sumlab_auto: add as output of peakotron mean

user/sumlab_auto.py

@@ -69,7 +69,7 @@ print("=======================================\033[0m")
 
 # Loop thorough SORTED files in alphabetical order!
 files_to_fit = sorted(files_to_fit, key=lambda x: x[0])
-G = []; d_G = []; G_prefit = []; d_G_prefit = []
+G = []; d_G = []; mu=[]; d_mu=[]; G_prefit = []; d_G_prefit = []; mu_prefit=[]; d_mu_prefit=[]
 V_bias = []
 t_gates=[]
 for i, (file, path) in enumerate(files_to_fit):
@@ -102,6 +102,8 @@ for i, (file, path) in enumerate(files_to_fit):
     prefit_val, prefit_err = f_data.GetPrefitResults(bin_units=False)
     print("\033[95m"+rf"Prefit: G = {prefit_val.get('G')
                                      } d_G = {prefit_err.get('G')}"+"\033[0m")
+    print("\033[95m"+rf"Prefit: mu = {prefit_val.get('mu')
+                                     } d_mu = {prefit_err.get('mu')}"+"\033[0m")
     for key, value in prefit_val.items():
         fit_out["prefit_{:s}".format(key)] = value
     for key, value in prefit_err.items():
@@ -111,6 +113,8 @@ for i, (file, path) in enumerate(files_to_fit):
         fit_val, fit_err = f_data.GetFitResults(bin_units=False)
         print("\033[95m"+rf"Fit: G = {fit_val.get('G')
                                       } d_G = {fit_err.get('G')}"+"\033[0m")
+        print("\033[95m"+rf"Fit: mu = {fit_val.get('mu')
+                                      } d_mu = {fit_err.get('mu')}"+"\033[0m")
         for key, value in fit_val.items():
             fit_out["{:s}".format(key)] = value
         for key, value in fit_err.items():
@@ -121,27 +125,42 @@ for i, (file, path) in enumerate(files_to_fit):
     df = pd.DataFrame.from_dict([fit_out])
     df.to_csv("{}/fit_results_{:s}.csv".format(folder, file[:-4]))
 
-    if not prefit_only and 'G' in fit_out and 'd_G' in fit_out:
+    if not prefit_only and 'G' in fit_out and 'd_G' in fit_out and 'mu' in fit_out and 'd_mu' in fit_out:
         G.append(fit_out['G'])
         d_G.append(fit_out['d_G'])
+        mu.append(fit_out['mu'])
+        d_mu.append(fit_out['d_mu'])
     else:
         G.append(0)
         d_G.append(0)
+        mu.append(0)
+        d_mu.append(0)
+
     G_prefit.append(fit_out['prefit_G'])
     d_G_prefit.append(fit_out['prefit_d_G'])
+    mu_prefit.append(fit_out['prefit_mu'])
+    d_mu_prefit.append(fit_out['prefit_d_mu'])
 
 G = np.array(G)
 d_G = np.array(d_G)
+mu=np.array(mu)
+d_mu= np.array(d_mu)
 G_prefit = np.array(G_prefit)
 d_G_prefit = np.array(d_G_prefit)
+mu_prefit=np.array(mu_prefit)
+d_mu_prefit=np.array(d_mu_prefit)
 V_bias = np.array(V_bias)
 t_gates = np.array(t_gates)
 
 with h5py.File(f"{folder}/{os.path.basename(folder)}.h5", 'w') as f:
     f.create_dataset('G', data=G)
     f.create_dataset('d_G', data=d_G)
+    f.create_dataset('mu',data=mu)
+    f.create_dataset('d_mu',data=d_mu)
     f.create_dataset('G_prefit', data=G_prefit)
     f.create_dataset('d_G_prefit', data=d_G_prefit)
+    f.create_dataset('mu_prefit', data=mu_prefit)
+    f.create_dataset('d_mu_prefit', data=d_mu_prefit)
     f.create_dataset('V_bias', data=V_bias)
     f.create_dataset('integral_length', data=t_gates)