HEX

File: //opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyc
�
�9Zc@`s�ddlmZmZmZddddddgZdd	lmZdd
lmZm	Z	d�Z
d�Zd
�Zd�Z
d�Zdd�ZdS(i(tdivisiontabsolute_importtprint_functiont
atleast_1dt
atleast_2dt
atleast_3dtvstackthstacktstacki(tnumeric(t
asanyarraytnewaxiscG`sg}xT|D]L}t|�}t|j�dkrF|jd�}n|}|j|�q
Wt|�dkrw|dS|SdS(s)
    Convert inputs to arrays with at least one dimension.

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

    Parameters
    ----------
    arys1, arys2, ... : 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])]

    iiN(R
tlentshapetreshapetappend(tarystrestarytresult((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyR	s'
cG`s�g}x�|D]}}t|�}t|j�dkrI|jdd�}n4t|j�dkrw|tdd�f}n|}|j|�q
Wt|�dkr�|dS|SdS(sa
    View inputs as arrays with at least two dimensions.

    Parameters
    ----------
    arys1, arys2, ... : 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]])]

    iiN(R
RR
RRR(RRRR((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyR=s%
cG`s�g}x�|D]�}t|�}t|j�dkrL|jddd�}nnt|j�dkr}|tdd�tf}n=t|j�dkr�|dd�dd�tf}n|}|j|�q
Wt|�dkr�|dS|SdS(s�
    View inputs as arrays with at least three dimensions.

    Parameters
    ----------
    arys1, arys2, ... : 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)``, and 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)

    iiNi(R
RR
RRR(RRRR((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyRqs1
"cC`s)tjg|D]}t|�^q
d�S(s(
    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
    --------
    stack : Join a sequence of arrays along a new axis.
    hstack : Stack arrays in sequence horizontally (column wise).
    dstack : Stack arrays in sequence depth wise (along third dimension).
    concatenate : Join a sequence of arrays along an existing axis.
    vsplit : Split array into a list of multiple sub-arrays vertically.

    Notes
    -----
    Equivalent to ``np.concatenate(tup, axis=0)`` if `tup` contains arrays that
    are at least 2-dimensional.

    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]])

    i(t_nxtconcatenateR(ttupt_m((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyR�s2cC`sVg|D]}t|�^q}|djdkrBtj|d�Stj|d�SdS(s^
    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
    --------
    stack : Join a sequence of arrays along a new axis.
    vstack : Stack arrays in sequence vertically (row wise).
    dstack : Stack arrays in sequence depth wise (along third axis).
    concatenate : Join a sequence of arrays along an existing axis.
    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]])

    iiN(RtndimRR(RRtarrs((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyR�s+cC`s g|D]}t|�^q}|s4td��ntd�|D��}t|�dkrktd��n|djd}||ko�|kns�dj||�}t|��n|dkr�||7}ntd�f|t	j
f}g|D]}||^q�}t	j|d|�S(	s�
    Join a sequence of arrays along a new axis.

    The `axis` parameter specifies the index of the new axis in the dimensions
    of the result. For example, if ``axis=0`` it will be the first dimension
    and if ``axis=-1`` it will be the last dimension.

    .. versionadded:: 1.10.0

    Parameters
    ----------
    arrays : sequence of array_like
        Each array must have the same shape.
    axis : int, optional
        The axis in the result array along which the input arrays are stacked.

    Returns
    -------
    stacked : ndarray
        The stacked array has one more dimension than the input arrays.

    See Also
    --------
    concatenate : Join a sequence of arrays along an existing axis.
    split : Split array into a list of multiple sub-arrays of equal size.

    Examples
    --------
    >>> arrays = [np.random.randn(3, 4) for _ in range(10)]
    >>> np.stack(arrays, axis=0).shape
    (10, 3, 4)

    >>> np.stack(arrays, axis=1).shape
    (3, 10, 4)

    >>> np.stack(arrays, axis=2).shape
    (3, 4, 10)

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

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

    s need at least one array to stackcs`s|]}|jVqdS(N(R
(t.0tarr((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pys	<genexpr>Qsis)all input arrays must have the same shapeis"axis {0} out of bounds [-{1}, {1})taxisN(R
t
ValueErrortsetRRtformatt
IndexErrortslicetNoneRRR(tarraysRRtshapestresult_ndimtmsgtsltexpanded_arrays((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyRs3
N(t
__future__RRRt__all__tR	RR
RRRRRRR(((sH/opt/alt/python27/lib64/python2.7/site-packages/numpy/core/shape_base.pyt<module>s		4	4	C	4	2