What do you mean by "efficient"? Are you trying to get it execute faster? Or using less memory? Or have more concise source code?
Less memory: - numpy.vectorize would let you get to the end result without any intermediate arrays but will be slow. - Using the "out" parameter of numpy.logical_and will let you avoid one of the intermediate arrays. More speed?: Perhaps putting all three boolean temporary results into a single boolean array (using the "out" parameter of numpy.greater, etc) and using numpy.all might benefit from logical short-circuiting. And watch out for divide-by-zero from "aNirChannel/aBlueChannel". Regards, Richard Hattersley On 19 March 2012 11:04, Matthieu Rigal <ri...@rapideye.net> wrote: > Dear Numpy fellows, > > I have actually a double question, which only aims to answer a single one : > how to get the following line being processed more efficiently : > > array = numpy.logical_and(numpy.logical_and(aBlueChannel < 1.0, aNirChannel > > (aBlueChannel * 1.0)), aNirChannel < (aBlueChannel * 1.8)) > > One possibility would have been to have the logical_and being able to handle > more than two arrays > > Another one would have been to be able to make a "double comparison" or a > "between", like following one : > > array = numpy.logical_and((aBlueChannel < 1.0), (1.0 < > aNirChannel/aBlueChannel < 1.8)) > > Is there any way to get the things work this way ? Would it else be a possible > improvement for 1.7 or a later version ? > > Best Regards, > Matthieu Rigal > > RapidEye AG > Molkenmarkt 30 > 14776 Brandenburg an der Havel > Germany > > Follow us on Twitter! www.twitter.com/rapideye_ag > > Head Office/Sitz der Gesellschaft: Brandenburg an der Havel > Management Board/Vorstand: Ryan Johnson > Chairman of Supervisory Board/Vorsitzender des Aufsichtsrates: > Robert Johnson > Commercial Register/Handelsregister Potsdam HRB 24742 P > Tax Number/Steuernummer: 048/100/00053 > VAT-Ident-Number/Ust.-ID: DE 199331235 > DIN EN ISO 9001 certified > > > _______________________________________________ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
