ENH: reimplement may_share_memory in C to improve its performance

1 files changed, 1 insertions, 44 deletions

diff --git a/numpy/core/function_base.py b/numpy/core/function_base.py
index 05fea557a..c82c9bb6b 100644
--- a/numpy/core/function_base.py
+++ b/numpy/core/function_base.py
@@ -1,6 +1,6 @@
 from __future__ import division, absolute_import, print_function
 
-__all__ = ['logspace', 'linspace', 'may_share_memory']
+__all__ = ['logspace', 'linspace']
 
 from . import numeric as _nx
 from .numeric import result_type, NaN, shares_memory, MAY_SHARE_BOUNDS, TooHardError
@@ -201,46 +201,3 @@ def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None):
     if dtype is None:
         return _nx.power(base, y)
     return _nx.power(base, y).astype(dtype)
-
-
-def may_share_memory(a, b, max_work=None):
-    """Determine if two arrays can share memory
-
-    A return of True does not necessarily mean that the two arrays
-    share any element.  It just means that they *might*.
-
-    Only the memory bounds of a and b are checked by default.
-
-    Parameters
-    ----------
-    a, b : ndarray
-        Input arrays
-    max_work : int, optional
-        Effort to spend on solving the overlap problem.  See
-        `shares_memory` for details.  Default for ``may_share_memory``
-        is to do a bounds check.
-
-    Returns
-    -------
-    out : bool
-
-    See Also
-    --------
-    shares_memory
-
-    Examples
-    --------
-    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
-    False
-    >>> x = np.zeros([3, 4])
-    >>> np.may_share_memory(x[:,0], x[:,1])
-    True
-
-    """
-    if max_work is None:
-        max_work = MAY_SHARE_BOUNDS
-    try:
-        return shares_memory(a, b, max_work=max_work)
-    except (TooHardError, OverflowError):
-        # Unable to determine, assume yes
-        return True