shares_memory(a, b, /, max_work=None)
This function can be exponentially slow for some inputs, unless :None:None:`max_work` is set to a finite number or MAY_SHARE_BOUNDS
. If in doubt, use :None:None:`numpy.may_share_memory` instead.
Input arrays
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.
Exceeded max_work.
Determine if two arrays share memory.
>>> x = np.array([1, 2, 3, 4])This example is valid syntax, but we were not able to check execution
... np.shares_memory(x, np.array([5, 6, 7])) False
>>> np.shares_memory(x[::2], x) TrueThis example is valid syntax, but we were not able to check execution
>>> 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, :None:None:`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 :None:None:`max_work` set takes around 1 minute for this case. It is possible to find problems that take still significantly longer.
The following pages refer to to this document either explicitly or contain code examples using this.
numpy.may_share_memory
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