, choose

Notes
    -----
    If `x` and `y` are given and input arrays are 1-D, `where` is
    equivalent to::

[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]

Examples
    --------
    >>> np.where([[True, False], [True, True]],
    ...          [[1, 2], [3, 4]],
    ...          [[9, 8], [7, 6]])
    array([[1, 8],
           [3, 4]])

>>> np.where([[0, 1], [1, 0]])
    (array([0, 1]), array([1, 0]))

>>> x = np.arange(9.).reshape(3, 3)
    >>> np.where( x > 5 )
    (array([2, 2, 2]), array([0, 1, 2]))
    >>> x[np.where( x > 3.0 )]               # Note: result is 1D.
    array([ 4.,  5.,  6.,  7.,  8.])
    >>> np.where(x < 5, x, -1)               # Note: broadcasting.
    array([[ 0.,  1.,  2.],
           [ 3.,  4., -1.],
           [-1., -1., -1.]])

Find the indices of elements of `x` that are in `goodvalues`.

>>> goodvalues = [3, 4, 7]
    >>> ix = np.in1d(x.ravel(), goodvalues).reshape(x.shape)
    >>> ix
    array([[False, False, False],
           [ True,  True, False],
           [False,  True, False]], dtype=bool)
    >>> np.where(ix)
    (array([1, 1, 2]), array([0, 1, 1]))

""")

add_newdoc('numpy.core.multiarray', 'lexsort',
    """
    lexsort(keys, axis=-1)

Perform an indirect sort using a sequence of keys.

Given multiple sorting keys, which can be interpreted as columns in a
    spreadsheet, lexsort returns an array of integer indices that describes
    the sort order by multiple columns. The last key in the sequence is used
    for the primary sort order, the second-to-last key for the secondary sort
    order, and so on. The keys argument must be a sequence of objects that
    can be converted to arrays of the same shape. If a 2D array is provided
    for the keys argument, it's rows are interpreted as the sorting keys and
    sorting is according to the last row, second last row etc.

Parameters
    ----------
    keys : (k, N) array or tuple containing k (N,)-shaped sequences
        The `k` different "columns" to be sorted.  The last column (or row if
        `keys` is a 2D array) is the primary sort key.
    axis : int, optional
        Axis to be indirectly sorted.  By default, sort over the last axis.

Returns
    -------
    indices : (N,) ndarray of ints
        Array of indices that sort the keys along the specified axis.

See Also
    --------
    argsort : Indirect sort.
    ndarray.sort : In-place sort.
    sort : Return a sorted copy of an array.

Examples
    --------
    Sort names: first by surname, then by name.

>>> surnames =    ('Hertz',    'Galilei', 'Hertz')
    >>> first_names = ('Heinrich', 'Galileo', 'Gustav')
    >>> ind = np.lexsort((first_names, surnames))
    >>> ind
    array([1, 2, 0])

>>> [surnames[i] + ", " + first_names[i] for i in ind]
    ['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']

Sort two columns of numbers:

>>> a = [1,5,1,4,3,4,4] # First column
    >>> b = [9,4,0,4,0,2,1] # Second column
    >>> ind = np.lexsort((b,a)) # Sort by a, then by b
    >>> print(ind)
    [2 0 4 6 5 3 1]

>>> [(a[i],b[i]) for i in ind]
    [(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]

Note that sorting is first according to the elements of ``a``.
    Secondary sorting is according to the elements of ``b``.

A normal ``argsort`` would have yielded:

>>> [(a[i],b[i]) for i in np.argsort(a)]
    [(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]

Structured arrays are sorted lexically by ``argsort``:

>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
    ...              dtype=np.dtype([('x', int), ('y', int)]))

>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
    array([2, 0, 4, 6, 5, 3, 1])

""")

add_newdoc('numpy.core.multiarray', 'can_cast',
    """
    can_cast(from, totype, casting = 'safe')

Returns True if cast between data types can occur according to the
    casting rule.  If from is a scalar or array scalar, also returns
    True if the scalar value can be cast without overflow or truncation
    to an integer.

Parameters
    ----------
    from : dtype, dtype specifier, scalar, or array
        Data type, scalar, or array to cast from.
    totype : dtype or dtype specifier
        Data type to cast to.
    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        Controls what kind of data casting may occur.

Returns
    -------
    out : bool
        True if cast can occur according to the casting rule.

Notes
    -----
    Starting in NumPy 1.9, can_cast function now returns False in 'safe'
    casting mode for integer/float dtype and string dtype if the string dtype
    length is not long enough to store the max integer/float value converted
    to a string. Previously can_cast in 'safe' mode returned True for
    integer/float dtype and a string dtype of any length.

See also
    --------
    dtype, result_type

Examples
    --------
    Basic examples

>>> np.can_cast(np.int32, np.int64)
    True
    >>> np.can_cast(np.float64, np.complex)
    True
    >>> np.can_cast(np.complex, np.float)
    False

>>> np.can_cast('i8', 'f8')
    True
    >>> np.can_cast('i8', 'f4')
    False
    >>> np.can_cast('i4', 'S4')
    False

Casting scalars

>>> np.can_cast(100, 'i1')
    True
    >>> np.can_cast(150, 'i1')
    False
    >>> np.can_cast(150, 'u1')
    True

>>> np.can_cast(3.5e100, np.float32)
    False
    >>> np.can_cast(1000.0, np.float32)
    True

Array scalar checks the value, array does not

>>> np.can_cast(np.array(1000.0), np.float32)
    True
    >>> np.can_cast(np.array([1000.0]), np.float32)
    False

Using the casting rules

>>> np.can_cast('i8', 'i8', 'no')
    True
    >>> np.can_cast('<i8', '>i8', 'no')
    False

>>> np.can_cast('<i8', '>i8', 'equiv')
    True
    >>> np.can_cast('<i4', '>i8', 'equiv')
    False

>>> np.can_cast('<i4', '>i8', 'safe')
    True
    >>> np.can_cast('<i8', '>i4', 'safe')
    False

>>> np.can_cast('<i8', '>i4', 'same_kind')
    True
    >>> np.can_cast('<i8', '>u4', 'same_kind')
    False

>>> np.can_cast('<i8', '>u4', 'unsafe')
    True

""")

add_newdoc('numpy.core.multiarray', 'promote_types',
    """
    promote_types(type1, type2)

Returns the data type with the smallest size and smallest scalar
    kind to which both ``type1`` and ``type2`` may be safely cast.
    The returned data type is always in native byte order.

This function is symmetric and associative.

Parameters
    ----------
    type1 : dtype or dtype specifier
        First data type.
    type2 : dtype or dtype specifier
        Second data type.

Returns
    -------
    out : dtype
        The promoted data type.

Notes
    -----
    .. versionadded:: 1.6.0

Starting in NumPy 1.9, promote_types function now returns a valid string
    length when given an integer or float dtype as one argument and a string
    dtype as another argument. Previously it always returned the input string
    dtype, even if it wasn't long enough to store the max integer/float value
    converted to a string.

See Also
    --------
    result_type, dtype, can_cast

Examples
    --------
    >>> np.promote_types('f4', 'f8')
    dtype('float64')

>>> np.promote_types('i8', 'f4')
    dtype('float64')

>>> np.promote_types('>i8', '<c8')
    dtype('complex128')

>>> np.promote_types('i4', 'S8')
    dtype('S11')

""")

add_newdoc('numpy.core.multiarray', 'min_scalar_type',
    """
    min_scalar_type(a)

For scalar ``a``, returns the data type with the smallest size
    and smallest scalar kind which can hold its value.  For non-scalar
    array ``a``, returns the vector's dtype unmodified.

Floating point values are not demoted to integers,
    and complex values are not demoted to floats.

Parameters
    ----------
    a : scalar or array_like
        The value whose minimal data type is to be found.

Returns
    -------
    out : dtype
        The minimal data type.

Notes
    -----
    .. versionadded:: 1.6.0

See Also
    --------
    result_type, promote_types, dtype, can_cast

Examples
    --------
    >>> np.min_scalar_type(10)
    dtype('uint8')

>>> np.min_scalar_type(-260)
    dtype('int16')

>>> np.min_scalar_type(3.1)
    dtype('float16')

>>> np.min_scalar_type(1e50)
    dtype('float64')

>>> np.min_scalar_type(np.arange(4,dtype='f8'))
    dtype('float64')

""")

add_newdoc('numpy.core.multiarray', 'result_type',
    """
    result_type(*arrays_and_dtypes)

Returns the type that results from applying the NumPy
    type promotion rules to the arguments.

Type promotion in NumPy works similarly to the rules in languages
    like C++, with some slight differences.  When both scalars and
    arrays are used, the array's type takes precedence and the actual value
    of the scalar is taken into account.

For example, calculating 3*a, where a is an array of 32-bit floats,
    intuitively should result in a 32-bit float output.  If the 3 is a
    32-bit integer, the NumPy rules indicate it can't convert losslessly
    into a 32-bit float, so a 64-bit float should be the result type.
    By examining the value of the constant, '3', we see that it fits in
    an 8-bit integer, which can be cast losslessly into the 32-bit float.

Parameters
    ----------
    arrays_and_dtypes : list of arrays and dtypes
        The operands of some operation whose result type is needed.

Returns
    -------
    out : dtype
        The result type.

See also
    --------
    dtype, promote_types, min_scalar_type, can_cast

Notes
    -----
    .. versionadded:: 1.6.0

The specific algorithm used is as follows.

Categories are determined by first checking which of boolean,
    integer (int/uint), or floating point (float/complex) the maximum
    kind of all the arrays and the scalars are.

If there are only scalars or the maximum category of the scalars
    is higher than the maximum category of the arrays,
    the data types are combined with :func:`promote_types`
    to produce the return value.

Otherwise, `min_scalar_type` is called on each array, and
    the resulting data types are all combined with :func:`promote_types`
    to produce the return value.

The set of int values is not a subset of the uint values for types
    with the same number of bits, something not reflected in
    :func:`min_scalar_type`, but handled as a special case in `result_type`.

Examples
    --------
    >>> np.result_type(3, np.arange(7, dtype='i1'))
    dtype('int8')

>>> np.result_type('i4', 'c8')
    dtype('complex128')

>>> np.result_type(3.0, -2)
    dtype('float64')

""")

add_newdoc('numpy.core.multiarray', 'newbuffer',
    """
    newbuffer(size)

Return a new uninitialized buffer object.

Parameters
    ----------
    size : int
        Size in bytes of returned buffer object.

Returns
    -------
    newbuffer : buffer object
        Returned, uninitialized buffer object of `size` bytes.

""")

add_newdoc('numpy.core.multiarray', 'getbuffer',
    """
    getbuffer(obj [,offset[, size]])

Create a buffer object from the given object referencing a slice of
    length size starting at offset.

Default is the entire buffer. A read-write buffer is attempted followed
    by a read-only buffer.

Parameters
    ----------
    obj : object

offset : int, optional

size : int, optional

Returns
    -------
    buffer_obj : buffer

Examples
    --------
    >>> buf = np.getbuffer(np.ones(5), 1, 3)
    >>> len(buf)
    3
    >>> buf[0]
    '\\x00'
    >>> buf
    <read-write buffer for 0x8af1e70, size 3, offset 1 at 0x8ba4ec0>

""")

add_newdoc('numpy.core', 'dot',
    """
    dot(a, b, out=None)

Dot product of two arrays.

For 2-D arrays it is equivalent to matrix multiplication, and for 1-D
    arrays to inner product of vectors (without complex conjugation). For
    N dimensions it is a sum product over the last axis of `a` and
    the second-to-last of `b`::

dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])

Parameters
    ----------
    a : array_like
        First argument.
    b : array_like
        Second argument.
    out : ndarray, optional
        Output argument. This must have the exact kind that would be returned
        if it was not used. In particular, it must have the right type, must be
        C-contiguous, and its dtype must be the dtype that would be returned
        for `dot(a,b)`. This is a performance feature. Therefore, if these
        conditions are not met, an exception is raised, instead of attempting
        to be flexible.

Returns
    -------
    output : ndarray
        Returns the dot product of `a` and `b`.  If `a` and `b` are both
        scalars or both 1-D arrays then a scalar is returned; otherwise
        an array is returned.
        If `out` is given, then it is returned.

Raises
    ------
    ValueError
        If the last dimension of `a` is not the same size as
        the second-to-last dimension of `b`.

See Also
    --------
    vdot : Complex-conjugating dot product.
    tensordot : Sum products over arbitrary axes.
    einsum : Einstein summation convention.
    matmul : '@' operator as method with out parameter.

Examples
    --------
    >>> np.dot(3, 4)
    12

Neither argument is complex-conjugated:

>>> np.dot([2j, 3j], [2j, 3j])
    (-13+0j)

For 2-D arrays it is the matrix product:

>>> a = [[1, 0], [0, 1]]
    >>> b = [[4, 1], [2, 2]]
    >>> np.dot(a, b)
    array([[4, 1],
           [2, 2]])

>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
    >>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
    >>> np.dot(a, b)[2,3,2,1,2,2]
    499128
    >>> sum(a[2,3,2,:] * b[1,2,:,2])
    499128

""")

add_newdoc('numpy.core', 'matmul',
    """
    matmul(a, b, out=None)

Matrix product of two arrays.

The behavior depends on the arguments in the following way.

- If both arguments are 2-D they are multiplied like conventional
      matrices.
    - If either argument is N-D, N > 2, it is treated as a stack of
      matrices residing in the last two indexes and broadcast accordingly.
    - If the first argument is 1-D, it is promoted to a matrix by
      prepending a 1 to its dimensions. After matrix multiplication
      the prepended 1 is removed.
    - If the second argument is 1-D, it is promoted to a matrix by
      appending a 1 to its dimensions. After matrix multiplication
      the appended 1 is removed.

Multiplication by a scalar is not allowed, use ``*`` instead. Note that
    multiplying a stack of matrices with a vector will result in a stack of
    vectors, but matmul will not recognize it as such.

``matmul`` differs from ``dot`` in two important ways.

- Multiplication by scalars is not allowed.
    - Stacks of matrices are broadcast together as if the matrices
      were elements.

.. warning::
       This function is preliminary and included in Numpy 1.10 for testing
       and documentation. Its semantics will not change, but the number and
       order of the optional arguments will.

.. versionadded:: 1.10.0

Returns
    -------
    output : ndarray
        Returns the dot product of `a` and `b`.  If `a` and `b` are both
        1-D arrays then a scalar is returned; otherwise an array is
        returned.  If `out` is given, then it is returned.

Raises
    ------
    ValueError
        If the last dimension of `a` is not the same size as
        the second-to-last dimension of `b`.

If scalar value is passed.

See Also
    --------
    vdot : Complex-conjugating dot product.
    tensordot : Sum products over arbitrary axes.
    einsum : Einstein summation convention.
    dot : alternative matrix product with different broadcasting rules.

Notes
    -----
    The matmul function implements the semantics of the `@` operator introduced
    in Python 3.5 following PEP465.

Examples
    --------
    For 2-D arrays it is the matrix product:

>>> a = [[1, 0], [0, 1]]
    >>> b = [[4, 1], [2, 2]]
    >>> np.matmul(a, b)
    array([[4, 1],
           [2, 2]])

For 2-D mixed with 1-D, the result is the usual.

>>> a = [[1, 0], [0, 1]]
    >>> b = [1, 2]
    >>> np.matmul(a, b)
    array([1, 2])
    >>> np.matmul(b, a)
    array([1, 2])

Broadcasting is conventional for stacks of arrays

>>> a = np.arange(2*2*4).reshape((2,2,4))
    >>> b = np.arange(2*2*4).reshape((2,4,2))
    >>> np.matmul(a,b).shape
    (2, 2, 2)
    >>> np.matmul(a,b)[0,1,1]
    98
    >>> sum(a[0,1,:] * b[0,:,1])
    98

Vector, vector returns the scalar inner product, but neither argument
    is complex-conjugated:

>>> np.matmul([2j, 3j], [2j, 3j])
    (-13+0j)

Scalar multiplication raises an error.

>>> np.matmul([1,2], 3)
    Traceback (most recent call last):
    ...
    ValueError: Scalar operands are not allowed, use '*' instead

""")

add_newdoc('numpy.core', 'einsum',
    """
    einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe')

Evaluates the Einstein summation convention on the operands.

Using the Einstein summation convention, many common multi-dimensional
    array operations can be represented in a simple fashion.  This function
    provides a way to compute such summations. The best way to understand this
    function is to try the examples below, which show how many common NumPy
    functions can be implemented as calls to `einsum`.

Parameters
    ----------
    subscripts : str
        Specifies the subscripts for summation.
    operands : list of array_like
        These are the arrays for the operation.
    out : ndarray, optional
        If provided, the calculation is done into this array.
    dtype : data-type, optional
        If provided, forces the calculation to use the data type specified.
        Note that you may have to also give a more liberal `casting`
        parameter to allow the conversions.
    order : {'C', 'F', 'A', 'K'}, optional
        Controls the memory layout of the output. 'C' means it should
        be C contiguous. 'F' means it should be Fortran contiguous,
        'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
        'K' means it should be as close to the layout as the inputs as
        is possible, including arbitrarily permuted axes.
        Default is 'K'.
    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
        Controls what kind of data casting may occur.  Setting this to
        'unsafe' is not recommended, as it can adversely affect accumulations.

Returns
    -------
    output : ndarray
        The calculation based on the Einstein summation convention.

See Also
    --------
    dot, inner, outer, tensordot

Notes
    -----
    .. versionadded:: 1.6.0

The subscripts string is a comma-separated list of subscript labels,
    where each label refers to a dimension of the corresponding operand.
    Repeated subscripts labels in one operand take the diagonal.  For example,
    ``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.

Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``
    is equivalent to ``np.inner(a,b)``.  If a label appears only once,
    it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``
    with no changes.

The order of labels in the output is by default alphabetical.  This
    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
    ``np.einsum('ji', a)`` takes its transpose.

The output can be controlled by specifying output subscript labels
    as well.  This specifies the label order, and allows summing to
    be disallowed or forced when desired.  The call ``np.einsum('i->', a)``
    is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``
    is like ``np.diag(a)``.  The difference is that `einsum` does not
    allow broadcasting by default.

To enable and control broadcasting, use an ellipsis.  Default
    NumPy-style broadcasting is done by adding an ellipsis
    to the left of each term, like ``np.einsum('...ii->...i', a)``.
    To take the trace along the first and last axes,
    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
    product with the left-most indices instead of rightmost, you can do
    ``np.einsum('ij...,jk...->ik...', a, b)``.

When there is only one operand, no axes are summed, and no output
    parameter is provided, a view into the operand is returned instead
    of a new array.  Thus, taking the diagonal as ``np.einsum('ii->i', a)``
    produces a view.

An alternative way to provide the subscripts and operands is as
    ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples
    below have corresponding `einsum` calls with the two parameter methods.

.. versionadded:: 1.10.0

Views returned from einsum are now writeable whenever the input array
    is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
    have the same effect as ``np.swapaxes(a, 0, 2)`` and
    ``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
    of a 2D array.

Examples
    --------
    >>> a = np.arange(25).reshape(5,5)
    >>> b = np.arange(5)
    >>> c = np.arange(6).reshape(2,3)

>>> np.einsum('ii', a)
    60
    >>> np.einsum(a, [0,0])
    60
    >>> np.trace(a)
    60

>>> np.einsum('ii->i', a)
    array([ 0,  6, 12, 18, 24])
    >>> np.einsum(a, [0,0], [0])
    array([ 0,  6, 12, 18, 24])
    >>> np.diag(a)
    array([ 0,  6, 12, 18, 24])

>>> np.einsum('ij,j', a, b)
    array([ 30,  80, 130, 180, 230])
    >>> np.einsum(a, [0,1], b, [1])
    array([ 30,  80, 130, 180, 230])
    >>> np.dot(a, b)
    array([ 30,  80, 130, 180, 230])
    >>> np.einsum('...j,j', a, b)
    array([ 30,  80, 130, 180, 230])

>>> np.einsum('ji', c)
    array([[0, 3],
           [1, 4],
           [2, 5]])
    >>> np.einsum(c, [1,0])
    array([[0, 3],
           [1, 4],
           [2, 5]])
    >>> c.T
    array([[0, 3],
           [1, 4],
           [2, 5]])

>>> np.einsum('..., ...', 3, c)
    array([[ 0,  3,  6],
           [ 9, 12, 15]])
    >>> np.einsum(3, [Ellipsis], c, [Ellipsis])
    array([[ 0,  3,  6],
           [ 9, 12, 15]])
    >>> np.multiply(3, c)
    array([[ 0,  3,  6],
           [ 9, 12, 15]])

>>> np.einsum('i,i', b, b)
    30
    >>> np.einsum(b, [0], b, [0])
    30
    >>> np.inner(b,b)
    30

>>> np.einsum('i,j', np.arange(2)+1, b)
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])
    >>> np.einsum(np.arange(2)+1, [0], b, [1])
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])
    >>> np.outer(np.arange(2)+1, b)
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])

>>> np.einsum('i...->...', a)
    array([50, 55, 60, 65, 70])
    >>> np.einsum(a, [0,Ellipsis], [Ellipsis])
    array([50, 55, 60, 65, 70])
    >>> np.sum(a, axis=0)
    array([50, 55, 60, 65, 70])

>>> a = np.arange(60.).reshape(3,4,5)
    >>> b = np.arange(24.).reshape(4,3,2)
    >>> np.einsum('ijk,jil->kl', a, b)
    array([[ 4400.,  4730.],
           [ 4532.,  4874.],
           [ 4664.,  5018.],
           [ 4796.,  5162.],
           [ 4928.,  5306.]])
    >>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
    array([[ 4400.,  4730.],
           [ 4532.,  4874.],
           [ 4664.,  5018.],
           [ 4796.,  5162.],
           [ 4928.,  5306.]])
    >>> np.tensordot(a,b, axes=([1,0],[0,1]))
    array([[ 4400.,  4730.],
           [ 4532.,  4874.],
           [ 4664.,  5018.],
           [ 4796.,  5162.],
           [ 4928.,  5306.]])

>>> a = np.arange(6).reshape((3,2))
    >>> b = np.arange(12).reshape((4,3))
    >>> np.einsum('ki,jk->ij', a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])
    >>> np.einsum('ki,...k->i...', a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])
    >>> np.einsum('k...,jk', a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])

>>> # since version 1.10.0
    >>> a = np.zeros((3, 3))
    >>> np.einsum('ii->i', a)[:] = 1
    >>> a
    array([[ 1.,  0.,  0.],
           [ 0.,  1.,  0.],
           [ 0.,  0.,  1.]])

""")

add_newdoc('numpy.core', 'vdot',
    """
    vdot(a, b)

Return the dot product of two vectors.

The vdot(`a`, `b`) function handles complex numbers differently than
    dot(`a`, `b`).  If the first argument is complex the complex conjugate
    of the first argument is used for the calculation of the dot product.

Note that `vdot` handles multidimensional arrays differently than `dot`:
    it does *not* perform a matrix product, but flattens input arguments
    to 1-D vectors first. Consequently, it should only be used for vectors.

Parameters
    ----------
    a : array_like
        If `a` is complex the complex conjugate is taken before calculation
        of the dot product.
    b : array_like
        Second argument to the dot product.

Returns
    -------
    output : ndarray
        Dot product of `a` and `b`.  Can be an int, float, or
        complex depending on the types of `a` and `b`.

See Also
    --------
    dot : Return the dot product without using the complex conjugate of the
          first argument.

Examples
    --------
    >>> a = np.array([1+2j,3+4j])
    >>> b = np.array([5+6j,7+8j])
    >>> np.vdot(a, b)
    (70-8j)
    >>> np.vdot(b, a)
    (70+8j)

Note that higher-dimensional arrays are flattened!

>>> a = np.array([[1, 4], [5, 6]])
    >>> b = np.array([[4, 1], [2, 2]])
    >>> np.vdot(a, b)
    30
    >>> np.vdot(b, a)
    30
    >>> 1*4 + 4*1 + 5*2 + 6*2
    30

""")

##############################################################################
#
# Documentation for ndarray attributes and methods
#
##############################################################################

##############################################################################
#
# ndarray object
#
##############################################################################

add_newdoc('numpy.core.multiarray', 'ndarray',
    """
    ndarray(shape, dtype=float, buffer=None, offset=0,
            strides=None, order=None)

An array object represents a multidimensional, homogeneous array
    of fixed-size items.  An associated data-type object describes the
    format of each element in the array (its byte-order, how many bytes it
    occupies in memory, whether it is an integer, a floating point number,
    or something else, etc.)

Arrays should be constructed using `array`, `zeros` or `empty` (refer
    to the See Also section below).  The parameters given here refer to
    a low-level method (`ndarray(...)`) for instantiating an array.

For more information, refer to the `numpy` module and examine the
    the methods and attributes of an array.

Parameters
    ----------
    (for the __new__ method; see Notes below)

shape : tuple of ints
        Shape of created array.
    dtype : data-type, optional
        Any object that can be interpreted as a numpy data type.
    buffer : object exposing buffer interface, optional
        Used to fill the array with data.
    offset : int, optional
        Offset of array data in buffer.
    strides : tuple of ints, optional
        Strides of data in memory.
    order : {'C', 'F'}, optional
        Row-major (C-style) or column-major (Fortran-style) order.

Attributes
    ----------
    T : ndarray
        Transpose of the array.
    data : buffer
        The array's elements, in memory.
    dtype : dtype object
        Describes the format of the elements in the array.
    flags : dict
        Dictionary containing information related to memory use, e.g.,
        'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
    flat : numpy.flatiter object
        Flattened version of the array as an iterator.  The iterator
        allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
        assignment examples; TODO).
    imag : ndarray
        Imaginary part of the array.
    real : ndarray
        Real part of the array.
    size : int
        Number of elements in the array.
    itemsize : int
        The memory use of each array element in bytes.
    nbytes : int
        The total number of bytes required to store the array data,
        i.e., ``itemsize * size``.
    ndim : int
        The array's number of dimensions.
    shape : tuple of ints
        Shape of the array.
    strides : tuple of ints
        The step-size required to move from one element to the next in
        memory. For example, a contiguous ``(3, 4)`` array of type
        ``int16`` in C-order has strides ``(8, 2)``.  This implies that
        to move from element to element in memory requires jumps of 2 bytes.
        To move from row-to-row, one needs to jump 8 bytes at a time
        (``2 * 4``).
    ctypes : ctypes object
        Class containing properties of the array needed for interaction
        with ctypes.
    base : ndarray
        If the array is a view into another array, that array is its `base`
        (unless that array is also a view).  The `base` array is where the
        array data is actually stored.

See Also
    --------
    array : Construct an array.
    zeros : Create an array, each element of which is zero.
    empty : Create an array, but leave its allocated memory unchanged (i.e.,
            it contains "garbage").
    dtype : Create a data-type.

Notes
    -----
    There are two modes of creating an array using ``__new__``:

1. If `buffer` is None, then only `shape`, `dtype`, and `order`
       are used.
    2. If `buffer` is an object exposing the buffer interface, then
       all keywords are interpreted.

No ``__init__`` method is needed because the array is fully initialized
    after the ``__new__`` method.

Examples
    --------
    These examples illustrate the low-level `ndarray` constructor.  Refer
    to the `See Also` section above for easier ways of constructing an
    ndarray.

First mode, `buffer` is None:

>>> np.ndarray(shape=(2,2), dtype=float, order='F')
    array([[ -1.13698227e+002,   4.25087011e-303],
           [  2.88528414e-306,   3.27025015e-309]])         #random

Second mode:

>>> np.ndarray((2,), buffer=np.array([1,2,3]),
    ...            offset=np.int_().itemsize,
    ...            dtype=int) # offset = 1*itemsize, i.e. skip first element
    array([2, 3])

""")

##############################################################################
#
# ndarray attributes
#
##############################################################################

add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_interface__',
    """Array protocol: Python side."""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_finalize__',
    """None."""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__',
    """Array priority."""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__',
    """Array protocol: C-struct side."""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('_as_parameter_',
    """Allow the array to be interpreted as a ctypes object by returning the
    data-memory location as an integer

"""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('base',
    """
    Base object if memory is from some other object.

Examples
    --------
    The base of an array that owns its memory is None:

>>> x = np.array([1,2,3,4])
    >>> x.base is None
    True

Slicing creates a view, whose memory is shared with x:

>>> y = x[2:]
    >>> y.base is x
    True

"""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
    """
    An object to simplify the interaction of the array with the ctypes
    module.

This attribute creates an object that makes it easier to use arrays
    when calling shared libraries with the ctypes module. The returned
    object has, among others, data, shape, and strides attributes (see
    Notes below) which themselves return ctypes objects that can be used
    as arguments to a shared library.

Parameters
    ----------
    None

Returns
    -------
    c : Python object
        Possessing attributes data, shape, strides, etc.

See Also
    --------
    numpy.ctypeslib

Notes
    -----
    Below are the public attributes of this object which were documented
    in "Guide to NumPy" (we have omitted undocumented public attributes,
    as well as documented private attributes):

* data: A pointer to the memory area of the array as a Python integer.
      This memory area may contain data that is not aligned, or not in correct
      byte-order. The memory area may not even be writeable. The array
      flags and data-type of this array should be respected when passing this
      attribute to arbitrary C-code to avoid trouble that can include Python
      crashing. User Beware! The value of this attribute is exactly the same
      as self._array_interface_['data'][0].

* shape (c_intp*self.ndim): A ctypes array of length self.ndim where
      the basetype is the C-integer corresponding to dtype('p') on this
      platform. This base-type could be c_int, c_long, or c_longlong
      depending on the platform. The c_intp type is defined accordingly in
      numpy.ctypeslib. The ctypes array contains the shape of the underlying
      array.

* strides (c_intp*self.ndim): A ctypes array of length self.ndim where
      the basetype is the same as for the shape attribute. This ctypes array
      contains the strides information from the underlying array. This strides
      information is important for showing how many bytes must be jumped to
      get to the next element in the array.

* data_as(obj): Return the data pointer cast to a particular c-types object.
      For example, calling self._as_parameter_ is equivalent to
      self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a
      pointer to a ctypes array of floating-point data:
      self.data_as(ctypes.POINTER(ctypes.c_double)).

* shape_as(obj): Return the shape tuple as an array of some other c-types
      type. For example: self.shape_as(ctypes.c_short).

* strides_as(obj): Return the strides tuple as an array of some other
      c-types type. For example: self.strides_as(ctypes.c_longlong).

Be careful using the ctypes attribute - especially on temporary
    arrays or arrays constructed on the fly. For example, calling
    ``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory
    that is invalid because the array created as (a+b) is deallocated
    before the next Python statement. You can avoid this problem using
    either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will
    hold a reference to the array until ct is deleted or re-assigned.

If the ctypes module is not available, then the ctypes attribute
    of array objects still returns something useful, but ctypes objects
    are not returned and errors may be raised instead. In particular,
    the object will still have the as parameter attribute which will
    return an integer equal to the data attribute.

Examples
    --------
    >>> import ctypes
    >>> x
    array([[0, 1],
           [2, 3]])
    >>> x.ctypes.data
    30439712
    >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))
    <ctypes.LP_c_long object at 0x01F01300>
    >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents
    c_long(0)
    >>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents
    c_longlong(4294967296L)
    >>> x.ctypes.shape
    <numpy.core._internal.c_long_Array_2 object at 0x01FFD580>
    >>> x.ctypes.shape_as(ctypes.c_long)
    <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
    >>> x.ctypes.strides
    <numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
    >>> x.ctypes.strides_as(ctypes.c_longlong)
    <numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>

"""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
    """Python buffer object pointing to the start of the array's data."""))

add_newdoc('numpy.core.multiarray', 'ndarray', ('dtype',
    """
    Data-type of the array's elements.

Parameters
    ----------
    None

Returns
    -------
    d : numpy dtype object

See also
    --------
    result_type

Examples
    --------
    Using array-scalar type:

>>> np.dtype(np.int16)
    dtype('int16')

Structured type, one field name 'f1', containing int16:

>>> np.dtype([('f1', np.int16)])
    dtype([('f1', '<i2')])

Structured type, one field named 'f1', in itself containing a structured
    type with one field:

>>> np.dtype([('f1', [('f1', np.int16)])])
    dtype([('f1', [('f1', '<i2')])])

Structured type, two fields: the first field contains an unsigned int, the
    second an int32:

>>> np.dtype([('f1', np.uint), ('f2', np.int32)])
    dtype([('f1', '<u4'), ('f2', '<i4')])

Using array-protocol type strings:

>>> np.dtype([('a','f8'),('b','S10')])
    dtype([('a', '<f8'), ('b', '|S10')])

Using comma-separated field formats.  The shape is (2,3):

>>> np.dtype("i4, (2,3)f8")
    dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])

Using tuples.  ``int`` is a fixed type, 3 the field's shape.  ``void``
    is a flexible type, here of size 10:

>>> np.dtype([('hello',(np.int,3)),('world',np.void,10)])
    dtype([('hello', '<i4', 3), ('world', '|V10')])

Subdivide ``int16`` into 2 ``int8``'s, called x and y.  0 and 1 are
    the offsets in bytes:

>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
    dtype(('<i2', [('x', '|i1'), ('y', '|i1')]))

Using dictionaries.  Two fields named 'gender' and 'age':

>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
    dtype([('gender', '|S1'), ('age', '|u1')])

Offsets in bytes, here 0 and 25:

>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
    dtype([('surname', '|S25'), ('age', '|u1')])

""")

##############################################################################
#
# dtype attributes
#
##############################################################################

add_newdoc('numpy.core.multiarray', 'dtype', ('alignment',
    """
    The required alignment (bytes) of this data-type according to the compiler.

More information is available in the C-API section of the manual.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
    """
    A character indicating the byte-order of this data-type object.

One of:

===  ==============
    '='  native
    '<'  little-endian
    '>'  big-endian
    '|'  not applicable
    ===  ==============

All built-in data-type objects have byteorder either '=' or '|'.

Examples
    --------

>>> dt = np.dtype('i2')
    >>> dt.byteorder
    '='
    >>> # endian is not relevant for 8 bit numbers
    >>> np.dtype('i1').byteorder
    '|'
    >>> # or ASCII strings
    >>> np.dtype('S2').byteorder
    '|'
    >>> # Even if specific code is given, and it is native
    >>> # '=' is the byteorder
    >>> import sys
    >>> sys_is_le = sys.byteorder == 'little'
    >>> native_code = sys_is_le and '<' or '>'
    >>> swapped_code = sys_is_le and '>' or '<'
    >>> dt = np.dtype(native_code + 'i2')
    >>> dt.byteorder
    '='
    >>> # Swapped code shows up as itself
    >>> dt = np.dtype(swapped_code + 'i2')
    >>> dt.byteorder == swapped_code
    True

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('char',
    """A unique character code for each of the 21 different built-in types."""))

add_newdoc('numpy.core.multiarray', 'dtype', ('descr',
    """
    Array-interface compliant full description of the data-type.

The format is that required by the 'descr' key in the
    `__array_interface__` attribute.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('fields',
    """
    Dictionary of named fields defined for this data type, or ``None``.

The dictionary is indexed by keys that are the names of the fields.
    Each entry in the dictionary is a tuple fully describing the field::

(dtype, offset[, title])

If present, the optional title can be any object (if it is a string
    or unicode then it will also be a key in the fields dictionary,
    otherwise it's meta-data). Notice also that the first two elements
    of the tuple can be passed directly as arguments to the ``ndarray.getfield``
    and ``ndarray.setfield`` methods.

See Also
    --------
    ndarray.getfield, ndarray.setfield

Examples
    --------
    >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
    >>> print(dt.fields)
    {'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('flags',
    """
    Bit-flags describing how this data type is to be interpreted.

Bit-masks are in `numpy.core.multiarray` as the constants
    `ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
    `NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
    of these flags is in C-API documentation; they are largely useful
    for user-defined data-types.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('hasobject',
    """
    Boolean indicating whether this dtype contains any reference-counted
    objects in any fields or sub-dtypes.

Recall that what is actually in the ndarray memory representing
    the Python object is the memory address of that object (a pointer).
    Special handling may be required, and this attribute is useful for
    distinguishing data types that may contain arbitrary Python objects
    and data-types that won't.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
    """
    Integer indicating how this dtype relates to the built-in dtypes.

Read-only.

=  ========================================================================
    0  if this is a structured array type, with fields
    1  if this is a dtype compiled into numpy (such as ints, floats etc)
    2  if the dtype is for a user-defined numpy type
       A user-defined type uses the numpy C-API machinery to extend
       numpy to handle a new array type. See
       :ref:`user.user-defined-data-types` in the Numpy manual.
    =  ========================================================================

Examples
    --------
    >>> dt = np.dtype('i2')
    >>> dt.isbuiltin
    1
    >>> dt = np.dtype('f8')
    >>> dt.isbuiltin
    1
    >>> dt = np.dtype([('field1', 'f8')])
    >>> dt.isbuiltin
    0

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('isnative',
    """
    Boolean indicating whether the byte order of this dtype is native
    to the platform.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('isalignedstruct',
    """
    Boolean indicating whether the dtype is a struct which maintains
    field alignment. This flag is sticky, so when combining multiple
    structs together, it is preserved and produces new dtypes which
    are also aligned.
    """))

add_newdoc('numpy.core.multiarray', 'dtype', ('itemsize',
    """
    The element size of this data-type object.

For 18 of the 21 types this number is fixed by the data-type.
    For the flexible data-types, this number can be anything.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('kind',
    """
    A character code (one of 'biufcmMOSUV') identifying the general kind of data.

=  ======================
    b  boolean
    i  signed integer
    u  unsigned integer
    f  floating-point
    c  complex floating-point
    m  timedelta
    M  datetime
    O  object
    S  (byte-)string
    U  Unicode
    V  void
    =  ======================

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('name',
    """
    A bit-width name for this data-type.

Un-sized flexible data-type objects do not have this attribute.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('names',
    """
    Ordered list of field names, or ``None`` if there are no fields.

The names are ordered according to increasing byte offset. This can be
    used, for example, to walk through all of the named fields in offset order.

Examples
    --------
    >>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
    >>> dt.names
    ('name', 'grades')

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('num',
    """
    A unique number for each of the 21 different built-in types.

These are roughly ordered from least-to-most precision.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('shape',
    """
    Shape tuple of the sub-array if this data type describes a sub-array,
    and ``()`` otherwise.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('str',
    """The array-protocol typestring of this data-type object."""))

add_newdoc('numpy.core.multiarray', 'dtype', ('subdtype',
    """
    Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
    None otherwise.

The *shape* is the fixed shape of the sub-array described by this
    data type, and *item_dtype* the data type of the array.

If a field whose dtype object has this attribute is retrieved,
    then the extra dimensions implied by *shape* are tacked on to
    the end of the retrieved array.

"""))

add_newdoc('numpy.core.multiarray', 'dtype', ('type',
    """The type object used to instantiate a scalar of this data-type."""))

##############################################################################
#
# dtype methods
#
##############################################################################

add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
    """
    newbyteorder(new_order='S')

Return a new dtype with a different byte order.

Changes are also made in all fields and sub-arrays of the data type.

Parameters
    ----------
    new_order : string, optional
        Byte order to force; a value from the byte order specifications
        below.  The default value ('S') results in swapping the current
        byte order.  `new_order` codes can be any of:

The code does a case-insensitive check on the first letter of
        `new_order` for these alternatives.  For example, any of '>'
        or 'B' or 'b' or 'brian' are valid to specify big-endian.

Returns
    -------
    new_dtype : dtype
        New dtype object with the given change to the byte order.

Notes
    -----
    Changes are also made in all fields and sub-arrays of the data type.

Examples
    --------
    >>> import sys
    >>> sys_is_le = sys.byteorder == 'little'
    >>> native_code = sys_is_le and '<' or '>'
    >>> swapped_code = sys_is_le and '>' or '<'
    >>> native_dt = np.dtype(native_code+'i2')
    >>> swapped_dt = np.dtype(swapped_code+'i2')
    >>> native_dt.newbyteorder('S') == swapped_dt
    True
    >>> native_dt.newbyteorder() == swapped_dt
    True
    >>> native_dt == swapped_dt.newbyteorder('S')
    True
    >>> native_dt == swapped_dt.newbyteorder('=')
    True
    >>> native_dt == swapped_dt.newbyteorder('N')
    True
    >>> native_dt == native_dt.newbyteorder('|')
    True
    >>> np.dtype('<i2') == native_dt.newbyteorder('<')
    True
    >>> np.dtype('<i2') == native_dt.newbyteorder('L')
    True
    >>> np.dtype('>i2') == native_dt.newbyteorder('>')
    True
    >>> np.dtype('>i2') == native_dt.newbyteorder('B')
    True

"""))

##############################################################################
#
# Datetime-related Methods
#
##############################################################################

add_newdoc('numpy.core.multiarray', 'busdaycalendar',
    """
    busdaycalendar(weekmask='1111100', holidays=None)

A business day calendar object that efficiently stores information
    defining valid days for the busday family of functions.

The default valid days are Monday through Friday ("business days").
    A busdaycalendar object can be specified with any set of weekly
    valid days, plus an optional "holiday" dates that always will be invalid.

Once a busdaycalendar object is created, the weekmask and holidays
    cannot be modified.

.. versionadded:: 1.7.0

Parameters
    ----------
    weekmask : str or array_like of bool, optional
        A seven-element array indicating which of Monday through Sunday are
        valid days. May be specified as a length-seven list or array, like
        [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
        like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
        weekdays, optionally separated by white space. Valid abbreviations
        are: Mon Tue Wed Thu Fri Sat Sun
    holidays : array_like of datetime64[D], optional
        An array of dates to consider as invalid dates, no matter which
        weekday they fall upon.  Holiday dates may be specified in any
        order, and NaT (not-a-time) dates are ignored.  This list is
        saved in a normalized form that is suited for fast calculations
        of valid days.

Returns
    -------
    out : busdaycalendar
        A business day calendar object containing the specified
        weekmask and holidays values.

See Also
    --------
    is_busday : Returns a boolean array indicating valid days.
    busday_offset : Applies an offset counted in valid days.
    busday_count : Counts how many valid days are in a half-open date range.

Attributes
    ----------
    Note: once a busdaycalendar object is created, you cannot modify the
    weekmask or holidays.  The attributes return copies of internal data.
    weekmask : (copy) seven-element array of bool
    holidays : (copy) sorted array of datetime64[D]

Examples
    --------
    >>> # Some important days in July
    ... bdd = np.busdaycalendar(
    ...             holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
    >>> # Default is Monday to Friday weekdays
    ... bdd.weekmask
    array([ True,  True,  True,  True,  True, False, False], dtype='bool')
    >>> # Any holidays already on the weekend are removed
    ... bdd.holidays
    array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')
    """)

add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('weekmask',
    """A copy of the seven-element boolean mask indicating valid days."""))

add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('holidays',
    """A copy of the holiday array indicating additional invalid days."""))

add_newdoc('numpy.core.multiarray', 'is_busday',
    """
    is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)

Calculates which of the given dates are valid days, and which are not.

.. versionadded:: 1.7.0

Parameters
    ----------
    dates : array_like of datetime64[D]
        The array of dates to process.
    weekmask : str or array_like of bool, optional
        A seven-element array indicating which of Monday through Sunday are
        valid days. May be specified as a length-seven list or array, like
        [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
        like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
        weekdays, optionally separated by white space. Valid abbreviations
        are: Mon Tue Wed Thu Fri Sat Sun
    holidays : array_like of datetime64[D], optional
        An array of dates to consider as invalid dates.  They may be
        specified in any order, and NaT (not-a-time) dates are ignored.
        This list is saved in a normalized form that is suited for
        fast calculations of valid days.
    busdaycal : busdaycalendar, optional
        A `busdaycalendar` object which specifies the valid days. If this
        parameter is provided, neither weekmask nor holidays may be
        provided.
    out : array of bool, optional
        If provided, this array is filled with the result.

Returns
    -------
    out : array of bool
        An array with the same shape as ``dates``, containing True for
        each valid day, and False for each invalid day.

See Also
    --------
    busdaycalendar: An object that specifies a custom set of valid days.
    busday_offset : Applies an offset counted in valid days.
    busday_count : Counts how many valid days are in a half-open date range.

Examples
    --------
    >>> # The weekdays are Friday, Saturday, and Monday
    ... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
    ...                 holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
    array([False, False,  True], dtype='bool')
    """)

add_newdoc('numpy.core.multiarray', 'busday_offset',
    """
    busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)

First adjusts the date to fall on a valid day according to
    the ``roll`` rule, then applies offsets to the given dates
    counted in valid days.

.. versionadded:: 1.7.0

Parameters
    ----------
    dates : array_like of datetime64[D]
        The array of dates to process.
    offsets : array_like of int
        The array of offsets, which is broadcast with ``dates``.
    roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
        How to treat dates that do not fall on a valid day. The default
        is 'raise'.

* 'raise' means to raise an exception for an invalid day.
          * 'nat' means to return a NaT (not-a-time) for an invalid day.
          * 'forward' and 'following' mean to take the first valid day
            later in time.
          * 'backward' and 'preceding' mean to take the first valid day
            earlier in time.
          * 'modifiedfollowing' means to take the first valid day
            later in time unless it is across a Month boundary, in which
            case to take the first valid day earlier in time.
          * 'modifiedpreceding' means to take the first valid day
            earlier in time unless it is across a Month boundary, in which
            case to take the first valid day later in time.
    weekmask : str or array_like of bool, optional
        A seven-element array indicating which of Monday through Sunday are
        valid days. May be specified as a length-seven list or array, like
        [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
        like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
        weekdays, optionally separated by white space. Valid abbreviations
        are: Mon Tue Wed Thu Fri Sat Sun
    holidays : array_like of datetime64[D], optional
        An array of dates to consider as invalid dates.  They may be
        specified in any order, and NaT (not-a-time) dates are ignored.
        This list is saved in a normalized form that is suited for
        fast calculations of valid days.
    busdaycal : busdaycalendar, optional
        A `busdaycalendar` object which specifies the valid days. If this
        parameter is provided, neither weekmask nor holidays may be
        provided.
    out : array of datetime64[D], optional
        If provided, this array is filled with the result.

Returns
    -------
    out : array of datetime64[D]
        An array with a shape from broadcasting ``dates`` and ``offsets``
        together, containing the dates with offsets applied.

See Also
    --------
    busdaycalendar: An object that specifies a custom set of valid days.
    is_busday : Returns a boolean array indicating valid days.
    busday_count : Counts how many valid days are in a half-open date range.

Examples
    --------
    >>> # First business day in October 2011 (not accounting for holidays)
    ... np.busday_offset('2011-10', 0, roll='forward')
    numpy.datetime64('2011-10-03','D')
    >>> # Last business day in February 2012 (not accounting for holidays)
    ... np.busday_offset('2012-03', -1, roll='forward')
    numpy.datetime64('2012-02-29','D')
    >>> # Third Wednesday in January 2011
    ... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
    numpy.datetime64('2011-01-19','D')
    >>> # 2012 Mother's Day in Canada and the U.S.
    ... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
    numpy.datetime64('2012-05-13','D')

>>> # First business day on or after a date
    ... np.busday_offset('2011-03-20', 0, roll='forward')
    numpy.datetime64('2011-03-21','D')
    >>> np.busday_offset('2011-03-22', 0, roll='forward')
    numpy.datetime64('2011-03-22','D')
    >>> # First business day after a date
    ... np.busday_offset('2011-03-20', 1, roll='backward')
    numpy.datetime64('2011-03-21','D')
    >>> np.busday_offset('2011-03-22', 1, roll='backward')
    numpy.datetime64('2011-03-23','D')
    """)

add_newdoc('numpy.core.multiarray', 'busday_count',
    """
    busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)

Counts the number of valid days between `begindates` and
    `enddates`, not including the day of `enddates`.

If ``enddates`` specifies a date value that is earlier than the
    corresponding ``begindates`` date value, the count will be negative.

.. versionadded:: 1.7.0

Parameters
    ----------
    begindates : array_like of datetime64[D]
        The array of the first dates for counting.
    enddates : array_like of datetime64[D]
        The array of the end dates for counting, which are excluded
        from the count themselves.
    weekmask : str or array_like of bool, optional
        A seven-element array indicating which of Monday through Sunday are
        valid days. May be specified as a length-seven list or array, like
        [1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
        like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
        weekdays, optionally separated by white space. Valid abbreviations
        are: Mon Tue Wed Thu Fri Sat Sun
    holidays : array_like of datetime64[D], optional
        An array of dates to consider as invalid dates.  They may be
        specified in any order, and NaT (not-a-time) dates are ignored.
        This list is saved in a normalized form that is suited for
        fast calculations of valid days.
    busdaycal : busdaycalendar, optional
        A `busdaycalendar` object which specifies the valid days. If this
        parameter is provided, neither weekmask nor holidays may be
        provided.
    out : array of int, optional
        If provided, this array is filled with the result.

Returns
    -------
    out : array of int
        An array with a shape from broadcasting ``begindates`` and ``enddates``
        together, containing the number of valid days between
        the begin and end dates.

See Also
    --------
    busdaycalendar: An object that specifies a custom set of valid days.
    is_busday : Returns a boolean array indicating valid days.
    busday_offset : Applies an offset counted in valid days.

Examples
    --------
    >>> # Number of weekdays in January 2011
    ... np.busday_count('2011-01', '2011-02')
    21
    >>> # Number of weekdays in 2011
    ...  np.busday_count('2011', '2012')
    260
    >>> # Number of Saturdays in 2011
    ... np.busday_count('2011', '2012', weekmask='Sat')
    53
    """)

##############################################################################
#
# nd_grid instances
#
##############################################################################

add_newdoc('numpy.lib.index_tricks', 'mgrid',
    """
    `nd_grid` instance which returns a dense multi-dimensional "meshgrid".

An instance of `numpy.lib.index_tricks.nd_grid` which returns an dense
    (or fleshed out) mesh-grid when indexed, so that each returned argument
    has the same shape.  The dimensions and number of the output arrays are
    equal to the number of indexing dimensions.  If the step length is not a
    complex number, then the stop is not inclusive.

However, if the step length is a **complex number** (e.g. 5j), then
    the integer part of its magnitude is interpreted as specifying the
    number of points to create between the start and stop values, where
    the stop value **is inclusive**.

Returns
    ----------
    mesh-grid `ndarrays` all of the same dimensions

See Also
    --------
    numpy.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
    ogrid : like mgrid but returns open (not fleshed out) mesh grids
    r_ : array concatenator

Examples
    --------
    >>> np.mgrid[0:5,0:5]
    array([[[0, 0, 0, 0, 0],
            [1, 1, 1, 1, 1],
            [2, 2, 2, 2, 2],
            [3, 3, 3, 3, 3],
            [4, 4, 4, 4, 4]],
           [[0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4],
            [0, 1, 2, 3, 4]]])
    >>> np.mgrid[-1:1:5j]
    array([-1. , -0.5,  0. ,  0.5,  1. ])

""")

add_newdoc('numpy.lib.index_tricks', 'ogrid',
    """
    `nd_grid` instance which returns an open multi-dimensional "meshgrid".

An instance of `numpy.lib.index_tricks.nd_grid` which returns an open
    (i.e. not fleshed out) mesh-grid when indexed, so that only one dimension
    of each returned array is greater than 1.  The dimension and number of the
    output arrays are equal to the number of indexing dimensions.  If the step
    length is not a complex number, then the stop is not inclusive.

Returns
    ----------
    mesh-grid `ndarrays` with only one dimension :math:`\\neq 1`

See Also
    --------
    np.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
    mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
    r_ : array concatenator

Examples
    --------
    >>> from numpy import ogrid
    >>> ogrid[-1:1:5j]
    array([-1. , -0.5,  0. ,  0.5,  1. ])
    >>> ogrid[0:5,0:5]
    [array([[0],
            [1],
            [2],
            [3],
            [4]]), array([[0, 1, 2, 3, 4]])]

""")

##############################################################################
#
# Documentation for `generic` attributes and methods
#
##############################################################################

add_newdoc('numpy.core.numerictypes', 'generic',
    """
    Base class for numpy scalar types.

Class from which most (all?) numpy scalar types are derived.  For
    consistency, exposes the same API as `ndarray`, despite many
    consequent attributes being either "get-only," or completely irrelevant.
    This is the class from which it is strongly suggested users should derive
    custom scalar types.

""")

# Attributes

add_newdoc('numpy.core.numerictypes', 'generic', ('T',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class so as to
    provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('base',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class so as to
    a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('data',
    """Pointer to start of data."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('dtype',
    """Get array data-descriptor."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('flags',
    """The integer value of flags."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('flat',
    """A 1-D view of the scalar."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('imag',
    """The imaginary part of the scalar."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('itemsize',
    """The length of one element in bytes."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('nbytes',
    """The length of the scalar in bytes."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('ndim',
    """The number of array dimensions."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('real',
    """The real part of the scalar."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('shape',
    """Tuple of array dimensions."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('size',
    """The number of elements in the gentype."""))

add_newdoc('numpy.core.numerictypes', 'generic', ('strides',
    """Tuple of bytes steps in each dimension."""))

# Methods

add_newdoc('numpy.core.numerictypes', 'generic', ('all',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('any',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('argmax',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('argmin',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('argsort',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('astype',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('byteswap',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class so as to
    provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('choose',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('clip',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('compress',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('conjugate',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('copy',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('cumprod',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('cumsum',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('diagonal',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('dump',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('dumps',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('fill',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('flatten',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('getfield',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('item',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('itemset',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('max',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('mean',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('min',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('newbyteorder',
    """
    newbyteorder(new_order='S')

Return a new `dtype` with a different byte order.

Changes are also made in all fields and sub-arrays of the data type.

The `new_order` code can be any from the following:

* 'S' - swap dtype from current to opposite endian
    * {'<', 'L'} - little endian
    * {'>', 'B'} - big endian
    * {'=', 'N'} - native order
    * {'|', 'I'} - ignore (no change to byte order)

Parameters
    ----------
    new_order : str, optional
        Byte order to force; a value from the byte order specifications
        above.  The default value ('S') results in swapping the current
        byte order. The code does a case-insensitive check on the first
        letter of `new_order` for the alternatives above.  For example,
        any of 'B' or 'b' or 'biggish' are valid to specify big-endian.

Returns
    -------
    new_dtype : dtype
        New `dtype` object with the given change to the byte order.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('nonzero',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('prod',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('ptp',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('put',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('ravel',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('repeat',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('reshape',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('resize',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('round',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('searchsorted',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('setfield',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('setflags',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class so as to
    provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('sort',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('squeeze',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('std',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('sum',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('swapaxes',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('take',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('tofile',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('tolist',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('tostring',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('trace',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('transpose',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('var',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

add_newdoc('numpy.core.numerictypes', 'generic', ('view',
    """
    Not implemented (virtual attribute)

Class generic exists solely to derive numpy scalars from, and possesses,
    albeit unimplemented, all the attributes of the ndarray class
    so as to provide a uniform API.

See Also
    --------
    The corresponding attribute of the derived class of interest.

"""))

##############################################################################
#
# Documentation for other scalar classes
#
##############################################################################

add_newdoc('numpy.core.numerictypes', 'bool_',
    """Numpy's Boolean type.  Character code: ``?``.  Alias: bool8""")

add_newdoc('numpy.core.numerictypes', 'complex64',
    """
    Complex number type composed of two 32 bit floats. Character code: 'F'.

""")

add_newdoc('numpy.core.numerictypes', 'complex128',
    """
    Complex number type composed of two 64 bit floats. Character code: 'D'.
    Python complex compatible.

""")

add_newdoc('numpy.core.numerictypes', 'complex256',
    """
    Complex number type composed of two 128-bit floats. Character code: 'G'.

""")

add_newdoc('numpy.core.numerictypes', 'float32',
    """
    32-bit floating-point number. Character code 'f'. C float compatible.

""")

add_newdoc('numpy.core.numerictypes', 'float64',
    """
    64-bit floating-point number. Character code 'd'. Python float compatible.

""")

add_newdoc('numpy.core.numerictypes', 'float96',
    """
    """)

add_newdoc('numpy.core.numerictypes', 'float128',
    """
    128-bit floating-point number. Character code: 'g'. C long float
    compatible.

""")

add_newdoc('numpy.core.numerictypes', 'int8',
    """8-bit integer. Character code ``b``. C char compatible.""")

add_newdoc('numpy.core.numerictypes', 'int16',
    """16-bit integer. Character code ``h``. C short compatible.""")

add_newdoc('numpy.core.numerictypes', 'int32',
    """32-bit integer. Character code 'i'. C int compatible.""")

add_newdoc('numpy.core.numerictypes', 'int64',
    """64-bit integer. Character code 'l'. Python int compatible.""")

add_newdoc('numpy.core.numerictypes', 'object_',
    """Any Python object.  Character code: 'O'.""")