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:
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
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