From: Sebastian H. <ha...@ms...> - 2004-07-02 15:27:11
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On Tuesday 29 June 2004 05:05 pm, Sebastian Haase wrote: > Hi, > > Is this a bug?: > >>> # (import numarray as na ; 'd' is a 3 dimensional array) > >>> d.type() > > Float32 > > >>> d[80, 136, 122] > > 80.3997039795 > > >>> na.maximum.reduce(d[:,136, 122]) > > 85.8426361084 > > >>> na.maximum.reduce(d) [136, 122] > > 37.3658103943 > > >>> na.maximum.reduce(d,0)[136, 122] > > 37.3658103943 > > >>> na.maximum.reduce(d,1)[136, 122] > > Traceback (most recent call last): > File "<input>", line 1, in ? > IndexError: Index out of range > > I was using na.maximum.reduce(d) to get a "pixelwise" maximum along Z > (axis 0). But as seen above it does not get it right. I then tried to > reproduce > > this with some simple arrays, but here it works just fine: > >>> a = na.arange(4*4*4) > >>> a.shape=(4,4,4) > >>> na.maximum.reduce(a) > > [[48 49 50 51] > [52 53 54 55] > [56 57 58 59] > [60 61 62 63]] > > >>> a = na.arange(4*4*4).astype(na.Float32) > >>> a.shape=(4,4,4) > >>> na.maximum.reduce(a) > > [[ 48. 49. 50. 51.] > [ 52. 53. 54. 55.] > [ 56. 57. 58. 59.] > [ 60. 61. 62. 63.]] > > > Any hint ? > > Regards, > Sebastian Haase Hi again, I think the reason that no one responded to this is that it just sounds to unbelievable ... Sorry for the missing piece of information, but 'd' is actually a memmapped array ! >>> d.info() class: <class 'numarray.numarraycore.NumArray'> shape: (80, 150, 150) strides: (90000, 600, 4) byteoffset: 0 bytestride: 4 itemsize: 4 aligned: 1 contiguous: 1 data: <MemmapSlice of length:7290000 readonly> byteorder: big byteswap: 1 type: Float32 >>> dd = d.copy() >>> na.maximum.reduce(dd[:,136, 122]) 85.8426361084 >>> na.maximum.reduce(dd)[136, 122] 85.8426361084 >>> Apparently we are using memmap so frequently now that I didn't even think about that - which is good news for everyone, because it means that it works (mostly). I just see that 'byteorder' is 'big' - I'm running this on an Intel Linux PC. Could this be the problem? Please some comments ! Thanks, Sebastian |