Re: [Numpy-discussion] numpy log and exceptions
Julian Taylor googlemail.com> writes: > What platform are you using? > whether you get exceptions or not depends on your math library. > Windows 7. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] numpy log and exceptions
I would have thought that this snippet would raise an exception: import numpy numpy.seterr(all='raise') a = numpy.array([1.0,0.0,-1.0]) b = numpy.log(a) I get as a result (in b): [0, -Inf, NaN] It's basically the same issue as: http://numpy-discussion.10968.n7.nabble.com/numpy-log-does-not-raise- exceptions-td5854.html Except that I have explicitly set the error flags to raise exceptions. It works fine for sqrt(), but not for log(). I've checked numpy 1.4.0 and 1.7.1 and both have the same behavior. Is there a way to force the log (and log10) function to raise an exception on invalid input? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Bug in pickling an ndarray?
Thanks Travis and Robert for the clarification; it is much more clear what is going on now. As the demo code shows, also a.flags['OWNDATA'] is different on its way out of the pickle; which also makes sense now. So using that flag instead of checking a.base for None is equivalent, at least in this situation. So is it a bug, then, that, on Windows, .base is set to None (of course, this may be something that was fixed in later versions of numpy; I was only able to test Windows with numpy 1.4.1). I'll just make a copy and discard the original to work around the situation (which is what I already had done, but the inconsistent behavior across versions and platforms made me think it was a bug). Thanks again for the clear explanation of what is going on. On Sat, Jun 30, 2012 at 6:33 PM, Daniel Hyams wrote: > Hmmm, I wouldn't think that it is correct behavior; I would think that > *any* ndarray arising from pickling would have its .base attribute set to > None. If not, then who is really the one that owns the data? > > It was my understanding that .base should hold a reference to another > ndarray that the data is really coming from, or it's None. It certainly > shouldn't be some random string, should it? > > And yes, it is causing a problem for me, which is why I noticed it. In my > application, ndarrays can come from various sources, pickling being one of > them. Later in the app, I was wanting to resize the array, which you > cannot do if the data is not really owned by that array...I had explicit > check for myarray.base==None, which it is not when I get the ndarray from a > pickle. > > > -- > Daniel Hyams > dhy...@gmail.com > -- Daniel Hyams dhy...@gmail.com ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Bug in pickling an ndarray?
Hmmm, I wouldn't think that it is correct behavior; I would think that *any* ndarray arising from pickling would have its .base attribute set to None. If not, then who is really the one that owns the data? It was my understanding that .base should hold a reference to another ndarray that the data is really coming from, or it's None. It certainly shouldn't be some random string, should it? And yes, it is causing a problem for me, which is why I noticed it. In my application, ndarrays can come from various sources, pickling being one of them. Later in the app, I was wanting to resize the array, which you cannot do if the data is not really owned by that array...I had explicit check for myarray.base==None, which it is not when I get the ndarray from a pickle. -- Daniel Hyams dhy...@gmail.com ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Bug in pickling an ndarray?
I am having trouble pickling (and then unpickling) an ndarray. Upon
unpickling, the "base" attribute of the ndarray is set to some very strange
string ("base" was None when the ndarray was pickled, so it should remain
None).
I have tried on various platforms and versions of numpy, with inconclusive
results:
# tested: Linux (Suse 11.1), numpy 1.5.1 BUG
# Linux (Suse 11,0), numpy 1.6.1 OK
# Linux (Mint Debian), numpy 1.6.1 BUG
# Linux (Mint Debian), numpy 1.6.2 BUG
# OSX (Snow Leopard), numpy 1.5.1rc1 BUG
# OSX (Snow Leopard), numpy 1.6.2 BUG
# Windows 7, numpy 1.4.1 OK
I have attached a script below that can be used to check for the problem; I
suppose that this is a bug report, unless I'm doing something terribly
wrong or my expectations for the base attribute are off.
cut here -
# this little demo shows a problem with the base attribute of an ndarray,
when
# pickling. Before pickling, dset.base is None, but after pickling, it is
some
# strange string.
import cPickle as pickle
import numpy
print numpy.__version__
#import pickle
dset = numpy.ones((2,2))
print "BEFORE PICKLING"
print dset
print "base = ",dset.base
print dset.flags
# pickle.
s = pickle.dumps(dset)
# now unpickle.
dset = pickle.loads(s)
print "AFTER PICKLING AND THEN IMMEDIATELY UNPICKLING"
print dset
print "base = ",dset.base
print dset.flags
--
Daniel Hyams
dhy...@gmail.com
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] Taking a large number of dot products at once
This is probably so easy, I'm embarrassed to ask it...but I've been casting around trying things to no avail for the last hour and a half, so here goes... I have a lot of dot products to take. The length-3 vectors that I want to dot are stacked in a 2D array like this: U = [u1 u2 u3] and V = [v1 v2 v3] So both of these arrays, are, say, 3x100 each. I just want to take the dot product of each of the corresponding vectors, so that the result is [u1.v1 u2.v2 u3.v3 ] which would be a 1x100 array in this case. Which function do I need to use? I thought tensordot() was the one, but I couldn't make it workpure user error I'm sure. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
