From: William Carithers <wccarithers@lb...>  20091201 03:46:53

Hi John, Yes, that is true if the data is truly gaussian. In my case, I know that the data have nongaussian tails which tend to dominate the calculation of the standard deviation. I should have been clearer in my post that what I actually wanted to do was fit a gaussian to the truncated "central" part of the distribution so that I was not so sensitive to the tails. A better statement of the problem is that I would like to fit a gaussian to the part of the data that I suspect is actually gaussian while ignoring the part that isn't. Unfortunately, if I calculate the standard deviation for the truncated distribution, then I will underestimate the "sigma" parameter of the gaussian needed to get a good fit. I'll take at the scipy.stats.norm . Thanks for your help. Bill On 11/30/09 7:22 PM, "John Hunter" <jdh2358@...> wrote: > On Mon, Nov 30, 2009 at 6:44 PM, William Carithers <wccarithers@...> > wrote: >> I would like to fit a gaussian to a histogram and then overplot it. I can >> write the code to do this but most plotting packages support such fitting. >> However I can't find it for pyplot even after scanning documentation, >> googling, etc. In fact, the only fitting functionality I could find was the >> polynomial fitting for numpy that is layered underneath matplotlib, i.e. >> Numpy.polyfit(...). >> >> Does anyone know if/how this might be built into matplotlib? > > For a Gaussian distribution, the best fit is provided by the normal > distribution which has the same mean and stddev as your empirical data > (this is not true in general for other distributions). Once you have > the mean and stddev from the data, you can use normpdf to plot the > analytic density  see for example > > http://matplotlib.sourceforge.net/search.html?q=normpdf > > For more powerful density fitting and sampling, see scipy.stats > functions, eg scipy.stats.norm.fit > > JDH 