DOC: Remove docstrings which are duplicated from `numpy/core/multiarray.py`

1 files changed, 0 insertions, 156 deletions

diff --git a/numpy/core/_add_newdocs.py b/numpy/core/_add_newdocs.py
index 8ed6e431b..c6e051a04 100644
--- a/numpy/core/_add_newdocs.py
+++ b/numpy/core/_add_newdocs.py
@@ -3227,87 +3227,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
     """))
 
 
-add_newdoc('numpy.core.multiarray', 'shares_memory',
-    """
-    shares_memory(a, b, max_work=None)
-
-    Determine if two arrays share memory
-
-    Parameters
-    ----------
-    a, b : ndarray
-        Input arrays
-    max_work : int, optional
-        Effort to spend on solving the overlap problem (maximum number
-        of candidate solutions to consider). The following special
-        values are recognized:
-
-        max_work=MAY_SHARE_EXACT  (default)
-            The problem is solved exactly. In this case, the function returns
-            True only if there is an element shared between the arrays.
-        max_work=MAY_SHARE_BOUNDS
-            Only the memory bounds of a and b are checked.
-
-    Raises
-    ------
-    numpy.TooHardError
-        Exceeded max_work.
-
-    Returns
-    -------
-    out : bool
-
-    See Also
-    --------
-    may_share_memory
-
-    Examples
-    --------
-    >>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
-    False
-
-    """)
-
-
-add_newdoc('numpy.core.multiarray', 'may_share_memory',
-    """
-    may_share_memory(a, b, max_work=None)
-
-    Determine if two arrays might 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
-
-    """)
-
-
 add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
     """
     arr.newbyteorder(new_order='S')
@@ -3409,81 +3328,6 @@ add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
 
     """))
 
-add_newdoc('numpy.core.multiarray', 'copyto',
-    """
-    copyto(dst, src, casting='same_kind', where=True)
-
-    Copies values from one array to another, broadcasting as necessary.
-
-    Raises a TypeError if the `casting` rule is violated, and if
-    `where` is provided, it selects which elements to copy.
-
-    .. versionadded:: 1.7.0
-
-    Parameters
-    ----------
-    dst : ndarray
-        The array into which values are copied.
-    src : array_like
-        The array from which values are copied.
-    casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
-        Controls what kind of data casting may occur when copying.
-
-          * 'no' means the data types should not be cast at all.
-          * 'equiv' means only byte-order changes are allowed.
-          * 'safe' means only casts which can preserve values are allowed.
-          * 'same_kind' means only safe casts or casts within a kind,
-            like float64 to float32, are allowed.
-          * 'unsafe' means any data conversions may be done.
-    where : array_like of bool, optional
-        A boolean array which is broadcasted to match the dimensions
-        of `dst`, and selects elements to copy from `src` to `dst`
-        wherever it contains the value True.
-
-    """)
-
-add_newdoc('numpy.core.multiarray', 'putmask',
-    """
-    putmask(a, mask, values)
-
-    Changes elements of an array based on conditional and input values.
-
-    Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
-
-    If `values` is not the same size as `a` and `mask` then it will repeat.
-    This gives behavior different from ``a[mask] = values``.
-
-    Parameters
-    ----------
-    a : array_like
-        Target array.
-    mask : array_like
-        Boolean mask array. It has to be the same shape as `a`.
-    values : array_like
-        Values to put into `a` where `mask` is True. If `values` is smaller
-        than `a` it will be repeated.
-
-    See Also
-    --------
-    place, put, take, copyto
-
-    Examples
-    --------
-    >>> x = np.arange(6).reshape(2, 3)
-    >>> np.putmask(x, x>2, x**2)
-    >>> x
-    array([[ 0,  1,  2],
-           [ 9, 16, 25]])
-
-    If `values` is smaller than `a` it is repeated:
-
-    >>> x = np.arange(5)
-    >>> np.putmask(x, x>1, [-33, -44])
-    >>> x
-    array([  0,   1, -33, -44, -33])
-
-    """)
-
 
 add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
     """