On Thu, Apr 18, 2013 at 10:04 PM, K.-Michael Aye <kmichael.aye@gmail.com> wrote:
On 2013-04-19 01:02:59 +0000, Benjamin Root said:
So why is there an error in the 2nd case, but no error in the first case? Is there a logic to it?
When you change a dtype like that in the first one, you aren't really upcasting anything. You are changing how numpy interprets the underlying bits. Because you went from a 32-bit element size to a 64-bit element size, you are actually seeing the double-precision representation of 2 of your original data points together.
I was wondering what would happen if there were not the right number of points available... In [225]: a = np.array((2.0, 3.0), dtype=np.float32) In [226]: a.dtype=np.float64 In [227]: a Out[227]: array([ 32.00000763]) OK , but: In [228]: a = np.array((2.0,), dtype=np.float32) In [229]: a.dtype=np.float64 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-229-0d494747aee1> in <module>() ----> 1 a.dtype=np.float64 ValueError: new type not compatible with array. so numpy is smart enough to not let you do it .. good thing. Final note -- changing the dtype in place like that is a very powerful and useful tool, but not likely to be used often -- it's really for things like working with odd binary data and the like. -Chris -- 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