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?")
            ]
           )

add_newdoc('numpy.core', 'flatiter',
           [('__array__',
"""__array__(type=None)
Get array from iterator"""),
            ('copy',
"""copy()
Get a copy of the iterator as a 1-d array"""),
            ('coords', "An N-d tuple of current coordinates.")
            ]
           )

add_newdoc('numpy.core', 'broadcast',
           [('size', "total size of broadcasted result"),
            ('index', "current index in broadcasted result"),
            ('shape', "shape of broadcasted result"),
            ('iters', "tuple of individual iterators"),
            ('numiter', "number of iterators"),
            ('nd', "number of dimensions of broadcasted result")
            ]
           )

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='')

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, then the entire file is read,
otherwise count is the number of items of the given type read in.  If
sep is '' then read a binary file, otherwise it gives the separator
between elements in a text file.

WARNING: This function should be used sparingly, as it is not a
platform-independent method of persistence.  But it can be useful to
read 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)

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 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
#
# Todo:
#
# expand and reformat documentation.
#
# do all methods prior to Extended methods added 2005
#
##############################################################################


##############################################################################
#
# 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',
"""Equivalent to self.transpose() except self is returned for self.ndim < 2."""))


##############################################################################
#
# ndarray methods
#
##############################################################################

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 given 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', ('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', ('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', ('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', ('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.  If an integer axis
  is given, this equals:
      a.sum(axis, dtype) * 1.0 / len(a).
  If axis is None, this equals:
      a.sum(axis, dtype) * 1.0 / product(a.shape)
  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(values, indices, mode) sets a.flat[n] = values[n] for
each n in indices. v can be scalar or shorter than indices, and
it will repeat.

"""))


add_newdoc('numpy.core.multiarray', 'ndarray', ('putmask',
"""a.putmask(values, mask) sets a.flat[n] = v[n] for each n where
mask.flat[n] is true. v can be scalar.

"""))


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', ('round',
"""a.round(decimals=0, out=None)

"""))


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

 Assuming that a is a 1-D array, in ascending order and represents
 bin boundaries, then a.searchsorted(values) gives an array of bin
 numbers, giving the bin into which each value would be placed.
 This method is helpful for histograming.  Note: No warning is
 given if the boundaries, in a, are not in ascending order.;

"""))


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)     |      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)).

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

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])

"""))


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', ('trace',
"""a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
return the sum along the offset diagonal of the arrays 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

"""))