Hi, I'm running in this strange issue when using some pretty large float32 arrays. In the following code I create a large array filled with ones, and calculate mean and sum, first with a float64 version, then with a float32 version. Note the difference between the two. NB the float64 version is obviously right :-)
In [2]: areaGrid = numpy.ones((11334, 16002)) In [3]: print(areaGrid.dtype) float64 In [4]: print(areaGrid.shape, areaGrid.min(), areaGrid.max(), areaGrid.mean(), areaGrid.sum()) ((11334, 16002), 1.0, 1.0, 1.0, 181366668.0) In [5]: areaGrid = numpy.ones((11334, 16002), numpy.float32) In [6]: print(areaGrid.dtype) float32 In [7]: print(areaGrid.shape, areaGrid.min(), areaGrid.max(), areaGrid.mean(), areaGrid.sum()) ((11334, 16002), 1.0, 1.0, 0.092504406598019437, 16777216.0) Can anybody confirm this? And better: explain it? Am I running into a for me till now hidden ieee float 'feature'? Or is it a bug somewhere? Btw I'd like to use float32 arrays, as precision is not really an issue in this case, but memory usage is... This is using python 2.7, numpy from git (yesterday's checkout), on arch linux 64bit. Best, Vincent. _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
