__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)

