On Sat, Jun 8, 2013 at 7:05 PM, Sebastian Berg <[email protected]> wrote: > On Sat, 2013-06-08 at 08:52 -0400, [email protected] wrote: >> Is there anything to require a numpy array with a minimum numeric dtype? >> >> To avoid lower precision calculations and be upwards compatible, something >> like >> >> x = np.asarray(x, >=np.float64) > > np.result_type(arr, np.float64) uses the usual numpy promotion rules. > But it doesn't do the "asarray" part. Its still the closest thing I can > think of right now.
Thank you, that looks close enough And I will soon switch to numpy 1.6 and can read up on some old What's new. Josef > > - Sebastian > >> >> that converts ints, bool and lower precision to float64 but leaves >> higher precision float and complex double alone. >> >> >> Josef >> _______________________________________________ >> NumPy-Discussion mailing list >> [email protected] >> http://mail.scipy.org/mailman/listinfo/numpy-discussion >> > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
