from lib import add_newdoc

add_newdoc('numpy.core','dtype',
           [('fields', "Fields of the data-type or None if no fields"),
            ('names', "Names of fields or None if no fields"),
            ('alignment', "Needed alignment for this data-type"),
            ('byteorder',
             "Little-endian (<), big-endian (>), native (=), or "\
             "not-applicable (|)"),
            ('char', "Letter typecode for this data-type"),
            ('type', "Type object associated with this data-type"),
            ('kind', "Character giving type-family of this data-type"),
            ('itemsize', "Size of each item"),
            ('hasobject', "Non-zero if Python objects are in "\
             "this data-type"),
            ('num', "Internally-used number for builtin base"),
            ('newbyteorder',
"""self.newbyteorder(<endian>)
returns a copy of the dtype object with altered byteorders.
If <endian> is not given all byteorders are swapped.
Otherwise endian can be '>', '<', or '=' to force a particular
byteorder.  Data-types in all fields are also updated in the
new dtype object.
"""),
            ("__reduce__", "self.__reduce__() for pickling"),
            ("__setstate__", "self.__setstate__() for pickling"),
            ("subdtype", "A tuple of (descr, shape) or None"),
            ("descr", "The array_interface data-type descriptor."),
            ("str", "The array interface typestring."),
            ("name", "The name of the true data-type"),
            ("base", "The base data-type or self if no subdtype"),
            ("shape", "The shape of the subdtype or (1,)"),
            ("isbuiltin", "Is this a built-in data-type?"),
            ("isnative", "Is the byte-order of this data-type native?")
            ]
           )

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

# attributes
add_newdoc('numpy.core', 'flatiter', ('base',
    """documentation needed

    """))



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

    """))



add_newdoc('numpy.core', 'flatiter', ('index',
    """documentation needed

    """))



# 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

    """))


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

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

    """))


add_newdoc('numpy.core', 'broadcast', ('iters',
    """tuple of individual iterators

    """))


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

    """))


add_newdoc('numpy.core', 'broadcast', ('numiter',
    """number of iterators

    """))


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

    """))


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

    """))


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

add_newdoc('numpy.core.multiarray','array',
    """array(object, dtype=None, copy=1,order=None, subok=0,ndmin=0)

    Return an array from object with the specified date-type.

    Inputs:
      object - an array, any object exposing the array interface, any
                object whose __array__ method returns an array, or any
                (nested) sequence.
      dtype  - 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   - If true, then force a copy.  Otherwise a copy will only occur
                if __array__ returns a copy, obj is a nested sequence, or
                a copy is needed to satisfy any of the other requirements
      order  - Specify the order of the array.  If order is 'C', then the
                array will be in C-contiguous order (last-index varies the
                fastest).  If order is 'FORTRAN', then the returned array
                will be in Fortran-contiguous order (first-index varies the
                fastest).  If order is None, then the returned array may
                be in either C-, or Fortran-contiguous order or even
                discontiguous.
      subok  - If True, then sub-classes will be passed-through, otherwise
                the returned array will be forced to be a base-class array
      ndmin  - Specifies the minimum number of dimensions that the resulting
                array should have.  1's will be pre-pended to the shape as
                needed to meet this requirement.

    """)

add_newdoc('numpy.core.multiarray','empty',
    """empty((d1,...,dn),dtype=float,order='C')

    Return a new array of shape (d1,...,dn) and given type with all its
    entries uninitialized. This can be faster than zeros.

    """)


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

    Return a new scalar array of the given type initialized with
    obj. Mainly for pickle support.  The 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 zeros for all other
    types.

    """)

add_newdoc('numpy.core.multiarray','zeros',
    """zeros((d1,...,dn),dtype=float,order='C')

    Return a new array of shape (d1,...,dn) and type typecode with all
    it's entries initialized to zero.

    """)

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='')

    Return a new 1d array initialized from the raw binary data in string.

    If count is positive, the new array will have count elements, otherwise its
    size is determined by the size of string.  If sep is not empty then the
    string is interpreted in ASCII mode and converted to the desired number type
    using sep as the separator between elements (extra whitespace is ignored).

    """)

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

    Return a new 1d array initialized from iterable. If count is
    nonegative, the new array will have count elements, otherwise it's
    size is determined by the generator.

    """)

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

    Required arguments:
        file -- open file object or string containing file name.

    Keyword arguments:
        dtype -- type and order of the returned array (default float)
        count -- number of items to input (default all)
        sep -- separater between items if file is a text file (default "")

    Return an array of the given data type from a text or binary file. The
    'file' argument can be an open file or a string with the name of a file to
    read from.  If 'count' == -1 the entire file is read, otherwise count is the
    number of items of the given type to read in.  If 'sep' is "" it means to
    read binary data from the file using the specified dtype, otherwise it gives
    the separator between elements in a text file. The 'dtype' value is also
    used to determine the size and order of the items in binary files.


    Data written using the tofile() method can be conveniently recovered using
    this function.

    WARNING: This function should be used sparingly as the binary files are not
    platform independent. In particular, they contain no endianess or datatype
    information. Nevertheless it can be useful for reading in simply formatted
    or binary data quickly.

    """)

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

    Returns a 1-d array of data type dtype from buffer. The buffer
    argument must be an object that exposes the buffer interface.  If
    count is -1 then the entire buffer is used, otherwise, count is the
    size of the output.  If offset is given then jump that far into the
    buffer. If the buffer has data that is out not in machine byte-order,
    than use a propert data type descriptor. The data will not be
    byteswapped, but the array will manage it in future operations.

    """)

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

    Join arrays together.

    The tuple of sequences (a1, a2, ...) are joined along the given axis
    (default is the first one) into a single numpy array.

    Example:

    >>> concatenate( ([0,1,2], [5,6,7]) )
    array([0, 1, 2, 5, 6, 7])

    """)

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

    Returns the dot product of two arrays, which has shape a.shape[:-1] +
    b.shape[:-1] with elements computed by the product of the elements
    from the last dimensions of a and b.

    """)

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)

    For integer arguments, just like range() except it returns an array
    whose type can be specified by the keyword argument dtype.  If dtype
    is not specified, the type of the result is deduced from the type of
    the arguments.

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

    """)

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)

    Set the python function f to be the function used to obtain a pretty
    printable string version of an array whenever an array is printed.
    f(M) should expect an array argument M, and should return a string
    consisting of the desired representation of M for printing.

    """)

add_newdoc('numpy.core.multiarray','set_numeric_ops',
    """set_numeric_ops(op=func, ...)

    Set some or all of the number methods for all array objects.  Don't
    forget **dict can be used as the argument list.  Return the functions
    that were replaced, which can be stored and set later.

    """)

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

    The result is shaped like condition and has elements of x and y where
    condition is respectively true or false.  If x or y are not given,
    then it is equivalent to condition.nonzero().

    To group the indices by element, rather than dimension, use

        transpose(where(condition, | x, y))

    instead. This always results in a 2d array, with a row of indices for
    each element that satisfies the condition.

    """)


add_newdoc('numpy.core.multiarray','lexsort',
    """lexsort(keys=, axis=-1) -> array of indices. argsort with list of keys.

    Return an array of indices similar to argsort, except the sorting is
    done using the provided sorting keys.  First the sort is done using
    key[0], then the resulting list of indices is further manipulated by
    sorting on key[1], and so forth. The result is a sort on multiple
    keys.  If the keys represented columns of a spreadsheet, for example,
    this would sort using multiple columns (the last key being 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 things that can be 
    converted to arrays of the same shape.

    """)

add_newdoc('numpy.core.multiarray','can_cast',
    """can_cast(from=d1, to=d2)

    Returns True if data type d1 can be cast to data type d2 without
    losing precision.

    """)

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

    Return a new 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.

    """)

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


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


add_newdoc('numpy.core.multiarray', 'ndarray',
    """An array object represents a multidimensional, homogeneous array
    of fixed-size items.  An associated data-type-descriptor object
    details the data-type in an array (including byteorder and any
    fields).  An array can be constructed using the numpy.array
    command. Arrays are sequence, mapping and numeric objects.
    More information is available in the numpy module and by looking
    at the methods and attributes of an array.

    ndarray.__new__(subtype, shape=, dtype=float, buffer=None,
                    offset=0, strides=None, order=None)

     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 exporting the buffer interface, then
        all keywords are interpreted.
     The dtype parameter can be any object that can be interpreted
        as a numpy.dtype object.

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

    """)


##############################################################################
#
# 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.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
    """A ctypes interface object.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
    """Buffer object pointing to the start of the data.

    """))


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

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
    """Imaginary part of the array.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
    """Length of one element in bytes.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
    """Special object providing array flags.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
    """A 1-d flat iterator.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
    """Number of bytes in the array.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
    """Number of array dimensions.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
    """Real part of the array.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
    """Tuple of array dimensions.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
    """Number of elements in the array.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
    """Tuple of bytes to step in each dimension.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
    """Same as self.transpose() except self is returned for self.ndim < 2.

    """))


##############################################################################
#
# 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_wrap__',
    """a.__array_wrap__(obj) -> Object of same type as a from ndarray obj.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
    """a.__copy__(|order) -> copy, possibly with different order.

    Return a copy of the array.

    Argument:
        order -- Order of returned copy (default 'C')
            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 m is already 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, typecode, isfortran, rawdata)

    For unpickling.

    Arguments:
        version -- optional pickle version. If omitted defaults to 0.
        shape -- a tuple giving the shape
        typecode -- a typecode
        isFortran --  a bool stating if Fortran or no
        rawdata -- a binary string with the data (or a list if Object array)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
    """ a.all(axis=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
    """ a.any(axis=None, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
    """ a.argmax(axis=None, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
    """ a.argmin(axis=None, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
    """a.argsort(axis=-1, kind='quicksort') -> indices that sort a along axis.

    Keyword arguments:

    axis -- axis to be indirectly sorted (default -1)
    kind -- sorting algorithm (default 'quicksort')
            Possible values: 'quicksort', 'mergesort', or 'heapsort'

    Returns: array of indices that sort a along the specified axis.

    This method executes an indirect sort along the given axis using the
    algorithm specified by the kind keyword. It returns an array of indices of
    the same shape as 'a' that index data along the given axis in sorted order.

    The various sorts are characterized by average speed, worst case
    performance, need for work space, and whether they are stable. A stable
    sort keeps items with the same key in the same relative order. The three
    available algorithms have the following properties:

    |------------------------------------------------------|
    |    kind   | speed |  worst case | work space | stable|
    |------------------------------------------------------|
    |'quicksort'|   1   | O(n^2)      |     0      |   no  |
    |'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
    |'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
    |------------------------------------------------------|

    All the sort algorithms make temporary copies of the data when the sort is
    not along the last axis. Consequently, sorts along the last axis are faster
    and use less space than sorts along other axis.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
    """a.astype(t) -> Copy of array cast to type t.

    Cast array m to type t.  t can be either a string representing a typecode,
    or a python type object of type int, float, or complex.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
    """a.byteswap(False) -> View or copy. Swap the bytes in the array.

    Swap the bytes in the array.  Return the byteswapped array.  If the first
    argument is TRUE, byteswap in-place and return a reference to self.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
    """ a.choose(b0, b1, ..., bn, out=None, mode='raise')

    Return an array that merges the b_i arrays together using 'a' as
    the index The b_i arrays and 'a' must all be broadcastable to the
    same shape.  The output at a particular position is the input
    array b_i at that position depending on the value of 'a' at that
    position.  Therefore, 'a' must be an integer array with entries
    from 0 to n+1.;

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
    """a.clip(min=, max=, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
    """a.compress(condition=, axis=None, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
    """a.conj()

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
    """a.conjugate()

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
    """a.copy(|order) -> copy, possibly with different order.

    Return a copy of the array.

    Argument:
        order -- Order of returned copy (default 'C')
            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 m is already in fortran order.;

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
    """a.cumprod(axis=None, dtype=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
    """a.cumsum(axis=None, dtype=None, out=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
    """a.diagonal(offset=0, axis1=0, axis2=1)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
    """a.dump(file) Dump to specified file.

    Arguments:
        file -- string naming the dump file.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
    """a.dumps() -> string containing the dump?

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
    """a.fill(value) -> None. Fill the array with the scalar value.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
    """a.flatten([fortran]) return a 1-d array (always copy)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
    """a.getfield(dtype, offset) -> field of array as given type.

    Returns a field of the given array as a certain type. A field is a view of
    the array's data with each itemsize determined by the given type and the
    offset into the current array.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
    """a.item() ->  copy of first array item as Python scalar.

    Copy the first element of array to a standard Python scalar and return
    it. The array must be of size one.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
    """a.max(axis=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
    """a.mean(axis=None, dtype=None)

      Average the array over the given axis.  If the axis is None,
      average over all dimensions of the array.  Equivalent to

        a.sum(axis, dtype) * 1.0 / size(a, axis).

      The optional dtype argument is the data type for intermediate
      calculations in the sum.;

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
    """a.min(axis=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
    """a.newbyteorder(<byteorder>) is equivalent to
    a.view(a.dtype.newbytorder(<byteorder>))

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
    """a.nonzero() returns a tuple of arrays

    Returns a tuple of arrays, one for each dimension of a,
    containing the indices of the non-zero elements in that
    dimension.  The corresponding non-zero values can be obtained
    with
        a[a.nonzero()].

    To group the indices by element, rather than dimension, use
        transpose(a.nonzero())
    instead. The result of this is always a 2d array, with a row for
    each non-zero element.;

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
    """a.prod(axis=None, dtype=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
    """a.ptp(axis=None) a.max(axis)-a.min(axis)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
    """a.put(indices, values, mode) sets a.flat[n] = values[n] for
    each n in indices. If values is shorter than indices then it
    will repeat.
    """))


add_newdoc('numpy.core.multiarray', 'putmask',
    """putmask(a, mask, values) sets a.flat[n] = values[n] for each n where
    mask.flat[n] is true.  If values is not the same size of a and mask then
    it will repeat.  This gives different behavior than a[mask] = values.
    """)


add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
    """a.ravel([fortran]) return a 1-d array (copy only if needed)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
    """a.repeat(repeats=, axis=none)

    copy elements of a, repeats times.  the repeats argument must be a sequence
    of length a.shape[axis] or a scalar.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
    """a.reshape(d1, d2, ..., dn, order='c')

    Return a new array from this one.  The new array must have the same number
    of elements as self.  Also always returns a view or raises a ValueError if
    that is impossible.;

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
    """a.resize(new_shape, refcheck=True, order=False) -> None. Change array shape.

    Change size and shape of self inplace.  Array must own its own memory and
    not be referenced by other arrays.    Returns None.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
    """a.round(decimals=0, out=None) -> out (a). Rounds to 'decimals' places.

    Keyword arguments:
        decimals -- number of decimals to round to (default 0). May be negative.
        out -- existing array to use for output (default a).

    Return:
        Reference to out, where None specifies the original array a.

    Round to the specified number of decimals. When 'decimals' is negative it
    specifies the number of positions to the left of the decimal point. The
    real and imaginary parts of complex numbers are rounded separately. Nothing
    is done if the array is not of float type and 'decimals' is >= 0.

    The keyword 'out' may be used to specify a different array to hold the
    result rather than the default 'a'. If the type of the array specified by
    'out' differs from that of 'a', the result is cast to the new type,
    otherwise the original type is kept. Floats round to floats by default.

    Numpy rounds to even. Thus 1.5 and 2.5 round to 2.0, -0.5 and 0.5 round to
    0.0, etc. Results may also be surprising due to the inexact representation
    of decimal fractions in IEEE floating point and the errors introduced in
    scaling the numbers when 'decimals' is something other than 0.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
    """a.searchsorted(v, side='left') -> index array.

    Required arguments:
        v -- array of keys to be searched for in a.

    Keyword arguments:
        side -- {'left', 'right'}, (default 'left').

    Returns:
        index array with the same shape as keys.

    The array to be searched must be 1-D and is assumed to be sorted in
    ascending order.

    The method call

        a.searchsorted(v, side='left')

    returns an index array with the same shape as v such that for each value i
    in the index and the corresponding key in v the following holds:

           a[j] < key <= a[i] for all j < i,

    If such an index does not exist, a.size() is used. Consequently, i is the
    index of the first item in 'a' that is >= key. If the key were to be
    inserted into a in the slot before the index i, then the order of a would
    be preserved and i would be the smallest index with that property.

    The method call

        a.searchsorted(v, side='right')

    returns an index array with the same shape as v such that for each value i
    in the index and the corresponding key in v the following holds:

           a[j] <= key < a[i] for all j < i,

    If such an index does not exist, a.size() is used. Consequently, i is the
    index of the first item in 'a' that is > key. If the key were to be
    inserted into a in the slot before the index i, then the order of a would
    be preserved and i would be the largest index with that property.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
    """m.setfield(value, dtype, offset) -> None.
    places val into field of the given array defined by the data type and offset.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
    """a.setflags(write=None, align=None, uic=None)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
    """a.sort(axis=-1, kind='quicksort') -> None. Sort a along the given axis.

    Keyword arguments:

    axis -- axis to be sorted (default -1)
    kind -- sorting algorithm (default 'quicksort')
            Possible values: 'quicksort', 'mergesort', or 'heapsort'.

    Returns: None.

    This method sorts 'a' in place along the given axis using the algorithm
    specified by the kind keyword.

    The various sorts may characterized by average speed, worst case
    performance, need for work space, and whether they are stable. A stable
    sort keeps items with the same key in the same relative order and is most
    useful when used with argsort where the key might differ from the items
    being sorted. The three available algorithms have the following properties:

    |------------------------------------------------------|
    |    kind   | speed |  worst case | work space | stable|
    |------------------------------------------------------|
    |'quicksort'|   1   | O(n^2)      |     0      |   no  |
    |'mergesort'|   2   | O(n*log(n)) |    ~n/2    |   yes |
    |'heapsort' |   3   | O(n*log(n)) |     0      |   no  |
    |------------------------------------------------------|

    All the sort algorithms make temporary copies of the data when the sort is
    not along the last axis. Consequently, sorts along the last axis are faster
    and use less space than sorts along other axis.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
    """m.squeeze() eliminate all length-1 dimensions

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
    """a.std(axis=None, dtype=None, out=None) -> standard deviation.

    The standard deviation isa measure of the spread of a
    distribution.

    The standard deviation is the square root of the average of the
    squared deviations from the mean, i.e.
        std = sqrt(mean((x - x.mean())**2,axis=0)).

    For multidimensional arrays, std is computed by default along the
    first axis.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
    """a.sum(axis=None, dtype=None) -> Sum of array over given axis.

    Sum the array over the given axis.  If the axis is None, sum over
    all dimensions of the array.

    The optional dtype argument is the data type for the returned
    value and intermediate calculations.  The default is to upcast
    (promote) smaller integer types to the platform-dependent int.
    For example, on 32-bit platforms:

      a.dtype                         default sum dtype
      ---------------------------------------------------
      bool, int8, int16, int32        int32

    Warning: The arithmetic is modular and no error is raised on overflow.

    Examples:

    >>> array([0.5, 1.5]).sum()
    2.0
    >>> array([0.5, 1.5]).sum(dtype=int32)
    1
    >>> array([[0, 1], [0, 5]]).sum(axis=0)
    array([0, 6])
    >>> array([[0, 1], [0, 5]]).sum(axis=1)
    array([1, 5])
    >>> ones(128, dtype=int8).sum(dtype=int8) # overflow!
    -128

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
    """a.swapaxes(axis1, axis2) -> new view with axes swapped.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
    """a.take(indices, axis=None, out=None, mode='raise') -> new array.

    The new array is formed from the elements of a indexed by indices along the
    given axis.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
    """a.tofile(fid, sep="", format="%s") -> None. Write the data to a file.

    Required arguments:
        file -- an open file object or a string containing a filename

    Keyword arguments:
        sep -- separator for text output. Write binary if empty (default "")
        format -- format string for text file output (default "%s")

    A convenience function for quick storage of array data. Information on
    endianess 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 endianess. Some of these problems can be overcome by outputting
    the data as text files at the expense of speed and file size.

    If 'sep' is empty this method is equivalent to file.write(a.tostring()). If
    'sep' is not empty each data item is converted to the nearest Python type
    and formatted using "format"%item. The resulting strings are written to the
    file separated by the contents of 'sep'. The data is always written in "C"
    (row major) order independent of the order of 'a'.

    The data produced by this method can be recovered by using the function
    fromfile().

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
    """a.tolist() -> Array as hierarchical list.

    Copy the data portion of the array to a hierarchical python list and return
    that list. Data items are converted to the nearest compatible Python type.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('tostring',
    """a.tostring(order='C') -> raw copy of array data as a Python string.

    Keyword arguments:
        order -- order of the data item in the copy {"C","F","A"} (default "C")

    Construct a Python string containing the raw bytes in the array. The order
    of the data in arrays with ndim > 1 is specified by the 'order' keyword and
    this keyword overrides the order of the array. The
    choices are:

        "C"       -- C order (row major)
        "Fortran" -- Fortran order (column major)
        "Any"     -- Current order of array.
        None      -- Same as "Any"

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
    """a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
    return the sum along the offset diagonal of the array's indicated
    axis1 and axis2.

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
    """a.transpose(*axes)

    Returns a view of 'a' with axes transposed. If no axes are given,
    or None is passed, switches the order of the axes. For a 2-d
    array, this is the usual matrix transpose. If axes are given,
    they describe how the axes are permuted.

    Example:
    >>> a = 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)

    """))


add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
    """a.view(<type>) -> new view of array with same data.

    Type can be either a new sub-type object or a data-descriptor object

    """))