Fit to function values with numpy/scipy

[Machiel Kolstein]

If I have an array with values, which are distributed according to a Gaussian function, then I can fit with: 
   (fit_mu, fit_sigma) = stats.norm.fit(x_array)

However, now, I have one array with values for the xbins (e.g., 0.0, 0.1, 0.2, 0.3, ..., up till 1.0) and one value for the corresponding y-value (e.g. 0.0, 0.3, 0.6, 1.2, 5.0, 10.0, 5.0, 1.2, 0.6, 0.3, 0.0).
(These values are just an example). 
Now I want to fit this, with a Gauss. So, obviously I don't want to fit over neither the values in xbins, nor the y_array (neither of which is normal distributed) but over the y values for each x bin. 
The only thing I can think of is looping over all bins, and then filling an artificial array: 

for i in range(0, Nbins): 
   x = xbinvalue(i)
   weight = y_value_for_this_x(x)
   for w in range(0, weight)
       numpy.vstack((tmp_array, x)
(fit_mu, fit_sigma) = scipy.stats.norm.fit(tmp_array)

But this seems a rather silly way of doing this. Is there an other way?

Cheers, 

Machiel   

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