On Wed, Jun 17, 2015 at 11:13 PM, Nathaniel Smith <njs@pobox.com> wrote:
there's some argument that in Python, doing explicit type checks like this is usually a sign that one is doing something awkward,
I tend to agree with that. On the other hand, numpy itself is kind-of sort-of statically typed. But in that case, if you need to know the type of an array -- check the array's dtype. Also:
a = np.zeros(7, int) n = a[3] type(n) <type 'numpy.int64'>
I Never liked declaring numpy arrays with the python types like "int" or "float" -- in numpy you usually care more about the type, so should simple use "int64" if you want a 64 bit int. And "float64" if you want a 64 bit float. Granted, pyton floats have always been float64 (on all platfroms??), and python ints used to a be a reasonable int type, but now that python ints are bigInts in py3, it really makes sense to be clear. And now that I think about it, in py2, int is 32 bit on win64 and 64 bit on *nix64 -- so you're really better off being explicit with your numpy arrays. -CHB -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/OR&R (206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception Chris.Barker@noaa.gov