Am Donnerstag, 28. Juli 2011, 17:42:38 schrieb Matthew Brett: > If I understand you correctly, the problem is that, for 1.5.1: > >>> class Test(np.ndarray): pass > >>> type(np.min(Test((1,)))) > > <type 'numpy.float64'> > > and for 1.6.0 (and current trunk): > >>> class Test(np.ndarray): pass > >>> type(np.min(Test((1,)))) > > <class '__main__.Test'>
Correct. This is the behavior change we're talking (/complaining) about. > So, 1.6.0 is returning a zero-dimensional scalar of the given type, > and 1.5.1 returns a python scalar. I am not sure about the terminology; I would have said that 1.5.1 returns a "numpy array scalar" (as opposed to a built-in python scalar), but at least we agree on the /scalar/ part here. OTOH, 1.6.0 does not return a zero-dimensional /scalar/, but a zero-rank /array/, which may be used to represent scalars in some contexts. But IMO this makes array subclassing much harder, since you need to special-case zero- dimensional arrays in all your member functions. IOW, it is hard to make a zero-dimensional instance of your array subclass "quack" like a scalar. And I do not see any need / reason for the above change. Right now, I found http://projects.scipy.org/numpy/wiki/ZeroRankArray which is an interesting read in this context, but does not seem to mention this change (age of that page is 5-6 years). > Zero dimensional scalars are designed to behave in a similar way to > python scalars, so the change should be all but invisible in practice. Now I am not sure if you're confusing something. I do not complain about min returning a numpy.float64 instead of a float, but about numpy wrapping the scalar in an instance of my *array* subclass. This is also inconsistent with ndarray, and the matrix class contains an explicit workaround, returning self[0,0] in cases where a scalar is expected: class matrix(N.ndarray): [...] def min(self, axis=None, out=None): """...""" return N.ndarray.min(self, axis, out)._align(axis) def _align(self, axis): """A convenience function for operations that need to preserve axis orientation. """ if axis is None: return self[0,0] <=== HERE elif axis==0: return self elif axis==1: return self.transpose() else: raise ValueError, "unsupported axis" > Was there a particular case you ran into where this was a problem? Yes, but it is not easy to reproduce for me right now (changed versions of numpy, our software, etc.). Basically, the problem arose because our ndarray subclass does not support zero-rank-instances fully. (And previously, there was no need for that.) Have a nice day, Hans _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
