LMFIT confidence interval uncertainty estimates error on python
<p>the output error is :
 MinimizerException: 
Cannot determine Confidence Intervals without sensible uncertainty estimates </p>

<p>Why I got this error? How can I calculate uncertainty estimates and solve this problem??</p>

<pre><code>for dosya1 in glob.glob("mean*"):
     data1=np.genfromtxt(dosya1, skip_header=0, skip_footer=0, names=["wavelength","mean"])
     x=data1["wavelength"]

     mod=VoigtModel()
     pars = mod.guess(y, x=x)
     pars['gamma'].set(value=0.7, vary=True, expr="")     
out=mod.fit(y,pars, x=x)

     pars=lmfit.Parameters()
     pars.add_many(('amp', out.params["amplitude"].value), ('sig', out.params["sigma"].value), ("gam",out.params["gamma"].value),("cent",out.params["center"].value))

 def residual(p):
     amp=p["amp"].value
     sig=p["sig"].value
     gam=p["gam"].value
     cent=p["cent"].value  
     return  ((wofz((x-cent + wofz(gam).imag)/(sig*(sqrt(2)))).real) / (sig*(sqrt(2))))- y

 mini = lmfit.Minimizer(residual, pars)
 result=mini.minimize()

 ci = lmfit.conf_interval(mini, result)
 lmfit.printfuncs.report_ci(ci)  
</code></pre>