It gets even worse with data similar to those I will be using. With:
x0 = numpy.linspace(-1,1, 10000) niter = 2000 I get 24 seconds for option1 and 0.64 seconds for option2. Considering I expect between 5 and 50 times that number of iterations, the difference is already quite considerable. Armando Quoting Vicente Sole <s...@esrf.fr>: >>> ? Well a loop or list comparison seems like a good choice to me. It is >>> much more obvious at the expense of two LOCs. Did you profile the two >>> possibilities and are they actually performance-critical? >>> >>> cheers >>> > > > The second is between 8 and ten times faster on my machine. > > import numpy > import time > x0 = numpy.arange(10000.) > niter = 2000 # I expect between 10000 and 100000 > > > def option1(x, delta=0.2): > y = [x[0]] > for value in x: > if (value - y[-1]) > delta: > y.append(value) > return numpy.array(y) > > def option2(x, delta=0.2): > y = numpy.cumsum((x[1:]-x[:-1])/delta).astype(numpy.int) > i1 = numpy.nonzero(y[1:]> y[:-1]) > return numpy.take(x, i1) > > > t0 = time.time() > for i in range(niter): > t = option1(x0) > print "Elapsed = ", time.time() - t0 > t0 = time.time() > for i in range(niter): > t = option2(x0) > print "Elapsed = ", time.time() - t0 > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
