[Numpy-discussion] How to remove fortran-like loops with numpy?

[Robert Kern]

On Mon, Jun 8, 2009 at 17:04, David Goldsmith<d_l_goldsmith at 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