On Thu, 24 Apr 2008, Zachary Pincus wrote: > The only remaining mystery is how 'loc' and 'scale' -- the parameters of > numpy.random.normal -- map to 'mean' and 'standard deviation', which is > how a normal distribution is usually parameterized. Fortunately, the > documentation reveals this: > > >>> print numpy.random.normal.__doc__ > Normal distribution (mean=loc, stdev=scale). > > normal(loc=0.0, scale=1.0, size=None) -> random values
Zachary, Thank you. I looked in the printed manual, not the built-in docs. > If you need an alternate parameterization of the normal (e.g. in terms > of the y value of the inflection point), just solve that out > analytically from the definition of the normal in terms of mean and std. > > However, it looks like you're trying to plot the normal function, not > get random samples. Just evaluate the function (as above) at the x > positions: > > mean, std = (0, 1) > x = numpy.linspace(-10, 10, 200) # 200 evenly-spaced points from -10 > to 10 > y = 1/(std*numpy.sqrt(2*numpy.pi))*numpy.exp(-(x-mean)**2/(2*std**2)) OK. I'll work with this as well as the arange. Rich -- Richard B. Shepard, Ph.D. | Integrity Credibility Applied Ecosystem Services, Inc. | Innovation <http://www.appl-ecosys.com> Voice: 503-667-4517 Fax: 503-667-8863 _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
