I am wondering why numpy.zeros takes up such little space?
x = numpy.zeros(200000000)
This takes up no memory while,
x = numpy.repeat(0,200000000)
takes up around 1.5GB. Does numpy.zeros create an array of empty pointers?
If so, is there a way to set the pointer back to empty in the array after changing it in cython? If I use:
x = numpy.zeros(200000000) x[0:200000000] = 0.0
The memory usage goes way up. Is there a way to change a value, and then change it back to the format numpy.zeros originally had it in python or cython?
Are you using Linux? Linux has lazy allocation of memory. The underlying calls to
calloc in numpy always ‘succeed’. No memory is actually allocated until the memory is first accessed.
zeros function will use
calloc which zeros any allocated memory before it is first accessed. Therfore, numpy need not explicitly zero the array and so the array will be lazily initialised. Whereas, the
repeat function cannot rely on
calloc to initialise the array. Instead it must use
malloc and then copy the repeated to all elements in the array (thus forcing immediate allocation).