Crash in visualization about iminuit HOT 4 CLOSED

Thanks for reporting, that is indeed a bug. I will see how this can be fixed. A temporary workaround for you is

# Minuit fit
p0 = np.array([2., 2., 0., -1., -1.5])
mFit = LeastSquares(x, y, ey, fitFun)
mFit.visualize = lambda x, *args: None
m = Minuit(mFit, p0)  # pass starting values
m.migrad()
# m.hesse()

from iminuit.

If you want to fit a smooth function $f(x)$ through your data points under the condition $f(x) \ge 0$, you can also use Bernstein polynomials. They are guaranteed to be positive inside the domain, if all the parameters are positive. You cannot safely extrapolate Bernstein polynomials outside their domain, though. If you need to query the function outside the fitted range, make the domain of the polynomial as large as you need to query it.

from numba_stats import bernstein

def model(x, p):
    domain_of_polynomial = (-0.4, 0.4)
    return bernstein.density(x, p, *domain_of_polynomial)

start = np.ones(4)  # increase this to increase flexibility of Bernstein polynomial
m = Minuit(LeastSquares(x, y, ey, model), start) 
m.limits = (0, None)  # ensure positiveness
m.migrad()

from iminuit.

Yes I know :)
We are working with people that like splines (I dont), so here we are....
C.

from iminuit.

I like splines, too :). Bernstein polynomials are a special case of splines.

from iminuit.