add few gauss and lorrentzian funcs with peak center bounds options

Author: SarthakJariwala

PythonGUI_apps/Spectrum_analysis/Spectra_fit_funcs.py

@@ -55,6 +55,15 @@ def gaussian_model(self):
         pars = gmodel.guess(y, x=x) # parameters - center, width, height
         result = gmodel.fit(y, pars, x=x, nan_policy='propagate')
         return result
+    
+    def gaussian_model_w_lims(self, center_min=None, center_max=None):
+        x,y = self.background_correction()
+        gmodel = GaussianModel(prefix = 'g1_') # calling gaussian model
+        pars = gmodel.guess(y, x=x) # parameters - center, width, height
+#        pars['g1_center'].set(800, min = 795, max = 820)
+        gmodel.set_param_hint('g1_center', min=center_min, max=center_max)
+        result = gmodel.fit(y, pars, x=x, nan_policy='propagate')
+        return result
 
 class Single_Lorentzian(Spectra_Fit):
     """Fit a single Lorentzian to the spectrum
@@ -70,6 +79,14 @@ def lorentzian_model(self):
         pars = lmodel.guess(y, x=x) # parameters - center, width, height
         result = lmodel.fit(y, pars, x=x, nan_policy='propagate')
         return result
+    
+    def lorentzian_model_w_lims(self, center_min = None, center_max = None):
+        x,y = self.background_correction()
+        lmodel = LorentzianModel(prefix = 'l1_') # calling lorentzian model
+        pars = lmodel.guess(y, x=x) # parameters - center, width, height
+        lmodel.set_param_hint('l1_center', min = center_min, max = center_max)
+        result = lmodel.fit(y, pars, x=x, nan_policy='propagate')
+        return result
 
 class Double_Gaussian(Spectra_Fit):
     """Fit two gaussians to the spectrum