If you are going to start a new topic, please start a new thread as well, instead of replying to a message. Thanks.
Vikalpa Jetly wrote: > I am reading a very large array (~9000,11000) of 1 byte image values. I need > to change values in the array that meet a certain condition so I am running > something like: > > b = numpy.where(a>200,0,1) > > to create a new array with the changed values. However, I get a > "MemoryError" everytime I try this. I have over 3gb of RAM on my machine > (most of which is available). The process runs fine on smaller datasets. Is > there a maximum array size that numpy handles? Any alternatives/workarounds? There is no predefined limit on the array size, just your memory. However, note that what you are doing here is creating an int32 (or int64 if you are on a 64-bit machine) array since you are using Python integers in your where() function. You can save quite a bit of memory by using the array from (a>200) and simply casting it to the uint8 type. That way, you only have two arrays in memory at any time, a and b. b = (a > 200).astype(numpy.uint8) -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco ------------------------------------------------------------------------- Take Surveys. Earn Cash. Influence the Future of IT Join SourceForge.net's Techsay panel and you'll get the chance to share your opinions on IT & business topics through brief surveys -- and earn cash http://www.techsay.com/default.php?page=join.php&p=sourceforge&CID=DEVDEV _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion
