On Mon, Apr 18, 2011 at 17:08, Russell E. Owen <ro...@uw.edu> wrote: > I stumbled across code that looks like this: > > imageArr = # a 2-d array of floats > noiseArr = numpy.random.poisson(imageArr) > > This works fine in numpy 1.5.1 and seems to do what I would hope: return > an array of random ints whose "expectation of interval" is set by the > corresponding element of the input array. Very nice! > > However, I can't find any documentation supporting this usage. The > standard help says: > > poisson(lam=1.0, size=None) > > Draw samples from a Poisson distribution. > > The Poisson distribution is the limit of the Binomial > distribution for large N. > > Parameters > ---------- > lam : float > Expectation of interval, should be >= 0. > size : int or tuple of ints, optional > Output shape. If the given shape is, e.g., ``(m, n, k)``, then > ``m * n * k`` samples are drawn. > > Which suggest that Iam must be a scalar. > > So... is the usage of passing in an array for Iam actually > supported/safe to use?
Yes, it is safe. All of the univariate distribution parameters will broadcast against each other as they would with ufuncs. I don't think we do it for multinomial and a couple of the other ones where the semantics would have made my head hurt. > And is there some general rule I could have used to predict that? Just the one I stated, which is limited to numpy.random. It might even be documented somewhere. Unfortunately, most of the individual methods had their parameters documented before this capability was added. -- Robert Kern "I have come to believe that the whole world is an enigma, a harmless enigma that is made terrible by our own mad attempt to interpret it as though it had an underlying truth." -- Umberto Eco _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
