Vincent Schut-2 wrote: > > Oh, and minor issue: creating a array of zeros and then multiplying with > -999 still makes an array of zeros... I'd incorporated an array of > *ones* multiplied with -999, because for the last chunk of days you > could end up with a 8day array only partly filled with real data. E.g. > if you'd have only 3 days of data left in your last chunk, 8dayData[0:3] > would be data, and to prevent the rest ([3:8]) to be incorporated into > the average calculation, these need to be -999. Currently these pixels > will be 0, which is > nodatavalue, and thus infuencing your average (the > pixelcount will be incremented for these 0 values). >
Ok I hadn't thought about it in that way but you are of course right! I have amended it. Vincent Schut-2 wrote: > > Alternatively, you could simply take the sum over axis=0 of the weight > array to get the pixel count (e.g. "pixelcount=weight.sum(axis=0)"). > Ok I see your point here as well. So I tried implementing your suggestion, as I understand it weights = data8days > nodatavalue will make and 8, nrows, ncols array containing true and false. as you said I can get the pixel count I was after by using weights.sum(axis=0). However when I try to do the averaging step: avg8days = np.average(data8days, axis=0, weights=weights) I get the error msg " in average raise ZeroDivisionError, "Weights sum to zero, can't be normalized" ZeroDivisionError: Weights sum to zero, can't be normalized" Which I guess (but don't know) comes from the trying do weight by a pixel count of zero. So not sure what has happened here? Thanks -- View this message in context: http://old.nabble.com/Help-making-better-use-of-numpy-array-functions-tp26503657p26529681.html Sent from the Numpy-discussion mailing list archive at Nabble.com. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
