Update PeakOTron.py

PeakOTron.py

@@ -1692,7 +1692,7 @@ class PeakOTron:
         
        
         minuit_lin_var = Minuit(fit_lin_var, **fit_dict_lin_var)
-        
+        minuit_lin_var.limits["m"] = (self._eps, None)
         
         
         
@@ -1725,7 +1725,7 @@ class PeakOTron:
         
         minuit_lin_G = Minuit(fit_lin_G, **fit_dict_lin_G)
         
-        
+        minuit_lin_G.limits["m"] = (self._eps, None)
         minuit_lin_G.strategy=2
         
         minuit_lin_G.migrad(ncall= self._n_call_minuit,
@@ -1736,8 +1736,6 @@ class PeakOTron:
         
 
             
-        if((minuit_lin_var.values["m"]>self._eps) and minuit_lin_var.valid):
-            
         self._GD_data["fit_var0"] = minuit_lin_var.values["c"]
         self._GD_data["fit_var0_err"] = minuit_lin_var.errors["c"]
         self._GD_data["fit_var1"] =  minuit_lin_var.values["m"]
@@ -1748,34 +1746,13 @@ class PeakOTron:
         self._GD_data["fit_sigma0_err"] = (0.5/self._GD_data["fit_sigma0"])*self._GD_data["fit_var0_err"]
         self._GD_data["fit_sigma1"] =  np.sqrt(self._GD_data["fit_var1"])
         self._GD_data["fit_sigma1_err"] = (0.5/self._GD_data["fit_sigma1"])*self._GD_data["fit_var1_err"]
-        else:
-            if(self._verbose):
-                print("Linear fit to peak variance returned invalid. Using basic estimated parameters instead...")
-        
-            self._GD_data["fit_sigma0"] =  self._peak_data["x_width_s"][0]*self._FWHM2Sigma
-            self._GD_data["fit_sigma0_err"] = 0.1*self._GD_data["fit_sigma0"]
-            self._GD_data["fit_sigma1"] =  abs(np.mean(np.diff(self._peak_data["x_width_s"]*self._FWHM2Sigma)/np.diff(self._peak_data["n_peak_s"])))
-            self._GD_data["fit_sigma1_err"] =  0.1*self._GD_data["fit_sigma1"]
 
-            self._GD_data["fit_var0"] = self._GD_data["fit_sigma0"]**2
-            self._GD_data["fit_var0_err"] = 2*self._GD_data["fit_sigma0"]*self._GD_data["fit_sigma0_err"]
-            self._GD_data["fit_var1"] =  self._GD_data["fit_sigma1"]**2
-            self._GD_data["fit_var1_err"] =  2*self._GD_data["fit_sigma1"]*self._GD_data["fit_sigma1_err"]
 
- 
-        
-        if((minuit_lin_G.values["m"]>self._eps) and minuit_lin_G.valid):
         self._GD_data["fit_G"] = minuit_lin_G.values["m"]
         self._GD_data["fit_G_err"] = minuit_lin_G.errors["m"]
         self._GD_data["fit_x_0"] = minuit_lin_G.values["c"]
         self._GD_data["fit_x_0_err"] = minuit_lin_G.errors["c"]
-        else:
-            if(self._verbose):
-                print("Linear fit to peak mean returned invalid. Using basic estimated parameters instead...")
-            self._GD_data["fit_G"] = self._GD_data["fit_G"]
-            self._GD_data["fit_G_err"] = self._GD_data["fit_G_err"]
-            self._GD_data["fit_x_0"] =  self._GD_data["fit_x_0"]
-            self._GD_data["fit_x_0_err"] = self._GD_data["fit_x_0_err"]
+