[email protected] wrote: > On Fri, Jun 5, 2009 at 1:48 AM, Robert Cimrman <[email protected]> wrote: >> [email protected] wrote: >>> On Thu, Jun 4, 2009 at 4:30 PM, Gael Varoquaux >>> <[email protected]> wrote: >>>> On Thu, Jun 04, 2009 at 10:27:11PM +0200, Kim Hansen wrote: >>>>> "in(b)" or "in_iterable(b)" method, such that you could do a.in(b) >>>>> which would return a boolean array of the same shape as a with >>>>> elements true if the equivalent a members were members in the iterable >>>>> b. >>>> That would really by what I would be looking for. >>>> >>> Just using "in" might promise more than it does, eg. it works only for >>> one dimensional arrays, maybe "in1d". With "in", I would expect a >>> generic function as in python that works with many array types and >>> dimensions. (But I haven't checked whether it would work with a 1d >>> structured array or object array.) >>> >>> I found arraysetops because of unique1d, but I didn't figure out what >>> the subpackage really does, because I was reading "arrayse-tops" >>> instead of array-set-ops" >> I am bad in choosing names, but note that numpy sub-modules usually do >> not use underscores, so array_set_ops would not fit well. > > I would have chosen something like setfun. Since this is in numpy > that sets refers to arrays should be implied.
Yes, good idea. I am not sure how to proceed, if people agree (name contest is open!) What about making an alias name setfun, and deprecate the name arraysetops? >>> BTW, for the docs, I haven't found a counter example where >>> np.setdiff1d gives the wrong answer for non-unique arrays. >> In [4]: np.setmember1d( [1, 1, 2, 4, 2], [3, 2, 4] ) >> Out[4]: array([ True, False, True, True, True], dtype=bool) > > setdiff1d diff not member > Looking at the source, I think setdiff always works even if for > non-unique arrays. Whoops, sorry. setdiff1d seems really to work for non-unique arrays - it relies on the behaviour above though :) - there is always one correct False even for repeated entries in the first array. r. _______________________________________________ Numpy-discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
