On 18 October 2012 03:34, Simon Lieschke <[email protected]> wrote: > I've discovered calling numpy.arange(1.1, 17.1) and numpy(1.1, 16.1) both > return the same results. Could this be a numpy bug, or is there some > behaviour I'm possibly not aware of here?
Not a bug, it's because you're using floating point arguments. The docstring (http://docs.scipy.org/doc/numpy/reference/generated/numpy.arange.html) tells you that "For floating point arguments, the length of the result is ceil((stop - start)/step)". If you try In [1]: (17.1-1.1)/1.0 Out[1]: 16.0 In [2]: (16.1-1.1)/1.0 Out[2]: 15.000000000000002 In [3]: np.ceil((17.1-1.1)/1.0) Out[3]: 16.0 In [4]: np.ceil((16.1-1.1)/1.0) Out[4]: 16.0 you see that the length of the output array ends up being the same due to floating point round-off effects. You can achieve what you want using np.linspace(1.1, 17.1, num=17) etc.. Cheers, Scott _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
