On Sun, Jun 10, 2018 at 11:53 PM, Ralf Gommers <ralf.gomm...@gmail.com> wrote: > > On Sun, Jun 10, 2018 at 11:15 PM, Robert Kern <robert.k...@gmail.com> wrote: >> >> The intention of this code is to shuffle two same-length sequences in the >> same way. So now if I write my code well to call np.random.seed() once at >> the start of my program, this function comes along and obliterates that with >> a fixed seed just so it can reuse the seed again to replicate the shuffle. > > > Yes, that's a big no-no. There are situations conceivable where a library > has to set a seed, but I think the right pattern in that case would be > something like > > old_state = np.random.get_state() > np.random.seed(some_int) > do_stuff() > np.random.set_state(**old._state)
This will seem to work fine in testing, and then when someone tries to use your library in a multithreaded program everything will break in complicated and subtle ways :-(. I really don't think there's any conceivable situation where a library (as opposed to an application) can correctly use the global random state. -n -- Nathaniel J. Smith -- https://vorpus.org _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@python.org https://mail.python.org/mailman/listinfo/numpy-discussion
