What is the difference of putmask and where? As it seems the only difference is the inplace behavior. This becomes more and more complicated as we have this subtle difference at many places (ravel vs. flat) and in future also the augmented assignment stuff, which also works for arrays now, although I do not know, if it's really an inplace assignment. What are the next functions for which a spacesaving variant is introduced? Would it be better to come to another convention for this type of optimization? As a side note the order of arguments is different for putmask and where. __Janko Paul F. Dubois writes:

There is (in CVS) a new function, putmask:

c = greater(x, 0) putmask(y, c, v) putmask(z, c, u+2)

The documentation is now online. Briefly: putmask(a, m, v) sets a to v where m is true.

a must be a contiguous array m must be the same total size as a (shape ignored) v will be repeated as needed to that size

The underlying work is done in C.

-----Original Message----- From: numpy-discussion-admin@lists.sourceforge.net [mailto:numpy-discussion-admin@lists.sourceforge.net]On Behalf Of Daehyok Shin Sent: Friday, October 13, 2000 5:26 PM To: Numpy Discussion Subject: [Numpy-discussion] [Q]Best way for an array operation?

What is the best Numpy way for the following work?

for i in range(len(x)): if x[i] > 0: y[i] = v[i] z[i] = u[i]+2

Daehyok Shin (Peter)

_______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net http://lists.sourceforge.net/mailman/listinfo/numpy-discussion _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net http://lists.sourceforge.net/mailman/listinfo/numpy-discussion

-----Original Message----- ...snip what I want is minimum.reduce(a,1), except that the zeros would be ignored. At first glace, Masked arrays seemed the perfect option (as that's what the zero means, no real data), but there doesn't seem to be a minimum.reduce() in MA. --> I'll add this to my list. Meanwhile you could do something like this, where x is your array: y = MA.masked_equal(x,0,copy=0) Numeric.minimum.reduce(y.filled(100000), 1) If x is floating point use masked_value rather than masked_equal. --- By the way, what is the downside to Masked Arrays? they seem to be a really powerful option. Is there any performance hit to using a masked array over a regular onw if there is no mask? If not, would it make sense to move toward just using Masked arrays for everything? thanks, -Chris --> There are two problems. There is a hit, even in the case of no mask. It is hard to quantify because it depends on what operations you do. Much of that could be eliminated if MA was in C but it is Python. It would have driven me mad to write it in C, however. More seriously, there are a few semantic issues where it isn't clear what you mean if a mask is present.