On Tue, Oct 6, 2009 at 11:33 PM, Pierre GM <pgmdevl...@gmail.com> wrote:
> > On Oct 7, 2009, at 12:10 AM, Gökhan Sever wrote: > > > Created the ticket http://projects.scipy.org/numpy/ticket/1253 > > Want even more confusion ? > >>> x = ma.array([1,2,3],mask=[0,1,0], dtype=int) > >>> x[0].dtype > dtype('int64') > >>> x[1].dtype > dtype('float64') > >>> x[2].dtype > dtype('int64') > > Yet another illustration of the masked constant... The more I think > about it, the more I think we should have a specific object > ("MaskedConstant") that would do nothing but tell us that it is masked. > Confusing indeed. One more from me: I[1]: a = np.arange(5) I[2]: mask = 999 I[6]: a[3] = 999 I[7]: am = ma.masked_equal(a, mask) I[8]: am O[8]: masked_array(data = [0 1 2 -- 4], mask = [False False False True False], fill_value = 999999) Where does this fill_value come from? To me it is little confusing having a "value" and "fill_value" in masked array method arguments. > > > > Could you tell me briefly what was the source of leak in arccos case? > > No idea, as I still haven't figured why you were having the problem in > the first place > Probably you can pin-point the error by testing a 1.3.0 version numpy. Not too many arc function with masked array users around I guess :) > > > And how do you write a test code for these cases? > > assert(np.arccos(ma.masked), ma.masked) would be the simplest. > Good to know this. The more I spend time with numpy the more I understand the importance of testing the code automatically. This said, I still find the test-driven-development approach somewhat bizarre. Start only by writing test code and keep implementing your code until all the tests are satisfied. Very interesting...These software engineers... > > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Gökhan
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