John Hunter wrote: >>>>>> "Erin" == Erin Sheldon <[EMAIL PROTECTED]> writes: >>>>>> >>>>> > > Erin> The question I have been asking myself is "what is the > Erin> advantage of such an approach?". It would be faster, but by > > In the use case that prompted this message, the pull from mysql took > almost 3 seconds, and the conversion from lists to numpy arrays took > more that 4 seconds. We have a list of about 500000 2 tuples of > floats. > > Digging in a little bit, we found that numpy is about 3x slower than > Numeric here > > peds-pc311:~> python test.py > with dtype: 4.25 elapsed seconds > w/o dtype 5.79 elapsed seconds > Numeric 1.58 elapsed seconds > 24.0b2 > 1.0.1.dev3432 > > Hmm... So maybe the question is -- is there some low hanging fruit > here to get numpy speeds up? > > import time > import numpy > import numpy.random > rand = numpy.random.rand > > x = [(rand(), rand()) for i in xrange(500000)] > tnow = time.time() > y = numpy.array(x, dtype=numpy.float_) > tdone = time.time() > print 'with dtype: %1.2f elapsed seconds'%(tdone - tnow) > > tnow = time.time() > y = numpy.array(x) > tdone = time.time() > print 'w/o dtype %1.2f elapsed seconds'%(tdone - tnow) > > import Numeric > tnow = time.time() > y = Numeric.array(x, Numeric.Float) > tdone = time.time() > print 'Numeric %1.2f elapsed seconds'%(tdone - tnow) > > print Numeric.__version__ > print numpy.__version__ > >
I just adapted Numarray's version of array (using the fromlist method) to NumPy. This new change needs some testing as it is called in many, many ways. But, I think it should be right (all tests of numpy and scipy pass with it). With the change I get: with dtype: 0.22 elapsed seconds w/o dtype 5.02 elapsed seconds Numeric 7.38 elapsed seconds numarray 0.55 elapsed seconds 24.2 1.0.1.dev3437 1.5.1 -Travis _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
