>> destructive + propagating = the data point is truly missing (satellite fell >> into >> the ocean; dog ate my source datasheet, or whatever), this is the nature of >> that >> data point, such missingness should be replicated in elementwise operations, >> and >> the missingness SHOULD interfere with reduction operations that involve that >> datapoint (np.sum([1,MISSING])=MISSING) > > Right. > > >> destructive + non-propagating = the data point is truly missing, this is the >> nature of that data point, such missingness should be replicated in >> elementwise >> operations, but such missingness should NOT interfere with reduction >> operations >> that involve that datapoint (np.sum([1,MISSING])=1) > > What do you define as element-wise operations? > > Is a sum on an array an element-wise operation? > > >>> [1, MISSING]+2 > [1, MISSING] > > Or is it just a form of reduction (after shape broadcasting)? > > >>> [1, MISSING]+2 > [3, 2] > > For me it's the second, so the only time where special values "propagate" in a > non-propagating scenario is when you slice an array.
Let's say I want to re-scale a column (or remove the mean from a column). I wouldn't want that to change my "missingness". Thus, I'm thinking: >>> x = [1,2,MISSING] >>> x*3 [3, 6, MISSING] >>> x = [1,2,MISSING] >>> x - x.mean() [-0.5, 0.5, MISSING] To me it makes sense to have identical operations for the temporary IGNORE case below (versus the permanent MISSING case here). Note, the reason to independently have separate IGNORE and MISSING is so that I can (for example) temporarily IGNORE entire rows in my 2D array (which may have scattered MISSING elements), and when I undo the IGNORE operation the MISSING elements are still MISSING. The question does still remain what to do when performing operations like those above in IGNORE cases. Perform the operation "underneath"? Or not? >> non-destructive + propagating = I want to ignore this datapoint for now; >> element-wise operations should replicate this "ignore" designation, and >> missingness of this type SHOULD interfere with reduction operations that >> involve >> this datapoint (np.sum([1,IGNORE])=IGNORE) > > Right. > > >> non-destructive + non-propagating = I want to ignore this datapoint for now; >> element-wise operations should replicate this "ignore" designation, but >> missingness of this type SHOULD NOT interfere with reduction operations that >> involve this datapoint (np.sum([1,IGNORE])=1) > > Same concerns as above. > > > Lluis > > The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
