> > > -Travis > > > However, what is the timing/memory cost of converting a large numpy array > that already exists into python list of lists? If all my processing before > the munkres step is using NumPy, converting it into python lists has a cost. > Also, your timings indicate only ~2x slowdown, while the timing tests done by > eat show an order-of-magnitude difference. I suspect there is great room for > improvement before even starting to worry about the array access issues. >
If you are also doing scalar math with the results returned from NumPy array element access, then a.item(i,j) will be faster because it returns a Python object which will use it's scalar math instead of re-using vectorized math operations which a[i,j] will do. I haven't looked at the code yet, just re-emphasizing known issues with trying to use NumPy as an arbitrary container of "elements" rather than a container of bytes that you do vectorized operations on. -Travis
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
