numpy.shares_memory(a, b, /, max_work=-1)
Determine if two arrays share memory.
Warning
This function can be exponentially slow for some inputs, unless max_work is set to zero or a positive integer. If in doubt, use
instead.
Parameters:a, bndarrayInput arrays
max_workint, optionalEffort to spend on solving the overlap problem (maximum number of candidate solutions to consider). The following special values are recognized:
max_work=-1 (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=0Only the memory bounds of a and b are checked. This is equivalent to using may_share_memory().
Returns:outboolRaises:numpy.exceptions.TooHardErrorExceeded max_work.
Examples
>>> importnumpyasnp>>> 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])FalseChecking 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:
>>> fromnumpy.lib.stride_tricksimportas_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.exceptions.TooHardError: Exceeded max_workRunning 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.