Excellent suggestions...just a few comments: Pierre GM wrote:

On Monday 23 April 2007 10:37:57 Mark.Miller wrote:

Greetings:

In some of my code, I need to use large matrix of random numbers that meet specific criteria (i.e., some random numbers need to be removed and replaces with new ones).

I have been working with .any() and .where() to facilitate this process.

Have you tried nonzero() ?

Nonzero isn't quite what I'm after, as the tests are more complicated than what I illustrated in my example.

a[a<0] = numpy.random.normal(0,1)

This is a neat construct that I didn't realize was possible. However, it has the undesirable (in my case) effect of placing a single new random number in each locations where a<0. While this could work, I ideally need a different random number chosen for each replaced value. Does that make sense?

will put a random number from the normal distribution where your initial a is negative. No Python loops needed, no Python temps.

Traceback (most recent call last): File "

", line 1, in <module> while (0 The double condition (00,a<1) or (a>0) & (a<1)

Note the () around each condition in case #2.

This makes perfect sense. Thanks for pointing it out to me. It should easily do the trick. Any and all additional suggestions are greatly appreciated, -Mark

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