On Fri, Jul 18, 2008 at 12:03 PM, Charles R Harris < [EMAIL PROTECTED]> wrote: <snip>
> > > Simpler test case: > > import sys, gc > import numpy as np > > def main() : > t = np.dtype(np.float32) > print sys.getrefcount(t) > for i in range(100) : > np.float32() > gc.collect() > print sys.getrefcount(t) > > if __name__ == "__main__" : > main() > > Result > > [EMAIL PROTECTED] ~]$ python debug.py > 5 > 105 > > So there is a leak. The question is the proper fix. I want to take a closer > look at PyArray_Return and also float32() and relations. > The reference leak seems specific to the float32 and complex64 types called with default arguments. In [1]: import sys, gc In [2]: t = float32 In [3]: sys.getrefcount(dtype(t)) Out[3]: 4 In [4]: for i in range(10) : t(); ...: In [5]: sys.getrefcount(dtype(t)) Out[5]: 14 In [6]: for i in range(10) : t(0); ...: In [7]: sys.getrefcount(dtype(t)) Out[7]: 14 In [8]: t = complex64 In [9]: sys.getrefcount(dtype(t)) Out[9]: 4 In [10]: for i in range(10) : t(); ....: In [11]: sys.getrefcount(dtype(t)) Out[11]: 14 In [12]: t = float64 In [13]: sys.getrefcount(dtype(t)) Out[13]: 19 In [14]: for i in range(10) : t(); ....: In [15]: sys.getrefcount(dtype(t)) Out[15]: 19 This shouldn't actually leak any memory as these types are singletons, but it points up a logic flaw somewhere. Chuck
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