Hi all, I'm wondering if you think the following behavior in numpy.clip is a bug (it certainly confused me for a while): >>> x = np.arange(5.) >>> xx = x.clip(None,3.) >>> xx array([0.0, 1.0, 2.0, 3.0, 3.0], dtype=object) Since xx now has the dtype of object, doing things like >>> np.exp(xx) AttributeError Traceback (most recent call last) <ipython-input-6-30aa315cc2b1> in <module>() ----> 1 np.exp(xx) Which, if you don't know about the change in the dtype is a very confusing error message. It seems to me that either clip should give an error message when None is given for the a_min argument or, better, should not change the dtype of the input array as it does. This comes up because I want to only clip the maximum. Of course you can clip the minimum by simply omitting the second argument. The asymmetry of this is not good, I think. I suppose that using the maximum function is better in this situation (as I recently found out), though the docs make it seem that one needs to supply two arrays -- even though you don't.
Jon -- ______________________________________________________________ Jonathan D. Slavin Harvard-Smithsonian CfA jsla...@cfa.harvard.edu 60 Garden Street, MS 83 phone: (617) 496-7981 Cambridge, MA 02138-1516 cell: (781) 363-0035 USA ______________________________________________________________ _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
