comparison DEPENDENCIES/mingw32/Python27/Lib/site-packages/numpy/add_newdocs.py @ 87:2a2c65a20a8b

Add Python libs and headers

+"""
+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 C-contiguous style, with 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' (default), 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', then the returned array may
+        be in any order (either C-, Fortran-contiguous, or even
+        discontiguous).
+    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 C (row-major) or
+        Fortran (column-major) order in memory.
+
+    Returns
+    -------
+    out : ndarray
+        Array of uninitialized (arbitrary) data with the given
+        shape, dtype, and order.
+
+    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
+        .. versionadded:: 1.6.0
+        Overrides the data type of the result.
+    order : {'C', 'F', 'A', or 'K'}, optional
+        .. versionadded:: 1.6.0
+        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.
+    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=numpy.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 together.
+
+    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).
+    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 of 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.
+
+          * '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.
+
+    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.
+
+    Examples
+    --------
+    >>> np.dot(3, 4)
+    12
+
+    Neither argument is complex-conjugated:
+
+    >>> np.dot([2j, 3j], [2j, 3j])
+    (-13+0j)
+
+    For 2-D arrays it's 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', '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 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.
+
+          * '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.
+
+    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.
+
+    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]])
+
+    """)
+
+add_newdoc('numpy.core', 'alterdot',
+    """
+    Change `dot`, `vdot`, and `inner` to use accelerated BLAS functions.
+
+    Typically, as a user of Numpy, you do not explicitly call this function. If
+    Numpy is built with an accelerated BLAS, this function is automatically
+    called when Numpy is imported.
+
+    When Numpy is built with an accelerated BLAS like ATLAS, these functions
+    are replaced to make use of the faster implementations.  The faster
+    implementations only affect float32, float64, complex64, and complex128
+    arrays. Furthermore, the BLAS API only includes matrix-matrix,
+    matrix-vector, and vector-vector products. Products of arrays with larger
+    dimensionalities use the built in functions and are not accelerated.
+
+    See Also
+    --------
+    restoredot : `restoredot` undoes the effects of `alterdot`.
+
+    """)
+
+add_newdoc('numpy.core', 'restoredot',
+    """
+    Restore `dot`, `vdot`, and `innerproduct` to the default non-BLAS
+    implementations.
+
+    Typically, the user will only need to call this when troubleshooting and
+    installation problem, reproducing the conditions of a build without an
+    accelerated BLAS, or when being very careful about benchmarking linear
+    algebra operations.
+
+    See Also
+    --------
+    alterdot : `restoredot` undoes the effects of `alterdot`.
+
+    """)
+
+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 or column-major 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
+    --------
+    numpy.dtype
+
+    Examples
+    --------
+    >>> x
+    array([[0, 1],
+           [2, 3]])
+    >>> x.dtype
+    dtype('int32')
+    >>> type(x.dtype)
+    <type 'numpy.dtype'>
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
+    """
+    The imaginary part of the array.
+
+    Examples
+    --------
+    >>> x = np.sqrt([1+0j, 0+1j])
+    >>> x.imag
+    array([ 0.        ,  0.70710678])
+    >>> x.imag.dtype
+    dtype('float64')
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
+    """
+    Length of one array element in bytes.
+
+    Examples
+    --------
+    >>> x = np.array([1,2,3], dtype=np.float64)
+    >>> x.itemsize
+    8
+    >>> x = np.array([1,2,3], dtype=np.complex128)
+    >>> x.itemsize
+    16
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
+    """
+    Information about the memory layout of the array.
+
+    Attributes
+    ----------
+    C_CONTIGUOUS (C)
+        The data is in a single, C-style contiguous segment.
+    F_CONTIGUOUS (F)
+        The data is in a single, Fortran-style contiguous segment.
+    OWNDATA (O)
+        The array owns the memory it uses or borrows it from another object.
+    WRITEABLE (W)
+        The data area can be written to.  Setting this to False locks
+        the data, making it read-only.  A view (slice, etc.) inherits WRITEABLE
+        from its base array at creation time, but a view of a writeable
+        array may be subsequently locked while the base array remains writeable.
+        (The opposite is not true, in that a view of a locked array may not
+        be made writeable.  However, currently, locking a base object does not
+        lock any views that already reference it, so under that circumstance it
+        is possible to alter the contents of a locked array via a previously
+        created writeable view onto it.)  Attempting to change a non-writeable
+        array raises a RuntimeError exception.
+    ALIGNED (A)
+        The data and all elements are aligned appropriately for the hardware.
+    UPDATEIFCOPY (U)
+        This array is a copy of some other array. When this array is
+        deallocated, the base array will be updated with the contents of
+        this array.
+    FNC
+        F_CONTIGUOUS and not C_CONTIGUOUS.
+    FORC
+        F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
+    BEHAVED (B)
+        ALIGNED and WRITEABLE.
+    CARRAY (CA)
+        BEHAVED and C_CONTIGUOUS.
+    FARRAY (FA)
+        BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
+
+    Notes
+    -----
+    The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),
+    or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag
+    names are only supported in dictionary access.
+
+    Only the UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be changed by
+    the user, via direct assignment to the attribute or dictionary entry,
+    or by calling `ndarray.setflags`.
+
+    The array flags cannot be set arbitrarily:
+
+    - UPDATEIFCOPY can only be set ``False``.
+    - ALIGNED can only be set ``True`` if the data is truly aligned.
+    - WRITEABLE can only be set ``True`` if the array owns its own memory
+      or the ultimate owner of the memory exposes a writeable buffer
+      interface or is a string.
+
+    Arrays can be both C-style and Fortran-style contiguous simultaneously.
+    This is clear for 1-dimensional arrays, but can also be true for higher
+    dimensional arrays.
+
+    Even for contiguous arrays a stride for a given dimension
+    ``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``
+    or the array has no elements.
+    It does *not* generally hold that ``self.strides[-1] == self.itemsize``
+    for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for
+    Fortran-style contiguous arrays is true.
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
+    """
+    A 1-D iterator over the array.
+
+    This is a `numpy.flatiter` instance, which acts similarly to, but is not
+    a subclass of, Python's built-in iterator object.
+
+    See Also
+    --------
+    flatten : Return a copy of the array collapsed into one dimension.
+
+    flatiter
+
+    Examples
+    --------
+    >>> x = np.arange(1, 7).reshape(2, 3)
+    >>> x
+    array([[1, 2, 3],
+           [4, 5, 6]])
+    >>> x.flat[3]
+    4
+    >>> x.T
+    array([[1, 4],
+           [2, 5],
+           [3, 6]])
+    >>> x.T.flat[3]
+    5
+    >>> type(x.flat)
+    <type 'numpy.flatiter'>
+
+    An assignment example:
+
+    >>> x.flat = 3; x
+    array([[3, 3, 3],
+           [3, 3, 3]])
+    >>> x.flat[[1,4]] = 1; x
+    array([[3, 1, 3],
+           [3, 1, 3]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
+    """
+    Total bytes consumed by the elements of the array.
+
+    Notes
+    -----
+    Does not include memory consumed by non-element attributes of the
+    array object.
+
+    Examples
+    --------
+    >>> x = np.zeros((3,5,2), dtype=np.complex128)
+    >>> x.nbytes
+    480
+    >>> np.prod(x.shape) * x.itemsize
+    480
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
+    """
+    Number of array dimensions.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 3])
+    >>> x.ndim
+    1
+    >>> y = np.zeros((2, 3, 4))
+    >>> y.ndim
+    3
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
+    """
+    The real part of the array.
+
+    Examples
+    --------
+    >>> x = np.sqrt([1+0j, 0+1j])
+    >>> x.real
+    array([ 1.        ,  0.70710678])
+    >>> x.real.dtype
+    dtype('float64')
+
+    See Also
+    --------
+    numpy.real : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
+    """
+    Tuple of array dimensions.
+
+    Notes
+    -----
+    May be used to "reshape" the array, as long as this would not
+    require a change in the total number of elements
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 3, 4])
+    >>> x.shape
+    (4,)
+    >>> y = np.zeros((2, 3, 4))
+    >>> y.shape
+    (2, 3, 4)
+    >>> y.shape = (3, 8)
+    >>> y
+    array([[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
+           [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]])
+    >>> y.shape = (3, 6)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    ValueError: total size of new array must be unchanged
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
+    """
+    Number of elements in the array.
+
+    Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's
+    dimensions.
+
+    Examples
+    --------
+    >>> x = np.zeros((3, 5, 2), dtype=np.complex128)
+    >>> x.size
+    30
+    >>> np.prod(x.shape)
+    30
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
+    """
+    Tuple of bytes to step in each dimension when traversing an array.
+
+    The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
+    is::
+
+        offset = sum(np.array(i) * a.strides)
+
+    A more detailed explanation of strides can be found in the
+    "ndarray.rst" file in the NumPy reference guide.
+
+    Notes
+    -----
+    Imagine an array of 32-bit integers (each 4 bytes)::
+
+      x = np.array([[0, 1, 2, 3, 4],
+                    [5, 6, 7, 8, 9]], dtype=np.int32)
+
+    This array is stored in memory as 40 bytes, one after the other
+    (known as a contiguous block of memory).  The strides of an array tell
+    us how many bytes we have to skip in memory to move to the next position
+    along a certain axis.  For example, we have to skip 4 bytes (1 value) to
+    move to the next column, but 20 bytes (5 values) to get to the same
+    position in the next row.  As such, the strides for the array `x` will be
+    ``(20, 4)``.
+
+    See Also
+    --------
+    numpy.lib.stride_tricks.as_strided
+
+    Examples
+    --------
+    >>> y = np.reshape(np.arange(2*3*4), (2,3,4))
+    >>> y
+    array([[[ 0,  1,  2,  3],
+            [ 4,  5,  6,  7],
+            [ 8,  9, 10, 11]],
+           [[12, 13, 14, 15],
+            [16, 17, 18, 19],
+            [20, 21, 22, 23]]])
+    >>> y.strides
+    (48, 16, 4)
+    >>> y[1,1,1]
+    17
+    >>> offset=sum(y.strides * np.array((1,1,1)))
+    >>> offset/y.itemsize
+    17
+
+    >>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
+    >>> x.strides
+    (32, 4, 224, 1344)
+    >>> i = np.array([3,5,2,2])
+    >>> offset = sum(i * x.strides)
+    >>> x[3,5,2,2]
+    813
+    >>> offset / x.itemsize
+    813
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
+    """
+    Same as self.transpose(), except that self is returned if
+    self.ndim < 2.
+
+    Examples
+    --------
+    >>> x = np.array([[1.,2.],[3.,4.]])
+    >>> x
+    array([[ 1.,  2.],
+           [ 3.,  4.]])
+    >>> x.T
+    array([[ 1.,  3.],
+           [ 2.,  4.]])
+    >>> x = np.array([1.,2.,3.,4.])
+    >>> x
+    array([ 1.,  2.,  3.,  4.])
+    >>> x.T
+    array([ 1.,  2.,  3.,  4.])
+
+    """))
+
+
+##############################################################################
+#
+# ndarray methods
+#
+##############################################################################
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
+    """ a.__array__(|dtype) -> reference if type unchanged, copy otherwise.
+
+    Returns either a new reference to self if dtype is not given or a new array
+    of provided data type if dtype is different from the current dtype of the
+    array.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__',
+    """a.__array_prepare__(obj) -> Object of same type as ndarray object obj.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__',
+    """a.__array_wrap__(obj) -> Object of same type as ndarray object a.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
+    """a.__copy__([order])
+
+    Return a copy of the array.
+
+    Parameters
+    ----------
+    order : {'C', 'F', 'A'}, optional
+        If order is 'C' (False) then the result is contiguous (default).
+        If order is 'Fortran' (True) then the result has fortran order.
+        If order is 'Any' (None) then the result has fortran order
+        only if the array already is in fortran order.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
+    """a.__deepcopy__() -> Deep copy of array.
+
+    Used if copy.deepcopy is called on an array.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__reduce__',
+    """a.__reduce__()
+
+    For pickling.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('__setstate__',
+    """a.__setstate__(version, shape, dtype, isfortran, rawdata)
+
+    For unpickling.
+
+    Parameters
+    ----------
+    version : int
+        optional pickle version. If omitted defaults to 0.
+    shape : tuple
+    dtype : data-type
+    isFortran : bool
+    rawdata : string or list
+        a binary string with the data (or a list if 'a' is an object array)
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
+    """
+    a.all(axis=None, out=None)
+
+    Returns True if all elements evaluate to True.
+
+    Refer to `numpy.all` for full documentation.
+
+    See Also
+    --------
+    numpy.all : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
+    """
+    a.any(axis=None, out=None)
+
+    Returns True if any of the elements of `a` evaluate to True.
+
+    Refer to `numpy.any` for full documentation.
+
+    See Also
+    --------
+    numpy.any : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
+    """
+    a.argmax(axis=None, out=None)
+
+    Return indices of the maximum values along the given axis.
+
+    Refer to `numpy.argmax` for full documentation.
+
+    See Also
+    --------
+    numpy.argmax : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
+    """
+    a.argmin(axis=None, out=None)
+
+    Return indices of the minimum values along the given axis of `a`.
+
+    Refer to `numpy.argmin` for detailed documentation.
+
+    See Also
+    --------
+    numpy.argmin : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
+    """
+    a.argsort(axis=-1, kind='quicksort', order=None)
+
+    Returns the indices that would sort this array.
+
+    Refer to `numpy.argsort` for full documentation.
+
+    See Also
+    --------
+    numpy.argsort : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('argpartition',
+    """
+    a.argpartition(kth, axis=-1, kind='introselect', order=None)
+
+    Returns the indices that would partition this array.
+
+    Refer to `numpy.argpartition` for full documentation.
+
+    .. versionadded:: 1.8.0
+
+    See Also
+    --------
+    numpy.argpartition : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
+    """
+    a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
+
+    Copy of the array, cast to a specified type.
+
+    Parameters
+    ----------
+    dtype : str or dtype
+        Typecode or data-type to which the array is cast.
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout order of the result.
+        '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.
+        Default is 'K'.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        Controls what kind of data casting may occur. Defaults to 'unsafe'
+        for backwards compatibility.
+
+          * '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.
+    subok : bool, optional
+        If True, then sub-classes will be passed-through (default), otherwise
+        the returned array will be forced to be a base-class array.
+    copy : bool, optional
+        By default, astype always returns a newly allocated array. If this
+        is set to false, and the `dtype`, `order`, and `subok`
+        requirements are satisfied, the input array is returned instead
+        of a copy.
+
+    Returns
+    -------
+    arr_t : ndarray
+        Unless `copy` is False and the other conditions for returning the input
+        array are satisfied (see description for `copy` input paramter), `arr_t`
+        is a new array of the same shape as the input array, with dtype, order
+        given by `dtype`, `order`.
+
+    Notes
+    -----
+    Starting in NumPy 1.9, astype method now returns an error if the string
+    dtype to cast to is not long enough in 'safe' casting mode to hold the max
+    value of integer/float array that is being casted. Previously the casting
+    was allowed even if the result was truncated.
+
+    Raises
+    ------
+    ComplexWarning
+        When casting from complex to float or int. To avoid this,
+        one should use ``a.real.astype(t)``.
+
+    Examples
+    --------
+    >>> x = np.array([1, 2, 2.5])
+    >>> x
+    array([ 1. ,  2. ,  2.5])
+
+    >>> x.astype(int)
+    array([1, 2, 2])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
+    """
+    a.byteswap(inplace)
+
+    Swap the bytes of the array elements
+
+    Toggle between low-endian and big-endian data representation by
+    returning a byteswapped array, optionally swapped in-place.
+
+    Parameters
+    ----------
+    inplace : bool, optional
+        If ``True``, swap bytes in-place, default is ``False``.
+
+    Returns
+    -------
+    out : ndarray
+        The byteswapped array. If `inplace` is ``True``, this is
+        a view to self.
+
+    Examples
+    --------
+    >>> A = np.array([1, 256, 8755], dtype=np.int16)
+    >>> map(hex, A)
+    ['0x1', '0x100', '0x2233']
+    >>> A.byteswap(True)
+    array([  256,     1, 13090], dtype=int16)
+    >>> map(hex, A)
+    ['0x100', '0x1', '0x3322']
+
+    Arrays of strings are not swapped
+
+    >>> A = np.array(['ceg', 'fac'])
+    >>> A.byteswap()
+    array(['ceg', 'fac'],
+          dtype='|S3')
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
+    """
+    a.choose(choices, out=None, mode='raise')
+
+    Use an index array to construct a new array from a set of choices.
+
+    Refer to `numpy.choose` for full documentation.
+
+    See Also
+    --------
+    numpy.choose : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
+    """
+    a.clip(a_min, a_max, out=None)
+
+    Return an array whose values are limited to ``[a_min, a_max]``.
+
+    Refer to `numpy.clip` for full documentation.
+
+    See Also
+    --------
+    numpy.clip : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
+    """
+    a.compress(condition, axis=None, out=None)
+
+    Return selected slices of this array along given axis.
+
+    Refer to `numpy.compress` for full documentation.
+
+    See Also
+    --------
+    numpy.compress : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
+    """
+    a.conj()
+
+    Complex-conjugate all elements.
+
+    Refer to `numpy.conjugate` for full documentation.
+
+    See Also
+    --------
+    numpy.conjugate : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
+    """
+    a.conjugate()
+
+    Return the complex conjugate, element-wise.
+
+    Refer to `numpy.conjugate` for full documentation.
+
+    See Also
+    --------
+    numpy.conjugate : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
+    """
+    a.copy(order='C')
+
+    Return a copy of the array.
+
+    Parameters
+    ----------
+    order : {'C', 'F', 'A', 'K'}, optional
+        Controls the memory layout of the copy. '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. (Note that this function and :func:numpy.copy are very
+        similar, but have different default values for their order=
+        arguments.)
+
+    See also
+    --------
+    numpy.copy
+    numpy.copyto
+
+    Examples
+    --------
+    >>> x = np.array([[1,2,3],[4,5,6]], order='F')
+
+    >>> y = x.copy()
+
+    >>> x.fill(0)
+
+    >>> x
+    array([[0, 0, 0],
+           [0, 0, 0]])
+
+    >>> y
+    array([[1, 2, 3],
+           [4, 5, 6]])
+
+    >>> y.flags['C_CONTIGUOUS']
+    True
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
+    """
+    a.cumprod(axis=None, dtype=None, out=None)
+
+    Return the cumulative product of the elements along the given axis.
+
+    Refer to `numpy.cumprod` for full documentation.
+
+    See Also
+    --------
+    numpy.cumprod : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
+    """
+    a.cumsum(axis=None, dtype=None, out=None)
+
+    Return the cumulative sum of the elements along the given axis.
+
+    Refer to `numpy.cumsum` for full documentation.
+
+    See Also
+    --------
+    numpy.cumsum : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
+    """
+    a.diagonal(offset=0, axis1=0, axis2=1)
+
+    Return specified diagonals. In NumPy 1.9 the returned array is a
+    read-only view instead of a copy as in previous NumPy versions.  In
+    NumPy 1.10 the read-only restriction will be removed.
+
+    Refer to :func:`numpy.diagonal` for full documentation.
+
+    See Also
+    --------
+    numpy.diagonal : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('dot',
+    """
+    a.dot(b, out=None)
+
+    Dot product of two arrays.
+
+    Refer to `numpy.dot` for full documentation.
+
+    See Also
+    --------
+    numpy.dot : equivalent function
+
+    Examples
+    --------
+    >>> a = np.eye(2)
+    >>> b = np.ones((2, 2)) * 2
+    >>> a.dot(b)
+    array([[ 2.,  2.],
+           [ 2.,  2.]])
+
+    This array method can be conveniently chained:
+
+    >>> a.dot(b).dot(b)
+    array([[ 8.,  8.],
+           [ 8.,  8.]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
+    """a.dump(file)
+
+    Dump a pickle of the array to the specified file.
+    The array can be read back with pickle.load or numpy.load.
+
+    Parameters
+    ----------
+    file : str
+        A string naming the dump file.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
+    """
+    a.dumps()
+
+    Returns the pickle of the array as a string.
+    pickle.loads or numpy.loads will convert the string back to an array.
+
+    Parameters
+    ----------
+    None
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
+    """
+    a.fill(value)
+
+    Fill the array with a scalar value.
+
+    Parameters
+    ----------
+    value : scalar
+        All elements of `a` will be assigned this value.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2])
+    >>> a.fill(0)
+    >>> a
+    array([0, 0])
+    >>> a = np.empty(2)
+    >>> a.fill(1)
+    >>> a
+    array([ 1.,  1.])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
+    """
+    a.flatten(order='C')
+
+    Return a copy of the array collapsed into one dimension.
+
+    Parameters
+    ----------
+    order : {'C', 'F', 'A'}, optional
+        Whether to flatten in C (row-major), Fortran (column-major) order,
+        or preserve the C/Fortran ordering from `a`.
+        The default is 'C'.
+
+    Returns
+    -------
+    y : ndarray
+        A copy of the input array, flattened to one dimension.
+
+    See Also
+    --------
+    ravel : Return a flattened array.
+    flat : A 1-D flat iterator over the array.
+
+    Examples
+    --------
+    >>> a = np.array([[1,2], [3,4]])
+    >>> a.flatten()
+    array([1, 2, 3, 4])
+    >>> a.flatten('F')
+    array([1, 3, 2, 4])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
+    """
+    a.getfield(dtype, offset=0)
+
+    Returns a field of the given array as a certain type.
+
+    A field is a view of the array data with a given data-type. The values in
+    the view are determined by the given type and the offset into the current
+    array in bytes. The offset needs to be such that the view dtype fits in the
+    array dtype; for example an array of dtype complex128 has 16-byte elements.
+    If taking a view with a 32-bit integer (4 bytes), the offset needs to be
+    between 0 and 12 bytes.
+
+    Parameters
+    ----------
+    dtype : str or dtype
+        The data type of the view. The dtype size of the view can not be larger
+        than that of the array itself.
+    offset : int
+        Number of bytes to skip before beginning the element view.
+
+    Examples
+    --------
+    >>> x = np.diag([1.+1.j]*2)
+    >>> x[1, 1] = 2 + 4.j
+    >>> x
+    array([[ 1.+1.j,  0.+0.j],
+           [ 0.+0.j,  2.+4.j]])
+    >>> x.getfield(np.float64)
+    array([[ 1.,  0.],
+           [ 0.,  2.]])
+
+    By choosing an offset of 8 bytes we can select the complex part of the
+    array for our view:
+
+    >>> x.getfield(np.float64, offset=8)
+    array([[ 1.,  0.],
+       [ 0.,  4.]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
+    """
+    a.item(*args)
+
+    Copy an element of an array to a standard Python scalar and return it.
+
+    Parameters
+    ----------
+    \\*args : Arguments (variable number and type)
+
+        * none: in this case, the method only works for arrays
+          with one element (`a.size == 1`), which element is
+          copied into a standard Python scalar object and returned.
+
+        * int_type: this argument is interpreted as a flat index into
+          the array, specifying which element to copy and return.
+
+        * tuple of int_types: functions as does a single int_type argument,
+          except that the argument is interpreted as an nd-index into the
+          array.
+
+    Returns
+    -------
+    z : Standard Python scalar object
+        A copy of the specified element of the array as a suitable
+        Python scalar
+
+    Notes
+    -----
+    When the data type of `a` is longdouble or clongdouble, item() returns
+    a scalar array object because there is no available Python scalar that
+    would not lose information. Void arrays return a buffer object for item(),
+    unless fields are defined, in which case a tuple is returned.
+
+    `item` is very similar to a[args], except, instead of an array scalar,
+    a standard Python scalar is returned. This can be useful for speeding up
+    access to elements of the array and doing arithmetic on elements of the
+    array using Python's optimized math.
+
+    Examples
+    --------
+    >>> x = np.random.randint(9, size=(3, 3))
+    >>> x
+    array([[3, 1, 7],
+           [2, 8, 3],
+           [8, 5, 3]])
+    >>> x.item(3)
+    2
+    >>> x.item(7)
+    5
+    >>> x.item((0, 1))
+    1
+    >>> x.item((2, 2))
+    3
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('itemset',
+    """
+    a.itemset(*args)
+
+    Insert scalar into an array (scalar is cast to array's dtype, if possible)
+
+    There must be at least 1 argument, and define the last argument
+    as *item*.  Then, ``a.itemset(*args)`` is equivalent to but faster
+    than ``a[args] = item``.  The item should be a scalar value and `args`
+    must select a single item in the array `a`.
+
+    Parameters
+    ----------
+    \*args : Arguments
+        If one argument: a scalar, only used in case `a` is of size 1.
+        If two arguments: the last argument is the value to be set
+        and must be a scalar, the first argument specifies a single array
+        element location. It is either an int or a tuple.
+
+    Notes
+    -----
+    Compared to indexing syntax, `itemset` provides some speed increase
+    for placing a scalar into a particular location in an `ndarray`,
+    if you must do this.  However, generally this is discouraged:
+    among other problems, it complicates the appearance of the code.
+    Also, when using `itemset` (and `item`) inside a loop, be sure
+    to assign the methods to a local variable to avoid the attribute
+    look-up at each loop iteration.
+
+    Examples
+    --------
+    >>> x = np.random.randint(9, size=(3, 3))
+    >>> x
+    array([[3, 1, 7],
+           [2, 8, 3],
+           [8, 5, 3]])
+    >>> x.itemset(4, 0)
+    >>> x.itemset((2, 2), 9)
+    >>> x
+    array([[3, 1, 7],
+           [2, 0, 3],
+           [8, 5, 9]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('setasflat',
+    """
+    a.setasflat(arr)
+
+    Equivalent to a.flat = arr.flat, but is generally more efficient.
+    This function does not check for overlap, so if ``arr`` and ``a``
+    are viewing the same data with different strides, the results will
+    be unpredictable.
+
+    Parameters
+    ----------
+    arr : array_like
+        The array to copy into a.
+
+    Examples
+    --------
+    >>> a = np.arange(2*4).reshape(2,4)[:,:-1]; a
+    array([[0, 1, 2],
+           [4, 5, 6]])
+    >>> b = np.arange(3*3, dtype='f4').reshape(3,3).T[::-1,:-1]; b
+    array([[ 2.,  5.],
+           [ 1.,  4.],
+           [ 0.,  3.]], dtype=float32)
+    >>> a.setasflat(b)
+    >>> a
+    array([[2, 5, 1],
+           [4, 0, 3]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
+    """
+    a.max(axis=None, out=None)
+
+    Return the maximum along a given axis.
+
+    Refer to `numpy.amax` for full documentation.
+
+    See Also
+    --------
+    numpy.amax : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
+    """
+    a.mean(axis=None, dtype=None, out=None)
+
+    Returns the average of the array elements along given axis.
+
+    Refer to `numpy.mean` for full documentation.
+
+    See Also
+    --------
+    numpy.mean : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
+    """
+    a.min(axis=None, out=None)
+
+    Return the minimum along a given axis.
+
+    Refer to `numpy.amin` for full documentation.
+
+    See Also
+    --------
+    numpy.amin : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'may_share_memory',
+    """
+    Determine if two arrays can share memory
+
+    The memory-bounds of a and b are computed.  If they overlap then
+    this function returns True.  Otherwise, it returns False.
+
+    A return of True does not necessarily mean that the two arrays
+    share any element.  It just means that they *might*.
+
+    Parameters
+    ----------
+    a, b : ndarray
+
+    Returns
+    -------
+    out : bool
+
+    Examples
+    --------
+    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
+    False
+
+    """)
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
+    """
+    arr.newbyteorder(new_order='S')
+
+    Return the array with the same data viewed with a different byte order.
+
+    Equivalent to::
+
+        arr.view(arr.dtype.newbytorder(new_order))
+
+    Changes are also made in all fields and sub-arrays of the array data
+    type.
+
+
+
+    Parameters
+    ----------
+    new_order : string, optional
+        Byte order to force; a value from the byte order specifications
+        above. `new_order` codes can be any of::
+
+         * 'S' - swap dtype from current to opposite endian
+         * {'<', 'L'} - little endian
+         * {'>', 'B'} - big endian
+         * {'=', 'N'} - native order
+         * {'|', 'I'} - ignore (no change to byte order)
+
+        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_arr : array
+        New array object with the dtype reflecting given change to the
+        byte order.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
+    """
+    a.nonzero()
+
+    Return the indices of the elements that are non-zero.
+
+    Refer to `numpy.nonzero` for full documentation.
+
+    See Also
+    --------
+    numpy.nonzero : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
+    """
+    a.prod(axis=None, dtype=None, out=None)
+
+    Return the product of the array elements over the given axis
+
+    Refer to `numpy.prod` for full documentation.
+
+    See Also
+    --------
+    numpy.prod : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
+    """
+    a.ptp(axis=None, out=None)
+
+    Peak to peak (maximum - minimum) value along a given axis.
+
+    Refer to `numpy.ptp` for full documentation.
+
+    See Also
+    --------
+    numpy.ptp : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
+    """
+    a.put(indices, values, mode='raise')
+
+    Set ``a.flat[n] = values[n]`` for all `n` in indices.
+
+    Refer to `numpy.put` for full documentation.
+
+    See Also
+    --------
+    numpy.put : equivalent function
+
+    """))
+
+add_newdoc('numpy.core.multiarray', 'copyto',
+    """
+    copyto(dst, src, casting='same_kind', where=None)
+
+    Copies values from one array to another, broadcasting as necessary.
+
+    Raises a TypeError if the `casting` rule is violated, and if
+    `where` is provided, it selects which elements to copy.
+
+    .. versionadded:: 1.7.0
+
+    Parameters
+    ----------
+    dst : ndarray
+        The array into which values are copied.
+    src : array_like
+        The array from which values are copied.
+    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
+        Controls what kind of data casting may occur when copying.
+
+          * '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.
+    where : array_like of bool, optional
+        A boolean array which is broadcasted to match the dimensions
+        of `dst`, and selects elements to copy from `src` to `dst`
+        wherever it contains the value True.
+
+    """)
+
+add_newdoc('numpy.core.multiarray', 'putmask',
+    """
+    putmask(a, mask, values)
+
+    Changes elements of an array based on conditional and input values.
+
+    Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
+
+    If `values` is not the same size as `a` and `mask` then it will repeat.
+    This gives behavior different from ``a[mask] = values``.
+
+    Parameters
+    ----------
+    a : array_like
+        Target array.
+    mask : array_like
+        Boolean mask array. It has to be the same shape as `a`.
+    values : array_like
+        Values to put into `a` where `mask` is True. If `values` is smaller
+        than `a` it will be repeated.
+
+    See Also
+    --------
+    place, put, take, copyto
+
+    Examples
+    --------
+    >>> x = np.arange(6).reshape(2, 3)
+    >>> np.putmask(x, x>2, x**2)
+    >>> x
+    array([[ 0,  1,  2],
+           [ 9, 16, 25]])
+
+    If `values` is smaller than `a` it is repeated:
+
+    >>> x = np.arange(5)
+    >>> np.putmask(x, x>1, [-33, -44])
+    >>> x
+    array([  0,   1, -33, -44, -33])
+
+    """)
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
+    """
+    a.ravel([order])
+
+    Return a flattened array.
+
+    Refer to `numpy.ravel` for full documentation.
+
+    See Also
+    --------
+    numpy.ravel : equivalent function
+
+    ndarray.flat : a flat iterator on the array.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
+    """
+    a.repeat(repeats, axis=None)
+
+    Repeat elements of an array.
+
+    Refer to `numpy.repeat` for full documentation.
+
+    See Also
+    --------
+    numpy.repeat : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
+    """
+    a.reshape(shape, order='C')
+
+    Returns an array containing the same data with a new shape.
+
+    Refer to `numpy.reshape` for full documentation.
+
+    See Also
+    --------
+    numpy.reshape : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
+    """
+    a.resize(new_shape, refcheck=True)
+
+    Change shape and size of array in-place.
+
+    Parameters
+    ----------
+    new_shape : tuple of ints, or `n` ints
+        Shape of resized array.
+    refcheck : bool, optional
+        If False, reference count will not be checked. Default is True.
+
+    Returns
+    -------
+    None
+
+    Raises
+    ------
+    ValueError
+        If `a` does not own its own data or references or views to it exist,
+        and the data memory must be changed.
+
+    SystemError
+        If the `order` keyword argument is specified. This behaviour is a
+        bug in NumPy.
+
+    See Also
+    --------
+    resize : Return a new array with the specified shape.
+
+    Notes
+    -----
+    This reallocates space for the data area if necessary.
+
+    Only contiguous arrays (data elements consecutive in memory) can be
+    resized.
+
+    The purpose of the reference count check is to make sure you
+    do not use this array as a buffer for another Python object and then
+    reallocate the memory. However, reference counts can increase in
+    other ways so if you are sure that you have not shared the memory
+    for this array with another Python object, then you may safely set
+    `refcheck` to False.
+
+    Examples
+    --------
+    Shrinking an array: array is flattened (in the order that the data are
+    stored in memory), resized, and reshaped:
+
+    >>> a = np.array([[0, 1], [2, 3]], order='C')
+    >>> a.resize((2, 1))
+    >>> a
+    array([[0],
+           [1]])
+
+    >>> a = np.array([[0, 1], [2, 3]], order='F')
+    >>> a.resize((2, 1))
+    >>> a
+    array([[0],
+           [2]])
+
+    Enlarging an array: as above, but missing entries are filled with zeros:
+
+    >>> b = np.array([[0, 1], [2, 3]])
+    >>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
+    >>> b
+    array([[0, 1, 2],
+           [3, 0, 0]])
+
+    Referencing an array prevents resizing...
+
+    >>> c = a
+    >>> a.resize((1, 1))
+    Traceback (most recent call last):
+    ...
+    ValueError: cannot resize an array that has been referenced ...
+
+    Unless `refcheck` is False:
+
+    >>> a.resize((1, 1), refcheck=False)
+    >>> a
+    array([[0]])
+    >>> c
+    array([[0]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
+    """
+    a.round(decimals=0, out=None)
+
+    Return `a` with each element rounded to the given number of decimals.
+
+    Refer to `numpy.around` for full documentation.
+
+    See Also
+    --------
+    numpy.around : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
+    """
+    a.searchsorted(v, side='left', sorter=None)
+
+    Find indices where elements of v should be inserted in a to maintain order.
+
+    For full documentation, see `numpy.searchsorted`
+
+    See Also
+    --------
+    numpy.searchsorted : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
+    """
+    a.setfield(val, dtype, offset=0)
+
+    Put a value into a specified place in a field defined by a data-type.
+
+    Place `val` into `a`'s field defined by `dtype` and beginning `offset`
+    bytes into the field.
+
+    Parameters
+    ----------
+    val : object
+        Value to be placed in field.
+    dtype : dtype object
+        Data-type of the field in which to place `val`.
+    offset : int, optional
+        The number of bytes into the field at which to place `val`.
+
+    Returns
+    -------
+    None
+
+    See Also
+    --------
+    getfield
+
+    Examples
+    --------
+    >>> x = np.eye(3)
+    >>> x.getfield(np.float64)
+    array([[ 1.,  0.,  0.],
+           [ 0.,  1.,  0.],
+           [ 0.,  0.,  1.]])
+    >>> x.setfield(3, np.int32)
+    >>> x.getfield(np.int32)
+    array([[3, 3, 3],
+           [3, 3, 3],
+           [3, 3, 3]])
+    >>> x
+    array([[  1.00000000e+000,   1.48219694e-323,   1.48219694e-323],
+           [  1.48219694e-323,   1.00000000e+000,   1.48219694e-323],
+           [  1.48219694e-323,   1.48219694e-323,   1.00000000e+000]])
+    >>> x.setfield(np.eye(3), np.int32)
+    >>> x
+    array([[ 1.,  0.,  0.],
+           [ 0.,  1.,  0.],
+           [ 0.,  0.,  1.]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
+    """
+    a.setflags(write=None, align=None, uic=None)
+
+    Set array flags WRITEABLE, ALIGNED, and UPDATEIFCOPY, respectively.
+
+    These Boolean-valued flags affect how numpy interprets the memory
+    area used by `a` (see Notes below). The ALIGNED flag can only
+    be set to True if the data is actually aligned according to the type.
+    The UPDATEIFCOPY flag can never be set to True. The flag WRITEABLE
+    can only be set to True if the array owns its own memory, or the
+    ultimate owner of the memory exposes a writeable buffer interface,
+    or is a string. (The exception for string is made so that unpickling
+    can be done without copying memory.)
+
+    Parameters
+    ----------
+    write : bool, optional
+        Describes whether or not `a` can be written to.
+    align : bool, optional
+        Describes whether or not `a` is aligned properly for its type.
+    uic : bool, optional
+        Describes whether or not `a` is a copy of another "base" array.
+
+    Notes
+    -----
+    Array flags provide information about how the memory area used
+    for the array is to be interpreted. There are 6 Boolean flags
+    in use, only three of which can be changed by the user:
+    UPDATEIFCOPY, WRITEABLE, and ALIGNED.
+
+    WRITEABLE (W) the data area can be written to;
+
+    ALIGNED (A) the data and strides are aligned appropriately for the hardware
+    (as determined by the compiler);
+
+    UPDATEIFCOPY (U) this array is a copy of some other array (referenced
+    by .base). When this array is deallocated, the base array will be
+    updated with the contents of this array.
+
+    All flags can be accessed using their first (upper case) letter as well
+    as the full name.
+
+    Examples
+    --------
+    >>> y
+    array([[3, 1, 7],
+           [2, 0, 0],
+           [8, 5, 9]])
+    >>> y.flags
+      C_CONTIGUOUS : True
+      F_CONTIGUOUS : False
+      OWNDATA : True
+      WRITEABLE : True
+      ALIGNED : True
+      UPDATEIFCOPY : False
+    >>> y.setflags(write=0, align=0)
+    >>> y.flags
+      C_CONTIGUOUS : True
+      F_CONTIGUOUS : False
+      OWNDATA : True
+      WRITEABLE : False
+      ALIGNED : False
+      UPDATEIFCOPY : False
+    >>> y.setflags(uic=1)
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    ValueError: cannot set UPDATEIFCOPY flag to True
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
+    """
+    a.sort(axis=-1, kind='quicksort', order=None)
+
+    Sort an array, in-place.
+
+    Parameters
+    ----------
+    axis : int, optional
+        Axis along which to sort. Default is -1, which means sort along the
+        last axis.
+    kind : {'quicksort', 'mergesort', 'heapsort'}, optional
+        Sorting algorithm. Default is 'quicksort'.
+    order : list, optional
+        When `a` is an array with fields defined, this argument specifies
+        which fields to compare first, second, etc.  Not all fields need be
+        specified.
+
+    See Also
+    --------
+    numpy.sort : Return a sorted copy of an array.
+    argsort : Indirect sort.
+    lexsort : Indirect stable sort on multiple keys.
+    searchsorted : Find elements in sorted array.
+    partition: Partial sort.
+
+    Notes
+    -----
+    See ``sort`` for notes on the different sorting algorithms.
+
+    Examples
+    --------
+    >>> a = np.array([[1,4], [3,1]])
+    >>> a.sort(axis=1)
+    >>> a
+    array([[1, 4],
+           [1, 3]])
+    >>> a.sort(axis=0)
+    >>> a
+    array([[1, 3],
+           [1, 4]])
+
+    Use the `order` keyword to specify a field to use when sorting a
+    structured array:
+
+    >>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
+    >>> a.sort(order='y')
+    >>> a
+    array([('c', 1), ('a', 2)],
+          dtype=[('x', '|S1'), ('y', '<i4')])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('partition',
+    """
+    a.partition(kth, axis=-1, kind='introselect', order=None)
+
+    Rearranges the elements in the array in such a way that value of the
+    element in kth position is in the position it would be in a sorted array.
+    All elements smaller than the kth element are moved before this element and
+    all equal or greater are moved behind it. The ordering of the elements in
+    the two partitions is undefined.
+
+    .. versionadded:: 1.8.0
+
+    Parameters
+    ----------
+    kth : int or sequence of ints
+        Element index to partition by. The kth element value will be in its
+        final sorted position and all smaller elements will be moved before it
+        and all equal or greater elements behind it.
+        The order all elements in the partitions is undefined.
+        If provided with a sequence of kth it will partition all elements
+        indexed by kth of them into their sorted position at once.
+    axis : int, optional
+        Axis along which to sort. Default is -1, which means sort along the
+        last axis.
+    kind : {'introselect'}, optional
+        Selection algorithm. Default is 'introselect'.
+    order : list, optional
+        When `a` is an array with fields defined, this argument specifies
+        which fields to compare first, second, etc.  Not all fields need be
+        specified.
+
+    See Also
+    --------
+    numpy.partition : Return a parititioned copy of an array.
+    argpartition : Indirect partition.
+    sort : Full sort.
+
+    Notes
+    -----
+    See ``np.partition`` for notes on the different algorithms.
+
+    Examples
+    --------
+    >>> a = np.array([3, 4, 2, 1])
+    >>> a.partition(a, 3)
+    >>> a
+    array([2, 1, 3, 4])
+
+    >>> a.partition((1, 3))
+    array([1, 2, 3, 4])
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
+    """
+    a.squeeze(axis=None)
+
+    Remove single-dimensional entries from the shape of `a`.
+
+    Refer to `numpy.squeeze` for full documentation.
+
+    See Also
+    --------
+    numpy.squeeze : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
+    """
+    a.std(axis=None, dtype=None, out=None, ddof=0)
+
+    Returns the standard deviation of the array elements along given axis.
+
+    Refer to `numpy.std` for full documentation.
+
+    See Also
+    --------
+    numpy.std : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
+    """
+    a.sum(axis=None, dtype=None, out=None)
+
+    Return the sum of the array elements over the given axis.
+
+    Refer to `numpy.sum` for full documentation.
+
+    See Also
+    --------
+    numpy.sum : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
+    """
+    a.swapaxes(axis1, axis2)
+
+    Return a view of the array with `axis1` and `axis2` interchanged.
+
+    Refer to `numpy.swapaxes` for full documentation.
+
+    See Also
+    --------
+    numpy.swapaxes : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
+    """
+    a.take(indices, axis=None, out=None, mode='raise')
+
+    Return an array formed from the elements of `a` at the given indices.
+
+    Refer to `numpy.take` for full documentation.
+
+    See Also
+    --------
+    numpy.take : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
+    """
+    a.tofile(fid, sep="", format="%s")
+
+    Write array to a file as text or binary (default).
+
+    Data is always written in 'C' order, independent of the order of `a`.
+    The data produced by this method can be recovered using the function
+    fromfile().
+
+    Parameters
+    ----------
+    fid : file or str
+        An open file object, or a string containing a filename.
+    sep : str
+        Separator between array items for text output.
+        If "" (empty), a binary file is written, equivalent to
+        ``file.write(a.tobytes())``.
+    format : str
+        Format string for text file output.
+        Each entry in the array is formatted to text by first converting
+        it to the closest Python type, and then using "format" % item.
+
+    Notes
+    -----
+    This is a convenience function for quick storage of array data.
+    Information on endianness and precision is lost, so this method is not a
+    good choice for files intended to archive data or transport data between
+    machines with different endianness. Some of these problems can be overcome
+    by outputting the data as text files, at the expense of speed and file
+    size.
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
+    """
+    a.tolist()
+
+    Return the array as a (possibly nested) list.
+
+    Return a copy of the array data as a (nested) Python list.
+    Data items are converted to the nearest compatible Python type.
+
+    Parameters
+    ----------
+    none
+
+    Returns
+    -------
+    y : list
+        The possibly nested list of array elements.
+
+    Notes
+    -----
+    The array may be recreated, ``a = np.array(a.tolist())``.
+
+    Examples
+    --------
+    >>> a = np.array([1, 2])
+    >>> a.tolist()
+    [1, 2]
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> list(a)
+    [array([1, 2]), array([3, 4])]
+    >>> a.tolist()
+    [[1, 2], [3, 4]]
+
+    """))
+
+
+tobytesdoc = """
+    a.{name}(order='C')
+
+    Construct Python bytes containing the raw data bytes in the array.
+
+    Constructs Python bytes showing a copy of the raw contents of
+    data memory. The bytes object can be produced in either 'C' or 'Fortran',
+    or 'Any' order (the default is 'C'-order). 'Any' order means C-order
+    unless the F_CONTIGUOUS flag in the array is set, in which case it
+    means 'Fortran' order.
+
+    {deprecated}
+
+    Parameters
+    ----------
+    order : {{'C', 'F', None}}, optional
+        Order of the data for multidimensional arrays:
+        C, Fortran, or the same as for the original array.
+
+    Returns
+    -------
+    s : bytes
+        Python bytes exhibiting a copy of `a`'s raw data.
+
+    Examples
+    --------
+    >>> x = np.array([[0, 1], [2, 3]])
+    >>> x.tobytes()
+    b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
+    >>> x.tobytes('C') == x.tobytes()
+    True
+    >>> x.tobytes('F')
+    b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
+
+    """
+
+add_newdoc('numpy.core.multiarray', 'ndarray',
+           ('tostring', tobytesdoc.format(name='tostring',
+                                          deprecated=
+                                          'This function is a compatibility '
+                                          'alias for tobytes. Despite its '
+                                          'name it returns bytes not '
+                                          'strings.')))
+add_newdoc('numpy.core.multiarray', 'ndarray',
+           ('tobytes', tobytesdoc.format(name='tobytes',
+                                         deprecated='.. versionadded:: 1.9.0')))
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
+    """
+    a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
+
+    Return the sum along diagonals of the array.
+
+    Refer to `numpy.trace` for full documentation.
+
+    See Also
+    --------
+    numpy.trace : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
+    """
+    a.transpose(*axes)
+
+    Returns a view of the array with axes transposed.
+
+    For a 1-D array, this has no effect. (To change between column and
+    row vectors, first cast the 1-D array into a matrix object.)
+    For a 2-D array, this is the usual matrix transpose.
+    For an n-D array, if axes are given, their order indicates how the
+    axes are permuted (see Examples). If axes are not provided and
+    ``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
+    ``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
+
+    Parameters
+    ----------
+    axes : None, tuple of ints, or `n` ints
+
+     * None or no argument: reverses the order of the axes.
+
+     * tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
+       `i`-th axis becomes `a.transpose()`'s `j`-th axis.
+
+     * `n` ints: same as an n-tuple of the same ints (this form is
+       intended simply as a "convenience" alternative to the tuple form)
+
+    Returns
+    -------
+    out : ndarray
+        View of `a`, with axes suitably permuted.
+
+    See Also
+    --------
+    ndarray.T : Array property returning the array transposed.
+
+    Examples
+    --------
+    >>> a = np.array([[1, 2], [3, 4]])
+    >>> a
+    array([[1, 2],
+           [3, 4]])
+    >>> a.transpose()
+    array([[1, 3],
+           [2, 4]])
+    >>> a.transpose((1, 0))
+    array([[1, 3],
+           [2, 4]])
+    >>> a.transpose(1, 0)
+    array([[1, 3],
+           [2, 4]])
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('var',
+    """
+    a.var(axis=None, dtype=None, out=None, ddof=0)
+
+    Returns the variance of the array elements, along given axis.
+
+    Refer to `numpy.var` for full documentation.
+
+    See Also
+    --------
+    numpy.var : equivalent function
+
+    """))
+
+
+add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
+    """
+    a.view(dtype=None, type=None)
+
+    New view of array with the same data.
+
+    Parameters
+    ----------
+    dtype : data-type or ndarray sub-class, optional
+        Data-type descriptor of the returned view, e.g., float32 or int16. The
+        default, None, results in the view having the same data-type as `a`.
+        This argument can also be specified as an ndarray sub-class, which
+        then specifies the type of the returned object (this is equivalent to
+        setting the ``type`` parameter).
+    type : Python type, optional
+        Type of the returned view, e.g., ndarray or matrix.  Again, the
+        default None results in type preservation.
+
+    Notes
+    -----
+    ``a.view()`` is used two different ways:
+
+    ``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
+    of the array's memory with a different data-type.  This can cause a
+    reinterpretation of the bytes of memory.
+
+    ``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
+    returns an instance of `ndarray_subclass` that looks at the same array
+    (same shape, dtype, etc.)  This does not cause a reinterpretation of the
+    memory.
+
+    For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
+    bytes per entry than the previous dtype (for example, converting a
+    regular array to a structured array), then the behavior of the view
+    cannot be predicted just from the superficial appearance of ``a`` (shown
+    by ``print(a)``). It also depends on exactly how ``a`` is stored in
+    memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
+    defined as a slice or transpose, etc., the view may give different
+    results.
+
+
+    Examples
+    --------
+    >>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
+
+    Viewing array data using a different type and dtype:
+
+    >>> y = x.view(dtype=np.int16, type=np.matrix)
+    >>> y
+    matrix([[513]], dtype=int16)
+    >>> print type(y)
+    <class 'numpy.matrixlib.defmatrix.matrix'>
+
+    Creating a view on a structured array so it can be used in calculations
+
+    >>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
+    >>> xv = x.view(dtype=np.int8).reshape(-1,2)
+    >>> xv
+    array([[1, 2],
+           [3, 4]], dtype=int8)
+    >>> xv.mean(0)
+    array([ 2.,  3.])
+
+    Making changes to the view changes the underlying array
+
+    >>> xv[0,1] = 20
+    >>> print x
+    [(1, 20) (3, 4)]
+
+    Using a view to convert an array to a record array:
+
+    >>> z = x.view(np.recarray)
+    >>> z.a
+    array([1], dtype=int8)
+
+    Views share data:
+
+    >>> x[0] = (9, 10)
+    >>> z[0]
+    (9, 10)
+
+    Views that change the dtype size (bytes per entry) should normally be
+    avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
+
+    >>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
+    >>> y = x[:, 0:2]
+    >>> y
+    array([[1, 2],
+           [4, 5]], dtype=int16)
+    >>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    ValueError: new type not compatible with array.
+    >>> z = y.copy()
+    >>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
+    array([[(1, 2)],
+           [(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
+    """))
+
+
+##############################################################################
+#
+# umath functions
+#
+##############################################################################
+
+add_newdoc('numpy.core.umath', 'frompyfunc',
+    """
+    frompyfunc(func, nin, nout)
+
+    Takes an arbitrary Python function and returns a Numpy ufunc.
+
+    Can be used, for example, to add broadcasting to a built-in Python
+    function (see Examples section).
+
+    Parameters
+    ----------
+    func : Python function object
+        An arbitrary Python function.
+    nin : int
+        The number of input arguments.
+    nout : int
+        The number of objects returned by `func`.
+
+    Returns
+    -------
+    out : ufunc
+        Returns a Numpy universal function (``ufunc``) object.
+
+    Notes
+    -----
+    The returned ufunc always returns PyObject arrays.
+
+    Examples
+    --------
+    Use frompyfunc to add broadcasting to the Python function ``oct``:
+
+    >>> oct_array = np.frompyfunc(oct, 1, 1)
+    >>> oct_array(np.array((10, 30, 100)))
+    array([012, 036, 0144], dtype=object)
+    >>> np.array((oct(10), oct(30), oct(100))) # for comparison
+    array(['012', '036', '0144'],
+          dtype='|S4')
+
+    """)
+
+add_newdoc('numpy.core.umath', 'geterrobj',
+    """
+    geterrobj()
+
+    Return the current object that defines floating-point error handling.
+
+    The error object contains all information that defines the error handling
+    behavior in Numpy. `geterrobj` is used internally by the other
+    functions that get and set error handling behavior (`geterr`, `seterr`,
+    `geterrcall`, `seterrcall`).
+
+    Returns
+    -------
+    errobj : list
+        The error object, a list containing three elements:
+        [internal numpy buffer size, error mask, error callback function].
+
+        The error mask is a single integer that holds the treatment information
+        on all four floating point errors. The information for each error type
+        is contained in three bits of the integer. If we print it in base 8, we
+        can see what treatment is set for "invalid", "under", "over", and
+        "divide" (in that order). The printed string can be interpreted with
+
+        * 0 : 'ignore'
+        * 1 : 'warn'
+        * 2 : 'raise'
+        * 3 : 'call'
+        * 4 : 'print'
+        * 5 : 'log'
+
+    See Also
+    --------
+    seterrobj, seterr, geterr, seterrcall, geterrcall
+    getbufsize, setbufsize
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    Examples
+    --------
+    >>> np.geterrobj()  # first get the defaults
+    [10000, 0, None]
+
+    >>> def err_handler(type, flag):
+    ...     print "Floating point error (%s), with flag %s" % (type, flag)
+    ...
+    >>> old_bufsize = np.setbufsize(20000)
+    >>> old_err = np.seterr(divide='raise')
+    >>> old_handler = np.seterrcall(err_handler)
+    >>> np.geterrobj()
+    [20000, 2, <function err_handler at 0x91dcaac>]
+
+    >>> old_err = np.seterr(all='ignore')
+    >>> np.base_repr(np.geterrobj()[1], 8)
+    '0'
+    >>> old_err = np.seterr(divide='warn', over='log', under='call',
+                            invalid='print')
+    >>> np.base_repr(np.geterrobj()[1], 8)
+    '4351'
+
+    """)
+
+add_newdoc('numpy.core.umath', 'seterrobj',
+    """
+    seterrobj(errobj)
+
+    Set the object that defines floating-point error handling.
+
+    The error object contains all information that defines the error handling
+    behavior in Numpy. `seterrobj` is used internally by the other
+    functions that set error handling behavior (`seterr`, `seterrcall`).
+
+    Parameters
+    ----------
+    errobj : list
+        The error object, a list containing three elements:
+        [internal numpy buffer size, error mask, error callback function].
+
+        The error mask is a single integer that holds the treatment information
+        on all four floating point errors. The information for each error type
+        is contained in three bits of the integer. If we print it in base 8, we
+        can see what treatment is set for "invalid", "under", "over", and
+        "divide" (in that order). The printed string can be interpreted with
+
+        * 0 : 'ignore'
+        * 1 : 'warn'
+        * 2 : 'raise'
+        * 3 : 'call'
+        * 4 : 'print'
+        * 5 : 'log'
+
+    See Also
+    --------
+    geterrobj, seterr, geterr, seterrcall, geterrcall
+    getbufsize, setbufsize
+
+    Notes
+    -----
+    For complete documentation of the types of floating-point exceptions and
+    treatment options, see `seterr`.
+
+    Examples
+    --------
+    >>> old_errobj = np.geterrobj()  # first get the defaults
+    >>> old_errobj
+    [10000, 0, None]
+
+    >>> def err_handler(type, flag):
+    ...     print "Floating point error (%s), with flag %s" % (type, flag)
+    ...
+    >>> new_errobj = [20000, 12, err_handler]
+    >>> np.seterrobj(new_errobj)
+    >>> np.base_repr(12, 8)  # int for divide=4 ('print') and over=1 ('warn')
+    '14'
+    >>> np.geterr()
+    {'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
+    >>> np.geterrcall() is err_handler
+    True
+
+    """)
+
+
+##############################################################################
+#
+# lib._compiled_base functions
+#
+##############################################################################
+
+add_newdoc('numpy.lib._compiled_base', 'digitize',
+    """
+    digitize(x, bins, right=False)
+
+    Return the indices of the bins to which each value in input array belongs.
+
+    Each index ``i`` returned is such that ``bins[i-1] <= x < bins[i]`` if
+    `bins` is monotonically increasing, or ``bins[i-1] > x >= bins[i]`` if
+    `bins` is monotonically decreasing. If values in `x` are beyond the
+    bounds of `bins`, 0 or ``len(bins)`` is returned as appropriate. If right
+    is True, then the right bin is closed so that the index ``i`` is such
+    that ``bins[i-1] < x <= bins[i]`` or bins[i-1] >= x > bins[i]`` if `bins`
+    is monotonically increasing or decreasing, respectively.
+
+    Parameters
+    ----------
+    x : array_like
+        Input array to be binned. It has to be 1-dimensional.
+    bins : array_like
+        Array of bins. It has to be 1-dimensional and monotonic.
+    right : bool, optional
+        Indicating whether the intervals include the right or the left bin
+        edge. Default behavior is (right==False) indicating that the interval
+        does not include the right edge. The left bin and is open in this
+        case. Ie., bins[i-1] <= x < bins[i] is the default behavior for
+        monotonically increasing bins.
+
+    Returns
+    -------
+    out : ndarray of ints
+        Output array of indices, of same shape as `x`.
+
+    Raises
+    ------
+    ValueError
+        If the input is not 1-dimensional, or if `bins` is not monotonic.
+    TypeError
+        If the type of the input is complex.
+
+    See Also
+    --------
+    bincount, histogram, unique
+
+    Notes
+    -----
+    If values in `x` are such that they fall outside the bin range,
+    attempting to index `bins` with the indices that `digitize` returns
+    will result in an IndexError.
+
+    Examples
+    --------
+    >>> x = np.array([0.2, 6.4, 3.0, 1.6])
+    >>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
+    >>> inds = np.digitize(x, bins)
+    >>> inds
+    array([1, 4, 3, 2])
+    >>> for n in range(x.size):
+    ...   print bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]]
+    ...
+    0.0 <= 0.2 < 1.0
+    4.0 <= 6.4 < 10.0
+    2.5 <= 3.0 < 4.0
+    1.0 <= 1.6 < 2.5
+
+    >>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
+    >>> bins = np.array([0,5,10,15,20])
+    >>> np.digitize(x,bins,right=True)
+    array([1, 2, 3, 4, 4])
+    >>> np.digitize(x,bins,right=False)
+    array([1, 3, 3, 4, 5])
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'bincount',
+    """
+    bincount(x, weights=None, minlength=None)
+
+    Count number of occurrences of each value in array of non-negative ints.
+
+    The number of bins (of size 1) is one larger than the largest value in
+    `x`. If `minlength` is specified, there will be at least this number
+    of bins in the output array (though it will be longer if necessary,
+    depending on the contents of `x`).
+    Each bin gives the number of occurrences of its index value in `x`.
+    If `weights` is specified the input array is weighted by it, i.e. if a
+    value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
+    of ``out[n] += 1``.
+
+    Parameters
+    ----------
+    x : array_like, 1 dimension, nonnegative ints
+        Input array.
+    weights : array_like, optional
+        Weights, array of the same shape as `x`.
+    minlength : int, optional
+        .. versionadded:: 1.6.0
+
+        A minimum number of bins for the output array.
+
+    Returns
+    -------
+    out : ndarray of ints
+        The result of binning the input array.
+        The length of `out` is equal to ``np.amax(x)+1``.
+
+    Raises
+    ------
+    ValueError
+        If the input is not 1-dimensional, or contains elements with negative
+        values, or if `minlength` is non-positive.
+    TypeError
+        If the type of the input is float or complex.
+
+    See Also
+    --------
+    histogram, digitize, unique
+
+    Examples
+    --------
+    >>> np.bincount(np.arange(5))
+    array([1, 1, 1, 1, 1])
+    >>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
+    array([1, 3, 1, 1, 0, 0, 0, 1])
+
+    >>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
+    >>> np.bincount(x).size == np.amax(x)+1
+    True
+
+    The input array needs to be of integer dtype, otherwise a
+    TypeError is raised:
+
+    >>> np.bincount(np.arange(5, dtype=np.float))
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+    TypeError: array cannot be safely cast to required type
+
+    A possible use of ``bincount`` is to perform sums over
+    variable-size chunks of an array, using the ``weights`` keyword.
+
+    >>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
+    >>> x = np.array([0, 1, 1, 2, 2, 2])
+    >>> np.bincount(x,  weights=w)
+    array([ 0.3,  0.7,  1.1])
+
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'ravel_multi_index',
+    """
+    ravel_multi_index(multi_index, dims, mode='raise', order='C')
+
+    Converts a tuple of index arrays into an array of flat
+    indices, applying boundary modes to the multi-index.
+
+    Parameters
+    ----------
+    multi_index : tuple of array_like
+        A tuple of integer arrays, one array for each dimension.
+    dims : tuple of ints
+        The shape of array into which the indices from ``multi_index`` apply.
+    mode : {'raise', 'wrap', 'clip'}, optional
+        Specifies how out-of-bounds indices are handled.  Can specify
+        either one mode or a tuple of modes, one mode per index.
+
+        * 'raise' -- raise an error (default)
+        * 'wrap' -- wrap around
+        * 'clip' -- clip to the range
+
+        In 'clip' mode, a negative index which would normally
+        wrap will clip to 0 instead.
+    order : {'C', 'F'}, optional
+        Determines whether the multi-index should be viewed as indexing in
+        C (row-major) order or FORTRAN (column-major) order.
+
+    Returns
+    -------
+    raveled_indices : ndarray
+        An array of indices into the flattened version of an array
+        of dimensions ``dims``.
+
+    See Also
+    --------
+    unravel_index
+
+    Notes
+    -----
+    .. versionadded:: 1.6.0
+
+    Examples
+    --------
+    >>> arr = np.array([[3,6,6],[4,5,1]])
+    >>> np.ravel_multi_index(arr, (7,6))
+    array([22, 41, 37])
+    >>> np.ravel_multi_index(arr, (7,6), order='F')
+    array([31, 41, 13])
+    >>> np.ravel_multi_index(arr, (4,6), mode='clip')
+    array([22, 23, 19])
+    >>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
+    array([12, 13, 13])
+
+    >>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
+    1621
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'unravel_index',
+    """
+    unravel_index(indices, dims, order='C')
+
+    Converts a flat index or array of flat indices into a tuple
+    of coordinate arrays.
+
+    Parameters
+    ----------
+    indices : array_like
+        An integer array whose elements are indices into the flattened
+        version of an array of dimensions ``dims``. Before version 1.6.0,
+        this function accepted just one index value.
+    dims : tuple of ints
+        The shape of the array to use for unraveling ``indices``.
+    order : {'C', 'F'}, optional
+        .. versionadded:: 1.6.0
+
+        Determines whether the indices should be viewed as indexing in
+        C (row-major) order or FORTRAN (column-major) order.
+
+    Returns
+    -------
+    unraveled_coords : tuple of ndarray
+        Each array in the tuple has the same shape as the ``indices``
+        array.
+
+    See Also
+    --------
+    ravel_multi_index
+
+    Examples
+    --------
+    >>> np.unravel_index([22, 41, 37], (7,6))
+    (array([3, 6, 6]), array([4, 5, 1]))
+    >>> np.unravel_index([31, 41, 13], (7,6), order='F')
+    (array([3, 6, 6]), array([4, 5, 1]))
+
+    >>> np.unravel_index(1621, (6,7,8,9))
+    (3, 1, 4, 1)
+
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'add_docstring',
+    """
+    add_docstring(obj, docstring)
+
+    Add a docstring to a built-in obj if possible.
+    If the obj already has a docstring raise a RuntimeError
+    If this routine does not know how to add a docstring to the object
+    raise a TypeError
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'add_newdoc_ufunc',
+    """
+    add_ufunc_docstring(ufunc, new_docstring)
+
+    Replace the docstring for a ufunc with new_docstring.
+    This method will only work if the current docstring for
+    the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
+
+    Parameters
+    ----------
+    ufunc : numpy.ufunc
+        A ufunc whose current doc is NULL.
+    new_docstring : string
+        The new docstring for the ufunc.
+
+    Notes
+    -----
+    This method allocates memory for new_docstring on
+    the heap. Technically this creates a mempory leak, since this
+    memory will not be reclaimed until the end of the program
+    even if the ufunc itself is removed. However this will only
+    be a problem if the user is repeatedly creating ufuncs with
+    no documentation, adding documentation via add_newdoc_ufunc,
+    and then throwing away the ufunc.
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'packbits',
+    """
+    packbits(myarray, axis=None)
+
+    Packs the elements of a binary-valued array into bits in a uint8 array.
+
+    The result is padded to full bytes by inserting zero bits at the end.
+
+    Parameters
+    ----------
+    myarray : array_like
+        An integer type array whose elements should be packed to bits.
+    axis : int, optional
+        The dimension over which bit-packing is done.
+        ``None`` implies packing the flattened array.
+
+    Returns
+    -------
+    packed : ndarray
+        Array of type uint8 whose elements represent bits corresponding to the
+        logical (0 or nonzero) value of the input elements. The shape of
+        `packed` has the same number of dimensions as the input (unless `axis`
+        is None, in which case the output is 1-D).
+
+    See Also
+    --------
+    unpackbits: Unpacks elements of a uint8 array into a binary-valued output
+                array.
+
+    Examples
+    --------
+    >>> a = np.array([[[1,0,1],
+    ...                [0,1,0]],
+    ...               [[1,1,0],
+    ...                [0,0,1]]])
+    >>> b = np.packbits(a, axis=-1)
+    >>> b
+    array([[[160],[64]],[[192],[32]]], dtype=uint8)
+
+    Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
+    and 32 = 0010 0000.
+
+    """)
+
+add_newdoc('numpy.lib._compiled_base', 'unpackbits',
+    """
+    unpackbits(myarray, axis=None)
+
+    Unpacks elements of a uint8 array into a binary-valued output array.
+
+    Each element of `myarray` represents a bit-field that should be unpacked
+    into a binary-valued output array. The shape of the output array is either
+    1-D (if `axis` is None) or the same shape as the input array with unpacking
+    done along the axis specified.
+
+    Parameters
+    ----------
+    myarray : ndarray, uint8 type
+       Input array.
+    axis : int, optional
+       Unpacks along this axis.
+
+    Returns
+    -------
+    unpacked : ndarray, uint8 type
+       The elements are binary-valued (0 or 1).
+
+    See Also
+    --------
+    packbits : Packs the elements of a binary-valued array into bits in a uint8
+               array.
+
+    Examples
+    --------
+    >>> a = np.array([[2], [7], [23]], dtype=np.uint8)
+    >>> a
+    array([[ 2],
+           [ 7],
+           [23]], dtype=uint8)
+    >>> b = np.unpackbits(a, axis=1)
+    >>> b
+    array([[0, 0, 0, 0, 0, 0, 1, 0],
+           [0, 0, 0, 0, 0, 1, 1, 1],
+           [0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)
+
+    """)
+
+
+##############################################################################
+#
+# Documentation for ufunc attributes and methods
+#
+##############################################################################
+
+
+##############################################################################
+#
+# ufunc object
+#
+##############################################################################
+
+add_newdoc('numpy.core', 'ufunc',
+    """
+    Functions that operate element by element on whole arrays.
+
+    To see the documentation for a specific ufunc, use np.info().  For
+    example, np.info(np.sin).  Because ufuncs are written in C
+    (for speed) and linked into Python with NumPy's ufunc facility,
+    Python's help() function finds this page whenever help() is called
+    on a ufunc.
+
+    A detailed explanation of ufuncs can be found in the "ufuncs.rst"
+    file in the NumPy reference guide.
+
+    Unary ufuncs:
+    =============
+
+    op(X, out=None)
+    Apply op to X elementwise
+
+    Parameters
+    ----------
+    X : array_like
+        Input array.
+    out : array_like
+        An array to store the output. Must be the same shape as `X`.
+
+    Returns
+    -------
+    r : array_like
+        `r` will have the same shape as `X`; if out is provided, `r`
+        will be equal to out.
+
+    Binary ufuncs:
+    ==============
+
+    op(X, Y, out=None)
+    Apply `op` to `X` and `Y` elementwise. May "broadcast" to make
+    the shapes of `X` and `Y` congruent.
+
+    The broadcasting rules are:
+
+    * Dimensions of length 1 may be prepended to either array.
+    * Arrays may be repeated along dimensions of length 1.
+
+    Parameters
+    ----------
+    X : array_like
+        First input array.
+    Y : array_like
+        Second input array.
+    out : array_like
+        An array to store the output. Must be the same shape as the
+        output would have.
+
+    Returns
+    -------
+    r : array_like
+        The return value; if out is provided, `r` will be equal to out.
+
+    """)
+
+
+##############################################################################
+#
+# ufunc attributes
+#
+##############################################################################
+
+add_newdoc('numpy.core', 'ufunc', ('identity',
+    """
+    The identity value.
+
+    Data attribute containing the identity element for the ufunc, if it has one.
+    If it does not, the attribute value is None.
+
+    Examples
+    --------
+    >>> np.add.identity
+    0
+    >>> np.multiply.identity
+    1
+    >>> np.power.identity
+    1
+    >>> print np.exp.identity
+    None
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('nargs',
+    """
+    The number of arguments.
+
+    Data attribute containing the number of arguments the ufunc takes, including
+    optional ones.
+
+    Notes
+    -----
+    Typically this value will be one more than what you might expect because all
+    ufuncs take  the optional "out" argument.
+
+    Examples
+    --------
+    >>> np.add.nargs
+    3
+    >>> np.multiply.nargs
+    3
+    >>> np.power.nargs
+    3
+    >>> np.exp.nargs
+    2
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('nin',
+    """
+    The number of inputs.
+
+    Data attribute containing the number of arguments the ufunc treats as input.
+
+    Examples
+    --------
+    >>> np.add.nin
+    2
+    >>> np.multiply.nin
+    2
+    >>> np.power.nin
+    2
+    >>> np.exp.nin
+    1
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('nout',
+    """
+    The number of outputs.
+
+    Data attribute containing the number of arguments the ufunc treats as output.
+
+    Notes
+    -----
+    Since all ufuncs can take output arguments, this will always be (at least) 1.
+
+    Examples
+    --------
+    >>> np.add.nout
+    1
+    >>> np.multiply.nout
+    1
+    >>> np.power.nout
+    1
+    >>> np.exp.nout
+    1
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('ntypes',
+    """
+    The number of types.
+
+    The number of numerical NumPy types - of which there are 18 total - on which
+    the ufunc can operate.
+
+    See Also
+    --------
+    numpy.ufunc.types
+
+    Examples
+    --------
+    >>> np.add.ntypes
+    18
+    >>> np.multiply.ntypes
+    18
+    >>> np.power.ntypes
+    17
+    >>> np.exp.ntypes
+    7
+    >>> np.remainder.ntypes
+    14
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('types',
+    """
+    Returns a list with types grouped input->output.
+
+    Data attribute listing the data-type "Domain-Range" groupings the ufunc can
+    deliver. The data-types are given using the character codes.
+
+    See Also
+    --------
+    numpy.ufunc.ntypes
+
+    Examples
+    --------
+    >>> np.add.types
+    ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
+    'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
+    'GG->G', 'OO->O']
+
+    >>> np.multiply.types
+    ['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
+    'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
+    'GG->G', 'OO->O']
+
+    >>> np.power.types
+    ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
+    'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
+    'OO->O']
+
+    >>> np.exp.types
+    ['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
+
+    >>> np.remainder.types
+    ['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
+    'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
+
+    """))
+
+
+##############################################################################
+#
+# ufunc methods
+#
+##############################################################################
+
+add_newdoc('numpy.core', 'ufunc', ('reduce',
+    """
+    reduce(a, axis=0, dtype=None, out=None, keepdims=False)
+
+    Reduces `a`'s dimension by one, by applying ufunc along one axis.
+
+    Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`.  Then
+    :math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
+    the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
+    ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
+    For a one-dimensional array, reduce produces results equivalent to:
+    ::
+
+     r = op.identity # op = ufunc
+     for i in range(len(A)):
+       r = op(r, A[i])
+     return r
+
+    For example, add.reduce() is equivalent to sum().
+
+    Parameters
+    ----------
+    a : array_like
+        The array to act on.
+    axis : None or int or tuple of ints, optional
+        Axis or axes along which a reduction is performed.
+        The default (`axis` = 0) is perform a reduction over the first
+        dimension of the input array. `axis` may be negative, in
+        which case it counts from the last to the first axis.
+
+        .. versionadded:: 1.7.0
+
+        If this is `None`, a reduction is performed over all the axes.
+        If this is a tuple of ints, a reduction is performed on multiple
+        axes, instead of a single axis or all the axes as before.
+
+        For operations which are either not commutative or not associative,
+        doing a reduction over multiple axes is not well-defined. The
+        ufuncs do not currently raise an exception in this case, but will
+        likely do so in the future.
+    dtype : data-type code, optional
+        The type used to represent the intermediate results. Defaults
+        to the data-type of the output array if this is provided, or
+        the data-type of the input array if no output array is provided.
+    out : ndarray, optional
+        A location into which the result is stored. If not provided, a
+        freshly-allocated array is returned.
+    keepdims : bool, optional
+        If this is set to True, the axes which are reduced are left
+        in the result as dimensions with size one. With this option,
+        the result will broadcast correctly against the original `arr`.
+
+        .. versionadded:: 1.7.0
+
+    Returns
+    -------
+    r : ndarray
+        The reduced array. If `out` was supplied, `r` is a reference to it.
+
+    Examples
+    --------
+    >>> np.multiply.reduce([2,3,5])
+    30
+
+    A multi-dimensional array example:
+
+    >>> X = np.arange(8).reshape((2,2,2))
+    >>> X
+    array([[[0, 1],
+            [2, 3]],
+           [[4, 5],
+            [6, 7]]])
+    >>> np.add.reduce(X, 0)
+    array([[ 4,  6],
+           [ 8, 10]])
+    >>> np.add.reduce(X) # confirm: default axis value is 0
+    array([[ 4,  6],
+           [ 8, 10]])
+    >>> np.add.reduce(X, 1)
+    array([[ 2,  4],
+           [10, 12]])
+    >>> np.add.reduce(X, 2)
+    array([[ 1,  5],
+           [ 9, 13]])
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('accumulate',
+    """
+    accumulate(array, axis=0, dtype=None, out=None)
+
+    Accumulate the result of applying the operator to all elements.
+
+    For a one-dimensional array, accumulate produces results equivalent to::
+
+      r = np.empty(len(A))
+      t = op.identity        # op = the ufunc being applied to A's  elements
+      for i in range(len(A)):
+          t = op(t, A[i])
+          r[i] = t
+      return r
+
+    For example, add.accumulate() is equivalent to np.cumsum().
+
+    For a multi-dimensional array, accumulate is applied along only one
+    axis (axis zero by default; see Examples below) so repeated use is
+    necessary if one wants to accumulate over multiple axes.
+
+    Parameters
+    ----------
+    array : array_like
+        The array to act on.
+    axis : int, optional
+        The axis along which to apply the accumulation; default is zero.
+    dtype : data-type code, optional
+        The data-type used to represent the intermediate results. Defaults
+        to the data-type of the output array if such is provided, or the
+        the data-type of the input array if no output array is provided.
+    out : ndarray, optional
+        A location into which the result is stored. If not provided a
+        freshly-allocated array is returned.
+
+    Returns
+    -------
+    r : ndarray
+        The accumulated values. If `out` was supplied, `r` is a reference to
+        `out`.
+
+    Examples
+    --------
+    1-D array examples:
+
+    >>> np.add.accumulate([2, 3, 5])
+    array([ 2,  5, 10])
+    >>> np.multiply.accumulate([2, 3, 5])
+    array([ 2,  6, 30])
+
+    2-D array examples:
+
+    >>> I = np.eye(2)
+    >>> I
+    array([[ 1.,  0.],
+           [ 0.,  1.]])
+
+    Accumulate along axis 0 (rows), down columns:
+
+    >>> np.add.accumulate(I, 0)
+    array([[ 1.,  0.],
+           [ 1.,  1.]])
+    >>> np.add.accumulate(I) # no axis specified = axis zero
+    array([[ 1.,  0.],
+           [ 1.,  1.]])
+
+    Accumulate along axis 1 (columns), through rows:
+
+    >>> np.add.accumulate(I, 1)
+    array([[ 1.,  1.],
+           [ 0.,  1.]])
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('reduceat',
+    """
+    reduceat(a, indices, axis=0, dtype=None, out=None)
+
+    Performs a (local) reduce with specified slices over a single axis.
+
+    For i in ``range(len(indices))``, `reduceat` computes
+    ``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th
+    generalized "row" parallel to `axis` in the final result (i.e., in a
+    2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
+    `axis = 1`, it becomes the i-th column).  There are three exceptions to this:
+
+    * when ``i = len(indices) - 1`` (so for the last index),
+      ``indices[i+1] = a.shape[axis]``.
+    * if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
+      simply ``a[indices[i]]``.
+    * if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.
+
+    The shape of the output depends on the size of `indices`, and may be
+    larger than `a` (this happens if ``len(indices) > a.shape[axis]``).
+
+    Parameters
+    ----------
+    a : array_like
+        The array to act on.
+    indices : array_like
+        Paired indices, comma separated (not colon), specifying slices to
+        reduce.
+    axis : int, optional
+        The axis along which to apply the reduceat.
+    dtype : data-type code, optional
+        The type used to represent the intermediate results. Defaults
+        to the data type of the output array if this is provided, or
+        the data type of the input array if no output array is provided.
+    out : ndarray, optional
+        A location into which the result is stored. If not provided a
+        freshly-allocated array is returned.
+
+    Returns
+    -------
+    r : ndarray
+        The reduced values. If `out` was supplied, `r` is a reference to
+        `out`.
+
+    Notes
+    -----
+    A descriptive example:
+
+    If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as
+    ``ufunc.reduceat(a, indices)[::2]`` where `indices` is
+    ``range(len(array) - 1)`` with a zero placed
+    in every other element:
+    ``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.
+
+    Don't be fooled by this attribute's name: `reduceat(a)` is not
+    necessarily smaller than `a`.
+
+    Examples
+    --------
+    To take the running sum of four successive values:
+
+    >>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
+    array([ 6, 10, 14, 18])
+
+    A 2-D example:
+
+    >>> x = np.linspace(0, 15, 16).reshape(4,4)
+    >>> x
+    array([[  0.,   1.,   2.,   3.],
+           [  4.,   5.,   6.,   7.],
+           [  8.,   9.,  10.,  11.],
+           [ 12.,  13.,  14.,  15.]])
+
+    ::
+
+     # reduce such that the result has the following five rows:
+     # [row1 + row2 + row3]
+     # [row4]
+     # [row2]
+     # [row3]
+     # [row1 + row2 + row3 + row4]
+
+    >>> np.add.reduceat(x, [0, 3, 1, 2, 0])
+    array([[ 12.,  15.,  18.,  21.],
+           [ 12.,  13.,  14.,  15.],
+           [  4.,   5.,   6.,   7.],
+           [  8.,   9.,  10.,  11.],
+           [ 24.,  28.,  32.,  36.]])
+
+    ::
+
+     # reduce such that result has the following two columns:
+     # [col1 * col2 * col3, col4]
+
+    >>> np.multiply.reduceat(x, [0, 3], 1)
+    array([[    0.,     3.],
+           [  120.,     7.],
+           [  720.,    11.],
+           [ 2184.,    15.]])
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('outer',
+    """
+    outer(A, B)
+
+    Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
+
+    Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
+    ``op.outer(A, B)`` is an array of dimension M + N such that:
+
+    .. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
+       op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
+
+    For `A` and `B` one-dimensional, this is equivalent to::
+
+      r = empty(len(A),len(B))
+      for i in range(len(A)):
+          for j in range(len(B)):
+              r[i,j] = op(A[i], B[j]) # op = ufunc in question
+
+    Parameters
+    ----------
+    A : array_like
+        First array
+    B : array_like
+        Second array
+
+    Returns
+    -------
+    r : ndarray
+        Output array
+
+    See Also
+    --------
+    numpy.outer
+
+    Examples
+    --------
+    >>> np.multiply.outer([1, 2, 3], [4, 5, 6])
+    array([[ 4,  5,  6],
+           [ 8, 10, 12],
+           [12, 15, 18]])
+
+    A multi-dimensional example:
+
+    >>> A = np.array([[1, 2, 3], [4, 5, 6]])
+    >>> A.shape
+    (2, 3)
+    >>> B = np.array([[1, 2, 3, 4]])
+    >>> B.shape
+    (1, 4)
+    >>> C = np.multiply.outer(A, B)
+    >>> C.shape; C
+    (2, 3, 1, 4)
+    array([[[[ 1,  2,  3,  4]],
+            [[ 2,  4,  6,  8]],
+            [[ 3,  6,  9, 12]]],
+           [[[ 4,  8, 12, 16]],
+            [[ 5, 10, 15, 20]],
+            [[ 6, 12, 18, 24]]]])
+
+    """))
+
+add_newdoc('numpy.core', 'ufunc', ('at',
+    """
+    at(a, indices, b=None)
+
+    Performs unbuffered in place operation on operand 'a' for elements
+    specified by 'indices'. For addition ufunc, this method is equivalent to
+    `a[indices] += b`, except that results are accumulated for elements that
+    are indexed more than once. For example, `a[[0,0]] += 1` will only
+    increment the first element once because of buffering, whereas
+    `add.at(a, [0,0], 1)` will increment the first element twice.
+
+    .. versionadded:: 1.8.0
+
+    Parameters
+    ----------
+    a : array_like
+        The array to perform in place operation on.
+    indices : array_like or tuple
+        Array like index object or slice object for indexing into first
+        operand. If first operand has multiple dimensions, indices can be a
+        tuple of array like index objects or slice objects.
+    b : array_like
+        Second operand for ufuncs requiring two operands. Operand must be
+        broadcastable over first operand after indexing or slicing.
+
+    Examples
+    --------
+    Set items 0 and 1 to their negative values:
+
+    >>> a = np.array([1, 2, 3, 4])
+    >>> np.negative.at(a, [0, 1])
+    >>> print(a)
+    array([-1, -2, 3, 4])
+
+    ::
+
+    Increment items 0 and 1, and increment item 2 twice:
+
+    >>> a = np.array([1, 2, 3, 4])
+    >>> np.add.at(a, [0, 1, 2, 2], 1)
+    >>> print(a)
+    array([2, 3, 5, 4])
+
+    ::
+
+    Add items 0 and 1 in first array to second array,
+    and store results in first array:
+
+    >>> a = np.array([1, 2, 3, 4])
+    >>> b = np.array([1, 2])
+    >>> np.add.at(a, [0, 1], b)
+    >>> print(a)
+    array([2, 4, 3, 4])
+
+    """))
+
+##############################################################################
+#
+# Documentation for dtype attributes and methods
+#
+##############################################################################
+
+##############################################################################
+#
+# dtype object
+#
+##############################################################################
+
+add_newdoc('numpy.core.multiarray', 'dtype',
+    """
+    dtype(obj, align=False, copy=False)
+
+    Create a data type object.
+
+    A numpy array is homogeneous, and contains elements described by a
+    dtype object. A dtype object can be constructed from different
+    combinations of fundamental numeric types.
+
+    Parameters
+    ----------
+    obj
+        Object to be converted to a data type object.
+    align : bool, optional
+        Add padding to the fields to match what a C compiler would output
+        for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
+        or a comma-separated string. If a struct dtype is being created,
+        this also sets a sticky alignment flag ``isalignedstruct``.
+    copy : bool, optional
+        Make a new copy of the data-type object. If ``False``, the result
+        may just be a reference to a built-in data-type object.
+
+    See also
+    --------
+    result_type
+
+    Examples
+    --------
+    Using array-scalar type:
+
+    >>> np.dtype(np.int16)
+    dtype('int16')
+
+    Record, one field name 'f1', containing int16:
+
+    >>> np.dtype([('f1', np.int16)])
+    dtype([('f1', '<i2')])
+
+    Record, one field named 'f1', in itself containing a record with one field:
+
+    >>> np.dtype([('f1', [('f1', np.int16)])])
+    dtype([('f1', [('f1', '<i2')])])
+
+    Record, 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 'biufcOSUV') identifying the general kind of data.
+
+    =  ======================
+    b  boolean
+    i  signed integer
+    u  unsigned integer
+    f  floating-point
+    c  complex floating-point
+    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::
+
+         * 'S' - swap dtype from current to opposite endian
+         * {'<', 'L'} - little endian
+         * {'>', 'B'} - big endian
+         * {'=', 'N'} - native order
+         * {'|', 'I'} - ignore (no change to byte order)
+
+        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.
+
+    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` 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:
+
+    * {'<', 'L'} - little endian
+    * {'>', 'B'} - big endian
+    * {'=', 'N'} - native order
+    * 'S' - swap dtype from current to opposite endian
+    * {'|', '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'.""")