On Wed, Nov 3, 2010 at 6:39 AM, Vincent Schut <sc...@sarvision.nl> wrote:
> > > On 11/03/2010 12:31 PM, Warren Weckesser wrote: > > > > > > On Wed, Nov 3, 2010 at 5:59 AM, Warren Weckesser > > <warren.weckes...@enthought.com <mailto:warren.weckes...@enthought.com>> > > wrote: > > > > > > > > On Wed, Nov 3, 2010 at 3:54 AM, Vincent Schut <sc...@sarvision.nl > > <mailto: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 > > > > > > > > > > By the way, you can override the dtype of the accumulator of the mean() > > function: > > > > In [61]: a = np.ones((11334,16002),np.float32) > > > > In [62]: a.mean() # Not correct > > Out[62]: 0.092504406598019437 > > > > In [63]: a.mean(dtype=np.float64) > > Out[63]: 1.0 > > Thanks for this. That at least gives me a temporary solution (I actually > need sum() instead of mean(), but the trick works for sum too). > sum() also has the dtype argument. Warren
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