On Sat, Jun 8, 2013 at 7:31 PM, <[email protected]> wrote: > 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.
browsing the numpy 1.5 documentation for functions starting with f (like fill), I found numpy.find_common_type which looks similar to `result_type` Josef > > 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
