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Bug in numpy.where #369
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Hey, thought about looking a bit at numpy code... and while I don't quite understand it, I think I found the bug. Maybe instead of PyArray_EquivTypes adding check for ->byteorder or such makes more sense. Here is a Diff fixing the issue for me:
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@seberg, thank you for your patch. Would you mind reviewing my PR (395) above? I also wrote tests. |
This was referenced Oct 16, 2012
luyahan
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feat: Add vget[q]_lane_f64
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On a Linux machine:
this example shows a problem with the where function:
Python 2.7.1 (r271:86832, Dec 21 2010, 11:19:43)
[GCC 4.1.2 20080704 (Red Hat 4.1.2-48)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
import numpy as np
print np.version
1.5.1
net = np.zeros(3, dtype='>f4')
net[1] = 0.00458849
net[2] = 0.605202
max_net = net.max()
test = np.where(net <= 0., max_net, net)
print test
[ -2.23910537e-35 4.58848989e-03 6.05202019e-01]
When I specified the dtype for net as '>f8', test[0] was 3.46244974e+68. It worked as expected (i.e. test[0] should be 0.605202) when I specified float(max_net) as the second argument to np.where.
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