Paulo van Breugel wrote: > And it seems to be the default behaviour by python/numpy:
It is, but ... > >>> import numpy as np > >>> np.random.random() > 0.8351426142559701 > >>> np.random.random() > 0.4813823441998394 > >>> np.random.random() > 0.7279314267025369 ... this example doesn't demonstrate that. Any PRNG returns different values for successive calls. The question is whether the PRNG's initial value should autmatically be seeded from some external source of entropy (e.g. the system clock), so that the sequence of values differs on different runs. In turn, that brings up questions about the quality of the entropy source. The ANSI C time() function typically only has one second granularity (indeed, POSIX requires this, as time_t is defined as seconds since the epoch), which is sufficiently course that successive runs may get the same seed. Other functions aren't portable, and even where available, the granularity isn't guaranteed. My main objection to automatic seeding is that people will inevitably produce non-repeatable results without even realising it. One possible solution would be to automatically add the seed to the history of any map generated by r.mapcalc (or possibly only those which use the rand() function). But that would still only help if the creator either provides access to the generated maps, or the output from r.info. Simply providing the commands used and the end result wouldn't help. -- Glynn Clements <gl...@gclements.plus.com> _______________________________________________ grass-dev mailing list grass-dev@lists.osgeo.org http://lists.osgeo.org/mailman/listinfo/grass-dev