diff --git a/scoop-based_V2/astra_example/input.ref.in b/scoop-based_V2/astra_example/input.ref.in
index 091243fe80f870db2859072c192eb69e417136d1..01ea40eef49ebee3f1e3ccd2719e843a72927f06 100644
--- a/scoop-based_V2/astra_example/input.ref.in
+++ b/scoop-based_V2/astra_example/input.ref.in
@@ -33,7 +33,7 @@
   Lsub_cor=T
   XYrms = {{lsr_xyrms}}
   Trms  = {{lsr_trms}}	
-  Qbunch=10
+  Qbunch=1
 /
 
 &OUTPUT
diff --git a/scoop-based_V2/astra_example/optimize.py b/scoop-based_V2/astra_example/optimize.py
index 2f3cae6ef6dd5063ced67793be9571d0bd6c5993..68f3b19ed92333ffdbc2da9c75b3c2115537f172 100755
--- a/scoop-based_V2/astra_example/optimize.py
+++ b/scoop-based_V2/astra_example/optimize.py
@@ -1,7 +1,7 @@
 
 import os
 import numpy as np
-import opt_func as optH
+import optHelper as optH
 #import generatorTool as gt 
 from shutil import copyfile, rmtree
 from scipy.stats import powerlaw, skew
@@ -29,11 +29,11 @@ final_z  = '2424'
 run      = 1
 Np       = 10000 
 # Ranges of the parameters
-myRanges = [(0.05, 0.32),
+myRanges = [(0.05, 4.0),
             (0.225e-3, 10e-3),
             (90, 120),
             (-15, 15),
-            (10.0, 16.0),
+            (10.0, 20.0),
             ( -20.0, 20.0),
             (10.0, 16.0), 
             (0.05, 0.32)]
@@ -159,7 +159,7 @@ def fitnessFunc(X):
     os.chdir(abs_path)
     # to be modified for each optimization
     # Constraints and filters, e.g. particle loss. Unfinshed.
-    if Nalive < Np - 10 or kinetic < 120:
+    if Nalive < Np - 10 or kinetic < 100:
         return [tuple(np.divide(badFitness, fScale)), (np.nan,)]
 # minimum emit along the beamline
     index=np.argwhere(zdist > dump)