On Wed, Nov 3, 2010 at 3:54 AM, Vincent Schut <sc...@sarvision.nl> wrote:
> 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. > > The problem kicks in with an array of ones of size 2**24. Note that np.float32(2**24) + np.float32(1.0) equals np.float32(2**24): In [41]: b = np.ones(2**24, np.float32) In [42]: b.size, b.sum() Out[42]: (16777216, 16777216.0) In [43]: b = np.ones(2**24+1, np.float32) In [44]: b.size, b.sum() Out[44]: (16777217, 16777216.0) In [45]: np.spacing(np.float32(2**24)) Out[45]: 2.0 In [46]: np.float32(2**24) + np.float32(1) Out[46]: 16777216.0 Warren
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