sonjaa wrote: > Serge Orlov wrote: > > sonjaa wrote: > > > Serge Orlov wrote: > > > > sonjaa wrote: > > > > > Hi > > > > > > > > > > I'm new to programming in python and I hope that this is the problem. > > > > > > > > > > I've created a cellular automata program in python with the numpy > > > > > array > > > > > extensions. After each cycle/iteration the memory used to examine and > > > > > change the array as determined by the transition rules is never freed. > > > > > I've tried using "del" on every variable possible, but that hasn't > > > > > worked. > > > > > > > > Python keeps track of number of references to every object if the > > > > object has more that one reference by the time you use "del" the object > > > > is not freed, only number of references is decremented. > > > > > > > > Print the number of references for all the objects you think should be > > > > freed after each cycle/iteration, if is not equal 2 that means you are > > > > holding extra references to those objects. You can get the number of > > > > references to any object by calling sys.getrefcount(obj) > > > > > > thanks for the info. I used this several variables/objects and > > > discovered that little counters i.e. k = k +1 have many references to > > > them, up tp 10000+. > > > Is there a way to free them? > > > > Although it's looks suspicious, even if you manage to free it you will > > gain only 12 bytes. I think you should concentrate on more fat > > objects ;) > > > Sent message to the NumPy forum as per Roberts suggestion. > An update after implimenting the suggestions: > > After doing this I see that iterative counters used to collect > occurrences > and nested loop counters (ii & jj) as seen in the code example below > are the culprits with the worst ones over 1M:
That means you have over 1M integers in your program. How did it happen if you're using numpy arrays? If I allocate a numpy array of one million bytes it is not using one million integers, whereas a python list of 1M integers creates 1M integers: >>> import numpy >>> a = numpy.zeros((1000000,), numpy.UnsignedInt8) >>> import sys >>> sys.getrefcount(0) 632 >>> b=[0]*1000000 >>> sys.getrefcount(0) 1000632 >>> But that doesn't explain why your program doesn't free memory. But the way, are you sure you have enough memory for one iteration of your program? -- http://mail.python.org/mailman/listinfo/python-list
