FM / core

/usr/lib64/python2.6/site-packages/numpy/core/

/usr/lib64/python2.6/site-packages/numpy/core

Name	Size	Mode	Actions
include/	-	0755	rm
lib/	-	0755	rm
tests/	-	0755	rm
arrayprint.py	16812	0644	edit dl rm
arrayprint.pyc	15601	0644	edit dl rm
arrayprint.pyo	15601	0644	edit dl rm
defchararray.py	69952	0644	edit dl rm
defchararray.pyc	78326	0644	edit dl rm
defchararray.pyo	78326	0644	edit dl rm
defmatrix.py	28094	0644	edit dl rm
defmatrix.pyc	30970	0644	edit dl rm
defmatrix.pyo	30970	0644	edit dl rm
fromnumeric.py	71135	0644	edit dl rm
fromnumeric.pyc	75079	0644	edit dl rm
fromnumeric.pyo	75079	0644	edit dl rm
function_base.py	5201	0644	edit dl rm
function_base.pyc	5569	0644	edit dl rm
function_base.pyo	5569	0644	edit dl rm
generate_numpy_api.py	6444	0644	edit dl rm
generate_numpy_api.pyc	6202	0644	edit dl rm
generate_numpy_api.pyo	6141	0644	edit dl rm
getlimits.py	8790	0644	edit dl rm
getlimits.pyc	10068	0644	edit dl rm
getlimits.pyo	10068	0644	edit dl rm
info.py	4635	0644	edit dl rm
info.pyc	4806	0644	edit dl rm
info.pyo	4806	0644	edit dl rm
machar.py	10658	0644	edit dl rm
machar.pyc	8694	0644	edit dl rm
machar.pyo	8694	0644	edit dl rm
memmap.py	9301	0644	edit dl rm
memmap.pyc	9534	0644	edit dl rm
memmap.pyo	9534	0644	edit dl rm
multiarray.so	584824	0755	edit dl rm
multiarray_tests.so	13008	0755	edit dl rm
numeric.py	68280	0644	edit dl rm
numeric.pyc	72033	0644	edit dl rm
numeric.pyo	72033	0644	edit dl rm
numerictypes.py	25088	0644	edit dl rm
numerictypes.pyc	24189	0644	edit dl rm
numerictypes.pyo	24144	0644	edit dl rm
records.py	26838	0644	edit dl rm
records.pyc	24437	0644	edit dl rm
records.pyo	24437	0644	edit dl rm
scalarmath.so	164928	0755	edit dl rm
scons_support.py	8352	0644	edit dl rm
scons_support.pyc	9342	0644	edit dl rm
scons_support.pyo	9342	0644	edit dl rm
setup.py	32450	0644	edit dl rm
setup.pyc	23292	0644	edit dl rm
setup.pyo	23292	0644	edit dl rm
setupscons.py	4516	0644	edit dl rm
setupscons.pyc	4087	0644	edit dl rm
setupscons.pyo	4087	0644	edit dl rm
setup_common.py	10069	0644	edit dl rm
setup_common.pyc	8353	0644	edit dl rm
setup_common.pyo	8353	0644	edit dl rm
shape_base.py	6294	0644	edit dl rm
shape_base.pyc	7123	0644	edit dl rm
shape_base.pyo	7123	0644	edit dl rm
umath.so	325904	0755	edit dl rm
umath_tests.so	12192	0755	edit dl rm
_dotblas.so	20712	0755	edit dl rm
_internal.py	10368	0644	edit dl rm
_internal.pyc	10870	0644	edit dl rm
_internal.pyo	10870	0644	edit dl rm
_mx_datetime_parser.py	33252	0644	edit dl rm
_mx_datetime_parser.pyc	29029	0644	edit dl rm
_mx_datetime_parser.pyo	29029	0644	edit dl rm
_sort.so	90736	0755	edit dl rm
__init__.py	983	0644	edit dl rm
__init__.pyc	1261	0644	edit dl rm
__init__.pyo	1261	0644	edit dl rm

Edit: /usr/lib64/python2.6/site-packages/numpy/core/shape_base.py (6294B)

__all__ = ['atleast_1d','atleast_2d','atleast_3d','vstack','hstack']

import numeric as _nx
from numeric import array, asarray, newaxis

def atleast_1d(*arys):
    """
    Convert inputs to arrays with at least one dimension.

Scalar inputs are converted to 1-dimensional arrays, whilst
    higher-dimensional inputs are preserved.

Parameters
    ----------
    array1, array2, ... : array_like
        One or more input arrays.

Returns
    -------
    ret : ndarray
        An array, or sequence of arrays, each with ``a.ndim >= 1``.
        Copies are made only if necessary.

See Also
    --------
    atleast_2d, atleast_3d

Examples
    --------
    >>> np.atleast_1d(1.0)
    array([ 1.])

>>> x = np.arange(9.0).reshape(3,3)
    >>> np.atleast_1d(x)
    array([[ 0.,  1.,  2.],
           [ 3.,  4.,  5.],
           [ 6.,  7.,  8.]])
    >>> np.atleast_1d(x) is x
    True

>>> np.atleast_1d(1, [3, 4])
    [array([1]), array([3, 4])]

"""
    res = []
    for ary in arys:
        res.append(array(ary,copy=False,subok=True,ndmin=1))
    if len(res) == 1:
        return res[0]
    else:
        return res

def atleast_2d(*arys):
    """
    View inputs as arrays with at least two dimensions.

Parameters
    ----------
    array1, array2, ... : array_like
        One or more array-like sequences.  Non-array inputs are converted
        to arrays.  Arrays that already have two or more dimensions are
        preserved.

Returns
    -------
    res, res2, ... : ndarray
        An array, or tuple of arrays, each with ``a.ndim >= 2``.
        Copies are avoided where possible, and views with two or more
        dimensions are returned.

See Also
    --------
    atleast_1d, atleast_3d

Examples
    --------
    >>> np.atleast_2d(3.0)
    array([[ 3.]])

>>> x = np.arange(3.0)
    >>> np.atleast_2d(x)
    array([[ 0.,  1.,  2.]])
    >>> np.atleast_2d(x).base is x
    True

>>> np.atleast_2d(1, [1, 2], [[1, 2]])
    [array([[1]]), array([[1, 2]]), array([[1, 2]])]

"""
    res = []
    for ary in arys:
        res.append(array(ary,copy=False,subok=True,ndmin=2))
    if len(res) == 1:
        return res[0]
    else:
        return res

def atleast_3d(*arys):
    """
    View inputs as arrays with at least three dimensions.

Parameters
    ----------
    array1, array2, ... : array_like
        One or more array-like sequences.  Non-array inputs are converted
        to arrays. Arrays that already have three or more dimensions are
        preserved.

Returns
    -------
    res1, res2, ... : ndarray
        An array, or tuple of arrays, each with ``a.ndim >= 3``.
        Copies are avoided where possible, and views with three or more
        dimensions are returned.  For example, a 1-D array of shape ``N``
        becomes a view of shape ``(1, N, 1)``.  A 2-D array of shape ``(M, N)``
        becomes a view of shape ``(M, N, 1)``.

See Also
    --------
    atleast_1d, atleast_2d

Examples
    --------
    >>> np.atleast_3d(3.0)
    array([[[ 3.]]])

>>> x = np.arange(3.0)
    >>> np.atleast_3d(x).shape
    (1, 3, 1)

>>> x = np.arange(12.0).reshape(4,3)
    >>> np.atleast_3d(x).shape
    (4, 3, 1)
    >>> np.atleast_3d(x).base is x
    True

>>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
    ...     print arr, arr.shape
    ...
    [[[1]
      [2]]] (1, 2, 1)
    [[[1]
      [2]]] (1, 2, 1)
    [[[1 2]]] (1, 1, 2)

"""
    res = []
    for ary in arys:
        ary = asarray(ary)
        if len(ary.shape) == 0:
            result = ary.reshape(1,1,1)
        elif len(ary.shape) == 1:
            result = ary[newaxis,:,newaxis]
        elif len(ary.shape) == 2:
            result = ary[:,:,newaxis]
        else:
            result = ary
        res.append(result)
    if len(res) == 1:
        return res[0]
    else:
        return res

def vstack(tup):
    """
    Stack arrays in sequence vertically (row wise).

Take a sequence of arrays and stack them vertically to make a single
    array. Rebuild arrays divided by `vsplit`.

Parameters
    ----------
    tup : sequence of ndarrays
        Tuple containing arrays to be stacked. The arrays must have the same
        shape along all but the first axis.

Returns
    -------
    stacked : ndarray
        The array formed by stacking the given arrays.

See Also
    --------
    hstack : Stack arrays in sequence horizontally (column wise).
    dstack : Stack arrays in sequence depth wise (along third dimension).
    concatenate : Join a sequence of arrays together.
    vsplit : Split array into a list of multiple sub-arrays vertically.

Notes
    -----
    Equivalent to ``np.concatenate(tup, axis=0)``

Examples
    --------
    >>> a = np.array([1, 2, 3])
    >>> b = np.array([2, 3, 4])
    >>> np.vstack((a,b))
    array([[1, 2, 3],
           [2, 3, 4]])

>>> a = np.array([[1], [2], [3]])
    >>> b = np.array([[2], [3], [4]])
    >>> np.vstack((a,b))
    array([[1],
           [2],
           [3],
           [2],
           [3],
           [4]])

"""
    return _nx.concatenate(map(atleast_2d,tup),0)

def hstack(tup):
    """
    Stack arrays in sequence horizontally (column wise).

Take a sequence of arrays and stack them horizontally to make
    a single array. Rebuild arrays divided by ``hsplit``.

Parameters
    ----------
    tup : sequence of ndarrays
        All arrays must have the same shape along all but the second axis.

Returns
    -------
    stacked : ndarray
        The array formed by stacking the given arrays.

See Also
    --------
    vstack : Stack arrays in sequence vertically (row wise).
    dstack : Stack arrays in sequence depth wise (along third axis).
    concatenate : Join a sequence of arrays together.
    hsplit : Split array along second axis.

Notes
    -----
    Equivalent to ``np.concatenate(tup, axis=1)``

Examples
    --------
    >>> a = np.array((1,2,3))
    >>> b = np.array((2,3,4))
    >>> np.hstack((a,b))
    array([1, 2, 3, 2, 3, 4])
    >>> a = np.array([[1],[2],[3]])
    >>> b = np.array([[2],[3],[4]])
    >>> np.hstack((a,b))
    array([[1, 2],
           [2, 3],
           [3, 4]])

"""
    return _nx.concatenate(map(atleast_1d,tup),1)

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