On May 3, 6:22 am, Charles Sanders <[EMAIL PROTECTED]> wrote: > y = a*b+c*d # Returns a proxy object > > x = y[4] # Computes x = a[4]*b[4] + c[4]*d[4] > > v = y.eval() # Evaluates all elements, returning Xarray > > z = ((a+b)*(c+d)).eval() # Also evaluates all elements
When I suggested this on the NumPy mailing list, I too suggested using the indexing operator to trigger the computations. But I am worried that if an expression like y = a*b+c*d returns a proxy, it is somehow possible to mess things up by creating cyclically dependent proxies. I may be wrong about this, in which case __getitem__ et al. will do the job. > Whether it would be any faster is doubtful, but it would eliminate > the temporaries. The Numexpr compiler in SciPy suggests that it can. It parses an expression like 'y = a*b+c*d' and evaluates it. Numexpr is only a slow prototype written in pure Python, but still it can sometimes give dramatical speed-ups. Here we do not even need all the machinery of Numexpr, as Python creates the parse tree on the fly. Inefficiency of binary operators that return temporary arrays is mainly an issue when the arrays in the expression is too large to fit in cache. RAM access can be very expensive, but cache access is usually quite cheap. One also avoids unnecessary allocation and deallocation of buffers to hold temporary arrays. Again, it is mainly an issue when arrays are large, as malloc and free can be rather efficient for small objects. -- http://mail.python.org/mailman/listinfo/python-list
