Gecko [ goons4good.org ]

Name	Size	Permission	Date
include	[ DIR ]	drwxr-xr-x	2021-09-16 10:54
lib	[ DIR ]	drwxr-xr-x	2021-09-16 10:54
tests	[ DIR ]	drwxr-xr-x	2021-09-16 10:54
__init__.py	1.71 KB	-rw-r--r--	2013-04-07 01:04
__init__.pyc	1.96 KB	-rw-r--r--	2018-04-10 19:40
__init__.pyo	1.96 KB	-rw-r--r--	2018-04-10 19:40
_dotblas.so	23.57 KB	-rwxr-xr-x	2018-04-10 19:40
_dummy.so	6.74 KB	-rwxr-xr-x	2018-04-10 19:40
_internal.py	16.37 KB	-rw-r--r--	2013-04-07 01:04
_internal.pyc	14.11 KB	-rw-r--r--	2018-04-10 19:40
_internal.pyo	14.11 KB	-rw-r--r--	2018-04-10 19:40
_methods.py	3.75 KB	-rw-r--r--	2013-04-07 01:04
_methods.pyc	4.18 KB	-rw-r--r--	2018-04-10 19:40
_methods.pyo	4.18 KB	-rw-r--r--	2018-04-10 19:40
arrayprint.py	25.21 KB	-rw-r--r--	2013-04-07 01:04
arrayprint.pyc	22.85 KB	-rw-r--r--	2018-04-10 19:40
arrayprint.pyo	22.85 KB	-rw-r--r--	2018-04-10 19:40
defchararray.py	70.67 KB	-rw-r--r--	2013-04-07 01:04
defchararray.pyc	78.49 KB	-rw-r--r--	2018-04-10 19:40
defchararray.pyo	78.49 KB	-rw-r--r--	2018-04-10 19:40
fromnumeric.py	79.16 KB	-rw-r--r--	2013-04-07 01:04
fromnumeric.pyc	81.98 KB	-rw-r--r--	2018-04-10 19:40
fromnumeric.pyo	81.98 KB	-rw-r--r--	2018-04-10 19:40
function_base.py	5.34 KB	-rw-r--r--	2013-04-07 01:04
function_base.pyc	5.7 KB	-rw-r--r--	2018-04-10 19:40
function_base.pyo	5.7 KB	-rw-r--r--	2018-04-10 19:40
generate_numpy_api.py	7.24 KB	-rw-r--r--	2013-04-07 01:04
generate_numpy_api.pyc	6.92 KB	-rw-r--r--	2018-04-10 19:40
generate_numpy_api.pyo	6.92 KB	-rw-r--r--	2018-04-10 19:40
getlimits.py	9.15 KB	-rw-r--r--	2013-04-07 01:04
getlimits.pyc	10.45 KB	-rw-r--r--	2018-04-10 19:40
getlimits.pyo	10.45 KB	-rw-r--r--	2018-04-10 19:40
info.py	4.53 KB	-rw-r--r--	2013-04-07 01:04
info.pyc	4.69 KB	-rw-r--r--	2018-04-10 19:40
info.pyo	4.69 KB	-rw-r--r--	2018-04-10 19:40
machar.py	10.39 KB	-rw-r--r--	2013-04-07 01:04
machar.pyc	8.44 KB	-rw-r--r--	2018-04-10 19:40
machar.pyo	8.44 KB	-rw-r--r--	2018-04-10 19:40
memmap.py	9.64 KB	-rw-r--r--	2013-04-07 01:04
memmap.pyc	9.64 KB	-rw-r--r--	2018-04-10 19:40
memmap.pyo	9.64 KB	-rw-r--r--	2018-04-10 19:40
multiarray.so	1.25 MB	-rwxr-xr-x	2018-04-10 19:40
multiarray_tests.so	15.23 KB	-rwxr-xr-x	2018-04-10 19:40
numeric.py	72.78 KB	-rw-r--r--	2013-04-07 01:04
numeric.pyc	75.76 KB	-rw-r--r--	2018-04-10 19:40
numeric.pyo	75.76 KB	-rw-r--r--	2018-04-10 19:40
numerictypes.py	28.31 KB	-rw-r--r--	2013-04-07 01:04
numerictypes.pyc	26.95 KB	-rw-r--r--	2018-04-10 19:40
numerictypes.pyo	26.9 KB	-rw-r--r--	2018-04-10 19:40
records.py	26.37 KB	-rw-r--r--	2013-04-07 01:04
records.pyc	23.77 KB	-rw-r--r--	2018-04-10 19:40
records.pyo	23.77 KB	-rw-r--r--	2018-04-10 19:40
scalarmath.so	187.89 KB	-rwxr-xr-x	2018-04-10 19:40
scons_support.py	8.16 KB	-rw-r--r--	2013-04-07 01:04
scons_support.pyc	9.03 KB	-rw-r--r--	2018-04-10 19:40
scons_support.pyo	9.03 KB	-rw-r--r--	2018-04-10 19:40
setup.py	37.73 KB	-rw-r--r--	2018-04-10 19:39
setup.pyc	25.07 KB	-rw-r--r--	2018-04-10 19:40
setup.pyo	25.07 KB	-rw-r--r--	2018-04-10 19:40
setup_common.py	10.38 KB	-rw-r--r--	2013-04-07 01:04
setup_common.pyc	8.83 KB	-rw-r--r--	2018-04-10 19:40
setup_common.pyo	8.83 KB	-rw-r--r--	2018-04-10 19:40
setupscons.py	4.41 KB	-rw-r--r--	2013-04-07 01:04
setupscons.pyc	3.95 KB	-rw-r--r--	2018-04-10 19:40
setupscons.pyo	3.95 KB	-rw-r--r--	2018-04-10 19:40
shape_base.py	6.66 KB	-rw-r--r--	2013-04-07 01:04
shape_base.pyc	7.16 KB	-rw-r--r--	2018-04-10 19:40
shape_base.pyo	7.16 KB	-rw-r--r--	2018-04-10 19:40
umath.so	377.02 KB	-rwxr-xr-x	2018-04-10 19:40
umath_tests.so	15.26 KB	-rwxr-xr-x	2018-04-10 19:40

Rename

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

import numeric as _nx
from numeric import array, asanyarray, 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
    ----------
    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])]

"""
    res = []
    for ary in arys:
        ary = asanyarray(ary)
        if len(ary.shape) == 0 :
            result = ary.reshape(1)
        else :
            result = ary
        res.append(result)
    if len(res) == 1:
        return res[0]
    else:
        return res

def atleast_2d(*arys):
    """
    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]])]

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

def atleast_3d(*arys):
    """
    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)

"""
    res = []
    for ary in arys:
        ary = asanyarray(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)`` 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]])

"""
    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]])

"""
    arrs = map(atleast_1d,tup)
    # As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
    if arrs[0].ndim == 1:
        return _nx.concatenate(arrs, 0)
    else:
        return _nx.concatenate(arrs, 1)