ENH: core: Add dtype= and order= parameters to zeros_like, ones_like, and empty_like

1 files changed, 12 insertions, 68 deletions

diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 43456b97f..fc935b4cb 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -62,17 +62,24 @@ ufunc = type(sin)
 
 
 # originally from Fernando Perez's IPython
-def zeros_like(a):
+def zeros_like(a, dtype=None, order='K'):
     """
     Return an array of zeros with the same shape and type as a given array.
 
-    Equivalent to ``a.copy().fill(0)``.
+    With default paramters, is equivalent to ``a.copy().fill(0)``.
 
     Parameters
     ----------
     a : array_like
         The shape and data-type of `a` define these same attributes of
         the returned array.
+    dtype : data-type, optional
+        Overrides the data type of the result.
+    order : {'C', 'F', 'A', or 'K'}, optional
+        Overrides the memory layout of the result. 'C' means C-order,
+        'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,
+        'C' otherwise. 'K' means match the layout of ``a`` as closely
+        as possible.
 
     Returns
     -------
@@ -105,72 +112,8 @@ def zeros_like(a):
     array([ 0.,  0.,  0.])
 
     """
-    if isinstance(a, ndarray):
-        res = ndarray.__new__(type(a), a.shape, a.dtype, order=a.flags.fnc)
-        res.fill(0)
-        return res
-    try:
-        wrap = a.__array_wrap__
-    except AttributeError:
-        wrap = None
-    a = asarray(a)
-    res = zeros(a.shape, a.dtype)
-    if wrap:
-        res = wrap(res)
-    return res
-
-def empty_like(a):
-    """
-    Return a new array with the same shape and type as a given array.
-
-    Parameters
-    ----------
-    a : array_like
-        The shape and data-type of `a` define these same attributes of the
-        returned array.
-
-    Returns
-    -------
-    out : ndarray
-        Array of random data with the same shape and type as `a`.
-
-    See Also
-    --------
-    ones_like : Return an array of ones with shape and type of input.
-    zeros_like : Return an array of zeros with shape and type of input.
-    empty : Return a new uninitialized array.
-    ones : Return a new array setting values to one.
-    zeros : Return a new array setting values to zero.
-
-    Notes
-    -----
-    This function does *not* initialize the returned array; to do that use
-    `zeros_like` or `ones_like` instead.  It may be marginally faster than
-    the functions that do set the array values.
-
-    Examples
-    --------
-    >>> a = ([1,2,3], [4,5,6])                         # a is array-like
-    >>> np.empty_like(a)
-    array([[-1073741821, -1073741821,           3],    #random
-           [          0,           0, -1073741821]])
-    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
-    >>> np.empty_like(a)
-    array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
-           [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])
-
-    """
-    if isinstance(a, ndarray):
-        res = ndarray.__new__(type(a), a.shape, a.dtype, order=a.flags.fnc)
-        return res
-    try:
-        wrap = a.__array_wrap__
-    except AttributeError:
-        wrap = None
-    a = asarray(a)
-    res = empty(a.shape, a.dtype)
-    if wrap:
-        res = wrap(res)
+    res = empty_like(a, dtype=dtype, order=order)
+    res.fill(0)
     return res
 
 # end Fernando's utilities
@@ -199,6 +142,7 @@ array = multiarray.array
 zeros = multiarray.zeros
 count_nonzero = multiarray.count_nonzero
 empty = multiarray.empty
+empty_like = multiarray.empty_like
 fromstring = multiarray.fromstring
 fromiter = multiarray.fromiter
 fromfile = multiarray.fromfile