ENH: missingdata: Add the maskna= parameter to np.ones and np.ones_like

10 files changed, 154 insertions, 87 deletions

diff --git a/numpy/core/code_generators/generate_umath.py b/numpy/core/code_generators/generate_umath.py
index 8771a040d..5fe31b945 100644
--- a/numpy/core/code_generators/generate_umath.py
+++ b/numpy/core/code_generators/generate_umath.py
@@ -330,9 +330,11 @@ defdict = {
           TD(ints+inexact),
           TD(O, f='Py_reciprocal'),
           ),
-'ones_like' :
+# This is no longer used as numpy.ones_like, however it is
+# still used by some internal calls.
+'_ones_like' :
     Ufunc(1, 1, None,
-          docstrings.get('numpy.core.umath.ones_like'),
+          docstrings.get('numpy.core.umath._ones_like'),
           'PyUFunc_OnesLikeTypeResolution',
           TD(noobj),
           TD(O, f='Py_get_one'),
diff --git a/numpy/core/code_generators/ufunc_docstrings.py b/numpy/core/code_generators/ufunc_docstrings.py
index 591a898ed..760617340 100644
--- a/numpy/core/code_generators/ufunc_docstrings.py
+++ b/numpy/core/code_generators/ufunc_docstrings.py
@@ -2490,24 +2490,16 @@ add_newdoc('numpy.core.umath', 'not_equal',
 
     """)
 
-add_newdoc('numpy.core.umath', 'ones_like',
+add_newdoc('numpy.core.umath', '_ones_like',
     """
-    Returns an array of ones with the same shape and type as a given array.
-
-    Equivalent to ``a.copy().fill(1)``.
-
-    Please refer to the documentation for `zeros_like` for further details.
+    This function used to be the numpy.ones_like, but now a
+    specific function for that has been written for consistency with
+    the other *_like functions. It is only used internally in a limited
+    fashion now.
 
     See Also
     --------
-    zeros_like, ones
-
-    Examples
-    --------
-    >>> a = np.array([[1, 2, 3], [4, 5, 6]])
-    >>> np.ones_like(a)
-    array([[1, 1, 1],
-           [1, 1, 1]])
+    ones_like
 
     """)
 
diff --git a/numpy/core/numeric.py b/numpy/core/numeric.py
index 00184505d..7a879719a 100644
--- a/numpy/core/numeric.py
+++ b/numpy/core/numeric.py
@@ -5,7 +5,7 @@ __all__ = ['newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
            'concatenate', 'fastCopyAndTranspose', 'lexsort', 'set_numeric_ops',
            'can_cast', 'promote_types', 'min_scalar_type', 'result_type',
            'asarray', 'asanyarray', 'ascontiguousarray', 'asfortranarray',
-           'isfortran', 'isna', 'empty_like', 'zeros_like',
+           'isfortran', 'isna', 'empty_like', 'zeros_like', 'ones_like',
            'correlate', 'convolve', 'inner', 'dot', 'einsum', 'outer', 'vdot',
            'alterdot', 'restoredot', 'roll', 'rollaxis', 'cross', 'tensordot',
            'array2string', 'get_printoptions', 'set_printoptions',
@@ -62,7 +62,6 @@ copyto = multiarray.copyto
 ufunc = type(sin)
 
 
-# originally from Fernando Perez's IPython
 def zeros_like(a, dtype=None, order='K', subok=True, maskna=False):
     """
     Return an array of zeros with the same shape and type as a given array.
@@ -119,7 +118,93 @@ def zeros_like(a, dtype=None, order='K', subok=True, maskna=False):
     multiarray.copyto(res, 0, casting='unsafe')
     return res
 
-# end Fernando's utilities
+def ones(shape, dtype=None, order='C', maskna=False):
+    """
+    Return a new array of given shape and type, filled with ones.
+
+    Please refer to the documentation for `zeros` for further details.
+
+    See Also
+    --------
+    zeros, ones_like
+
+    Examples
+    --------
+    >>> np.ones(5)
+    array([ 1.,  1.,  1.,  1.,  1.])
+
+    >>> np.ones((5,), dtype=np.int)
+    array([1, 1, 1, 1, 1])
+
+    >>> np.ones((2, 1))
+    array([[ 1.],
+           [ 1.]])
+
+    >>> s = (2,2)
+    >>> np.ones(s)
+    array([[ 1.,  1.],
+           [ 1.,  1.]])
+
+    """
+    a = empty(shape, dtype, order, maskna)
+    multiarray.copyto(a, 1, casting='unsafe')
+    return a
+
+def ones_like(a, dtype=None, order='K', subok=True, maskna=False):
+    """
+    Return an array of ones with the same shape and type as a given array.
+
+    With default parameters, is equivalent to ``a.copy().fill(1)``.
+
+    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.
+    maskna : boolean
+        If this is true, the returned array will have an NA mask.
+
+    Returns
+    -------
+    out : ndarray
+        Array of ones with the same shape and type as `a`.
+
+    See Also
+    --------
+    zeros_like : Return an array of zeros with shape and type of input.
+    empty_like : Return an empty array with shape and type of input.
+    zeros : Return a new array setting values to zero.
+    ones : Return a new array setting values to one.
+    empty : Return a new uninitialized array.
+
+    Examples
+    --------
+    >>> x = np.arange(6)
+    >>> x = x.reshape((2, 3))
+    >>> x
+    array([[0, 1, 2],
+           [3, 4, 5]])
+    >>> np.ones_like(x)
+    array([[1, 1, 1],
+           [1, 1, 1]])
+
+    >>> y = np.arange(3, dtype=np.float)
+    >>> y
+    array([ 0.,  1.,  2.])
+    >>> np.ones_like(y)
+    array([ 1.,  1.,  1.])
+
+    """
+    res = empty_like(a, dtype=dtype, order=order, subok=subok, maskna=maskna)
+    multiarray.copyto(res, 1, casting='unsafe')
+    return res
 
 
 def extend_all(module):
@@ -1808,38 +1893,6 @@ def _maketup(descr, val):
         res = [_maketup(fields[name][0],val) for name in dt.names]
         return tuple(res)
 
-def ones(shape, dtype=None, order='C'):
-    """
-    Return a new array of given shape and type, filled with ones.
-
-    Please refer to the documentation for `zeros` for further details.
-
-    See Also
-    --------
-    zeros, ones_like
-
-    Examples
-    --------
-    >>> np.ones(5)
-    array([ 1.,  1.,  1.,  1.,  1.])
-
-    >>> np.ones((5,), dtype=np.int)
-    array([1, 1, 1, 1, 1])
-
-    >>> np.ones((2, 1))
-    array([[ 1.],
-           [ 1.]])
-
-    >>> s = (2,2)
-    >>> np.ones(s)
-    array([[ 1.,  1.],
-           [ 1.,  1.]])
-
-    """
-    a = empty(shape, dtype, order)
-    multiarray.copyto(a, 1, casting='unsafe')
-    return a
-
 def identity(n, dtype=None):
     """
     Return the identity array.
diff --git a/numpy/core/src/multiarray/number.c b/numpy/core/src/multiarray/number.c
index 8bb9dfc3d..861ef5c05 100644
--- a/numpy/core/src/multiarray/number.c
+++ b/numpy/core/src/multiarray/number.c
@@ -52,7 +52,7 @@ PyArray_SetNumericOps(PyObject *dict)
     SET(power);
     SET(square);
     SET(reciprocal);
-    SET(ones_like);
+    SET(_ones_like);
     SET(sqrt);
     SET(negative);
     SET(absolute);
@@ -103,7 +103,7 @@ PyArray_GetNumericOps(void)
     GET(power);
     GET(square);
     GET(reciprocal);
-    GET(ones_like);
+    GET(_ones_like);
     GET(sqrt);
     GET(negative);
     GET(absolute);
@@ -348,7 +348,7 @@ fast_scalar_power(PyArrayObject *a1, PyObject *o2, int inplace)
                 fastop = n_ops.reciprocal;
             }
             else if (exp ==  0.0) {
-                fastop = n_ops.ones_like;
+                fastop = n_ops._ones_like;
             }
             else if (exp ==  0.5) {
                 fastop = n_ops.sqrt;
diff --git a/numpy/core/src/multiarray/number.h b/numpy/core/src/multiarray/number.h
index 8f1cb3b91..0018b7348 100644
--- a/numpy/core/src/multiarray/number.h
+++ b/numpy/core/src/multiarray/number.h
@@ -10,7 +10,7 @@ typedef struct {
     PyObject *power;
     PyObject *square;
     PyObject *reciprocal;
-    PyObject *ones_like;
+    PyObject *_ones_like;
     PyObject *sqrt;
     PyObject *negative;
     PyObject *absolute;
diff --git a/numpy/core/src/umath/loops.c.src b/numpy/core/src/umath/loops.c.src
index 0d1841b47..f4b0fc0df 100644
--- a/numpy/core/src/umath/loops.c.src
+++ b/numpy/core/src/umath/loops.c.src
@@ -611,7 +611,7 @@ BOOL_@kind@(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED
 /**end repeat**/
 
 NPY_NO_EXPORT void
-BOOL_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+BOOL__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         *((Bool *)op1) = 1;
@@ -642,7 +642,7 @@ BOOL_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNU
 #define @S@@TYPE@_fmin @S@@TYPE@_minimum
 
 NPY_NO_EXPORT void
-@S@@TYPE@_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+@S@@TYPE@__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         *((@s@@type@ *)op1) = 1;
@@ -985,7 +985,7 @@ TIMEDELTA_sign(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNU
  */
 
 NPY_NO_EXPORT void
-@TYPE@_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+@TYPE@__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         *((@type@ *)op1) = 1;
@@ -1458,7 +1458,7 @@ NPY_NO_EXPORT void
 }
 
 NPY_NO_EXPORT void
-@TYPE@_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+@TYPE@__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         *((@type@ *)op1) = 1;
@@ -1762,7 +1762,7 @@ HALF_reciprocal(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UN
 }
 
 NPY_NO_EXPORT void
-HALF_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+HALF__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         *((npy_half *)op1) = NPY_HALF_ONE;
@@ -2093,7 +2093,7 @@ C@TYPE@_reciprocal(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY
 }
 
 NPY_NO_EXPORT void
-C@TYPE@_ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
+C@TYPE@__ones_like(char **args, npy_intp *dimensions, npy_intp *steps, void *NPY_UNUSED(data))
 {
     OUTPUT_LOOP {
         ((@type@ *)op1)[0] = 1;
diff --git a/numpy/core/src/umath/loops.h b/numpy/core/src/umath/loops.h
index 2a792bf5b..9b16ed3fc 100644
--- a/numpy/core/src/umath/loops.h
+++ b/numpy/core/src/umath/loops.h
@@ -161,7 +161,7 @@ BOOL_logical_not(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(fu
 
 
 NPY_NO_EXPORT void
-BOOL_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+BOOL__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 /*
  *****************************************************************************
@@ -178,7 +178,7 @@ BOOL_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data
 #define BYTE_fmin BYTE_minimum
 
 NPY_NO_EXPORT void
-BYTE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+BYTE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 BYTE_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -301,7 +301,7 @@ BYTE_fmod(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
 #define UBYTE_fmin UBYTE_minimum
 
 NPY_NO_EXPORT void
-UBYTE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+UBYTE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 UBYTE_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -451,7 +451,7 @@ UBYTE_remainder(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(fun
 #define SHORT_fmin SHORT_minimum
 
 NPY_NO_EXPORT void
-SHORT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+SHORT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 SHORT_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -574,7 +574,7 @@ SHORT_fmod(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
 #define USHORT_fmin USHORT_minimum
 
 NPY_NO_EXPORT void
-USHORT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+USHORT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 USHORT_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -724,7 +724,7 @@ USHORT_remainder(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(fu
 #define INT_fmin INT_minimum
 
 NPY_NO_EXPORT void
-INT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+INT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 INT_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -847,7 +847,7 @@ INT_fmod(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
 #define UINT_fmin UINT_minimum
 
 NPY_NO_EXPORT void
-UINT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+UINT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 UINT_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -997,7 +997,7 @@ UINT_remainder(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func
 #define LONG_fmin LONG_minimum
 
 NPY_NO_EXPORT void
-LONG_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+LONG__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 LONG_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -1120,7 +1120,7 @@ LONG_fmod(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
 #define ULONG_fmin ULONG_minimum
 
 NPY_NO_EXPORT void
-ULONG_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+ULONG__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 ULONG_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -1270,7 +1270,7 @@ ULONG_remainder(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(fun
 #define LONGLONG_fmin LONGLONG_minimum
 
 NPY_NO_EXPORT void
-LONGLONG_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+LONGLONG__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 LONGLONG_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -1393,7 +1393,7 @@ LONGLONG_fmod(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func)
 #define ULONGLONG_fmin ULONGLONG_minimum
 
 NPY_NO_EXPORT void
-ULONGLONG_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+ULONGLONG__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 ULONGLONG_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -1662,7 +1662,7 @@ HALF_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(dat
 
 
 NPY_NO_EXPORT void
-HALF_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+HALF__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 HALF_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -1816,7 +1816,7 @@ FLOAT_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(da
 
 
 NPY_NO_EXPORT void
-FLOAT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+FLOAT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 FLOAT_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -1970,7 +1970,7 @@ DOUBLE_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(d
 
 
 NPY_NO_EXPORT void
-DOUBLE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+DOUBLE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 DOUBLE_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -2124,7 +2124,7 @@ LONGDOUBLE_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUS
 
 
 NPY_NO_EXPORT void
-LONGDOUBLE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+LONGDOUBLE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 LONGDOUBLE_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -2254,7 +2254,7 @@ NPY_NO_EXPORT void
 CFLOAT_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-CFLOAT_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+CFLOAT__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 CFLOAT_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -2370,7 +2370,7 @@ NPY_NO_EXPORT void
 CDOUBLE_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-CDOUBLE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+CDOUBLE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 CDOUBLE_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -2486,7 +2486,7 @@ NPY_NO_EXPORT void
 CLONGDOUBLE_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-CLONGDOUBLE_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+CLONGDOUBLE__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 CLONGDOUBLE_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -2547,7 +2547,7 @@ TIMEDELTA_sign(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func
 #line 432
 
 NPY_NO_EXPORT void
-DATETIME_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+DATETIME__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 #line 440
 NPY_NO_EXPORT void
@@ -2587,7 +2587,7 @@ DATETIME_minimum(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(fu
 #line 432
 
 NPY_NO_EXPORT void
-TIMEDELTA_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+TIMEDELTA__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 #line 440
 NPY_NO_EXPORT void
diff --git a/numpy/core/src/umath/loops.h.src b/numpy/core/src/umath/loops.h.src
index fdedc1933..0b59b3095 100644
--- a/numpy/core/src/umath/loops.h.src
+++ b/numpy/core/src/umath/loops.h.src
@@ -65,7 +65,7 @@ BOOL_@kind@(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
 /**end repeat**/
 
 NPY_NO_EXPORT void
-BOOL_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+BOOL__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 /*
  *****************************************************************************
@@ -90,7 +90,7 @@ BOOL_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data
 #define @S@@TYPE@_fmin @S@@TYPE@_minimum
 
 NPY_NO_EXPORT void
-@S@@TYPE@_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+@S@@TYPE@__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 @S@@TYPE@_square(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
@@ -255,7 +255,7 @@ NPY_NO_EXPORT void
 
 
 NPY_NO_EXPORT void
-@TYPE@_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+@TYPE@__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 @TYPE@_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -369,7 +369,7 @@ NPY_NO_EXPORT void
 C@TYPE@_reciprocal(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
-C@TYPE@_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+C@TYPE@__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 NPY_NO_EXPORT void
 C@TYPE@_conjugate(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func));
@@ -431,7 +431,7 @@ TIMEDELTA_sign(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(func
  */
 
 NPY_NO_EXPORT void
-@TYPE@_ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
+@TYPE@__ones_like(char **args, intp *dimensions, intp *steps, void *NPY_UNUSED(data));
 
 /**begin repeat1
  * #kind = equal, not_equal, greater, greater_equal, less, less_equal#
diff --git a/numpy/core/tests/test_maskna.py b/numpy/core/tests/test_maskna.py
index e284f841b..8fd3fb174 100644
--- a/numpy/core/tests/test_maskna.py
+++ b/numpy/core/tests/test_maskna.py
@@ -62,6 +62,12 @@ def test_array_maskna_construction():
     assert_(a.flags.maskna)
     assert_equal(np.isna(a), False)
 
+    a = np.ones((3,))
+    assert_(not a.flags.maskna)
+    a = np.ones((3,), maskna=True)
+    assert_(a.flags.maskna)
+    assert_equal(np.isna(a), False)
+
     # np.empty returns all NAs if maskna is set to True
     a = np.empty((3,))
     assert_(not a.flags.maskna)
diff --git a/numpy/core/tests/test_numeric.py b/numpy/core/tests/test_numeric.py
index 2f8c2fcc9..205bf56c4 100644
--- a/numpy/core/tests/test_numeric.py
+++ b/numpy/core/tests/test_numeric.py
@@ -1324,7 +1324,7 @@ class TestLikeFuncs(TestCase):
             if not value is None:
                 assert_(all(dz == value))
 
-        # Test the 'subok' parameter'
+        # Test the 'subok' parameter
         a = np.matrix([[1,2],[3,4]])
 
         b = like_function(a)
@@ -1333,6 +1333,20 @@ class TestLikeFuncs(TestCase):
         b = like_function(a, subok=False)
         assert_(not (type(b) is np.matrix))
 
+        # Test that 'maskna=True' works
+        a = np.arange(6).reshape(2,3)
+        res = like_function(a, maskna=True)
+        assert_(res.flags.maskna)
+        assert_(res.flags.ownmaskna)
+        assert_equal(res.shape, a.shape)
+        assert_equal(res.dtype, a.dtype)
+
+        # Test that no NA mask is created when the prototype is NA-masked
+        a = np.arange(6, maskna=True).reshape(2,3)
+        assert_(a.flags.maskna)
+        res = like_function(a)
+        assert_(not res.flags.maskna)
+
     def test_ones_like(self):
         self.check_like_function(np.ones_like, 1)