numpy.shares_memory

numpy.shares_memory(a, b, max_work=None)

Determine if two arrays share memory.

Warning

This function can be exponentially slow for some inputs, unless max_work is set to a finite number or MAY_SHARE_BOUNDS. If in doubt, use numpy.may_share_memory instead.

Parameters

a, bndarray

Input arrays

max_workint, 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. Finding the exact solution may take extremely long in some cases.

max_work=MAY_SHARE_BOUNDS

Only the memory bounds of a and b are checked.

Returns

outbool

Raises

numpy.TooHardError

Exceeded max_work.

See also

may_share_memory

Examples

>>> x = np.array([1, 2, 3, 4])
>>> np.shares_memory(x, np.array([5, 6, 7]))
False
>>> np.shares_memory(x[::2], x)
True
>>> np.shares_memory(x[::2], x[1::2])
False

Checking whether two arrays share memory is NP-complete, and runtime may increase exponentially in the number of dimensions. Hence, max_work should generally be set to a finite number, as it is possible to construct examples that take extremely long to run:

>>> from numpy.lib.stride_tricks import as_strided
>>> x = np.zeros([192163377], dtype=np.int8)
>>> x1 = as_strided(x, strides=(36674, 61119, 85569), shape=(1049, 1049, 1049))
>>> x2 = as_strided(x[64023025:], strides=(12223, 12224, 1), shape=(1049, 1049, 1))
>>> np.shares_memory(x1, x2, max_work=1000)
Traceback (most recent call last):
...
numpy.TooHardError: Exceeded max_work

Running np.shares_memory(x1, x2) without max_work set takes around 1 minute for this case. It is possible to find problems that take still significantly longer.

numpy.getbufsize numpy.may_share_memory