I am looking to replace a number with NaN in numpy and am looking for a function like numpy.nan_to_num, except in reverse. The number is likely to change as different arrays are processed because each can have a uniquely define NoDataValue. I have seen people using dictionaries, but the arrays are large and filled with both positive and negative floats. I suspect that it is not efficient to try to load all of these into anything to create keys. I tried using the following but numpy requires that I use any() or all(). I realize that I need to iterate element wise, but hope that a built-in function can achieve this. def replaceNoData(scanBlock, NDV): for n, i in enumerate(array): if i == NDV: scanBlock[n] = numpy.nan NDV is GDAL's no data value and array is a numpy array. Is a masked array the way to go perhaps? Continue reading...
