On 5/17/07, Anne Archibald <[EMAIL PROTECTED]> wrote:
Hi, Numpy has a max() function. It takes an array, and possibly some extra arguments (axis and default). Unfortunately, this means that >>> numpy.max(-1.3,2,7) -1.3 This can lead to surprising bugs in code that either explicitly expects it to behave like python's max() or implicitly expects that by doing "from numpy import max". I don't have a *suggestion*, exactly, for how to deal with this; checking the type of the axis argument, or even its value, when the first argument is a scalar, will still let some bugs slip through (e.g., max(-1,0)). But I've been bitten by this a few times even once I noticed it. Is there anything reasonable to do about this, beyond conditioning oneself to use amax?
I don't know what a good fix is, but I got bitten by that one the other day too. --bb
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