2009/6/8 Robert Kern <robert.k...@gmail.com>: > On Mon, Jun 8, 2009 at 17:04, David Goldsmith<d_l_goldsm...@yahoo.com> wrote: >> >> I look forward to an instructive reply: the "Pythonic" way to do it would be >> to take advantage of the facts that Numpy is "pre-vectorized" and uses >> broadcasting, but so far I haven't been able to figure out (though I haven't >> yet really buckled down and tried real hard) how to broadcast a >> conditionally-terminated iteration where the number of iterations will vary >> among the array elements. Hopefully someone else already has. :-) > > You can't, really. What you can do is just keep iterating with the > whole data set and ignore the parts that have already converged. Here > is an example:
Well, yes and no. This is only worth doing if the number of problem points that require many iterations is small - not the case here without some sort of periodicity detection - but you can keep an array of not-yet-converged points, which you iterate. When some converge, you store them in a results array (with fancy indexing) and remove them from your still-converging array. It's also worth remembering that the overhead of for loops is large but not enormous, so you can often remove only the inner for loop, in this case perhaps iterating over the image a line at a time. Anne > > z = np.zeros((201,201), dtype=complex) > Y, X = np.mgrid[1:-1:-201j, -1.5:0.5:201j] > c = np.empty_like(z) > c.real = X > c.imag = Y > N = np.zeros(z.shape, dtype=int) > > while ((N<30) | (abs(z)<2)).all(): > N += abs(z) < 2 > z = z ** 2 + c > > N[N>=30] = 0 > > -- > Robert Kern > > "I have come to believe that the whole world is an enigma, a harmless > enigma that is made terrible by our own mad attempt to interpret it as > though it had an underlying truth." > -- Umberto Eco > _______________________________________________ > 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
