diff --git a/.gitignore b/.gitignore
index 5ba68f2f6c6201d21e6ec0778108ccf6eef6e05f..c6ff4158df43f64e19ab62378e6ac6062e783b72 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,4 +2,6 @@ Results
 data
 .DS_Store
 __pycache__
-user
\ No newline at end of file
+user
+Icon*
+peakotron_logo.icns
\ No newline at end of file
diff --git a/user/sumlab_auto.py b/user/sumlab_auto.py
index 74ae8eeba984e7c10481129dbc18fb369f87c5ad..8801a5fd15aacc6156521a030350efd368208c94 100644
--- a/user/sumlab_auto.py
+++ b/user/sumlab_auto.py
@@ -85,12 +85,10 @@ for i, (file, path) in enumerate(files_to_fit):
                tau_R=f_tau_hmt*tau,
                bin_0=bin_0,
                truncate_nsigma0_up=truncate_nsigma0_up,
-               truncate_nsigma0_do=truncate_nsigma0_do
+               truncate_nsigma0_do=truncate_nsigma0_do,
+               prefit_only=prefit_only
                )
 
-    f_data.PlotFit(plot_in_bins=True, display=False,
-                   save_directory=f"{folder}/{file[:-4]}_fit.png")
-
     fit_out = {}
     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")
@@ -99,13 +97,15 @@ for i, (file, path) in enumerate(files_to_fit):
     for key, value in prefit_err.items():
         fit_out["prefit_d_{:s}".format(key)] = value
 
-    #if not prefit_only:
-    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")
-    for key, value in fit_val.items():
-        fit_out["{:s}".format(key)] = value
-    for key, value in fit_err.items():
-        fit_out["d_{:s}".format(key)] = value
+    if not prefit_only:
+        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")
+        for key, value in fit_val.items():
+            fit_out["{:s}".format(key)] = value
+        for key, value in fit_err.items():
+            fit_out["d_{:s}".format(key)] = value
+        f_data.PlotFit(plot_in_bins=True, display=False,
+                save_directory=f"{folder}/{file[:-4]}_fit.png")
 
     df = pd.DataFrame.from_dict([fit_out])
     df.to_csv("{}/fit_results_{:s}.csv".format(folder, file[:-4]))